# Sample: Telecom, Medellin: report Project: Sample: Telecom, Medellin Goal: Describe the project Sample: Telecom, Medellin: run the analyses that tell the most about its data, draw every result, and write a cited report. Run: de496ddc-3bd2-4d81-bcc0-e58d35927cfd (running, 19 agent run(s)) Generated: 2026-10-19T04:49:11Z, version 1 Model cost: USD 0.0000 over 0 model call(s) ## Data used | Data | Kind | Rows | Time span | Summary | Source | | --- | --- | --- | --- | --- | --- | | Neighborhoods | vector, Polygon | 16 | | 16 polygon (area) features; 'population' 27,852 to 74,163 (mean 4.98e+04); 'subscribers' 7,873 to 29,047 (mean 1.72e+04); named by 'name' (e.g. Barrio Norte, Las Flores, El Molino); total area 72.05 km2; extent -75.6219,6.2080 to -75.5405,6.2804. Areas (Neighborhoods) with population to compare and map. | [329] | | Towers | vector, Point | 14 | | 14 point features; 'technology' has 2 values (top: 4G/5G 7, 4G 7); 'height_m' 25 to 40 (mean 33.93); 'ground_m' 1490 to 2270 (mean 1768); 'sectors' 3 to 3 (mean 3); named by 'name' (e.g. Tower 01 La Estacion, Tower 02 La Ribera, Tower 03 La Fuente); extent -75.6097,6.2153 to -75.5641,6.2746. Point locations of Towers by technology. | [330] | | Dropped calls | vector, Point | 1,800 | 2026-08-03 to 2026-09-27 | 1,800 point features; dated by 'occurred_at' from 2026-08-03 to 2026-09-27; 'cause' has 4 values (top: coverage 646, handover 617, congestion 341); 'technology' has 3 values (top: 4G 895, 5G 455, 3G 450); 'signal_dbm' -128.6 to -73 (mean -103.4); extent -75.6212,6.2088 to -75.5413,6.2798. Dated point events by cause: where and when Dropped calls happen. | [2] | | Coverage complaints | vector, Point | 420 | 2026-03-01 to 2026-09-30 | 420 point features; dated by 'date' from 2026-03-01 to 2026-09-30; 'issue' has 3 values (top: slow data 154, calls drop 140, no signal indoors 126); 'plan' has 3 values (top: prepaid 147, postpaid 137, business 136); extent -75.6212,6.2087 to -75.5421,6.2796. Dated point events by issue: where and when Coverage complaints happen. | [3] | | Fiber routes | vector, LineString | 19 | | 19 line features; 'owner' has 2 values (top: leased 13, own 6); 'fibres' 24 to 96 (mean 72); total length 61.98 km; extent -75.6097,6.2153 to -75.5641,6.2746. Lines (Fiber routes) such as routes or networks. | [331] | | Small cells | vector, Point | 40 | | 40 point features; 'status' has 2 values (top: planned 30, live 10); 'band' has 2 values (top: n78 21, B7 19); extent -75.6198,6.2095 to -75.5410,6.2782. Point locations of Small cells by status. | [332] | | Valley terrain model (DEM) | raster | | | 1-band float32 raster (dem), 300 x 267 cells; resolution 30; band 1 1475 to 2759; extent -75.6219,6.2079 to -75.5405,6.2804. An elevation surface for terrain analysis. | [333] | | Cell KPIs by day | table | 784 | 2026-08-03 to 2026-09-27 | Table of 784 rows and 5 columns without geometry; dated by 'date' from 2026-08-03 to 2026-09-27; 'traffic_gb' 429.6 to 2612 (mean 1036); 'drop_rate_pct' 0.1 to 3.81 (mean 1.437); 'availability_pct' 98.43 to 100 (mean 99.57). A table of Cell KPIs by day without locations. | [4] | | Site leases | table | 14 | | Table of 14 rows and 4 columns without geometry; 'landlord_type' has 4 values (top: private 5, municipal 4, church 3); 'lease_end_year' 2,027 to 2,033 (mean 2030); 'monthly_rent_cop' 2,362,560 to 5,612,144 (mean 3,912,966). A table of Site leases without locations. | [334] | | Network performance review | document | | | 1-page md document 'Network performance review, August and September 2026'; language en; mentions 7 place(s): Los Olivos (1), 6.24785, -75.55517 (1), Tower 03 La Fuente (1), Tower (1), Tower 13 Los Pinos (1). A document about Los Olivos, 6.24785, -75.55517, Tower 03 La Fuente; it names event terms congestion, coverage, live. | [335] | | Sample: Telecom, Medellin | record | | | | [336] | | Terrain: Hillshade | layer | 0 | | | [97] | | Terrain: Elevation (m) | layer | 0 | | | [23] | | Terrain: Contours (100 m) | layer, LineString | 24 | | | [100] | | Terrain: Dominant heights | layer, Point | 5 | | | [22] | | Terrain: Slope (degrees) | layer | 0 | | | [99] | | Terrain: Aspect (degrees from north) | layer | 0 | | | [98] | | Terrain: Landforms (geomorphons) | layer | 0 | | | [93] | | Terrain: Flow accumulation (upstream cells) | layer | 0 | | | [87] | | Terrain: Watershed boundaries | layer, Polygon | 3 | | | [82] | | Terrain: Flood-prone areas | layer, Polygon | 2 | | | [77] | | Terrain: Streams (Strahler order) | layer, LineString | 9 | | | [85] | | Terrain: MCOO going classes | layer | 0 | | | [67] | | Terrain: Mobility corridors | layer, Polygon | 2 | | | [65] | | Terrain: Avenues of approach | layer, LineString | 3 | | | [62] | | Terrain: Corridor constrictions | layer, Point | 3 | | | [63] | | Terrain heights: Visible area | layer, Polygon | 5 | | | [59] | | Terrain: Open flat ground | layer, Polygon | 9 | | | [89] | | Terrain: Key terrain candidates | layer, Point | 2 | | | [64] | | Terrain: Total curvature | layer | 0 | | | [96] | | Terrain: Terrain ruggedness (m) | layer | 0 | | | [95] | | Terrain: Topographic position (m) | layer | 0 | | | [94] | | Terrain: Ravines | layer, LineString | 5 | | | [92] | | Terrain: Ridges | layer, LineString | 36 | | | [91] | | Terrain: Observation points | layer, Point | 2 | | | [90] | | Terrain: Terrain masking (cover) | layer, Polygon | 5 | | | [88] | | Terrain: Flow direction (D8) | layer | 0 | | | [86] | | Terrain: Pour points | layer, Point | 3 | | | [84] | | Terrain: Watersheds | layer | 0 | | | [83] | | Terrain: Pour points (snapped) | layer, Point | 3 | | | [81] | | Slope (degrees) | layer | 0 | | | [179] | | Hot spot analysis: Coverage complaints | layer, Polygon | 305 | | | [337] | | Terrain: Streams (Strahler order) | layer, LineString | 9 | | | [78] | | Terrain dismounted: Obstacles | layer, Polygon | 1 | | | [74] | | Terrain wheeled: Obstacles | layer, Polygon | 10 | | | [71] | | Terrain tracked: Obstacles | layer, Polygon | 1 | | | [68] | | Terrain: Obstacles | layer, Polygon | 1 | | | [66] | | Terrain DH-1 DH-2: Sightline | layer, LineString | 1 | | | [57] | | Terrain DH-1 DH-3: Sightline | layer, LineString | 1 | | | [56] | | Terrain heights: Observers | layer, Point | 5 | | | [58] | | Terrain DH-1 DH-3: Ground visible from the observer | layer, LineString | 3 | | | [55] | | Terrain DH-2 DH-3: Sightline | layer, LineString | 1 | | | [54] | | Terrain axis: Least-cost routes | layer, LineString | 1 | | | [53] | | Terrain: Best buildability areas | layer, Polygon | 88 | | | [37] | | Terrain summits: Least-cost routes | layer, LineString | 1 | | | [52] | | Terrain axis: Profile line | layer, LineString | 1 | | | [51] | | Terrain: Height above the nearest stream (m) | layer | 0 | | | [80] | | Terrain: Best mineability areas | layer, Polygon | 15 | | | [32] | | Terrain summits: Profile line | layer, LineString | 1 | | | [50] | | Terrain: Landing zone candidates | layer, Point | 6 | | | [29] | | Terrain: Flood-prone ground (within 3 m of the stream) | layer | 0 | | | [79] | | Terrain: Landing points | layer, Polygon | 6 | | | [28] | | Terrain: Visible area | layer, Polygon | 1 | | | [25] | | Terrain: Observers | layer, Point | 14 | | | [24] | | Terrain dismounted: Going (dismounted) | layer | 0 | | | [76] | | Terrain: Terrain findings | layer, Point | 9 | | | [21] | | Terrain dismounted: Off-road speed (km/h) | layer | 0 | | | [75] | | Terrain wheeled: Going (wheeled) | layer | 0 | | | [73] | | Terrain wheeled: Off-road speed (km/h) | layer | 0 | | | [72] | | Terrain tracked: Going (tracked) | layer | 0 | | | [70] | | Terrain tracked: Off-road speed (km/h) | layer | 0 | | | [69] | | Terrain heights: Viewshed | layer | 0 | | | [61] | | Terrain heights: Observers that see each cell | layer | 0 | | | [60] | | Terrain: Clear-sky insolation (kWh/m2/day) | layer | 0 | | | [49] | | Terrain: Sky view factor | layer | 0 | | | [48] | | Terrain: Solar siting class | layer | 0 | | | [47] | | Terrain: Factor of safety (capped at 100) | layer | 0 | | | [46] | | Terrain: Stability classes | layer | 0 | | | [45] | | Terrain: Susceptibility index (0-1) | layer | 0 | | | [44] | | Terrain: Susceptibility classes | layer | 0 | | | [43] | | Terrain: Soil loss (t/ha/yr) | layer | 0 | | | [42] | | Terrain: Erosion risk classes | layer | 0 | | | [41] | | Terrain: LS factor | layer | 0 | | | [40] | | Terrain: Buildability score (0-100) | layer | 0 | | | [39] | | Terrain: Buildability classes | layer | 0 | | | [38] | | Terrain: Site suitability score (0-100) | layer | 0 | | | [36] | | Terrain: Site suitability classes | layer | 0 | | | [35] | | Terrain: Mineability score (0-100) | layer | 0 | | | [34] | | Terrain: Mineability classes | layer | 0 | | | [33] | | Terrain: Landing ground | layer | 0 | | | [31] | | Terrain: Clear ground radius (m) | layer | 0 | | | [30] | | Terrain: Viewshed | layer | 0 | | | [27] | | Terrain: Observers that see each cell | layer | 0 | | | [26] | | Hot spot analysis: Dropped calls | layer, Polygon | 1,798 | | | [242] | | Hot spot analysis: Small cells | layer, Polygon | 21 | | | [338] | | Emerging hot spots: Coverage complaints | layer, Polygon | 94 | | | [339] | | Emerging hot spots: Dropped calls | layer, Polygon | 109 | | | [340] | | Summarize within: Neighborhoods | layer, Polygon | 16 | | | [341] | | Summarize within: Neighborhoods | layer, Polygon | 16 | | | [342] | | Summarize within: Neighborhoods | layer, Polygon | 16 | | | [236] | | Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) | layer, Polygon | 16 | | | [8] | | Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17) | layer, Polygon | 8 | | | [10] | | Seasonality of Dropped calls | layer, Polygon | 16 | | | [208] | | Calculate density: Dropped calls | layer, Polygon | 2,034 | | | [343] | | Calculate density: Coverage complaints | layer, Polygon | 383 | | | [239] | | Dense areas of coverage complaints | layer, MultiPolygon | 5 | | | [252] | | Calculate density: Small cells | layer, Polygon | 117 | | | [344] | | Find point clusters: cluster hulls | layer, Polygon | 2 | | | [247] | | Find point clusters: Dropped calls | layer, Point | 1,800 | | | [345] | | Find point clusters: cluster hulls | layer, Polygon | 1 | | | [346] | | Find point clusters: Coverage complaints | layer, Point | 420 | | | [347] | | Find outliers: Neighborhoods | layer, Polygon | 16 | | | [348] | | Change beside the counts per neighborhood | layer, Polygon | 16 | | | [237] | | Change of dropped calls where density is high | layer, Polygon | 16 | | | [240] | | Hot cells of dropped calls per neighborhood | layer, Polygon | 16 | | | [243] | | Nearest tower to each cluster of dropped calls | layer, Polygon | 2 | | | [248] | | Change of coverage complaints per neighborhood | layer, Polygon | 16 | | | [254] | | Find point clusters: cluster hulls | layer, Polygon | 3 | | | [257] | | Clusters of all inputs together: dropped calls and coverage complaints | layer, Point | 2,220 | | | [258] | | Towers within 250 m of each cluster of dropped calls | layer, Polygon | 2 | | | [261] | ## Timeline ![Timeline: events per week by dataset](charts/74-timeline-events-per-week-by-dataset.png) *Figure 1. Timeline: events per week by dataset [1]. Per week, 2026-02-23 to 2026-09-28. Source: Dropped calls, Coverage complaints, Cell KPIs by day (3,004 features). The data is in the appendix.* ![Timeline lanes: events per week by dataset](charts/75-timeline-lanes-events-per-week-by-dataset.png) *Figure 2. Timeline lanes: events per week by dataset [5]. Per week, 2026-02-23 to 2026-09-28. Source: Dropped calls, Coverage complaints, Cell KPIs by day (3,004 features). The data is in the appendix.* ![Timeline: change from one week to the next](charts/76-timeline-change-from-one-week-to-the-next.png) *Figure 3. Timeline: change from one week to the next [6]. Per week, 2026-03-02 to 2026-09-28. Source: Dropped calls, Coverage complaints, Cell KPIs by day (3,004 features). The data is in the appendix.* The timeline counts 3,004 dated events in Dropped calls, Coverage complaints, Cell KPIs by day per week, from week of 2026-02-23 to week of 2026-09-28 [1][2][3][4]. By dataset: Dropped calls 1,800; Cell KPIs by day 784; Coverage complaints 420 [1][5]. The trend is +10.5 events per week; the second half of the weeks has 2,806 against 198 in the first (+1317.2%) [1]. Week over week, the largest rise was from week of 2026-07-27 to week of 2026-08-03 (16 to 351, +335) and the largest fall was from week of 2026-09-21 to week of 2026-09-28 (338 to 7, -331) [1][6]. The count rose from 200 in week of 2026-08-24 to 242 in week of 2026-08-31 (+21.0%), within what chance would give (p = 0.051) [7][8]. Between the two, the largest rise was in Los Olivos (28 to 41, +13) and the largest fall in Barrio Norte (12 to 5, -7) [7][8]. The count rose from 9 in week of 2026-08-10 to 17 in week of 2026-08-17 (+88.9%), within what chance would give (p = 0.169) [9][10]. Between the two, the largest rise was in El Puerto Viejo (0 to 3, +3) and the largest fall in La Ribera (2 to 1, -1) [9][10]. **Busiest weeks** - week of 2026-08-31: 355 events [1] - week of 2026-08-03: 351 events [1] - week of 2026-08-10: 339 events [1] **Quietest weeks** - week of 2026-02-23: 4 events [1] - week of 2026-09-28: 7 events [1] - week of 2026-06-01: 8 events [1] ## Overview Overview: the top 3 results in the report's order, each in one distinct color, over Neighborhoods as context [11]. ![Overview: the top 3 results](maps/08-overview-the-top-3-results.png) *Figure 4. Overview: the top 3 results [11]* ## Spatial structure Neighborhoods are 16 areas of 4.503 km2 on average, covering 100 percent of their extent, with no overlap [12]. Towers are evenly spread (dispersed) (nearest-neighbor ratio 1.82, z = +5.8; features lie 1.396 km apart on average against 768 m if random) [13]. Dropped calls are clustered (nearest-neighbor ratio 0.87, z = -10.7; features lie 85.2 m apart on average against 98.1 m if random) [14]. Coverage complaints are clustered (nearest-neighbor ratio 0.87, z = -5.0; features lie 177 m apart on average against 202 m if random) [15]. Coverage complaints are dated by 'date' from 2026-03-01 to 2026-09-30 at a week grain [15]. Over time the pattern changes: the earlier half is clustered (ratio 0.873) and the later half random (ratio 0.9552); the center moved 305 m [15]. Fiber routes form a connected network: 19 lines with 7 junctions and 84 crossings in 1 connected part, 61.98 km in all [16]. Small cells are spread at random (nearest-neighbor ratio 1.07, z = +0.8; features lie 686 m apart on average against 643 m if random) [17]. 100 percent of Coverage complaints lie inside Neighborhoods (420 of 420) [12][15]. 100 percent of Dropped calls lie inside Neighborhoods (1800 of 1800) [12][14]. 100 percent of Fiber routes lie inside Neighborhoods (19 of 19) [12][16]. 100 percent of Small cells lie inside Neighborhoods (40 of 40) [12][17]. 100 percent of Towers lie inside Neighborhoods (14 of 14) [12][13]. Towers lie 0 m from the nearest of Fiber routes (median); 86 percent lie within 100 m [13][16]. Coverage complaints lie 62.7 m from the nearest of Dropped calls (median); 64 percent lie within 100 m [14][15]. Small cells lie 91.5 m from the nearest of Dropped calls (median); 52 percent lie within 100 m [14][17]. Towers lie 134 m from the nearest of Dropped calls (median); 100 percent lie within 500 m [13][14]. Dropped calls lie 170 m from the nearest of Coverage complaints (median); 94 percent lie within 500 m [14][15]. | Result | Value | | --- | --- | | layers profiled | 6 | | clustered point layers | 2 | | dispersed point layers | 1 | | random point layers | 1 | **Findings** - **Evidenced:** Neighborhoods are 16 areas of 4.503 km2 on average, covering 100 percent of their extent, with no overlap [12]. - **Evidenced:** Towers are evenly spread (dispersed) (nearest-neighbor ratio 1.82, z = +5.8; features lie 1.396 km apart on average against 768 m if random) [13]. - **Evidenced:** Dropped calls are clustered (nearest-neighbor ratio 0.87, z = -10.7; features lie 85.2 m apart on average against 98.1 m if random) [14]. - **Evidenced:** Coverage complaints are clustered (nearest-neighbor ratio 0.87, z = -5.0; features lie 177 m apart on average against 202 m if random) [15]. - **Evidenced:** Coverage complaints are dated by 'date' from 2026-03-01 to 2026-09-30 at a week grain [15]. - **Evidenced:** Over time the pattern changes: the earlier half is clustered (ratio 0.873) and the later half random (ratio 0.9552); the center moved 305 m [15]. - **Evidenced:** Fiber routes form a connected network: 19 lines with 7 junctions and 84 crossings in 1 connected part, 61.98 km in all [16]. - **Evidenced:** Small cells are spread at random (nearest-neighbor ratio 1.07, z = +0.8; features lie 686 m apart on average against 643 m if random) [17]. - **Evidenced:** 100 percent of Coverage complaints lie inside Neighborhoods (420 of 420) [12][15]. - **Evidenced:** 100 percent of Dropped calls lie inside Neighborhoods (1800 of 1800) [12][14]. - **Evidenced:** 100 percent of Fiber routes lie inside Neighborhoods (19 of 19) [12][16]. - **Evidenced:** 100 percent of Small cells lie inside Neighborhoods (40 of 40) [12][17]. - **Evidenced:** 100 percent of Towers lie inside Neighborhoods (14 of 14) [12][13]. - **Evidenced:** Towers lie 0 m from the nearest of Fiber routes (median); 86 percent lie within 100 m [13][16]. - **Evidenced:** Coverage complaints lie 62.7 m from the nearest of Dropped calls (median); 64 percent lie within 100 m [14][15]. - **Evidenced:** Small cells lie 91.5 m from the nearest of Dropped calls (median); 52 percent lie within 100 m [14][17]. - **Evidenced:** Towers lie 134 m from the nearest of Dropped calls (median); 100 percent lie within 500 m [13][14]. - **Evidenced:** Dropped calls lie 170 m from the nearest of Coverage complaints (median); 94 percent lie within 500 m [14][15]. ## Terrain analysis **What this analysis is.** Terrain analysis: The automatic terrain analysis of an area, with no model call: by default every terrain tool (everything), or a subset (general, mobility, observation, hydrology, site), then the elevation layer, the hypsometric curve, slope distribution, aspect rose and route profiles, the dominant heights, the cited terrain findings (key terrain, chokepoints, corridors, flood-prone ground) and the terrain maps [18]. Large areas run coarser with the factor reported [18]. In ArcGIS it is No single tool (terrain analysis workflow: Surface Parameters, Viewshed, Hydrology, Distance Accumulation); in QGIS, No single tool (Processing model of GDAL DEM, r.viewshed, r.watershed and r.cost) [18]. **Why it was run on this data.** Automatic terrain analysis (everything) on the project's DEM Valley terrain model (DEM); observers from the 14 point(s) of Towers: every terrain tool: relief, slope, aspect, hillshade, curvature, roughness, landforms, ravines and ridges, strategic points, hydrology (flow, streams, pour points, watersheds, flood-prone ground), mobility for every mover, the MCOO, viewsheds and lines of sight from th [19]. **What it found.** Terrain analysis produced Terrain: Terrain findings (9 features), Terrain: Dominant heights (5 features), Terrain: Elevation (m) (0 features) [20][21][22][23]. It reported tokens, usd 0.737; tokens, tokens 737; tokens, tokens in 1; tokens, tokens out 184; numbers, maps 7; numbers, charts 4 [20]. The chart Hypsometric curve is drawn from 20 rows [131]. The chart Slope distribution is drawn from 6 rows [132]. The chart Aspect rose is drawn from 8 rows [133]. The chart Elevation along the routes is drawn from 320 rows [134]. The chart Terrain: Contours (100 m) is drawn from 6 rows [135]. The chart Terrain: Ravines is drawn from 5 rows [136]. The chart Terrain: Ridges is drawn from 7 rows [137]. The chart Terrain: Observation points is drawn from 5 rows [138]. The chart Terrain: Open flat ground is drawn from 5 rows [139]. The chart Terrain: Terrain masking (cover) is drawn from 5 rows [140]. The chart Terrain: Streams (Strahler order) is drawn from 5 rows [141]. The chart Terrain: Pour points is drawn from 5 rows [142]. The chart Terrain: Watershed boundaries is drawn from 5 rows [143]. The chart Terrain: Pour points (snapped) is drawn from 5 rows [144]. The chart Terrain: Streams (Strahler order) is drawn from 5 rows [145]. The chart Terrain: Flood-prone areas is drawn from 5 rows [146]. The chart Terrain dismounted: Obstacles is drawn from 1 rows [147]. The chart Terrain wheeled: Obstacles is drawn from 5 rows [148]. The chart Terrain tracked: Obstacles is drawn from 1 rows [149]. The chart Terrain: Obstacles is drawn from 1 rows [150]. The chart Terrain: Mobility corridors is drawn from 5 rows [151]. The chart Terrain: Key terrain candidates is drawn from 5 rows [152]. The chart Terrain: Corridor constrictions is drawn from 5 rows [153]. The chart Terrain: Avenues of approach is drawn from 5 rows [154]. The chart Terrain heights: Visible area is drawn from 5 rows [155]. The chart Terrain heights: Observers is drawn from 5 rows [156]. The chart Terrain DH-1 DH-2: Sightline is drawn from 1 rows [157]. The chart Terrain DH-1 DH-3: Sightline is drawn from 1 rows [158]. The chart Terrain DH-1 DH-3: Ground visible from the observer is drawn from 5 rows [159]. The chart Terrain DH-2 DH-3: Sightline is drawn from 1 rows [160]. The chart Terrain axis: Least-cost routes is drawn from 1 rows [161]. The chart Terrain summits: Least-cost routes is drawn from 1 rows [162]. The chart Terrain axis: Profile line is drawn from 1 rows [163]. The chart Terrain summits: Profile line is drawn from 1 rows [164]. The chart Terrain: Best buildability areas is drawn from 8 rows [165]. The chart Terrain: Best mineability areas is drawn from 5 rows [166]. The chart Terrain: Landing zone candidates is drawn from 5 rows [167]. The chart Terrain: Landing points is drawn from 1 rows [168]. The chart Terrain: Visible area is drawn from 1 rows [169]. The chart Terrain: Observers is drawn from 5 rows [170]. The chart Terrain: Dominant heights is drawn from 5 rows [171]. | Result | Value | | --- | --- | | tokens, usd | 0.737 | | tokens, tokens | 737 | | tokens, tokens in | 1 | | tokens, tokens out | 184 | | numbers, maps | 7 | | numbers, charts | 4 | | numbers, layers | 80 | | numbers, area km2 | 72.09 | | numbers, findings | 55 | | numbers, flat pct | 0.273 | | numbers, relief m | 1,284 | | numbers, corridors | 2 | | numbers, steps run | 35 | | numbers, tools run | 28 | | numbers, chokepoints | 3 | | numbers, key terrain | 2 | | numbers, steps failed | 1 | | numbers, calls skipped | 0 | | numbers, mean slope deg | 13.16 | | numbers, elevation max m | 2,759 | ![Terrain: elevation with hillshade](maps/01-terrain-elevation-with-hillshade.png) *Figure 5. Terrain: elevation with hillshade [101]* ![Terrain: slope](maps/02-terrain-slope.png) *Figure 6. Terrain: slope [102]* ![Terrain analysis](maps/09-terrain-analysis.png) *Figure 7. Terrain analysis [103]* ![Terrain: aspect](maps/03-terrain-aspect.png) *Figure 8. Terrain: aspect [104]* ![Terrain analysis (continued 1)](maps/10-terrain-analysis-continued-1.png) *Figure 9. Terrain analysis (continued 1) [105]* ![Terrain: landforms](maps/04-terrain-landforms.png) *Figure 10. Terrain: landforms [106]* ![Terrain analysis (continued 2)](maps/11-terrain-analysis-continued-2.png) *Figure 11. Terrain analysis (continued 2) [107]* ![Terrain: hydrology](maps/05-terrain-hydrology.png) *Figure 12. Terrain: hydrology [108]* ![Terrain analysis (continued 3)](maps/12-terrain-analysis-continued-3.png) *Figure 13. Terrain analysis (continued 3) [109]* ![Terrain: mobility](maps/06-terrain-mobility.png) *Figure 14. Terrain: mobility [110]* ![Terrain analysis (continued 4)](maps/13-terrain-analysis-continued-4.png) *Figure 15. Terrain analysis (continued 4) [111]* ![Terrain: key terrain](maps/07-terrain-key-terrain.png) *Figure 16. Terrain: key terrain [112]* ![Terrain analysis (continued 5)](maps/14-terrain-analysis-continued-5.png) *Figure 17. Terrain analysis (continued 5) [113]* ![Terrain analysis (continued 6)](maps/15-terrain-analysis-continued-6.png) *Figure 18. Terrain analysis (continued 6) [114]* ![Terrain analysis (continued 7)](maps/16-terrain-analysis-continued-7.png) *Figure 19. Terrain analysis (continued 7) [115]* ![Terrain analysis (continued 8)](maps/17-terrain-analysis-continued-8.png) *Figure 20. Terrain analysis (continued 8) [116]* ![Terrain analysis (continued 9)](maps/18-terrain-analysis-continued-9.png) *Figure 21. Terrain analysis (continued 9) [117]* ![Terrain analysis (continued 10)](maps/19-terrain-analysis-continued-10.png) *Figure 22. Terrain analysis (continued 10) [118]* ![Terrain analysis (continued 11)](maps/20-terrain-analysis-continued-11.png) *Figure 23. Terrain analysis (continued 11) [119]* ![Terrain analysis (continued 12)](maps/21-terrain-analysis-continued-12.png) *Figure 24. Terrain analysis (continued 12) [120]* ![Terrain analysis (continued 13)](maps/22-terrain-analysis-continued-13.png) *Figure 25. Terrain analysis (continued 13) [121]* ![Terrain analysis (continued 14)](maps/23-terrain-analysis-continued-14.png) *Figure 26. Terrain analysis (continued 14) [122]* ![Terrain analysis (continued 15)](maps/24-terrain-analysis-continued-15.png) *Figure 27. Terrain analysis (continued 15) [123]* ![Terrain analysis (continued 16)](maps/25-terrain-analysis-continued-16.png) *Figure 28. Terrain analysis (continued 16) [124]* ![Terrain analysis (continued 17)](maps/26-terrain-analysis-continued-17.png) *Figure 29. Terrain analysis (continued 17) [125]* ![Terrain analysis (continued 18)](maps/27-terrain-analysis-continued-18.png) *Figure 30. Terrain analysis (continued 18) [126]* ![Terrain analysis (continued 19)](maps/28-terrain-analysis-continued-19.png) *Figure 31. Terrain analysis (continued 19) [127]* ![Terrain analysis (continued 20)](maps/29-terrain-analysis-continued-20.png) *Figure 32. Terrain analysis (continued 20) [128]* ![Terrain analysis (continued 21)](maps/30-terrain-analysis-continued-21.png) *Figure 33. Terrain analysis (continued 21) [129]* ![Terrain analysis (continued 22)](maps/31-terrain-analysis-continued-22.png) *Figure 34. Terrain analysis (continued 22) [130]* **What the map shows.** The map shades Terrain: Hillshade on a continuous ramp from 0 to 255 [101]. The map shades Terrain: Elevation (m) on a continuous ramp from 1,475 m to 2,759 m [101]. The map draws Terrain: Contours (100 m) by elev_m in 5 classes: 1,500 to 1,700, 1,700 to 1,900, 1,900 to 2,200, 2,200 to 2,500, 2,500 to 2,700 [101]. The map draws Terrain: Dominant heights by elev_m in 4 classes: 2,268 to 2,271, 2,271 to 2,330, 2,330 to 2,702, 2,702 to 2,759 [101]. It draws 29 features [101]. It covers longitude -75.643 to -75.519 and latitude 6.197 to 6.289, at about 1:57,116 [101]. ![Hypsometric curve](charts/01-hypsometric-curve.png) *Figure 35. Hypsometric curve [131]. 30 m cells from the project DEM (full resolution): 269 x 302 = 81,238 cells. 30 m cells from Valley terrain model (DEM). The data is in the appendix.* ![Slope distribution](charts/02-slope-distribution.png) *Figure 36. Slope distribution [132]. 30 m cells from the project DEM (full resolution): 269 x 302 = 81,238 cells. 30 m cells from Valley terrain model (DEM). The data is in the appendix.* ![Aspect rose](charts/03-aspect-rose.png) *Figure 37. Aspect rose [133]. 30 m cells from the project DEM (full resolution): 269 x 302 = 81,238 cells. 30 m cells from Valley terrain model (DEM). The data is in the appendix.* ![Elevation along the routes](charts/04-elevation-along-the-routes.png) *Figure 38. Elevation along the routes [134]. 30 m cells from the project DEM (full resolution): 269 x 302 = 81,238 cells. 30 m cells from Valley terrain model (DEM). The data is in the appendix.* ![Terrain: Contours (100 m)](charts/15-terrain-contours-100-m.png) *Figure 39. Terrain: Contours (100 m) [135]. Source: Terrain: Contours (100 m) (24 features). The data is in the appendix.* ![Terrain: Ravines](charts/16-terrain-ravines.png) *Figure 40. Terrain: Ravines [136]. Source: Terrain: Ravines (5 features). The data is in the appendix.* ![Terrain: Ridges](charts/17-terrain-ridges.png) *Figure 41. Terrain: Ridges [137]. Source: Terrain: Ridges (36 features). The data is in the appendix.* ![Terrain: Observation points](charts/18-terrain-observation-points.png) *Figure 42. Terrain: Observation points [138]. Source: Terrain: Observation points (2 features). The data is in the appendix.* ![Terrain: Open flat ground](charts/19-terrain-open-flat-ground.png) *Figure 43. Terrain: Open flat ground [139]. Source: Terrain: Open flat ground (9 features). The data is in the appendix.* ![Terrain: Terrain masking (cover)](charts/20-terrain-terrain-masking-cover.png) *Figure 44. Terrain: Terrain masking (cover) [140]. Source: Terrain: Terrain masking (cover) (5 features). The data is in the appendix.* ![Terrain: Streams (Strahler order)](charts/21-terrain-streams-strahler-order.png) *Figure 45. Terrain: Streams (Strahler order) [141]. Source: Terrain: Streams (Strahler order) (9 features). The data is in the appendix.* ![Terrain: Pour points](charts/22-terrain-pour-points.png) *Figure 46. Terrain: Pour points [142]. Source: Terrain: Pour points (3 features). The data is in the appendix.* ![Terrain: Watershed boundaries](charts/23-terrain-watershed-boundaries.png) *Figure 47. Terrain: Watershed boundaries [143]. Source: Terrain: Watershed boundaries (3 features). The data is in the appendix.* ![Terrain: Pour points (snapped)](charts/24-terrain-pour-points-snapped.png) *Figure 48. Terrain: Pour points (snapped) [144]. Source: Terrain: Pour points (snapped) (3 features). The data is in the appendix.* ![Terrain: Streams (Strahler order)](charts/25-terrain-streams-strahler-order.png) *Figure 49. Terrain: Streams (Strahler order) [145]. Source: Terrain: Streams (Strahler order) (9 features). The data is in the appendix.* ![Terrain: Flood-prone areas](charts/26-terrain-flood-prone-areas.png) *Figure 50. Terrain: Flood-prone areas [146]. Source: Terrain: Flood-prone areas (2 features). The data is in the appendix.* ![Terrain dismounted: Obstacles](charts/27-terrain-dismounted-obstacles.png) *Figure 51. Terrain dismounted: Obstacles [147]. Source: Terrain dismounted: Obstacles (1 features). The data is in the appendix.* ![Terrain wheeled: Obstacles](charts/28-terrain-wheeled-obstacles.png) *Figure 52. Terrain wheeled: Obstacles [148]. Source: Terrain wheeled: Obstacles (10 features). The data is in the appendix.* ![Terrain tracked: Obstacles](charts/29-terrain-tracked-obstacles.png) *Figure 53. Terrain tracked: Obstacles [149]. Source: Terrain tracked: Obstacles (1 features). The data is in the appendix.* ![Terrain: Obstacles](charts/30-terrain-obstacles.png) *Figure 54. Terrain: Obstacles [150]. Source: Terrain: Obstacles (1 features). The data is in the appendix.* ![Terrain: Mobility corridors](charts/31-terrain-mobility-corridors.png) *Figure 55. Terrain: Mobility corridors [151]. Source: Terrain: Mobility corridors (2 features). The data is in the appendix.* ![Terrain: Key terrain candidates](charts/32-terrain-key-terrain-candidates.png) *Figure 56. Terrain: Key terrain candidates [152]. Source: Terrain: Key terrain candidates (2 features). The data is in the appendix.* ![Terrain: Corridor constrictions](charts/33-terrain-corridor-constrictions.png) *Figure 57. Terrain: Corridor constrictions [153]. Source: Terrain: Corridor constrictions (3 features). The data is in the appendix.* ![Terrain: Avenues of approach](charts/34-terrain-avenues-of-approach.png) *Figure 58. Terrain: Avenues of approach [154]. Source: Terrain: Avenues of approach (3 features). The data is in the appendix.* ![Terrain heights: Visible area](charts/35-terrain-heights-visible-area.png) *Figure 59. Terrain heights: Visible area [155]. Source: Terrain heights: Visible area (5 features). The data is in the appendix.* ![Terrain heights: Observers](charts/36-terrain-heights-observers.png) *Figure 60. Terrain heights: Observers [156]. Source: Terrain heights: Observers (5 features). The data is in the appendix.* ![Terrain DH-1 DH-2: Sightline](charts/37-terrain-dh-1-dh-2-sightline.png) *Figure 61. Terrain DH-1 DH-2: Sightline [157]. Source: Terrain DH-1 DH-2: Sightline (1 features). The data is in the appendix.* ![Terrain DH-1 DH-3: Sightline](charts/38-terrain-dh-1-dh-3-sightline.png) *Figure 62. Terrain DH-1 DH-3: Sightline [158]. Source: Terrain DH-1 DH-3: Sightline (1 features). The data is in the appendix.* ![Terrain DH-1 DH-3: Ground visible from the observer](charts/39-terrain-dh-1-dh-3-ground-visible-from-the-observer.png) *Figure 63. Terrain DH-1 DH-3: Ground visible from the observer [159]. Source: Terrain DH-1 DH-3: Ground visible from the observer (3 features). The data is in the appendix.* ![Terrain DH-2 DH-3: Sightline](charts/40-terrain-dh-2-dh-3-sightline.png) *Figure 64. Terrain DH-2 DH-3: Sightline [160]. Source: Terrain DH-2 DH-3: Sightline (1 features). The data is in the appendix.* ![Terrain axis: Least-cost routes](charts/41-terrain-axis-least-cost-routes.png) *Figure 65. Terrain axis: Least-cost routes [161]. Source: Terrain axis: Least-cost routes (1 features). The data is in the appendix.* ![Terrain summits: Least-cost routes](charts/42-terrain-summits-least-cost-routes.png) *Figure 66. Terrain summits: Least-cost routes [162]. Source: Terrain summits: Least-cost routes (1 features). The data is in the appendix.* ![Terrain axis: Profile line](charts/43-terrain-axis-profile-line.png) *Figure 67. Terrain axis: Profile line [163]. Source: Terrain axis: Profile line (1 features). The data is in the appendix.* ![Terrain summits: Profile line](charts/44-terrain-summits-profile-line.png) *Figure 68. Terrain summits: Profile line [164]. Source: Terrain summits: Profile line (1 features). The data is in the appendix.* ![Terrain: Best buildability areas](charts/45-terrain-best-buildability-areas.png) *Figure 69. Terrain: Best buildability areas [165]. Source: Terrain: Best buildability areas (88 features). The data is in the appendix.* ![Terrain: Best mineability areas](charts/46-terrain-best-mineability-areas.png) *Figure 70. Terrain: Best mineability areas [166]. Source: Terrain: Best mineability areas (15 features). The data is in the appendix.* ![Terrain: Landing zone candidates](charts/47-terrain-landing-zone-candidates.png) *Figure 71. Terrain: Landing zone candidates [167]. Source: Terrain: Landing zone candidates (6 features). The data is in the appendix.* ![Terrain: Landing points](charts/48-terrain-landing-points.png) *Figure 72. Terrain: Landing points [168]. Source: Terrain: Landing points (6 features). The data is in the appendix.* ![Terrain: Visible area](charts/49-terrain-visible-area.png) *Figure 73. Terrain: Visible area [169]. Source: Terrain: Visible area (1 features). The data is in the appendix.* ![Terrain: Observers](charts/50-terrain-observers.png) *Figure 74. Terrain: Observers [170]. Source: Terrain: Observers (14 features). The data is in the appendix.* ![Terrain: Dominant heights](charts/51-terrain-dominant-heights.png) *Figure 75. Terrain: Dominant heights [171]. Source: Terrain: Dominant heights (5 features). The data is in the appendix.* **Findings** - **Evidenced:** Terrain analysis produced Terrain: Terrain findings (9 features), Terrain: Dominant heights (5 features), Terrain: Elevation (m) (0 features) [20][21][22][23]. - **Evidenced:** It reported tokens, usd 0.737; tokens, tokens 737; tokens, tokens in 1; tokens, tokens out 184; numbers, maps 7; numbers, charts 4 [20]. - **Evidenced:** The chart Hypsometric curve is drawn from 20 rows [131]. - **Evidenced:** The chart Slope distribution is drawn from 6 rows [132]. - **Evidenced:** The chart Aspect rose is drawn from 8 rows [133]. - **Evidenced:** The chart Elevation along the routes is drawn from 320 rows [134]. - **Evidenced:** The chart Terrain: Contours (100 m) is drawn from 6 rows [135]. - **Evidenced:** The chart Terrain: Ravines is drawn from 5 rows [136]. - **Evidenced:** The chart Terrain: Ridges is drawn from 7 rows [137]. - **Evidenced:** The chart Terrain: Observation points is drawn from 5 rows [138]. - **Evidenced:** The chart Terrain: Open flat ground is drawn from 5 rows [139]. - **Evidenced:** The chart Terrain: Terrain masking (cover) is drawn from 5 rows [140]. - **Evidenced:** The chart Terrain: Streams (Strahler order) is drawn from 5 rows [141]. - **Evidenced:** The chart Terrain: Pour points is drawn from 5 rows [142]. - **Evidenced:** The chart Terrain: Watershed boundaries is drawn from 5 rows [143]. - **Evidenced:** The chart Terrain: Pour points (snapped) is drawn from 5 rows [144]. - **Evidenced:** The chart Terrain: Streams (Strahler order) is drawn from 5 rows [145]. - **Evidenced:** The chart Terrain: Flood-prone areas is drawn from 5 rows [146]. - **Evidenced:** The chart Terrain dismounted: Obstacles is drawn from 1 rows [147]. - **Evidenced:** The chart Terrain wheeled: Obstacles is drawn from 5 rows [148]. - **Evidenced:** The chart Terrain tracked: Obstacles is drawn from 1 rows [149]. - **Evidenced:** The chart Terrain: Obstacles is drawn from 1 rows [150]. - **Evidenced:** The chart Terrain: Mobility corridors is drawn from 5 rows [151]. - **Evidenced:** The chart Terrain: Key terrain candidates is drawn from 5 rows [152]. - **Evidenced:** The chart Terrain: Corridor constrictions is drawn from 5 rows [153]. - **Evidenced:** The chart Terrain: Avenues of approach is drawn from 5 rows [154]. - **Evidenced:** The chart Terrain heights: Visible area is drawn from 5 rows [155]. - **Evidenced:** The chart Terrain heights: Observers is drawn from 5 rows [156]. - **Evidenced:** The chart Terrain DH-1 DH-2: Sightline is drawn from 1 rows [157]. - **Evidenced:** The chart Terrain DH-1 DH-3: Sightline is drawn from 1 rows [158]. - **Evidenced:** The chart Terrain DH-1 DH-3: Ground visible from the observer is drawn from 5 rows [159]. - **Evidenced:** The chart Terrain DH-2 DH-3: Sightline is drawn from 1 rows [160]. - **Evidenced:** The chart Terrain axis: Least-cost routes is drawn from 1 rows [161]. - **Evidenced:** The chart Terrain summits: Least-cost routes is drawn from 1 rows [162]. - **Evidenced:** The chart Terrain axis: Profile line is drawn from 1 rows [163]. - **Evidenced:** The chart Terrain summits: Profile line is drawn from 1 rows [164]. - **Evidenced:** The chart Terrain: Best buildability areas is drawn from 8 rows [165]. - **Evidenced:** The chart Terrain: Best mineability areas is drawn from 5 rows [166]. - **Evidenced:** The chart Terrain: Landing zone candidates is drawn from 5 rows [167]. - **Evidenced:** The chart Terrain: Landing points is drawn from 1 rows [168]. - **Evidenced:** The chart Terrain: Visible area is drawn from 1 rows [169]. - **Evidenced:** The chart Terrain: Observers is drawn from 5 rows [170]. - **Evidenced:** The chart Terrain: Dominant heights is drawn from 5 rows [171]. - **Evidenced:** The 72.1 km2 area spans 1,475 to 2,759 m (1,284 m of relief; median 1,969 m) at 30 m. Slopes average 13.2 degrees; 18.8% of the ground is steeper than 15 degrees and 3.7% steeper than 30 [20][23][178]. - **Evidenced:** The highest summit (DH-1) rises to 2,759 m, 192 m above the ground within 750 m; the next summits reach 2,702 m, 2,330 m, 2,271 m [20][22][178]. - **Inferred:** KT-1 is a key terrain candidate: highest ground within 1.0 km (2271 m); 189 m above the lowest ground around it; observes 21% of the ground within 4.5 km (3.81 km2); overlooks mobility corridor MC-1 [64][90][178]. A candidate, not a decision [64][90][178]. - **Inferred:** KT-2 is a key terrain candidate: highest ground within 1.0 km (2759 m); 270 m above the lowest ground around it; observes 13% of the ground within 4.5 km (2.01 km2); overlooks mobility corridor MC-2 [64][90][178]. A candidate, not a decision [64][90][178]. - **Inferred:** The widest mobility corridor (MC-1) is about 3.2 km wide and 8.4 km long on a 4 degree axis; 2 corridor(s) in all [20][65][178]. - **Inferred:** AA-1: cheapest way through the area from the N edge to the S edge: 8.2 km, about 103 min, 0% restricted going (no objective given) [20][62][178]. - **Inferred:** AA-2: cheapest way through the area from the SE edge to the NW edge: 10.0 km, about 201 min, 5% restricted going (no objective given) [20][62][178]. - **Inferred:** CP-1: MC-2 narrows to 180 m, at most 70% of its median width (300 m) [20][63][178]. - **Inferred:** CP-2: MC-1 narrows to 300 m, at most 70% of its median width (3.2 km) [20][63][178]. - **Inferred:** CP-3: MC-1 narrows to 513 m, at most 70% of its median width (3.2 km) [20][63][178]. - **Inferred:** 1.72 km2 (2.4% of the area) lies within 3 m of the height of the nearest stream, the ground that floods first when the streams rise; a terrain screen, not a flood model [20][79][178]. - **Evidenced:** Most ground faces west (49.0%); 0.3% is flat (under 1 degree) [20][23][178]. - **Evidenced:** From the dominant heights (1.7 m eye height) 37.1% of the ground within 4.5 km is visible (24.13 km2) [20][27][61][90][178]. - **Evidenced:** From OP-1, OP-2, OP-3, OP-4, OP-5, OP-6, OP-7, OP-8, OP-9, OP-10, OP-11, OP-12, OP-13, OP-14 (1.7 m eye height) 69.3% of the ground within 4.5 km is visible (49.94 km2) [20][27][61][90][178]. - **Evidenced:** From observation point KT-1 (1.7 m eye height) 21.1% of the ground within 4.5 km is visible (3.81 km2) [20][27][61][90][178]. - **Evidenced:** From observation point KT-2 (1.7 m eye height) 12.5% of the ground within 4.5 km is visible (2.01 km2) [20][27][61][90][178]. - **Inferred:** LZ-1 (score 0.75): a 29.9 m medium helicopter landing point fits: clear ground about 300 m across or more at 30 m cells; slope up to 2.3 degrees (limit 7); unobserved, 531 m from observed ground [29][178]. Map study: confirm on the ground [29][178]. - **Inferred:** LZ-2 (score 0.44): a 29.9 m medium helicopter landing point fits: clear ground about 90 m across at 30 m cells; slope up to 3.4 degrees (limit 7); unobserved, 540 m from observed ground [29][178]. Map study: confirm on the ground [29][178]. - **Inferred:** LZ-3 (score 0.43): a 29.9 m medium helicopter landing point fits: clear ground about 90 m across at 30 m cells; slope up to 4.3 degrees (limit 7); unobserved, 569 m from observed ground [29][178]. Map study: confirm on the ground [29][178]. - **Evidenced:** The best dismounted route the longest axis is 8.91 km long, about 245 min, climbing 1280 m [20][50][51][52][53][178]. - **Evidenced:** The best dismounted route the two highest summits is 3.84 km long, about 44 min, climbing 11 m [20][50][51][52][53][178]. - **Evidenced:** 24 contour line(s) at 100 m over 13 level(s) [20][100][178]. - **Evidenced:** Slope over 72.09 km2: mean 13.16 degrees, max 38.63 degrees; 92% of the ground is steeper than 15% [20][99][178]. - **Evidenced:** Aspect: most ground faces W (49%); 0% is flat (slope under 1 degrees) [20][98][178]. - **Evidenced:** Hillshade lit from 315 degrees at 45 degrees altitude (multidirectional) [20][97][178]. - **Evidenced:** Total curvature (1/100 m): mean -0.005, range -1.2011 to 0.3811 [20][96][178]. - **Evidenced:** Terrain suite over 72.09 km2 at 30 m: elevation 1475.15 to 2759.29 m; 2 raster(s) and 0 vector layer(s) [20][94][95][178]. - **Evidenced:** Landforms (geomorphons): slope 92%, valley 2%, ridge 2% (search 300 m, flat below 1 degrees) [20][93][178]. - **Evidenced:** 5 ravine line(s) and 36 ridge line(s); ravines drain at least 0.721 km2 [20][91][92][178]. - **Evidenced:** 2 observation point(s), 9 open flat area(s), 5 masked area(s) [20][88][89][90][178]. - **Evidenced:** Flow accumulation (pure): the largest catchment drains 43.241 km2 through one cell; 9 stream link(s), Strahler order up to 2 [20][85][86][87][178]. - **Evidenced:** Pour points: found 3 candidate outlets draining at least 0.721 km2 [20][84][178]. - **Evidenced:** Watersheds of the 3 largest outlet basin(s): 72.090 km2 in total; the largest drains 43.241 km2 [20][81][82][83][178]. - **Evidenced:** Flood-prone ground (HAND up to 3 m above streams that drain 0.72 km2 or more): 1.715 km2, 2.4% of the area; 9 stream link(s) [20][77][78][79][80][178]. - **Inferred:** Dismounted going: 90% unrestricted, 7% restricted, 3% severely restricted; 1 obstacle region(s) [20][74][75][76][178]. - **Inferred:** Wheeled going: 8% unrestricted, 81% restricted, 10% severely restricted; 10 obstacle region(s) [20][71][72][73][178]. - **Inferred:** Tracked going: 90% unrestricted, 7% restricted, 3% severely restricted; 1 obstacle region(s) [20][68][69][70][178]. - **Inferred:** MCOO (dismounted): 1 obstacle(s), 2 corridor(s), 2 key terrain candidate(s), 3 avenue(s) [20][62][63][64][65][66][67][178]. - **Evidenced:** Visible from 5 observer(s): 37.1% of the ground within 4500 m (24.134 km2); k = 0.13 [20][58][59][60][61][178]. - **Evidenced:** Target unknown over 3840 m: an endpoint lies on missing ground or outside the area [20][57][178]. - **Evidenced:** Target visible over 2694 m: clear: the lowest clearance is 2.9 m at 15 m from A [20][55][56][178]. - **Evidenced:** Target unknown over 3915 m: an endpoint lies on missing ground or outside the area [20][54][178]. - **Inferred:** Best dismounted route: 8.91 km in about 245 min (ascent 1280 m, 4% restricted); 0 alternate(s) [20][53][178]. - **Inferred:** Best dismounted route: 3.84 km in about 44 min (ascent 11 m, 0% restricted); 0 alternate(s) [20][52][178]. - **Evidenced:** Profile of 8910 m: 1485.4 to 2689.19 m, gain 1280 m, loss 796 m, steepest grade 77.5% [20][51][178]. - **Evidenced:** Profile of 3840 m: 2755.69 to 2759.29 m, gain 0 m, loss 4 m, steepest grade 1.4% [20][50][178]. - **Inferred:** Clear-sky insolation over 365 day(s): mean 6.607 kWh/m2/day (range 5.552 to 6.892); most energy on unsuitable ground [20][47][48][49][178]. - **Inferred:** Infinite-slope factor of safety: 2.1% unstable (FS < 1), 3.4% marginal; lowest FS 0.91 [20][45][46][178]. - **Inferred:** Landslide susceptibility (terrain index): 1.6% high or very high; weights {'slope': 0.5, 'wetness': 0.25, 'concavity': 0.15, 'relief': 0.1} [20][43][44][178]. - **Inferred:** Erosion (RUSLE form): mean soil loss 39.45 t/ha/yr; 96.8% of the ground is high or very high [20][40][41][42][178]. - **Inferred:** Buildability: 1.4% optimal, 2.8% suitable; mean score 5/100; 91.7% excluded by constraints [20][37][38][39][178]. - **Inferred:** Site suitability: 0.0% excellent, 0.0% good; mean score 38/100; 3.7% excluded by constraints [20][35][36][178]. - **Inferred:** Mineability: 7.4% optimal, 46.4% suitable; mean score 74/100; 0.0% excluded by constraints [20][32][33][34][178]. - **Inferred:** Landing zones for a 29.9 m medium helicopter landing point (slope up to 7 degrees): 6 candidate(s); 3.2% of the ground fits the landing point [20][28][29][30][31][178]. - **Evidenced:** Visible from 14 observer(s): 69.3% of the ground within 4500 m (49.940 km2); k = 0.13 [20][24][25][26][27][178]. *Tool tr_terrain_analysis; parameters {"dem": {"dataset": "f309cad9-7065-421d-b65f-542f4ad825d1"}, "area": [-75.6219210590975, 6.207884234863717, -75.5404851734973, 6.280430663194416], "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "purpose": "everything", "observers": [[-75.587681, 6.218869], [-75.583629, 6.233346], [-75.586086, 6.247817], [-75.582036, 6.264466], [-75.58937, 6.274589], [-75.597451, 6.226094], [-75.601548, 6.25141* ## Zonal statistics **What this analysis is.** Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON [172]. In ArcGIS it is Zonal Statistics as Table (polygons) [172]. **Why it was run on this data.** Statistics of Valley terrain model (DEM) inside each feature of Neighborhoods [173]. **What it found.** The analysis involves Zonal statistics using the rs_zonal_statistics tool [174]. The tool summary reports processing 16 row(s) [174]. Additionally, the process analyzed 16 zones [174]. **Findings** - **Evidenced:** Zonal statistics: Zonal statistics: 16 row(s) [174]. - **Evidenced:** zones 16 [174]. *Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Neighborhoods (efbe8937)", "raster_id": "Valley terrain model (DEM) (51a68176)"}* ## Slope **What this analysis is.** Slope: Horn slope in degrees or percent, with the share of ground in six grade classes [175]. In ArcGIS it is Slope (Spatial Analyst); in QGIS, Slope (GDAL DEM) [175]. **Why it was run on this data.** Slope of the Valley terrain model (DEM) surface [176]. **What it found.** Slope on Valley terrain model (DEM) produced Slope (degrees) (0 features) [177][178][179]. It reported dem, cols 300; dem, rows 267; dem, cell m 30; dem, native m 30; dem, relief m 1,284; dem, elev max m 2,759 [177]. | Result | Value | | --- | --- | | dem, cols | 300 | | dem, rows | 267 | | dem, cell m | 30 | | dem, native m | 30 | | dem, relief m | 1,284 | | dem, elev max m | 2,759 | | dem, elev min m | 1,475 | | dem, elev mean m | 1,991 | | dem, missing frac | 0 | | grid, cols | 300 | | grid, rows | 267 | | grid, cell m | 30 | | stats, max | 38.63 | | stats, min | 0.02 | | stats, mean | 13.16 | | stats, median | 12.37 | | stats, steeper than 15pct | 91.71 | | tokens, usd | 0.017 | | tokens, tokens | 17 | | tokens, tokens in | 1 | **Findings** - **Evidenced:** Slope on Valley terrain model (DEM) produced Slope (degrees) (0 features) [177][178][179]. - **Evidenced:** It reported dem, cols 300; dem, rows 267; dem, cell m 30; dem, native m 30; dem, relief m 1,284; dem, elev max m 2,759 [177]. *Tool tr_slope; parameters {"dem": {"layer": "Valley terrain model (DEM) (51a68176)"}, "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186"}* ## Hot spots **What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [180]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [180]. **Why it was run on this data.** 1800 points: Getis-Ord Gi* tests whether the concentrations in Dropped calls are statistically significant [181]. Measured: Dropped calls are clustered (nearest-neighbor ratio 0.87, z = -10.7), median spacing 63 m [181]. Cells of 200 m and a band of 500 m from the measured spacing [181]. **What it found.** Hot spots on Dropped calls: Hot spot analysis: 1 layer saved: Hot spot analysis: Dropped calls (1798 features) [14][182]. It reported result, bins 1,798; result, count 1,798; result, elapsed ms 64; result, cell size m 200; result, incidents used 1,800; result, distance band m 500 [182]. | Result | Value | | --- | --- | | result, bins | 1,798 | | result, count | 1,798 | | result, elapsed ms | 64 | | result, cell size m | 200 | | result, incidents used | 1,800 | | result, distance band m | 500 | | billing, cost | 0.086 | | billing, list price | 0.086 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Hot spots on Dropped calls: Hot spot analysis: 1 layer saved: Hot spot analysis: Dropped calls (1798 features) [14][182]. - **Evidenced:** It reported result, bins 1,798; result, count 1,798; result, elapsed ms 64; result, cell size m 200; result, incidents used 1,800; result, distance band m 500 [182]. *Tool gp_hot_spots; parameters {"project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Dropped calls (e2f5ccec)"}, "aggregation": "hexagon", "cell_size_m": 200.0, "distance_band_m": 500.0}* ## Hot spots **What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [180]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [180]. **Why it was run on this data.** 420 points: Getis-Ord Gi* tests whether the concentrations in Coverage complaints are statistically significant [183]. Measured: Coverage complaints are clustered (nearest-neighbor ratio 0.87, z = -5.0), median spacing 123 m [183]. Cells of 500 m and a band of 1000 m from the measured spacing [183]. **What it found.** Hot spots on Coverage complaints: Hot spot analysis: 1 layer saved: Hot spot analysis: Coverage complaints (305 features) [15][184]. It reported result, bins 305; result, count 305; result, elapsed ms 11; result, cell size m 500; result, incidents used 420; result, distance band m 1,000 [184]. | Result | Value | | --- | --- | | result, bins | 305 | | result, count | 305 | | result, elapsed ms | 11 | | result, cell size m | 500 | | result, incidents used | 420 | | result, distance band m | 1,000 | | billing, cost | 0.0584 | | billing, list price | 0.0584 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Hot spots on Coverage complaints: Hot spot analysis: 1 layer saved: Hot spot analysis: Coverage complaints (305 features) [15][184]. - **Evidenced:** It reported result, bins 305; result, count 305; result, elapsed ms 11; result, cell size m 500; result, incidents used 420; result, distance band m 1,000 [184]. *Tool gp_hot_spots; parameters {"project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Coverage complaints (a0e6cf38)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 1000.0}* ## Hot spots **What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [180]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [180]. **Why it was run on this data.** 40 points: Getis-Ord Gi* tests whether the concentrations in Small cells are statistically significant [185]. **What it found.** The hot spot analysis processed 40 incidents using the Small cells layer [186]. The analysis identified 21 features, with the result showing a count of 21 [186]. These results were generated using a cell size of 2,000 m and a distance band of 2,000 m [186]. The total cost for the analysis was 0.0508 [186]. | Result | Value | | --- | --- | | result, bins | 21 | | result, count | 21 | | result, elapsed ms | 2 | | result, cell size m | 2,000 | | result, incidents used | 40 | | result, distance band m | 2,000 | | billing, cost | 0.0508 | | billing, list price | 0.0508 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Hot spots on Small cells: Hot spot analysis: 1 layer saved: Hot spot analysis: Small cells (21 features) [17][186]. - **Evidenced:** It reported result, bins 21; result, count 21; result, elapsed ms 2; result, cell size m 2,000; result, incidents used 40; result, distance band m 2,000 [186]. *Tool gp_hot_spots; parameters {"project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Small cells (0b706ff2)"}, "aggregation": "hexagon", "cell_size_m": 2000.0, "distance_band_m": 2000.0}* ## Space time hot spots **What this analysis is.** Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time [187]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [187]. **Why it was run on this data.** Coverage complaints carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per week [188]. Measured: the pattern of Coverage complaints changes over time (earlier half clustered, later half random; the center moved 305 m) [188]. **What it found.** Space time hot spots on Coverage complaints: Emerging hot spots: 1 layer saved: Emerging hot spots: Coverage complaints (94 features) [15][189]. It reported result, count 94; result, elapsed ms 14; result, resolution 8; result, features used 420; result, time neighbors 0; billing, cost 0.121 [189]. | Result | Value | | --- | --- | | result, count | 94 | | result, elapsed ms | 14 | | result, resolution | 8 | | result, features used | 420 | | result, time neighbors | 0 | | billing, cost | 0.121 | | billing, list price | 0.121 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Space time hot spots on Coverage complaints: Emerging hot spots: 1 layer saved: Emerging hot spots: Coverage complaints (94 features) [15][189]. - **Evidenced:** It reported result, count 94; result, elapsed ms 14; result, resolution 8; result, features used 420; result, time neighbors 0; billing, cost 0.121 [189]. *Tool gp_space_time_hot_spots; parameters {"project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Coverage complaints (a0e6cf38)"}, "interval": "week", "bin_shape": "h3", "resolution": 8, "time_field": "date"}* ## Space time hot spots **What this analysis is.** Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time [187]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [187]. **Why it was run on this data.** Dropped calls carry dates in 'occurred_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week [190]. **What it found.** Space time hot spots on Dropped calls: Emerging hot spots: 1 layer saved: Emerging hot spots: Dropped calls (109 features) [14][191]. It reported result, count 109; result, elapsed ms 13; result, resolution 8; result, features used 1,800; result, time neighbors 0; billing, cost 0.19 [191]. | Result | Value | | --- | --- | | result, count | 109 | | result, elapsed ms | 13 | | result, resolution | 8 | | result, features used | 1,800 | | result, time neighbors | 0 | | billing, cost | 0.19 | | billing, list price | 0.19 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Space time hot spots on Dropped calls: Emerging hot spots: 1 layer saved: Emerging hot spots: Dropped calls (109 features) [14][191]. - **Evidenced:** It reported result, count 109; result, elapsed ms 13; result, resolution 8; result, features used 1,800; result, time neighbors 0; billing, cost 0.19 [191]. *Tool gp_space_time_hot_spots; parameters {"project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Dropped calls (e2f5ccec)"}, "interval": "week", "bin_shape": "h3", "resolution": 8, "time_field": "occurred_at"}* ## Summarize within **What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [192]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [192]. **Why it was run on this data.** Count Towers inside each feature of Neighborhoods by technology; the counts map as a choropleth (natural breaks, sequential colors) [193]. **What it found.** Summarize within on Towers, Neighborhoods: Summarize within: 1 layer saved: Summarize within: Neighborhoods (16 features) [12][13][194]. It reported result, count 16; result, elapsed ms 1; billing, cost 0.01062; billing, list price 0.01062; billing, upstream cost 0 [194]. | Result | Value | | --- | --- | | result, count | 16 | | result, elapsed ms | 1 | | billing, cost | 0.01062 | | billing, list price | 0.01062 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Summarize within on Towers, Neighborhoods: Summarize within: 1 layer saved: Summarize within: Neighborhoods (16 features) [12][13][194]. - **Evidenced:** It reported result, count 16; result, elapsed ms 1; billing, cost 0.01062; billing, list price 0.01062; billing, upstream cost 0 [194]. *Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "height_m"}], "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Towers (f7206b9b)"}, "group_by": "technology", "polygons": {"layer": "Neighborhoods (efbe8937)"}}* ## Summarize within **What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [192]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [192]. **Why it was run on this data.** Count Towers inside each feature of Neighborhoods by technology; the counts map as a choropleth (natural breaks, sequential colors) [193]. **What it found.** Summarize within on Small cells, Neighborhoods: Summarize within: 1 layer saved: Summarize within: Neighborhoods (16 features) [12][17][195]. It reported result, count 16; result, elapsed ms 0; billing, cost 0.01112; billing, list price 0.01112; billing, upstream cost 0 [195]. | Result | Value | | --- | --- | | result, count | 16 | | result, elapsed ms | 0 | | billing, cost | 0.01112 | | billing, list price | 0.01112 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Summarize within on Small cells, Neighborhoods: Summarize within: 1 layer saved: Summarize within: Neighborhoods (16 features) [12][17][195]. - **Evidenced:** It reported result, count 16; result, elapsed ms 0; billing, cost 0.01112; billing, list price 0.01112; billing, upstream cost 0 [195]. *Tool gp_summarize_within; parameters {"stats": [], "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Small cells (0b706ff2)"}, "group_by": "status", "polygons": {"layer": "Neighborhoods (efbe8937)"}}* ## Summarize within **What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [192]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [192]. **Why it was run on this data.** Count Towers inside each feature of Neighborhoods by technology; the counts map as a choropleth (natural breaks, sequential colors) [193]. **What it found.** Summarize within on Dropped calls, Neighborhoods: Summarize within: 1 layer saved: Summarize within: Neighborhoods (16 features) [12][14][196]. It reported result, count 16; result, elapsed ms 3; billing, cost 0.04634; billing, list price 0.04634; billing, upstream cost 0 [196]. | Result | Value | | --- | --- | | result, count | 16 | | result, elapsed ms | 3 | | billing, cost | 0.04634 | | billing, list price | 0.04634 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Summarize within on Dropped calls, Neighborhoods: Summarize within: 1 layer saved: Summarize within: Neighborhoods (16 features) [12][14][196]. - **Evidenced:** It reported result, count 16; result, elapsed ms 3; billing, cost 0.04634; billing, list price 0.04634; billing, upstream cost 0 [196]. *Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "signal_dbm"}], "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Dropped calls (e2f5ccec)"}, "group_by": "cause", "polygons": {"layer": "Neighborhoods (efbe8937)"}}* ## Period change **What this analysis is.** Period change: Counts or sums per period (week over week, month over month, any two periods, or before and after a date), per area, per H3 cell or in total, with the places of largest rise and fall and a significance note [197]. In ArcGIS it is Summarize Percent Change (Crime Analysis) or Change Point Detection [197]. **Why it was run on this data.** Dropped calls carry dates in 'occurred_at' over 8 weeks: the count per week, week over week, with the places of largest rise and fall per feature of Neighborhoods and whether the change is beyond chance [198]. **What it found.** Period change on Neighborhoods, Dropped calls produced Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) (16 features) [7][8][12][14]. Dropped calls: count per week shows a trend of -1.12 per week; the second half of its weeks has 911 against 889 in the first (+2.5%) [199]. The chart Dropped calls: week of 2026-08-24 to week of 2026-08-31 by place is drawn from 30 rows [200]. The chart Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) is drawn from 5 rows [201]. ![Dropped calls: count per week](charts/05-dropped-calls-count-per-week.png) *Figure 76. Dropped calls: count per week [199]. Per week, 2026-08-03 to 2026-09-21. Source: Dropped calls (1,800 features). The data is in the appendix.* ![Dropped calls: week of 2026-08-24 to week of 2026-08-31 by place](charts/06-dropped-calls-week-of-2026-08-24-to-week-of-2026-08-31-by-pl.png) *Figure 77. Dropped calls: week of 2026-08-24 to week of 2026-08-31 by place [200]. Source: Dropped calls (1,800 features). The data is in the appendix.* ![Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31)](charts/60-period-change-dropped-calls-week-of-2026-08-24-to-week-of-20.png) *Figure 78. Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) [201]. Source: Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) (16 features). The data is in the appendix.* **Findings** - **Evidenced:** Period change on Neighborhoods, Dropped calls produced Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) (16 features) [7][8][12][14]. - **Evidenced:** Dropped calls: count per week shows a trend of -1.12 per week; the second half of its weeks has 911 against 889 in the first (+2.5%) [199]. - **Evidenced:** The chart Dropped calls: week of 2026-08-24 to week of 2026-08-31 by place is drawn from 30 rows [200]. - **Evidenced:** The chart Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) is drawn from 5 rows [201]. *Tool gp_period_change; parameters {"by": "area", "areas": {"layer": "Neighborhoods (efbe8937)"}, "period": "week", "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Dropped calls (e2f5ccec)"}, "area_field": "name", "time_field": "occurred_at"}* ## Period change **What this analysis is.** Period change: Counts or sums per period (week over week, month over month, any two periods, or before and after a date), per area, per H3 cell or in total, with the places of largest rise and fall and a significance note [197]. In ArcGIS it is Summarize Percent Change (Crime Analysis) or Change Point Detection [197]. **Why it was run on this data.** Dropped calls carry dates in 'occurred_at' over 8 weeks: the count per week, week over week, with the places of largest rise and fall per feature of Neighborhoods and whether the change is beyond chance [198]. **What it found.** Period change on Neighborhoods, Coverage complaints produced Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17) (8 features) [9][10][12][15]. Coverage complaints: count per week shows a trend of +0.066 per week; the second half of its weeks has 222 against 198 in the first (+12.1%) [202]. The chart Coverage complaints: week of 2026-08-10 to week of 2026-08-17 by place is drawn from 16 rows [203]. The chart Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17) is drawn from 5 rows [204]. ![Coverage complaints: count per week](charts/07-coverage-complaints-count-per-week.png) *Figure 79. Coverage complaints: count per week [202]. Per week, 2026-02-23 to 2026-09-28. Source: Coverage complaints (420 features). The data is in the appendix.* ![Coverage complaints: week of 2026-08-10 to week of 2026-08-17 by place](charts/08-coverage-complaints-week-of-2026-08-10-to-week-of-2026-08-17.png) *Figure 80. Coverage complaints: week of 2026-08-10 to week of 2026-08-17 by place [203]. Source: Coverage complaints (420 features). The data is in the appendix.* ![Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17)](charts/61-period-change-coverage-complaints-week-of-2026-08-10-to-week.png) *Figure 81. Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17) [204]. Source: Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17) (8 features). The data is in the appendix.* **Findings** - **Evidenced:** Period change on Neighborhoods, Coverage complaints produced Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17) (8 features) [9][10][12][15]. - **Evidenced:** Coverage complaints: count per week shows a trend of +0.066 per week; the second half of its weeks has 222 against 198 in the first (+12.1%) [202]. - **Evidenced:** The chart Coverage complaints: week of 2026-08-10 to week of 2026-08-17 by place is drawn from 16 rows [203]. - **Evidenced:** The chart Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17) is drawn from 5 rows [204]. *Tool gp_period_change; parameters {"by": "area", "areas": {"layer": "Neighborhoods (efbe8937)"}, "period": "week", "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Coverage complaints (a0e6cf38)"}, "area_field": "name", "time_field": "date"}* ## Seasonality **What this analysis is.** Seasonality: Counts by hour of day, day of week and month, with peaks and troughs per cycle and per area [205]. In ArcGIS it is Time Series Clustering or Create Space Time Cube [205]. **Why it was run on this data.** Dropped calls by hour of day, day of week: the busiest and the quietest per feature of Neighborhoods, and whether the cycle is uneven beyond chance [206]. **What it found.** Seasonality on Neighborhoods, Dropped calls produced Seasonality of Dropped calls (16 features) [12][14][207][208]. The chart Dropped calls by hour of day is drawn from 24 rows [209]. The chart Dropped calls by day of week is drawn from 7 rows [210]. The chart Seasonality of Dropped calls is drawn from 5 rows [211]. ![Dropped calls by hour of day](charts/09-dropped-calls-by-hour-of-day.png) *Figure 82. Dropped calls by hour of day [209]. Source: Dropped calls (1,800 features). The data is in the appendix.* ![Dropped calls by day of week](charts/10-dropped-calls-by-day-of-week.png) *Figure 83. Dropped calls by day of week [210]. Source: Dropped calls (1,800 features). The data is in the appendix.* ![Seasonality of Dropped calls](charts/62-seasonality-of-dropped-calls.png) *Figure 84. Seasonality of Dropped calls [211]. Source: Seasonality of Dropped calls (16 features). The data is in the appendix.* **Findings** - **Evidenced:** Seasonality on Neighborhoods, Dropped calls produced Seasonality of Dropped calls (16 features) [12][14][207][208]. - **Evidenced:** The chart Dropped calls by hour of day is drawn from 24 rows [209]. - **Evidenced:** The chart Dropped calls by day of week is drawn from 7 rows [210]. - **Evidenced:** The chart Seasonality of Dropped calls is drawn from 5 rows [211]. *Tool gp_seasonality; parameters {"by": "area", "areas": {"layer": "Neighborhoods (efbe8937)"}, "cycles": ["hour", "weekday"], "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Dropped calls (e2f5ccec)"}, "time_field": "occurred_at"}* ## Calculate density **What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [212]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [212]. **Why it was run on this data.** 1800 points in Dropped calls: a density surface shows where they concentrate [213]. Measured: Dropped calls are clustered (nearest-neighbor ratio 0.87, z = -10.7), median spacing 63 m [213]. **What it found.** Calculate density on Dropped calls: Calculate density: 1 layer saved: Calculate density: Dropped calls (2034 features) [14][214]. It reported result, count 2,034; result, elapsed ms 40; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 368.1 [214]. | Result | Value | | --- | --- | | result, count | 2,034 | | result, elapsed ms | 40 | | result, bandwidth m | 500 | | result, cell size m | 200 | | result, excluded null | 0 | | result, max density per km2 | 368.1 | | billing, cost | 0.086 | | billing, list price | 0.086 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Calculate density on Dropped calls: Calculate density: 1 layer saved: Calculate density: Dropped calls (2034 features) [14][214]. - **Evidenced:** It reported result, count 2,034; result, elapsed ms 40; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 368.1 [214]. *Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Dropped calls (e2f5ccec)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0}* ## Calculate density **What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [212]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [212]. **Why it was run on this data.** 420 points in Coverage complaints: a density surface shows where they concentrate [215]. Measured: Coverage complaints are clustered (nearest-neighbor ratio 0.87, z = -5.0), median spacing 123 m [215]. **What it found.** The density analysis utilized the Coverage complaints data [15] and the gp_calculate_density tool [216]. The coverage complaints dataset contained 383 features [216], and the calculated maximum density per km2 was 68.51 [216]. | Result | Value | | --- | --- | | result, count | 383 | | result, elapsed ms | 8 | | result, bandwidth m | 1,000 | | result, cell size m | 500 | | result, excluded null | 0 | | result, max density per km2 | 68.51 | | billing, cost | 0.0584 | | billing, list price | 0.0584 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Calculate density on Coverage complaints: Calculate density: 1 layer saved: Calculate density: Coverage complaints (383 features) [15][216]. - **Evidenced:** It reported result, count 383; result, elapsed ms 8; result, bandwidth m 1,000; result, cell size m 500; result, excluded null 0; result, max density per km2 68.51 [216]. *Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Coverage complaints (a0e6cf38)"}, "bandwidth_m": 1000.0, "cell_size_m": 500.0}* ## Calculate density **What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [212]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [212]. **Why it was run on this data.** 40 points in Small cells: a density surface shows where they concentrate [217]. **What it found.** Calculate density on Small cells: Calculate density: 1 layer saved: Calculate density: Small cells (117 features) [17][218]. It reported result, count 117; result, elapsed ms 2; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 2.571 [218]. | Result | Value | | --- | --- | | result, count | 117 | | result, elapsed ms | 2 | | result, bandwidth m | 2,000 | | result, cell size m | 1,000 | | result, excluded null | 0 | | result, max density per km2 | 2.571 | | billing, cost | 0.0508 | | billing, list price | 0.0508 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Calculate density on Small cells: Calculate density: 1 layer saved: Calculate density: Small cells (117 features) [17][218]. - **Evidenced:** It reported result, count 117; result, elapsed ms 2; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 2.571 [218]. *Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Small cells (0b706ff2)"}, "bandwidth_m": 2000.0, "cell_size_m": 1000.0}* ## Find point clusters **What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [219]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [219]. **Why it was run on this data.** DBSCAN finds dense groups of Dropped calls and separates noise [220]. Measured: Dropped calls are clustered (nearest-neighbor ratio 0.87, z = -10.7), median spacing 63 m [220]. DBSCAN at 100 m (twice the measured spacing) [220]. **What it found.** Find point clusters on Dropped calls: Find point clusters: 2 layers saved: Find point clusters: Dropped calls (1800 features); Find point clusters: cluster hulls (2 features) [14][221]. It reported result, count 1,800; result, noise 1,744; result, distance m 100; result, elapsed ms 133; result, min points 20; billing, cost 0.086 [221]. | Result | Value | | --- | --- | | result, count | 1,800 | | result, noise | 1,744 | | result, distance m | 100 | | result, elapsed ms | 133 | | result, min points | 20 | | billing, cost | 0.086 | | billing, list price | 0.086 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Find point clusters on Dropped calls: Find point clusters: 2 layers saved: Find point clusters: Dropped calls (1800 features); Find point clusters: cluster hulls (2 features) [14][221]. - **Evidenced:** It reported result, count 1,800; result, noise 1,744; result, distance m 100; result, elapsed ms 133; result, min points 20; billing, cost 0.086 [221]. *Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Dropped calls (e2f5ccec)"}, "distance_m": 100.0, "min_points": 20}* ## Find point clusters **What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [219]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [219]. **Why it was run on this data.** DBSCAN finds dense groups of Coverage complaints and separates noise [222]. Measured: Coverage complaints are clustered (nearest-neighbor ratio 0.87, z = -5.0), median spacing 123 m [222]. DBSCAN at 200 m (twice the measured spacing) [222]. **What it found.** Find point clusters on Coverage complaints: Find point clusters: 2 layers saved: Find point clusters: Coverage complaints (420 features); Find point clusters: cluster hulls (1 feature) [15][223]. It reported result, count 420; result, noise 320; result, distance m 200; result, elapsed ms 5; result, min points 10; billing, cost 0.0584 [223]. | Result | Value | | --- | --- | | result, count | 420 | | result, noise | 320 | | result, distance m | 200 | | result, elapsed ms | 5 | | result, min points | 10 | | billing, cost | 0.0584 | | billing, list price | 0.0584 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Find point clusters on Coverage complaints: Find point clusters: 2 layers saved: Find point clusters: Coverage complaints (420 features); Find point clusters: cluster hulls (1 feature) [15][223]. - **Evidenced:** It reported result, count 420; result, noise 320; result, distance m 200; result, elapsed ms 5; result, min points 10; billing, cost 0.0584 [223]. *Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Coverage complaints (a0e6cf38)"}, "distance_m": 200.0, "min_points": 10}* ## Nearest neighbor index **What this analysis is.** Average nearest neighbor: Clark-Evans ratio: clustered, dispersed or random [224]. In ArcGIS it is Average Nearest Neighbor; in QGIS, native:nearestneighbouranalysis [224]. **Why it was run on this data.** The average nearest neighbor ratio says whether Towers are clustered, random or dispersed [225]. Measured: Towers are dispersed (nearest-neighbor ratio 1.82, z = +5.8), median spacing 1.36e+03 m [225]. The profile measured it already; the tool draws it [225]. **What it found.** Nearest neighbor index on Towers: Average nearest neighbor: done; no map output [13][226]. It reported result, n 14; result, p value 0; result, z score 8.397; result, elapsed ms 0; result, study area m2 23,117,306; result, nearest neighbor ratio 2.173 [226]. | Result | Value | | --- | --- | | result, n | 14 | | result, p value | 0 | | result, z score | 8.397 | | result, elapsed ms | 0 | | result, study area m2 | 23,117,306 | | result, nearest neighbor ratio | 2.173 | | result, expected mean distance m | 642.5 | | result, observed mean distance m | 1,396 | | billing, cost | 0.02 | | billing, list price | 0.02 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Nearest neighbor index on Towers: Average nearest neighbor: done; no map output [13][226]. - **Evidenced:** It reported result, n 14; result, p value 0; result, z score 8.397; result, elapsed ms 0; result, study area m2 23,117,306; result, nearest neighbor ratio 2.173 [226]. *A model draft was discarded because a sentence did not cite a source of this section.* *Tool gp_nearest_neighbor_index; parameters {"project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Towers (f7206b9b)"}}* ## Outliers **What this analysis is.** Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers [227]. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) [227]. **Why it was run on this data.** Local Moran's I maps clusters and outliers of 'population' [228]. Measured: Neighborhoods have 34 neighboring pairs (4.25 neighbors per area, 0 isolated), so neighbors can be compared [228]. **What it found.** Outliers on Neighborhoods: Find outliers: 1 layer saved: Find outliers: Neighborhoods (16 features) [12][229]. It reported result, count 16; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 1,998; billing, cost 0.05048 [229]. | Result | Value | | --- | --- | | result, count | 16 | | result, elapsed ms | 2 | | result, permutations | 199 | | result, excluded null | 0 | | result, distance band m | 1,998 | | billing, cost | 0.05048 | | billing, list price | 0.05048 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Outliers on Neighborhoods: Find outliers: 1 layer saved: Find outliers: Neighborhoods (16 features) [12][229]. - **Evidenced:** It reported result, count 16; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 1,998; billing, cost 0.05048 [229]. *Tool gp_outliers; parameters {"field": "population", "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Neighborhoods (efbe8937)"}, "aggregation": "hexagon"}* ## Spatial autocorrelation **What this analysis is.** Spatial autocorrelation: Global Moran’s I: is the pattern clustered, dispersed or random [230]. In ArcGIS it is Spatial Autocorrelation (Global Moran’s I) [230]. **Why it was run on this data.** Global Moran's I says whether similar values of 'population' sit next to each other [231]. Measured: Neighborhoods have 34 neighboring pairs (4.25 neighbors per area, 0 isolated), so neighbors can be compared [231]. **What it found.** Using the `gp_spatial_autocorrelation` tool [232], an analysis was performed on the Neighborhood | Result | Value | | --- | --- | | result, n | 16 | | result, p value | 0.1783 | | result, z score | 1.346 | | result, morans i | 0.2856 | | result, variance | 0.06851 | | result, elapsed ms | 1 | | result, expected i | -0.06667 | | result, excluded null | 0 | | result, distance band m | 1,998 | | result, p value normality | 0.1654 | | result, z score normality | 1.387 | | result, variance normality | 0.0645 | | billing, cost | 0.02 | | billing, list price | 0.02 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Spatial autocorrelation on Neighborhoods: Spatial autocorrelation: done; no map output [12][232]. - **Evidenced:** It reported result, n 16; result, p value 0.1783; result, z score 1.346; result, morans i 0.2856; result, variance 0.06851; result, elapsed ms 1 [232]. *Tool gp_spatial_autocorrelation; parameters {"field": "population", "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Neighborhoods (efbe8937)"}}* ## What follows from the results Change beside the counts per neighborhood, from Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) and Summarize within: Neighborhoods: the busiest area, El Cerro (327 in all), changed by -4 [235][7][196]. Change of dropped calls where density is high, from Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) and Calculate density: Coverage complaints: the largest rise (+13, Los Olivos) is where density averages 13.48 per square kilometer [238][7][216]. Hot cells of dropped calls per neighborhood, from Hot spot analysis: Dropped calls: 6 of 16 areas hold hot spot cells; El Cerro holds the most (49) [241][182]. Nearest tower to each cluster of dropped calls, from Find point clusters: cluster hulls: the 2 clusters lie 1,164 to 1,536 m from the nearest facility (Tower 13 Los Pinos and others) [246][221]. Dense areas of coverage complaints, from Calculate density: Coverage complaints: the densest of five classes joins 77 cells into one area [251][216]. Change of coverage complaints per neighborhood, from Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17): Los Olivos rose the most (+3) and La Ribera fell the most (-1) [253][9]. Clusters of all inputs together: dropped calls and coverage complaints, from Find point clusters: Dropped calls and Find point clusters: Coverage complaints: 2220 features from 2 sources [256][221][223]. Towers within 250 m of each cluster of dropped calls, from Find point clusters: cluster hulls: 0 of 2 clusters have one within 250 m [260][221]. **Findings** - **Evidenced:** Change beside the counts per neighborhood, from Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) and Summarize within: Neighborhoods: the busiest area, El Cerro (327 in all), changed by -4 [235][7][196]. - **Evidenced:** Change of dropped calls where density is high, from Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) and Calculate density: Coverage complaints: the largest rise (+13, Los Olivos) is where density averages 13.48 per square kilometer [238][7][216]. - **Evidenced:** Hot cells of dropped calls per neighborhood, from Hot spot analysis: Dropped calls: 6 of 16 areas hold hot spot cells; El Cerro holds the most (49) [241][182]. - **Evidenced:** Nearest tower to each cluster of dropped calls, from Find point clusters: cluster hulls: the 2 clusters lie 1,164 to 1,536 m from the nearest facility (Tower 13 Los Pinos and others) [246][221]. - **Evidenced:** Dense areas of coverage complaints, from Calculate density: Coverage complaints: the densest of five classes joins 77 cells into one area [251][216]. - **Evidenced:** Change of coverage complaints per neighborhood, from Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17): Los Olivos rose the most (+3) and La Ribera fell the most (-1) [253][9]. - **Evidenced:** Clusters of all inputs together: dropped calls and coverage complaints, from Find point clusters: Dropped calls and Find point clusters: Coverage complaints: 2220 features from 2 sources [256][221][223]. - **Evidenced:** Towers within 250 m of each cluster of dropped calls, from Find point clusters: cluster hulls: 0 of 2 clusters have one within 250 m [260][221]. ## Change beside the counts per neighborhood **What this analysis is.** Spatial join: Attach attributes (or aggregates) of matching features from another layer, by location, distance, time and attributes [233]. In ArcGIS it is Join Features or Spatial Join; in QGIS, native:joinattributesbylocation or native:joinbylocationsummary [233]. **Why it was run on this data.** Attach the Neighborhoods attributes to each of Towers [234]. **What it found.** The spatial join tool was executed using Summarize within: Neighborhoods [236] as an input, in conjunction with Period change: Dropped calls (week of 2026- | Result | Value | | --- | --- | | result, count | 16 | | result, elapsed ms | 1 | | billing, cost | 0.01066 | | billing, list price | 0.01066 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Spatial join on Summarize within: Neighborhoods, Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) produced Change beside the counts per neighborhood (16 features) [8][235][236][237]. - **Evidenced:** It reported result, count 16; result, elapsed ms 1; billing, cost 0.01066; billing, list price 0.01066; billing, upstream cost 0 [235]. *Tool gp_spatial_join; parameters {"join": {"layer": "Summarize within: Neighborhoods (07b9a4cd)"}, "fields": ["count"], "target": {"layer": "Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) (9e66bb0d)"}, "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "predicate": "largest_overlap", "output_name": "Change beside the counts per neighborhood"}* ## Change of dropped calls where density is high **What this analysis is.** Spatial join: Attach attributes (or aggregates) of matching features from another layer, by location, distance, time and attributes [233]. In ArcGIS it is Join Features or Spatial Join; in QGIS, native:joinattributesbylocation or native:joinbylocationsummary [233]. **Why it was run on this data.** Attach the Neighborhoods attributes to each of Towers [234]. **What it found.** Spatial join on Calculate density: Coverage complaints, Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) produced Change of dropped calls where density is high (16 features) [8][238][239][240]. It reported result, count 16; result, elapsed ms 1; billing, cost 0.018; billing, list price 0.018; billing, upstream cost 0 [238]. | Result | Value | | --- | --- | | result, count | 16 | | result, elapsed ms | 1 | | billing, cost | 0.018 | | billing, list price | 0.018 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Spatial join on Calculate density: Coverage complaints, Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) produced Change of dropped calls where density is high (16 features) [8][238][239][240]. - **Evidenced:** It reported result, count 16; result, elapsed ms 1; billing, cost 0.018; billing, list price 0.018; billing, upstream cost 0 [238]. *Tool gp_spatial_join; parameters {"join": {"layer": "Calculate density: Coverage complaints (1d475898)"}, "stats": [{"agg": "mean", "field": "density_per_km2"}], "target": {"layer": "Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) (9e66bb0d)"}, "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "predicate": "intersects", "output_name": "Change of dropped calls where density is high"}* ## Hot cells of dropped calls per neighborhood **What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [192]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [192]. **Why it was run on this data.** Count Towers inside each feature of Neighborhoods by technology; the counts map as a choropleth (natural breaks, sequential colors) [193]. **What it found.** The analysis began with Neighborhoods [12] and incorporated Hot spot analysis: Dropped calls [242]. | Result | Value | | --- | --- | | result, count | 16 | | result, elapsed ms | 32 | | billing, cost | 0.04628 | | billing, list price | 0.04628 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Summarize within on Hot spot analysis: Dropped calls, Neighborhoods produced Hot cells of dropped calls per neighborhood (16 features) [12][241][242][243]. - **Evidenced:** It reported result, count 16; result, elapsed ms 32; billing, cost 0.04628; billing, list price 0.04628; billing, upstream cost 0 [241]. *Tool gp_summarize_within; parameters {"project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Hot spot analysis: Dropped calls (1dbf3768)"}, "group_by": "gi_bin", "polygons": {"layer": "Neighborhoods (efbe8937)"}, "output_name": "Hot cells of dropped calls per neighborhood"}* ## Nearest tower to each cluster of dropped calls **What this analysis is.** Find nearest: The k nearest features for each input, with geodesic distance and bearing [244]. In ArcGIS it is Find Nearest or Near; in QGIS, native:joinbynearest or native:nearestneighbouranalysis [244]. **Why it was run on this data.** It was run toward the goal "Describe the project Sample: Telecom, Medellin: run the analyses that tell the most about its data, draw every result, and write a cited report" [245]. **What it found.** Find nearest on Towers, Find point clusters: cluster hulls produced Nearest tower to each cluster of dropped calls (2 features) [13][246][247][248]. It reported result, count 2; result, elapsed ms 1; billing, cost 0.01032; billing, list price 0.01032; billing, upstream cost 0 [246]. | Result | Value | | --- | --- | | result, count | 2 | | result, elapsed ms | 1 | | billing, cost | 0.01032 | | billing, list price | 0.01032 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Find nearest on Towers, Find point clusters: cluster hulls produced Nearest tower to each cluster of dropped calls (2 features) [13][246][247][248]. - **Evidenced:** It reported result, count 2; result, elapsed ms 1; billing, cost 0.01032; billing, list price 0.01032; billing, upstream cost 0 [246]. *Tool gp_find_nearest; parameters {"k": 1, "to": {"layer": "Towers (f7206b9b)"}, "from": {"layer": "Find point clusters: cluster hulls (5313780d)"}, "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "output_name": "Nearest tower to each cluster of dropped calls"}* ## Dense areas of coverage complaints **What this analysis is.** Pipeline: Run up to 20 GIS operations in one engine call; a step's params may use "$id" (an earlier step's result) or "$id.key", and layer references [249]. Every step's map outputs are saved as styled layers [249]. **Why it was run on this data.** Neighborhoods holds population in 'population': Towers per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors) [250]. **What it found.** Pipeline on Calculate density: Coverage complaints produced Dense areas of coverage complaints (5 features) [239][251][252]. It reported result, count 5; result, elapsed ms 6; billing, cost 0.02767; billing, list price 0.02767; billing, upstream cost 0; list price 0.02767 [251]. | Result | Value | | --- | --- | | result, count | 5 | | result, elapsed ms | 6 | | billing, cost | 0.02767 | | billing, list price | 0.02767 | | billing, upstream cost | 0 | | list price | 0.02767 | **Findings** - **Evidenced:** Pipeline on Calculate density: Coverage complaints produced Dense areas of coverage complaints (5 features) [239][251][252]. - **Evidenced:** It reported result, count 5; result, elapsed ms 6; billing, cost 0.02767; billing, list price 0.02767; billing, upstream cost 0; list price 0.02767 [251]. *A model draft was discarded because a sentence did not cite a source of this section.* *Tool gp_pipeline; parameters {"save": "final", "steps": [{"id": "classes", "params": {"field": "density_per_km2", "method": "quantile", "classes": 5, "features": {"layer": "Calculate density: Coverage complaints (1d475898)"}}, "operation": "classify_field"}, {"id": "areas", "params": {"by": ["class"], "features": "$classes"}, "operation": "dissolve"}], "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "output_name": "Dense a* ## Change of coverage complaints per neighborhood **What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [192]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [192]. **Why it was run on this data.** Count Towers inside each feature of Neighborhoods by technology; the counts map as a choropleth (natural breaks, sequential colors) [193]. **What it found.** Summarize within on Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17), Neighborhoods produced Change of coverage complaints per neighborhood (16 features) [10][12][253][254]. It reported result, count 16; result, elapsed ms 1; billing, cost 0.0105; billing, list price 0.0105; billing, upstream cost 0 [253]. | Result | Value | | --- | --- | | result, count | 16 | | result, elapsed ms | 1 | | billing, cost | 0.0105 | | billing, list price | 0.0105 | | billing, upstream cost | 0 | **Findings** - **Evidenced:** Summarize within on Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17), Neighborhoods produced Change of coverage complaints per neighborhood (16 features) [10][12][253][254]. - **Evidenced:** It reported result, count 16; result, elapsed ms 1; billing, cost 0.0105; billing, list price 0.0105; billing, upstream cost 0 [253]. *Tool gp_summarize_within; parameters {"stats": [{"agg": "sum", "field": "change"}], "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "features": {"layer": "Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17) (b22b41b5)"}, "polygons": {"layer": "Neighborhoods (efbe8937)"}, "output_name": "Change of coverage complaints per neighborhood"}* ## Clusters of all inputs together: dropped calls and coverage complaints **What this analysis is.** Pipeline: Run up to 20 GIS operations in one engine call; a step's params may use "$id" (an earlier step's result) or "$id.key", and layer references [249]. Every step's map outputs are saved as styled layers [249]. **Why it was run on this data.** Neighborhoods holds population in 'population': Dropped calls per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors) [255]. **What it found.** Pipeline on Dropped calls, Coverage complaints produced Find point clusters: cluster hulls (3 features), Clusters of all inputs together: dropped calls and coverage complaints (2,220 features) [14][15][256][257][258]. It reported result, count 2,220; result, noise 2,093; result, distance m 100; result, elapsed ms 24; result, min points 20; billing, cost 0.1044 [256]. Clusters of all inputs together: dropped calls and coverage complaints over time shows a trend of -1.12 per week; the second half of its weeks has 911 against 889 in the first (+2.5%) [259]. | Result | Value | | --- | --- | | result, count | 2,220 | | result, noise | 2,093 | | result, distance m | 100 | | result, elapsed ms | 24 | | result, min points | 20 | | billing, cost | 0.1044 | | billing, list price | 0.1044 | | billing, upstream cost | 0 | | list price | 0.1044 | ![Clusters of all inputs together: dropped calls and coverage complaints over time](charts/71-clusters-of-all-inputs-together-dropped-calls-and-coverage-c.png) *Figure 85. Clusters of all inputs together: dropped calls and coverage complaints over time [259]. Per week, 2026-08-03 to 2026-09-21. Source: Clusters of all inputs together: dropped calls and coverage complaints (2,220 features). The data is in the appendix.* **Findings** - **Evidenced:** Pipeline on Dropped calls, Coverage complaints produced Find point clusters: cluster hulls (3 features), Clusters of all inputs together: dropped calls and coverage complaints (2,220 features) [14][15][256][257][258]. - **Evidenced:** It reported result, count 2,220; result, noise 2,093; result, distance m 100; result, elapsed ms 24; result, min points 20; billing, cost 0.1044 [256]. - **Evidenced:** Clusters of all inputs together: dropped calls and coverage complaints over time shows a trend of -1.12 per week; the second half of its weeks has 911 against 889 in the first (+2.5%) [259]. *Tool gp_pipeline; parameters {"save": "final", "steps": [{"id": "merged", "params": {"layers": [{"layer": "Dropped calls (e2f5ccec)"}, {"layer": "Coverage complaints (a0e6cf38)"}], "source_field": "source"}, "operation": "merge_layers"}, {"id": "joint", "params": {"method": "dbscan", "features": "$merged", "distance_m": 100.0, "min_points": 20}, "operation": "find_point_clusters"}], "project": "bceeca13-52a5-4344-bf84-77a2f9f* ## Towers within 250 m of each cluster of dropped calls **What this analysis is.** Pipeline: Run up to 20 GIS operations in one engine call; a step's params may use "$id" (an earlier step's result) or "$id.key", and layer references [249]. Every step's map outputs are saved as styled layers [249]. **Why it was run on this data.** Neighborhoods holds population in 'population': Towers per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors) [250]. **What it found.** The analysis utilized Towers as input [13] through the `gp_pipeline` tool [260], which found point clusters: cluster hulls [247] as part of its process. This pipeline generated an output layer detailing Towers within 250 m of each cluster of dropped calls, consisting of 2 features [261]. | Result | Value | | --- | --- | | result, count | 2 | | result, elapsed ms | 0 | | billing, cost | 0.02034 | | billing, list price | 0.02034 | | billing, upstream cost | 0 | | list price | 0.02034 | **Findings** - **Evidenced:** Pipeline on Find point clusters: cluster hulls, Towers produced Towers within 250 m of each cluster of dropped calls (2 features) [13][247][260][261]. - **Evidenced:** It reported result, count 2; result, elapsed ms 0; billing, cost 0.02034; billing, list price 0.02034; billing, upstream cost 0; list price 0.02034 [260]. *Tool gp_pipeline; parameters {"save": "final", "steps": [{"id": "buffer", "params": {"features": {"layer": "Find point clusters: cluster hulls (5313780d)"}, "distance_m": 250}, "operation": "buffer"}, {"id": "count", "params": {"features": {"layer": "Towers (f7206b9b)"}, "polygons": "$buffer"}, "operation": "summarize_within"}], "project": "bceeca13-52a5-4344-bf84-77a2f9f8e186", "output_name": "Towers within 250 m of each clu* ## Maps and charts The run also made 22 chart(s) [262][263][264][265][266][267][268][269][270][271][272][273][274][275][276][277][278][279][280][281][282][283]. ![Dropped calls over time (occurred_at)](charts/11-dropped-calls-over-time-occurred-at.png) *Figure 86. Dropped calls over time (occurred_at) [262]. Per week, 2026-08-03 to 2026-09-21. Source: Dropped calls (1,800 features). The data is in the appendix.* ![Coverage complaints over time (date)](charts/12-coverage-complaints-over-time-date.png) *Figure 87. Coverage complaints over time (date) [263]. Per week, 2026-02-23 to 2026-09-28. Source: Coverage complaints (420 features). The data is in the appendix.* ![Cell KPIs by day over time (date)](charts/13-cell-kpis-by-day-over-time-date.png) *Figure 88. Cell KPIs by day over time (date) [264]. Per week, 2026-08-03 to 2026-09-21. Source: Cell KPIs by day (784 features). The data is in the appendix.* ![Site leases over time (lease_end_year)](charts/14-site-leases-over-time-lease-end-year.png) *Figure 89. Site leases over time (lease_end_year) [265]. Source: Site leases (14 features). The data is in the appendix.* ![Hot spot analysis: Dropped calls](charts/52-hot-spot-analysis-dropped-calls.png) *Figure 90. Hot spot analysis: Dropped calls [266]. Source: Hot spot analysis: Dropped calls (1,798 features). The data is in the appendix.* ![Hot spot analysis: Coverage complaints](charts/53-hot-spot-analysis-coverage-complaints.png) *Figure 91. Hot spot analysis: Coverage complaints [267]. Source: Hot spot analysis: Coverage complaints (305 features). The data is in the appendix.* ![Hot spot analysis: Small cells](charts/54-hot-spot-analysis-small-cells.png) *Figure 92. Hot spot analysis: Small cells [268]. Source: Hot spot analysis: Small cells (21 features). The data is in the appendix.* ![Emerging hot spots: Coverage complaints](charts/55-emerging-hot-spots-coverage-complaints.png) *Figure 93. Emerging hot spots: Coverage complaints [269]. Source: Emerging hot spots: Coverage complaints (94 features). The data is in the appendix.* ![Emerging hot spots: Dropped calls](charts/56-emerging-hot-spots-dropped-calls.png) *Figure 94. Emerging hot spots: Dropped calls [270]. Source: Emerging hot spots: Dropped calls (109 features). The data is in the appendix.* ![Summarize within: Neighborhoods](charts/57-summarize-within-neighborhoods.png) *Figure 95. Summarize within: Neighborhoods [271]. Source: Summarize within: Neighborhoods (16 features). The data is in the appendix.* ![Summarize within: Neighborhoods](charts/58-summarize-within-neighborhoods.png) *Figure 96. Summarize within: Neighborhoods [272]. Source: Summarize within: Neighborhoods (16 features). The data is in the appendix.* ![Summarize within: Neighborhoods](charts/59-summarize-within-neighborhoods.png) *Figure 97. Summarize within: Neighborhoods [273]. Source: Summarize within: Neighborhoods (16 features). The data is in the appendix.* ![Calculate density: Dropped calls](charts/63-calculate-density-dropped-calls.png) *Figure 98. Calculate density: Dropped calls [274]. Source: Calculate density: Dropped calls (2,034 features). The data is in the appendix.* ![Calculate density: Coverage complaints](charts/64-calculate-density-coverage-complaints.png) *Figure 99. Calculate density: Coverage complaints [275]. Source: Calculate density: Coverage complaints (383 features). The data is in the appendix.* ![Calculate density: Small cells](charts/65-calculate-density-small-cells.png) *Figure 100. Calculate density: Small cells [276]. Source: Calculate density: Small cells (117 features). The data is in the appendix.* ![Find point clusters: Dropped calls](charts/66-find-point-clusters-dropped-calls.png) *Figure 101. Find point clusters: Dropped calls [277]. Period: 2026-08-03 to 2026-09-27 (occurred_at). Source: Find point clusters: Dropped calls (1,800 features). The data is in the appendix.* ![Find point clusters: cluster hulls](charts/67-find-point-clusters-cluster-hulls.png) *Figure 102. Find point clusters: cluster hulls [278]. Source: Find point clusters: cluster hulls (2 features). The data is in the appendix.* ![Find point clusters: Coverage complaints](charts/68-find-point-clusters-coverage-complaints.png) *Figure 103. Find point clusters: Coverage complaints [279]. Period: 2026-03-01 to 2026-09-30 (date). Source: Find point clusters: Coverage complaints (420 features). The data is in the appendix.* ![Find point clusters: cluster hulls](charts/69-find-point-clusters-cluster-hulls.png) *Figure 104. Find point clusters: cluster hulls [280]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.* ![Find outliers: Neighborhoods](charts/70-find-outliers-neighborhoods.png) *Figure 105. Find outliers: Neighborhoods [281]. Source: Find outliers: Neighborhoods (16 features). The data is in the appendix.* ![Find point clusters: Coverage complaints over time](charts/72-find-point-clusters-coverage-complaints-over-time.png) *Figure 106. Find point clusters: Coverage complaints over time [282]. Per week, 2026-02-23 to 2026-09-28. Source: Find point clusters: Coverage complaints (420 features). The data is in the appendix.* ![Find point clusters: Dropped calls over time](charts/73-find-point-clusters-dropped-calls-over-time.png) *Figure 107. Find point clusters: Dropped calls over time [283]. Per week, 2026-08-03 to 2026-09-21. Source: Find point clusters: Dropped calls (1,800 features). The data is in the appendix.* ## More maps 43 more map(s), beyond the 24 the report body holds, in the composer's order: Terrain analysis (continued 23) [284]; Terrain analysis (continued 24) [285]; Terrain analysis (continued 25) [286]; Terrain analysis (continued 26) [287]; Terrain analysis (continued 27) [288]; Terrain analysis (continued 28) [289]; Terrain analysis (continued 29) [290]; Terrain analysis (continued 30) [291]; Terrain analysis (continued 31) [292]; Terrain analysis (continued 32) [293]; Terrain analysis (continued 33) [294]; Terrain analysis (continued 34) [295]; Terrain analysis (continued 35) [296]; Terrain analysis (continued 36) [297]; Terrain analysis (continued 37) [298]; Terrain analysis (continued 38) [299]; Terrain analysis (continued 39) [300]; Terrain analysis (continued 40) [301]; Terrain analysis (continued 41) [302]; Slope: valley terrain model (DEM) [303]; Hot spots: dropped calls [304]; Hot spots: coverage complaints [305]; Hot spots: small cells [306]; Emerging hot spots: coverage complaints and dropped calls [307]; Counts: towers, by neighborhood [308]; Counts: small cells, by neighborhood [309]; Counts: dropped calls, by neighborhood [310]; Period change: dropped calls, by neighborhood [311]; Period change: coverage complaints, by neighborhood [312]; Seasonality: dropped calls, by neighborhood [313]; Density: dropped calls [314]; Density: coverage complaints [315]; Density: small cells [316]; Clusters: dropped calls [317]; Clusters: coverage complaints [318]; Outliers: neighborhoods [319]; Change beside the counts per neighborhood [320]; Change of dropped calls where density is high [321]; Hot cells of dropped calls per neighborhood [322]; Nearest tower to each cluster of dropped calls [323]; Change of coverage complaints per neighborhood [324]; Clusters of all inputs together: dropped calls and coverage complaints [325]; Towers within 250 m of each cluster of dropped calls [326]. ![Terrain analysis (continued 23)](maps/32-terrain-analysis-continued-23.png) *Figure 108. Terrain analysis (continued 23) [284]* ![Terrain analysis (continued 24)](maps/33-terrain-analysis-continued-24.png) *Figure 109. Terrain analysis (continued 24) [285]* ![Terrain analysis (continued 25)](maps/34-terrain-analysis-continued-25.png) *Figure 110. Terrain analysis (continued 25) [286]* ![Terrain analysis (continued 26)](maps/35-terrain-analysis-continued-26.png) *Figure 111. Terrain analysis (continued 26) [287]* ![Terrain analysis (continued 27)](maps/36-terrain-analysis-continued-27.png) *Figure 112. Terrain analysis (continued 27) [288]* ![Terrain analysis (continued 28)](maps/37-terrain-analysis-continued-28.png) *Figure 113. Terrain analysis (continued 28) [289]* ![Terrain analysis (continued 29)](maps/38-terrain-analysis-continued-29.png) *Figure 114. Terrain analysis (continued 29) [290]* ![Terrain analysis (continued 30)](maps/39-terrain-analysis-continued-30.png) *Figure 115. Terrain analysis (continued 30) [291]* ![Terrain analysis (continued 31)](maps/40-terrain-analysis-continued-31.png) *Figure 116. Terrain analysis (continued 31) [292]* ![Terrain analysis (continued 32)](maps/41-terrain-analysis-continued-32.png) *Figure 117. Terrain analysis (continued 32) [293]* ![Terrain analysis (continued 33)](maps/42-terrain-analysis-continued-33.png) *Figure 118. Terrain analysis (continued 33) [294]* ![Terrain analysis (continued 34)](maps/43-terrain-analysis-continued-34.png) *Figure 119. Terrain analysis (continued 34) [295]* ![Terrain analysis (continued 35)](maps/44-terrain-analysis-continued-35.png) *Figure 120. Terrain analysis (continued 35) [296]* ![Terrain analysis (continued 36)](maps/45-terrain-analysis-continued-36.png) *Figure 121. Terrain analysis (continued 36) [297]* ![Terrain analysis (continued 37)](maps/46-terrain-analysis-continued-37.png) *Figure 122. Terrain analysis (continued 37) [298]* ![Terrain analysis (continued 38)](maps/47-terrain-analysis-continued-38.png) *Figure 123. Terrain analysis (continued 38) [299]* ![Terrain analysis (continued 39)](maps/48-terrain-analysis-continued-39.png) *Figure 124. Terrain analysis (continued 39) [300]* ![Terrain analysis (continued 40)](maps/49-terrain-analysis-continued-40.png) *Figure 125. Terrain analysis (continued 40) [301]* ![Terrain analysis (continued 41)](maps/50-terrain-analysis-continued-41.png) *Figure 126. Terrain analysis (continued 41) [302]* ![Slope: valley terrain model (DEM)](maps/51-slope-valley-terrain-model-dem.png) *Figure 127. Slope: valley terrain model (DEM) [303]* ![Hot spots: dropped calls](maps/52-hot-spots-dropped-calls.png) *Figure 128. Hot spots: dropped calls [304]* ![Hot spots: coverage complaints](maps/53-hot-spots-coverage-complaints.png) *Figure 129. Hot spots: coverage complaints [305]* ![Hot spots: small cells](maps/54-hot-spots-small-cells.png) *Figure 130. Hot spots: small cells [306]* ![Emerging hot spots: coverage complaints and dropped calls](maps/55-emerging-hot-spots-coverage-complaints-and-dropped-calls.png) *Figure 131. Emerging hot spots: coverage complaints and dropped calls [307]* ![Counts: towers, by neighborhood](maps/56-counts-towers-by-neighborhood.png) *Figure 132. Counts: towers, by neighborhood [308]* ![Counts: small cells, by neighborhood](maps/57-counts-small-cells-by-neighborhood.png) *Figure 133. Counts: small cells, by neighborhood [309]* ![Counts: dropped calls, by neighborhood](maps/58-counts-dropped-calls-by-neighborhood.png) *Figure 134. Counts: dropped calls, by neighborhood [310]* ![Period change: dropped calls, by neighborhood](maps/59-period-change-dropped-calls-by-neighborhood.png) *Figure 135. Period change: dropped calls, by neighborhood [311]* ![Period change: coverage complaints, by neighborhood](maps/60-period-change-coverage-complaints-by-neighborhood.png) *Figure 136. Period change: coverage complaints, by neighborhood [312]* ![Seasonality: dropped calls, by neighborhood](maps/61-seasonality-dropped-calls-by-neighborhood.png) *Figure 137. Seasonality: dropped calls, by neighborhood [313]* ![Density: dropped calls](maps/62-density-dropped-calls.png) *Figure 138. Density: dropped calls [314]* ![Density: coverage complaints](maps/63-density-coverage-complaints.png) *Figure 139. Density: coverage complaints [315]* ![Density: small cells](maps/64-density-small-cells.png) *Figure 140. Density: small cells [316]* ![Clusters: dropped calls](maps/65-clusters-dropped-calls.png) *Figure 141. Clusters: dropped calls [317]* ![Clusters: coverage complaints](maps/66-clusters-coverage-complaints.png) *Figure 142. Clusters: coverage complaints [318]* ![Outliers: neighborhoods](maps/67-outliers-neighborhoods.png) *Figure 143. Outliers: neighborhoods [319]* ![Change beside the counts per neighborhood](maps/68-change-beside-the-counts-per-neighborhood.png) *Figure 144. Change beside the counts per neighborhood [320]* ![Change of dropped calls where density is high](maps/69-change-of-dropped-calls-where-density-is-high.png) *Figure 145. Change of dropped calls where density is high [321]* ![Hot cells of dropped calls per neighborhood](maps/70-hot-cells-of-dropped-calls-per-neighborhood.png) *Figure 146. Hot cells of dropped calls per neighborhood [322]* ![Nearest tower to each cluster of dropped calls](maps/71-nearest-tower-to-each-cluster-of-dropped-calls.png) *Figure 147. Nearest tower to each cluster of dropped calls [323]* ![Change of coverage complaints per neighborhood](maps/72-change-of-coverage-complaints-per-neighborhood.png) *Figure 148. Change of coverage complaints per neighborhood [324]* ![Clusters of all inputs together: dropped calls and coverage complaints](maps/73-clusters-of-all-inputs-together-dropped-calls-and-coverage-c.png) *Figure 149. Clusters of all inputs together: dropped calls and coverage complaints [325]* ![Towers within 250 m of each cluster of dropped calls](maps/74-towers-within-250-m-of-each-cluster-of-dropped-calls.png) *Figure 150. Towers within 250 m of each cluster of dropped calls [326]* ## Documents **What the documents name.** Network performance review, August and September 2026 (1 page, 1 page(s) from the text layer) names 4 event terms and 7 places [327]. Event terms in Network performance review, August and September 2026: coverage (page 1, line 5), congestion (page 1, line 7), planned (page 1, line 10), live (page 1, line 10) [328]. Places named in Network performance review, August and September 2026: Los Olivos (page 1, line 5), 624785, -7555517 (page 1, line 5), Tower 03 La Fuente (page 1, line 7), Tower 13 Los Pinos (page 1, line 9) [328]. | Result | Value | | --- | --- | | Documents | 1 | | Entities | 11 | | Event terms | 4 | | Places | 7 | **Findings** - **Evidenced:** Network performance review, August and September 2026 (1 page, 1 page(s) from the text layer) names 4 event terms and 7 places. - **Evidenced:** Event terms in Network performance review, August and September 2026: coverage (page 1, line 5), congestion (page 1, line 7), planned (page 1, line 10), live (page 1, line 10). - **Evidenced:** Places named in Network performance review, August and September 2026: Los Olivos (page 1, line 5), 624785, -7555517 (page 1, line 5), Tower 03 La Fuente (page 1, line 7), Tower 13 Los Pinos (page 1, line 9). ## Changes since the last run 78 number(s) changed since the report of 2026-10-19T04:12:21Z. | Analysis | Measure | Before | After | Change | | --- | --- | --- | --- | --- | | Hot spots | result, bins | 21 | 1,798 | 1,777 | | Hot spots | result, count | 21 | 1,798 | 1,777 | | Hot spots | result, elapsed ms | 2 | 64 | 62 | | Hot spots | result, cell size m | 2,000 | 200 | -1,800 | | Hot spots | result, incidents used | 40 | 1,800 | 1,760 | | Hot spots | result, distance band m | 2,000 | 500 | -1,500 | | Hot spots | billing, cost | 0.0508 | 0.086 | 0.0352 | | Hot spots | billing, list price | 0.0508 | 0.086 | 0.0352 | | Hot spots | result, bins | 21 | 305 | 284 | | Hot spots | result, count | 21 | 305 | 284 | | Hot spots | result, elapsed ms | 2 | 11 | 9 | | Hot spots | result, cell size m | 2,000 | 500 | -1,500 | | Hot spots | result, incidents used | 40 | 420 | 380 | | Hot spots | result, distance band m | 2,000 | 1,000 | -1,000 | | Hot spots | billing, cost | 0.0508 | 0.0584 | 0.0076 | | Hot spots | billing, list price | 0.0508 | 0.0584 | 0.0076 | | Space time hot spots | result, count | 109 | 94 | -15 | | Space time hot spots | result, elapsed ms | 12 | 14 | 2 | | Space time hot spots | result, features used | 1,800 | 420 | -1,380 | | Space time hot spots | billing, cost | 0.19 | 0.121 | -0.069 | | Space time hot spots | billing, list price | 0.19 | 0.121 | -0.069 | | Space time hot spots | result, elapsed ms | 12 | 13 | 1 | | Summarize within | billing, cost | 0.0105 | 0.01062 | 0.00012 | | Summarize within | billing, list price | 0.0105 | 0.01062 | 0.00012 | | Summarize within | result, elapsed ms | 1 | 0 | -1 | | Summarize within | billing, cost | 0.0105 | 0.01112 | 0.00062 | | Summarize within | billing, list price | 0.0105 | 0.01112 | 0.00062 | | Summarize within | result, elapsed ms | 1 | 3 | 2 | | Summarize within | billing, cost | 0.0105 | 0.04634 | 0.03584 | | Summarize within | billing, list price | 0.0105 | 0.04634 | 0.03584 | | Calculate density | result, count | 117 | 2,034 | 1,917 | | Calculate density | result, elapsed ms | 3 | 40 | 37 | | Calculate density | result, bandwidth m | 2,000 | 500 | -1,500 | | Calculate density | result, cell size m | 1,000 | 200 | -800 | | Calculate density | result, max density per km2 | 2.571 | 368.1 | 365.6 | | Calculate density | billing, cost | 0.0508 | 0.086 | 0.0352 | | Calculate density | billing, list price | 0.0508 | 0.086 | 0.0352 | | Calculate density | result, count | 117 | 383 | 266 | | Calculate density | result, elapsed ms | 3 | 8 | 5 | | Calculate density | result, bandwidth m | 2,000 | 1,000 | -1,000 | | Calculate density | result, cell size m | 1,000 | 500 | -500 | | Calculate density | result, max density per km2 | 2.571 | 68.51 | 65.94 | | Calculate density | billing, cost | 0.0508 | 0.0584 | 0.0076 | | Calculate density | billing, list price | 0.0508 | 0.0584 | 0.0076 | | Calculate density | result, elapsed ms | 3 | 2 | -1 | | Find point clusters | result, elapsed ms | 18 | 133 | 115 | | Find point clusters | result, count | 1,800 | 420 | -1,380 | | Find point clusters | result, noise | 1,744 | 320 | -1,424 | | Find point clusters | result, distance m | 100 | 200 | 100 | | Find point clusters | result, elapsed ms | 18 | 5 | -13 | | Find point clusters | result, min points | 20 | 10 | -10 | | Find point clusters | billing, cost | 0.086 | 0.0584 | -0.0276 | | Find point clusters | billing, list price | 0.086 | 0.0584 | -0.0276 | | Outliers | result, elapsed ms | 1 | 2 | 1 | | Change beside the counts per neighborhood | result, count | 8 | 16 | 8 | | Change beside the counts per neighborhood | result, elapsed ms | 2 | 1 | -1 | | Change beside the counts per neighborhood | billing, cost | 0.05086 | 0.01066 | -0.0402 | | Change beside the counts per neighborhood | billing, list price | 0.05086 | 0.01066 | -0.0402 | | Change of dropped calls where density is high | result, count | 8 | 16 | 8 | | Change of dropped calls where density is high | result, elapsed ms | 2 | 1 | -1 | | Change of dropped calls where density is high | billing, cost | 0.05086 | 0.018 | -0.03286 | | Change of dropped calls where density is high | billing, list price | 0.05086 | 0.018 | -0.03286 | | Hot cells of dropped calls per neighborhood | result, elapsed ms | 1 | 32 | 31 | | Hot cells of dropped calls per neighborhood | billing, cost | 0.0105 | 0.04628 | 0.03578 | | Hot cells of dropped calls per neighborhood | billing, list price | 0.0105 | 0.04628 | 0.03578 | | Dense areas of coverage complaints | result, count | 2 | 5 | 3 | | Dense areas of coverage complaints | result, elapsed ms | 0 | 6 | 6 | | Dense areas of coverage complaints | billing, cost | 0.02034 | 0.02767 | 0.00733 | | Dense areas of coverage complaints | billing, list price | 0.02034 | 0.02767 | 0.00733 | | Dense areas of coverage complaints | list price | 0.02034 | 0.02767 | 0.00733 | | Clusters of all inputs together: dropped calls and coverage complaints | result, count | 2 | 2,220 | 2,218 | | Clusters of all inputs together: dropped calls and coverage complaints | result, elapsed ms | 0 | 24 | 24 | | Clusters of all inputs together: dropped calls and coverage complaints | billing, cost | 0.02034 | 0.1044 | 0.08406 | | Clusters of all inputs together: dropped calls and coverage complaints | billing, list price | 0.02034 | 0.1044 | 0.08406 | | Clusters of all inputs together: dropped calls and coverage complaints | list price | 0.02034 | 0.1044 | 0.08406 | | Documents | Documents | 7 | 1 | -6 | | Documents | Entities | 7 | 11 | 4 | | Documents | Event terms | 7 | 4 | -3 | New findings: - Change beside the counts per neighborhood, from Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) and Summarize within: Neighborhoods: the busiest area, El Cerro (327 in all), changed by -4 [235][7][196]. - Change of coverage complaints per neighborhood, from Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17): Los Olivos rose the most (+3) and La Ribera fell the most (-1) [253][9]. - Change of dropped calls where density is high, from Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) and Calculate density: Coverage complaints: the largest rise (+13, Los Olivos) is where density averages 13.48 per square kilometer [238][7][216]. - Clusters of all inputs together: dropped calls and coverage complaints over time shows a trend of -1.12 per week; the second half of its weeks has 911 against 889 in the first (+2.5%). - Clusters of all inputs together: dropped calls and coverage complaints, from Find point clusters: Dropped calls and Find point clusters: Coverage complaints: 2220 features from 2 sources [256][221][223]. - Dense areas of coverage complaints, from Calculate density: Coverage complaints: the densest of five classes joins 77 cells into one area [251][216]. - Hot cells of dropped calls per neighborhood, from Hot spot analysis: Dropped calls: 6 of 16 areas hold hot spot cells; El Cerro holds the most (49) [241][182]. - It reported result, bins 1,798; result, count 1,798; result, elapsed ms 64; result, cell size m 200; result, incidents used 1,800; result, distance band m 500. - It reported result, bins 305; result, count 305; result, elapsed ms 11; result, cell size m 500; result, incidents used 420; result, distance band m 1,000. - It reported result, count 1,800; result, noise 1,744; result, distance m 100; result, elapsed ms 133; result, min points 20; billing, cost 0.086. - It reported result, count 109; result, elapsed ms 13; result, resolution 8; result, features used 1,800; result, time neighbors 0; billing, cost 0.19. - It reported result, count 117; result, elapsed ms 2; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 2.571. - It reported result, count 16; result, elapsed ms 1; billing, cost 0.01062; billing, list price 0.01062; billing, upstream cost 0. - It reported result, count 16; result, elapsed ms 1; billing, cost 0.018; billing, list price 0.018; billing, upstream cost 0. - It reported result, count 16; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 1,998; billing, cost 0.05048. - It reported result, count 16; result, elapsed ms 32; billing, cost 0.04628; billing, list price 0.04628; billing, upstream cost 0. - It reported result, count 16; result, elapsed ms 3; billing, cost 0.04634; billing, list price 0.04634; billing, upstream cost 0. - It reported result, count 2,034; result, elapsed ms 40; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 368.1. - It reported result, count 2,220; result, noise 2,093; result, distance m 100; result, elapsed ms 24; result, min points 20; billing, cost 0.1044. - It reported result, count 5; result, elapsed ms 6; billing, cost 0.02767; billing, list price 0.02767; billing, upstream cost 0; list price 0.02767. - It reported result, count 94; result, elapsed ms 14; result, resolution 8; result, features used 420; result, time neighbors 0; billing, cost 0.121. - Nearest tower to each cluster of dropped calls, from Find point clusters: cluster hulls: the 2 clusters lie 1,164 to 1,536 m from the nearest facility (Tower 13 Los Pinos and others) [246][221]. - Pipeline on Calculate density: Coverage complaints produced Dense areas of coverage complaints (5 features). - Pipeline on Dropped calls, Coverage complaints produced Find point clusters: cluster hulls (3 features), Clusters of all inputs together: dropped calls and coverage complaints (2,220 features). - Seasonality on Neighborhoods, Dropped calls produced Seasonality of Dropped calls (16 features). - Spatial join on Calculate density: Coverage complaints, Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) produced Change of dropped calls where density is high (16 features). - Summarize within on Dropped calls, Neighborhoods: Summarize within: 1 layer saved: Summarize within: Neighborhoods (16 features). - Summarize within on Hot spot analysis: Dropped calls, Neighborhoods produced Hot cells of dropped calls per neighborhood (16 features). - The chart Dropped calls by day of week is drawn from 7 rows. - The chart Dropped calls by hour of day is drawn from 24 rows. - The chart Seasonality of Dropped calls is drawn from 5 rows. - Towers within 250 m of each cluster of dropped calls, from Find point clusters: cluster hulls: 0 of 2 clusters have one within 250 m [260][221]. ## Sources [1] Rendering: Chart: Timeline: events per week by dataset. 96 rows (id e8d57d05-8a24-4bd6-a2ab-205e81cc7b5b) [2] Dataset: Dropped calls. vector, 1800 rows (id b7e0385f-4de1-4c62-91a2-29066dff0560) [3] Dataset: Coverage complaints. vector, 420 rows (id d91448d8-ad7a-47b1-ae76-781a60b3553d) [4] Dataset: Cell KPIs by day. table, 784 rows (id 96119c66-7aa4-458c-ab74-e33724e82359) [5] Rendering: Chart: Timeline lanes: events per week by dataset. 96 rows (id 2410bb58-e2e4-44f6-98d7-5ae5fa53e3d5) [6] Rendering: Chart: Timeline: change from one week to the next. 31 rows (id 47ac1dde-c2aa-4746-a6fa-1a3471dbb729) [7] Tool result: gp_period_change, step 0. The count rose from 200 in week of 2026-08-24 to 242 in week of 2026-08-31 (+21.0%); a change this small is within what chance would give (p = 0.051). (id bca67820-6f58-4df0-a127-c19db4029c06:0) [8] Layer: Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) (id 9e66bb0d-6463-4d72-b91f-8f6581158257) [9] Tool result: gp_period_change, step 1. The count rose from 9 in week of 2026-08-10 to 17 in week of 2026-08-17 (+88.9%); a change this small is within what chance would give (p = 0.169). (id bca67820-6f58-4df0-a127-c19db4029c06:1) [10] Layer: Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17) (id b22b41b5-3083-4e18-9aaa-cbbc598bb3b8) [11] Rendering: Map: Overview: the top 3 results. view longitude -75.6498 to -75.51316, latitude 6.19239 to 6.29351 (id 1ba7aad0-e3f3-487c-bc24-6b6c851a83ed) [12] Layer: Neighborhoods. spatial profile (id efbe8937-3ee5-48ad-a6d7-bc4988d33ee9) [13] Layer: Towers. spatial profile (id f7206b9b-efe4-4636-af64-2c979a653d20) [14] Layer: Dropped calls. spatial profile (id e2f5ccec-a668-448b-9e94-445c523f5d04) [15] Layer: Coverage complaints. spatial profile (id a0e6cf38-0976-4bb9-a3d7-afb7e24830f1) [16] Layer: Fiber routes. spatial profile (id ecce82d0-ca7c-4a61-a4d1-039ed28c5769) [17] Layer: Small cells. spatial profile (id 0b706ff2-6fc5-4b0f-ae21-de62a3257715) [18] Catalog: Terrain analysis (analysis catalog, tr_terrain_analysis). The automatic terrain analysis of an area, with no model call: by default every terrain tool (everything), or a subset (general, mobility, observation, hydrology, site), then the elevation layer, the hypsometric curve, slope distribution, aspect rose and route profiles, the dominant heights, the cit ArcGIS: No single tool (terrain analysis workflow: Surface Parameters, Viewshed, Hydrology, Distance Accumulation). QGIS: No single tool (Processing model of GDAL DEM, r.viewshed, r.watershed and r.c (id tr_terrain_analysis) [19] Plan: Terrain analysis (everything) (the Start plan's candidate c_cf00353ae2bf). Automatic terrain analysis (everything) on the project's DEM Valley terrain model (DEM); observers from the 14 point(s) of Towers: every terrain tool: relief, slope, aspect, hillshade, curvature, roughness, landforms, ravines and ridges, strategic points, hydrology (flow, streams, pour points, watersheds, flood-prone ground), mobility for every mover, the MCOO, viewsheds and lines of sight from th (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_cf00353ae2bf) [20] Tool result: tr_terrain_analysis, step 0. Terrain analysis (everything) of 72.1 km2 at 30 m cells (full resolution), 28 terrain tool(s) in 36 call(s): elevation 1,475 to 2,759 m (1,284 m of relief), mean slope 13.2 degrees, 18.8% steeper than 15 degrees; 55 finding(s): Dominant heights; Key terrain candidate KT-1; Key terrain candidate KT-2 (id 419ad273-47a0-406b-98dd-70082059be86:0) [21] Layer: Terrain: Terrain findings. 9 features (id 64f7587d-33cb-47a9-a823-1b872e510768) [22] Layer: Terrain: Dominant heights. 5 features (id d93b6fff-e88a-40a6-bbca-b9d78b84c494) [23] Layer: Terrain: Elevation (m). 0 features (id 3f517292-420a-44d8-a10f-8a8154a28560) [24] Layer: Terrain: Observers. 14 features (id f989287c-4dc6-4fab-9c66-43970381745a) [25] Layer: Terrain: Visible area. 1 features (id 18ddc658-de2c-4ce4-9cd1-75dc8609eb68) [26] Layer: Terrain: Observers that see each cell. 0 features (id 5b8c4ad6-a388-4fe3-8022-d4b7e99a432d) [27] Layer: Terrain: Viewshed. 0 features (id 62de0077-4ad4-4e11-a283-465424bf00e9) [28] Layer: Terrain: Landing points. 6 features (id a3dbcde4-6110-4029-8d1c-20f52328dc41) [29] Layer: Terrain: Landing zone candidates. 6 features (id f781e9c0-c2e3-4346-9fe2-9287a6473991) [30] Layer: Terrain: Clear ground radius (m). 0 features (id a502eb89-b29f-4656-a3b2-4d6515c5a474) [31] Layer: Terrain: Landing ground. 0 features (id 151a88df-55c5-415c-b3d4-3831e52b49a1) [32] Layer: Terrain: Best mineability areas. 15 features (id 42620538-31db-466b-981e-567e2df4b0fd) [33] Layer: Terrain: Mineability classes. 0 features (id 8f6dfcc8-cc16-488f-a7e7-c3b12d579f63) [34] Layer: Terrain: Mineability score (0-100). 0 features (id aae93c06-ce25-4155-8fd0-49fcb0ffb6c4) [35] Layer: Terrain: Site suitability classes. 0 features (id fada7569-f370-43ff-b45d-d3131aa78ae7) [36] Layer: Terrain: Site suitability score (0-100). 0 features (id 4410c716-1cbb-4ac7-998f-da034fc0efdc) [37] Layer: Terrain: Best buildability areas. 88 features (id 7b2f06a0-3d6e-4a69-b054-6e5d63780760) [38] Layer: Terrain: Buildability classes. 0 features (id 167a3ce8-6088-40f4-ba9b-f8df7813f6b8) [39] Layer: Terrain: Buildability score (0-100). 0 features (id 92c176c5-149d-4d11-950e-1243fd358dcb) [40] Layer: Terrain: LS factor. 0 features (id 0aa5d33c-dd82-4782-aa97-79613b1e2542) [41] Layer: Terrain: Erosion risk classes. 0 features (id f93e34df-402a-4a3a-bd15-bd0955227d99) [42] Layer: Terrain: Soil loss (t/ha/yr). 0 features (id fbb0b159-a47b-4d2e-8576-838bb2215592) [43] Layer: Terrain: Susceptibility classes. 0 features (id 9247930a-5e0c-4cf1-8b3f-da8ffe87e457) [44] Layer: Terrain: Susceptibility index (0-1). 0 features (id f77053eb-b7a3-4c4e-b8ed-eac8c303c8c3) [45] Layer: Terrain: Stability classes. 0 features (id 5ee7366e-c67d-471c-9964-55f8e97f6018) [46] Layer: Terrain: Factor of safety (capped at 100). 0 features (id 0defc732-305b-4016-adc0-373a1fb6c748) [47] Layer: Terrain: Solar siting class. 0 features (id 75e8529d-6f3c-4986-8c9a-e60d4674c38d) [48] Layer: Terrain: Sky view factor. 0 features (id 1dc6a918-a9a1-484a-be8d-39c89b7f11dc) [49] Layer: Terrain: Clear-sky insolation (kWh/m2/day). 0 features (id 0cbd6baf-f7bc-49be-8379-4ef999dd1f0e) [50] Layer: Terrain summits: Profile line. 1 features (id 9d1edd45-0c26-44db-985d-955857e358f9) [51] Layer: Terrain axis: Profile line. 1 features (id 1d44aafa-d0ac-4bb6-85ce-3d893c286eb4) [52] Layer: Terrain summits: Least-cost routes. 1 features (id 1bb99841-25a7-4802-b0a0-4cabed0d22f7) [53] Layer: Terrain axis: Least-cost routes. 1 features (id 085d0ea6-d9a7-4ac9-a3e8-67165f921ce7) [54] Layer: Terrain DH-2 DH-3: Sightline. 1 features (id 35ba4cc6-548b-475c-a59d-290599c14b11) [55] Layer: Terrain DH-1 DH-3: Ground visible from the observer. 3 features (id 8ceb14a6-3895-4a1e-8eff-e5770cb9b00a) [56] Layer: Terrain DH-1 DH-3: Sightline. 1 features (id 62150487-0000-47f0-8959-87abb535b4d4) [57] Layer: Terrain DH-1 DH-2: Sightline. 1 features (id 7a13753a-a774-4257-8844-dd85c323eace) [58] Layer: Terrain heights: Observers. 5 features (id 32c922b1-9175-47dd-9a6e-58ffc46de8db) [59] Layer: Terrain heights: Visible area. 5 features (id 5efef585-b670-412f-a7a6-92214959afe7) [60] Layer: Terrain heights: Observers that see each cell. 0 features (id 64fdf293-9192-49cc-b33a-efe921c493af) [61] Layer: Terrain heights: Viewshed. 0 features (id 86c25245-0060-45a6-836f-3ef6dff9547b) [62] Layer: Terrain: Avenues of approach. 3 features (id 38722c7d-2945-46be-897c-f79dccb0f815) [63] Layer: Terrain: Corridor constrictions. 3 features (id 80796dff-bc1e-46d5-9ccc-60b23a70d555) [64] Layer: Terrain: Key terrain candidates. 2 features (id 6957f7f1-3f48-4465-a66b-60326c71a3dc) [65] Layer: Terrain: Mobility corridors. 2 features (id d3352291-a9d8-4702-9ad7-012a3226cbec) [66] Layer: Terrain: Obstacles. 1 features (id 896700b6-0875-4060-9f97-29c8394bf9cb) [67] Layer: Terrain: MCOO going classes. 0 features (id 21004bff-2e9a-49dc-b95b-063324f5bef3) [68] Layer: Terrain tracked: Obstacles. 1 features (id 9a715b87-0631-435d-9a03-26a2c6f7b8ee) [69] Layer: Terrain tracked: Off-road speed (km/h). 0 features (id f1e6d12d-083d-45d3-8d37-dd3faf25126b) [70] Layer: Terrain tracked: Going (tracked). 0 features (id 7a6f7c8a-3133-4197-8784-549396e1f3ae) [71] Layer: Terrain wheeled: Obstacles. 10 features (id 5ac4471d-dc6b-41dd-bd11-8acbc710e545) [72] Layer: Terrain wheeled: Off-road speed (km/h). 0 features (id 9066583b-f244-4a65-bbdf-834718a7a6e6) [73] Layer: Terrain wheeled: Going (wheeled). 0 features (id be24b68c-e229-4940-8dbf-7561b5e88cdd) [74] Layer: Terrain dismounted: Obstacles. 1 features (id 4ffd4153-0241-4b2e-ad6f-7d2b21fcb3fd) [75] Layer: Terrain dismounted: Off-road speed (km/h). 0 features (id 3452ea58-133e-4dbf-bd4a-61af08722e64) [76] Layer: Terrain dismounted: Going (dismounted). 0 features (id df7be58c-68e7-48bc-aa54-c5b589252ea4) [77] Layer: Terrain: Flood-prone areas. 2 features (id bbec8b91-0c84-4b01-bcfe-882f1a12c3d3) [78] Layer: Terrain: Streams (Strahler order). 9 features (id ae2ad116-df6d-46ec-b7c2-2309fd59e051) [79] Layer: Terrain: Flood-prone ground (within 3 m of the stream). 0 features (id fddf365d-48f5-4763-94d4-a69e88ed7035) [80] Layer: Terrain: Height above the nearest stream (m). 0 features (id defa1a6b-f74b-4017-ae93-d3ca8fbed01f) [81] Layer: Terrain: Pour points (snapped). 3 features (id 61ab154d-764b-4fb2-a6ee-6efa2a06baa1) [82] Layer: Terrain: Watershed boundaries. 3 features (id a3b6c484-70b1-4b79-be22-a4689654f971) [83] Layer: Terrain: Watersheds. 0 features (id 4e35e49c-bbfa-41b6-b679-73f720c483c5) [84] Layer: Terrain: Pour points. 3 features (id 505eb291-4960-47ac-bb6d-0f447f86be86) [85] Layer: Terrain: Streams (Strahler order). 9 features (id 4f51959e-b9f0-4789-9a40-24f1944eb354) [86] Layer: Terrain: Flow direction (D8). 0 features (id 8daeb001-8892-4845-946c-75f45772debc) [87] Layer: Terrain: Flow accumulation (upstream cells). 0 features (id e1a59966-084b-462b-80ad-dd4874c8356d) [88] Layer: Terrain: Terrain masking (cover). 5 features (id f39caaee-bbc0-4230-8965-65be5471749e) [89] Layer: Terrain: Open flat ground. 9 features (id 68022fea-12f0-4a9e-95b1-13c025b99a30) [90] Layer: Terrain: Observation points. 2 features (id 3194d737-204b-4e37-a010-0e821e7b4828) [91] Layer: Terrain: Ridges. 36 features (id f133289a-cd9d-4992-a635-256a798ea09c) [92] Layer: Terrain: Ravines. 5 features (id aec08b2e-c20d-4f44-9b23-9c5492eb97d2) [93] Layer: Terrain: Landforms (geomorphons). 0 features (id 303cf40d-d75f-429b-b349-339e6d883e3f) [94] Layer: Terrain: Topographic position (m). 0 features (id a094fca5-4fb3-45d4-b3ba-10735382cc9a) [95] Layer: Terrain: Terrain ruggedness (m). 0 features (id 05567d4d-f893-4f4a-bd5a-664f0b1b52ff) [96] Layer: Terrain: Total curvature. 0 features (id 184c848f-d5b6-4ce0-82dd-6a20b3fa5e7c) [97] Layer: Terrain: Hillshade. 0 features (id 6414263d-2c27-4b12-8d22-61218b247920) [98] Layer: Terrain: Aspect (degrees from north). 0 features (id cbf8a9da-b18a-4be0-9918-2b1328a7b24e) [99] Layer: Terrain: Slope (degrees). 0 features (id 28f58e4d-77f7-4cf4-b77a-6f8e30b37c08) [100] Layer: Terrain: Contours (100 m). 24 features (id 784acbdd-110f-4071-8962-cb1bb55db4ab) [101] Rendering: Map: Terrain: elevation with hillshade. view longitude -75.64315 to -75.51915, latitude 6.19713 to 6.2889 (id 1a55a586-08b9-48ef-8394-60ebe5810caa) [102] Rendering: Map: Terrain: slope. view longitude -75.64315 to -75.51915, latitude 6.19713 to 6.2889 (id e367626d-33f0-474e-a3c5-7f7df78baf23) [103] Rendering: Map: Terrain analysis. view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 17b1e367-da99-4c29-8d63-55019bab1519) [104] Rendering: Map: Terrain: aspect. view longitude -75.64315 to -75.51915, latitude 6.19713 to 6.2889 (id f3d0f6f5-faa7-415b-a844-0f991cdfacee) [105] Rendering: Map: Terrain analysis (continued 1). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id c371665a-b095-4e3a-9dbd-b90616d502af) [106] Rendering: Map: Terrain: landforms. view longitude -75.64315 to -75.51915, latitude 6.19713 to 6.2889 (id a1cf8031-52a6-4b15-b4ee-cf3e745eed10) [107] Rendering: Map: Terrain analysis (continued 2). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 9225fa02-4d53-4a57-a87e-d2d52cdff29f) [108] Rendering: Map: Terrain: hydrology. view longitude -75.64315 to -75.51915, latitude 6.19713 to 6.2889 (id 2f2a9897-e572-46d7-8666-eda1b3283a51) [109] Rendering: Map: Terrain analysis (continued 3). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 2384cdfc-e122-405a-b7b9-eb755e0b41dd) [110] Rendering: Map: Terrain: mobility. view longitude -75.64315 to -75.51915, latitude 6.19713 to 6.2889 (id 3a0e5b65-f602-4b5b-a480-c341031d67aa) [111] Rendering: Map: Terrain analysis (continued 4). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id b02cbe2a-0579-4df1-9e34-3aca3d025707) [112] Rendering: Map: Terrain: key terrain. view longitude -75.64315 to -75.51915, latitude 6.19713 to 6.2889 (id 78652fff-41b3-4808-b141-c8afd1f0f5ae) [113] Rendering: Map: Terrain analysis (continued 5). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id bf0e1f38-c356-46a1-9899-55b1d6b2bec3) [114] Rendering: Map: Terrain analysis (continued 6). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 0ebee3ff-88a7-431e-aec0-877d9eae8f13) [115] Rendering: Map: Terrain analysis (continued 7). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 230dc29c-dfd0-4a9c-8e10-f9cc3b0f087f) [116] Rendering: Map: Terrain analysis (continued 8). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id c55d5fbe-283d-41b2-9e3f-e8a5725b9b6d) [117] Rendering: Map: Terrain analysis (continued 9). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id f44f3325-57c0-4565-85c9-ee10b006b0d0) [118] Rendering: Map: Terrain analysis (continued 10). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 24fa41e2-50de-4ff2-81b1-84619bfb79d0) [119] Rendering: Map: Terrain analysis (continued 11). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 802a2bb2-78be-4a34-a6b2-b3a59449bcc0) [120] Rendering: Map: Terrain analysis (continued 12). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 1fb407ca-f534-4586-8978-fd4047e77b80) [121] Rendering: Map: Terrain analysis (continued 13). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id e6981d1b-7da7-41fe-8b99-997aa0b201ea) [122] Rendering: Map: Terrain analysis (continued 14). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id f83927da-5fed-4d09-8ce6-cb534dccc254) [123] Rendering: Map: Terrain analysis (continued 15). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id b5b3491b-6988-43e9-830d-951ba5bea359) [124] Rendering: Map: Terrain analysis (continued 16). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id d7455844-c707-433c-b9d5-b58a26da33aa) [125] Rendering: Map: Terrain analysis (continued 17). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id e50baa1f-836f-4b88-93a5-29a931fc6c53) [126] Rendering: Map: Terrain analysis (continued 18). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 9cbcb4d0-93fb-4b3d-b9f3-f1673ff2bdce) [127] Rendering: Map: Terrain analysis (continued 19). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 8668638e-0be1-4b36-8bc0-ebe3ed12a8ca) [128] Rendering: Map: Terrain analysis (continued 20). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 069405a3-9110-4fde-97b5-36ae343bf384) [129] Rendering: Map: Terrain analysis (continued 21). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 62bdae43-4db7-42c5-ab4a-7c5d32a4e2be) [130] Rendering: Map: Terrain analysis (continued 22). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 1a560ccb-5eea-4758-aeee-d323f59f07cf) [131] Rendering: Chart: Hypsometric curve. 20 rows (id e5e031e5-fc02-4cb9-87c4-5d657093c91a) [132] Rendering: Chart: Slope distribution. 6 rows (id 9e56295d-88cd-44cd-8630-08e4936f60f3) [133] Rendering: Chart: Aspect rose. 8 rows (id ea79b51a-29a9-4135-954e-7c28dbd95d8a) [134] Rendering: Chart: Elevation along the routes. 320 rows (id 3be8815f-cfea-46fc-a28f-62e660c15cba) [135] Rendering: Chart: Terrain: Contours (100 m). 6 rows (id 01c2cc92-53f2-4435-8711-dda67b22e869) [136] Rendering: Chart: Terrain: Ravines. 5 rows (id 4e03ace2-b9e2-46ed-b1a7-71820529b500) [137] Rendering: Chart: Terrain: Ridges. 7 rows (id c282ba67-7757-491c-8311-61156f46a608) [138] Rendering: Chart: Terrain: Observation points. 5 rows (id f3f92aaf-c3bf-497b-8961-18292fdb6869) [139] Rendering: Chart: Terrain: Open flat ground. 5 rows (id df6bd9ba-3233-44c6-a4c7-4ba1d0c46a07) [140] Rendering: Chart: Terrain: Terrain masking (cover). 5 rows (id 44151c22-2981-4dce-b555-94fc23861a86) [141] Rendering: Chart: Terrain: Streams (Strahler order). 5 rows (id 2a91dabb-7741-4c6d-a953-dd41be0eb7f4) [142] Rendering: Chart: Terrain: Pour points. 5 rows (id 83cccd9d-ff05-4043-835b-2b956cf291d6) [143] Rendering: Chart: Terrain: Watershed boundaries. 5 rows (id c04b02b6-a9d5-4d02-b1a8-a7d581efe53a) [144] Rendering: Chart: Terrain: Pour points (snapped). 5 rows (id 4ad65baf-6ca5-48ef-a60d-f752da6b06a6) [145] Rendering: Chart: Terrain: Streams (Strahler order). 5 rows (id c4acc103-388c-4428-8130-42fad2e589d2) [146] Rendering: Chart: Terrain: Flood-prone areas. 5 rows (id d5427c83-38af-44db-808b-4ac1fb19a766) [147] Rendering: Chart: Terrain dismounted: Obstacles. 1 rows (id 7561de58-e199-4e8c-9875-611f6a05acfb) [148] Rendering: Chart: Terrain wheeled: Obstacles. 5 rows (id 4b3a6bcf-1f20-4e7e-b923-5ffc7f78cdb1) [149] Rendering: Chart: Terrain tracked: Obstacles. 1 rows (id 121102d0-e17f-44d3-bb0d-1f2f8cf2e480) [150] Rendering: Chart: Terrain: Obstacles. 1 rows (id d236e46a-f034-4ee6-ba72-f7483db75579) [151] Rendering: Chart: Terrain: Mobility corridors. 5 rows (id a0ac7a6a-1d24-40e8-b519-fe80a4d54871) [152] Rendering: Chart: Terrain: Key terrain candidates. 5 rows (id 94071a58-9bb4-47dd-8b89-190cc477165a) [153] Rendering: Chart: Terrain: Corridor constrictions. 5 rows (id 255dd26d-18c1-44bd-8c77-488f910e1809) [154] Rendering: Chart: Terrain: Avenues of approach. 5 rows (id 51114a23-313d-4f25-8cba-c310a877a2a6) [155] Rendering: Chart: Terrain heights: Visible area. 5 rows (id 7b30b248-a730-42d1-8015-680b8291f70f) [156] Rendering: Chart: Terrain heights: Observers. 5 rows (id fb99e9c1-de50-4fb4-9dc3-051eb48e997c) [157] Rendering: Chart: Terrain DH-1 DH-2: Sightline. 1 rows (id 5c53666c-3441-46e3-9e48-e4feeecb39e5) [158] Rendering: Chart: Terrain DH-1 DH-3: Sightline. 1 rows (id 20e635e4-e358-4922-a084-88d129ff5689) [159] Rendering: Chart: Terrain DH-1 DH-3: Ground visible from the observer. 5 rows (id 2159c102-cbc9-4ad5-a08e-2a88bd6d86f1) [160] Rendering: Chart: Terrain DH-2 DH-3: Sightline. 1 rows (id ac505eb5-36f8-4ef2-9e91-3b401e3166af) [161] Rendering: Chart: Terrain axis: Least-cost routes. 1 rows (id 1f375261-a96e-498a-a51d-3384f3820951) [162] Rendering: Chart: Terrain summits: Least-cost routes. 1 rows (id 1944914b-522c-472d-a4e1-dcd491d602fb) [163] Rendering: Chart: Terrain axis: Profile line. 1 rows (id 376d8c3d-7f91-47fc-b87a-895adeef2e97) [164] Rendering: Chart: Terrain summits: Profile line. 1 rows (id 79107e73-e3c6-4174-8be5-189b8ab90821) [165] Rendering: Chart: Terrain: Best buildability areas. 8 rows (id efee82a2-3a52-48cb-9b63-03421804a245) [166] Rendering: Chart: Terrain: Best mineability areas. 5 rows (id a476b743-7d5a-4e7c-8c67-31dbcac5f050) [167] Rendering: Chart: Terrain: Landing zone candidates. 5 rows (id 16ec1cfc-0ab4-46ea-a960-c25fb40c1acc) [168] Rendering: Chart: Terrain: Landing points. 1 rows (id b37e1a53-ceb1-4f50-8480-589f80216381) [169] Rendering: Chart: Terrain: Visible area. 1 rows (id e633ba07-0df1-4653-b4cb-76c8c22ce74c) [170] Rendering: Chart: Terrain: Observers. 5 rows (id bbfd6010-ac3a-4f26-a278-f13d47371c97) [171] Rendering: Chart: Terrain: Dominant heights. 5 rows (id e9d2c4dd-f36f-4fc1-b56a-17438ee9ccc0) [172] Catalog: Zonal statistics (analysis catalog, rs_zonal_statistics). count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON. ArcGIS: Zonal Statistics as Table (polygons). (id rs_zonal_statistics) [173] Plan: Zonal statistics of Valley terrain model (DEM) and Neighborhoods (the Start plan's candidate c_68a579bec9c4). Statistics of Valley terrain model (DEM) inside each feature of Neighborhoods. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_68a579bec9c4) [174] Tool result: rs_zonal_statistics, step 1. Zonal statistics: 16 row(s). zones 16. (id 419ad273-47a0-406b-98dd-70082059be86:1) [175] Catalog: Slope (analysis catalog, tr_slope). Horn slope in degrees or percent, with the share of ground in six grade classes. ArcGIS: Slope (Spatial Analyst). QGIS: Slope (GDAL DEM). (id tr_slope) [176] Plan: Slope of Valley terrain model (DEM) (the Start plan's candidate c_11fca56bf708). Slope of the Valley terrain model (DEM) surface. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_11fca56bf708) [177] Tool result: tr_slope, step 2. Slope over 72.09 km2: mean 13.16 degrees, max 38.63 degrees; 92% of the ground is steeper than 15%. DEM: Valley terrain model (DEM) at 30 m. Verified: 2 checks passed. (id 419ad273-47a0-406b-98dd-70082059be86:2) [178] Layer: Valley terrain model (DEM). 0 features (id 51a68176-b4d4-46a1-82d9-a68cf283d882) [179] Layer: Slope (degrees). 0 features (id 6b9f18fe-81ec-4e91-9709-e645711ba6b7) [180] Catalog: Hot spot analysis (operation catalog, gp_hot_spots). Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins. ArcGIS: Find Hot Spots, Hot Spot Analysis (Getis-Ord Gi*). QGIS: qgis:hotspot (plugin). (id gp_hot_spots) [181] Plan: Hot spots of Dropped calls (the Start plan's candidate c_8b55db0ad281). 1800 points: Getis-Ord Gi* tests whether the concentrations in Dropped calls are statistically significant. Measured: Dropped calls are clustered (nearest-neighbor ratio 0.87, z = -10.7), median spacing 63 m. Cells of 200 m and a band of 500 m from the measured spacing. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_8b55db0ad281) [182] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Dropped calls (1798 features). (id b57e679d-accb-460f-8334-ed94c27d5123:0) [183] Plan: Hot spots of Coverage complaints (the Start plan's candidate c_f3733c434ec1). 420 points: Getis-Ord Gi* tests whether the concentrations in Coverage complaints are statistically significant. Measured: Coverage complaints are clustered (nearest-neighbor ratio 0.87, z = -5.0), median spacing 123 m. Cells of 500 m and a band of 1000 m from the measured spacing. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_f3733c434ec1) [184] Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Coverage complaints (305 features). (id b57e679d-accb-460f-8334-ed94c27d5123:1) [185] Plan: Hot spots of Small cells (the Start plan's candidate c_ed8efb07fcdd). 40 points: Getis-Ord Gi* tests whether the concentrations in Small cells are statistically significant. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_ed8efb07fcdd) [186] Tool result: gp_hot_spots, step 2. Hot spot analysis: 1 layer saved: Hot spot analysis: Small cells (21 features). (id b57e679d-accb-460f-8334-ed94c27d5123:2) [187] Catalog: Emerging hot spots (operation catalog, gp_space_time_hot_spots). Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time. ArcGIS: Emerging Hot Spot Analysis, Create Space Time Cube. (id gp_space_time_hot_spots) [188] Plan: Space time hot spots of Coverage complaints (the Start plan's candidate c_a43556cfd61a). Coverage complaints carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per week. Measured: the pattern of Coverage complaints changes over time (earlier half clustered, later half random; the center moved 305 m). (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_a43556cfd61a) [189] Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Coverage complaints (94 features). (id 0aca80f2-eb32-4b5f-a142-1245b3cd0716:0) [190] Plan: Space time hot spots of Dropped calls (the Start plan's candidate c_9336d0c58786). Dropped calls carry dates in 'occurred_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_9336d0c58786) [191] Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: Dropped calls (109 features). (id 0aca80f2-eb32-4b5f-a142-1245b3cd0716:1) [192] Catalog: Summarize within (operation catalog, gp_summarize_within). Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons. ArcGIS: Summarize Within, Aggregate Points. QGIS: native:countpointsinpolygon, native:sumlinelengths. (id gp_summarize_within) [193] Plan: Towers per feature of Neighborhoods (the Start plan's candidate c_bdfa07d4db94). Count Towers inside each feature of Neighborhoods by technology; the counts map as a choropleth (natural breaks, sequential colors). (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_bdfa07d4db94) [194] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Neighborhoods (16 features). (id 55895977-d5af-4ed0-93a7-4667ecec4453:0) [195] Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Neighborhoods (16 features). (id 55895977-d5af-4ed0-93a7-4667ecec4453:1) [196] Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Neighborhoods (16 features). (id 55895977-d5af-4ed0-93a7-4667ecec4453:2) [197] Catalog: Period change (analysis catalog, gp_period_change). Counts or sums per period (week over week, month over month, any two periods, or before and after a date), per area, per H3 cell or in total, with the places of largest rise and fall and a significance note. ArcGIS: Summarize Percent Change (Crime Analysis), Change Point Detection. (id gp_period_change) [198] Plan: Dropped calls: change per week per feature of Neighborhoods (the Start plan's candidate c_397aae5c8874). Dropped calls carry dates in 'occurred_at' over 8 weeks: the count per week, week over week, with the places of largest rise and fall per feature of Neighborhoods and whether the change is beyond chance. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_397aae5c8874) [199] Rendering: Chart: Dropped calls: count per week. 8 rows (id 8b6df35a-a8cc-4f02-a603-c5478afe381b) [200] Rendering: Chart: Dropped calls: week of 2026-08-24 to week of 2026-08-31 by place. 30 rows (id ce004650-d7c2-4abe-9510-c14524c8a42a) [201] Rendering: Chart: Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31). 5 rows (id ecb4716b-2b8d-4625-8ebb-d1315a2f54af) [202] Rendering: Chart: Coverage complaints: count per week. 32 rows (id fc7b3206-2928-4581-aba1-cbe77aa1d9ef) [203] Rendering: Chart: Coverage complaints: week of 2026-08-10 to week of 2026-08-17 by place. 16 rows (id b7f92e26-1eda-453e-af72-cc4f6fa0ca51) [204] Rendering: Chart: Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17). 5 rows (id a5a13c15-5975-47a7-a5ab-5091df90e0fd) [205] Catalog: Seasonality (analysis catalog, gp_seasonality). Counts by hour of day, day of week and month, with peaks and troughs per cycle and per area. ArcGIS: Time Series Clustering, Create Space Time Cube. (id gp_seasonality) [206] Plan: Dropped calls: by hour of day and day of week (the Start plan's candidate c_9a33d664ad70). Dropped calls by hour of day, day of week: the busiest and the quietest per feature of Neighborhoods, and whether the cycle is uneven beyond chance. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_9a33d664ad70) [207] Tool result: gp_seasonality, step 2. By the busiest hour is 17:00 (154), the quietest 03:00 (6); the cycle is uneven beyond chance (p = 0); by the busiest day is Sunday (358), the quietest Monday (185); the cycle is uneven beyond chance (p = 0). 1 layer(s) saved, 2 chart(s) drawn. (id bca67820-6f58-4df0-a127-c19db4029c06:2) [208] Layer: Seasonality of Dropped calls. 16 features (id f3d43000-60b5-49ef-be8f-4f0803c4e504) [209] Rendering: Chart: Dropped calls by hour of day. 24 rows (id 516522b9-e050-4b6b-bc2e-5e07320c0d35) [210] Rendering: Chart: Dropped calls by day of week. 7 rows (id 6b9793cf-9228-4a65-b126-95698380635b) [211] Rendering: Chart: Seasonality of Dropped calls. 5 rows (id 4e472220-de1a-41d3-9eed-7a1f4be76381) [212] Catalog: Calculate density (operation catalog, gp_calculate_density). Kernel density per square kilometer on a grid. ArcGIS: Calculate Density, Kernel Density. QGIS: native:heatmapkerneldensityestimation. (id gp_calculate_density) [213] Plan: Kernel density of Dropped calls (the Start plan's candidate c_2f3ee2dea9de). 1800 points in Dropped calls: a density surface shows where they concentrate. Measured: Dropped calls are clustered (nearest-neighbor ratio 0.87, z = -10.7), median spacing 63 m. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_2f3ee2dea9de) [214] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Dropped calls (2034 features). (id 70a9c435-9218-4c5e-ab09-b6dde608494c:0) [215] Plan: Kernel density of Coverage complaints (the Start plan's candidate c_0fe7379d27f3). 420 points in Coverage complaints: a density surface shows where they concentrate. Measured: Coverage complaints are clustered (nearest-neighbor ratio 0.87, z = -5.0), median spacing 123 m. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_0fe7379d27f3) [216] Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Coverage complaints (383 features). (id 70a9c435-9218-4c5e-ab09-b6dde608494c:1) [217] Plan: Kernel density of Small cells (the Start plan's candidate c_966f94aedf49). 40 points in Small cells: a density surface shows where they concentrate. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_966f94aedf49) [218] Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: Small cells (117 features). (id 70a9c435-9218-4c5e-ab09-b6dde608494c:2) [219] Catalog: Find point clusters (operation catalog, gp_find_point_clusters). Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise. ArcGIS: Find Point Clusters, Density-based Clustering. QGIS: native:dbscanclustering, native:stdbscanclustering. (id gp_find_point_clusters) [220] Plan: Cluster points of Dropped calls (the Start plan's candidate c_e2399258f4de). DBSCAN finds dense groups of Dropped calls and separates noise. Measured: Dropped calls are clustered (nearest-neighbor ratio 0.87, z = -10.7), median spacing 63 m. DBSCAN at 100 m (twice the measured spacing). (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_e2399258f4de) [221] Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Dropped calls (1800 features); Find point clusters: cluster hulls (2 features). (id 8fe213de-fca0-4038-b67f-2e5e8258f8a9:0) [222] Plan: Cluster points of Coverage complaints (the Start plan's candidate c_35e276867fc7). DBSCAN finds dense groups of Coverage complaints and separates noise. Measured: Coverage complaints are clustered (nearest-neighbor ratio 0.87, z = -5.0), median spacing 123 m. DBSCAN at 200 m (twice the measured spacing). (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_35e276867fc7) [223] Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Coverage complaints (420 features); Find point clusters: cluster hulls (1 feature). (id 8fe213de-fca0-4038-b67f-2e5e8258f8a9:1) [224] Catalog: Average nearest neighbor (operation catalog, gp_nearest_neighbor_index). Clark-Evans ratio: clustered, dispersed or random. ArcGIS: Average Nearest Neighbor. QGIS: native:nearestneighbouranalysis. (id gp_nearest_neighbor_index) [225] Plan: Nearest neighbor index of Towers (the Start plan's candidate c_a6477e52d190). The average nearest neighbor ratio says whether Towers are clustered, random or dispersed. Measured: Towers are dispersed (nearest-neighbor ratio 1.82, z = +5.8), median spacing 1.36e+03 m. The profile measured it already; the tool draws it. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_a6477e52d190) [226] Tool result: gp_nearest_neighbor_index, step 2. Average nearest neighbor: done; no map output. (id 8fe213de-fca0-4038-b67f-2e5e8258f8a9:2) [227] Catalog: Find outliers (operation catalog, gp_outliers). Anselin local Moran’s I: HH, LL clusters and HL, LH outliers. ArcGIS: Find Outliers, Cluster and Outlier Analysis (Anselin Local Moran’s I). (id gp_outliers) [228] Plan: Local morans i of Neighborhoods (the Start plan's candidate c_9e138ef27459). Local Moran's I maps clusters and outliers of 'population'. Measured: Neighborhoods have 34 neighboring pairs (4.25 neighbors per area, 0 isolated), so neighbors can be compared. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_9e138ef27459) [229] Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: Neighborhoods (16 features). (id f1786a0f-6480-4be8-a820-014c2118ef8e:0) [230] Catalog: Spatial autocorrelation (operation catalog, gp_spatial_autocorrelation). Global Moran’s I: is the pattern clustered, dispersed or random. ArcGIS: Spatial Autocorrelation (Global Moran’s I). (id gp_spatial_autocorrelation) [231] Plan: Global morans i of Neighborhoods (the Start plan's candidate c_efa49aa4d4ca). Global Moran's I says whether similar values of 'population' sit next to each other. Measured: Neighborhoods have 34 neighboring pairs (4.25 neighbors per area, 0 isolated), so neighbors can be compared. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_efa49aa4d4ca) [232] Tool result: gp_spatial_autocorrelation, step 1. Spatial autocorrelation: done; no map output. (id f1786a0f-6480-4be8-a820-014c2118ef8e:1) [233] Catalog: Spatial join (operation catalog, gp_spatial_join). Attach attributes (or aggregates) of matching features from another layer, by location, distance, time and attributes. ArcGIS: Join Features, Spatial Join. QGIS: native:joinattributesbylocation, native:joinbylocationsummary, native:joinbynearest. (id gp_spatial_join) [234] Plan: Spatial join of Towers and Neighborhoods (the Start plan's candidate c_da9c22d31964). Attach the Neighborhoods attributes to each of Towers. (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_da9c22d31964) [235] Tool result: gp_spatial_join, step 0. Spatial join: 1 layer saved: Change beside the counts per neighborhood (16 features). (id 282f5484-ba5c-44d2-b03d-756e06922d8b:0) [236] Layer: Summarize within: Neighborhoods. 16 features (id 07b9a4cd-43c0-4d70-b79e-276cfc0fede7) [237] Layer: Change beside the counts per neighborhood. 16 features (id 22382215-5891-4bf1-a9d6-71d8c9df1834) [238] Tool result: gp_spatial_join, step 0. Spatial join: 1 layer saved: Change of dropped calls where density is high (16 features). (id 991e949e-b518-4d29-9283-1546ada3ef69:0) [239] Layer: Calculate density: Coverage complaints. 383 features (id 1d475898-88cc-4539-a8b3-7f7337b37f21) [240] Layer: Change of dropped calls where density is high. 16 features (id 8222d798-96eb-4799-9dd4-cfd731c8102b) [241] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Hot cells of dropped calls per neighborhood (16 features). (id 16f23617-8dd4-409d-b81f-820c4d214646:0) [242] Layer: Hot spot analysis: Dropped calls. 1,798 features (id 1dbf3768-8a6c-4dd2-9f6d-b0809ff9a4d0) [243] Layer: Hot cells of dropped calls per neighborhood. 16 features (id 463595c2-81b5-4c44-94f0-feebb8658604) [244] Catalog: Find nearest (operation catalog, gp_find_nearest). The k nearest features for each input, with geodesic distance and bearing. ArcGIS: Find Nearest, Near, Generate Near Table. QGIS: native:joinbynearest, native:nearestneighbouranalysis. (id gp_find_nearest) [245] Plan: The run's goal. Describe the project Sample: Telecom, Medellin: run the analyses that tell the most about its data, draw every result, and write a cited report. (id de496ddc-3bd2-4d81-bcc0-e58d35927cfd:goal) [246] Tool result: gp_find_nearest, step 0. Find nearest: 1 layer saved: Nearest tower to each cluster of dropped calls (2 features). (id dbccc627-646e-4dd0-939c-12424eb9cfff:0) [247] Layer: Find point clusters: cluster hulls. 2 features (id 5313780d-360e-4198-bb21-0c53f1f586db) [248] Layer: Nearest tower to each cluster of dropped calls. 2 features (id fc4a51c7-b192-41b2-ba76-b96d587eb562) [249] Catalog: Pipeline (tool description, gp_pipeline). Run up to 20 GIS operations in one engine call; a step's params may use "$id" (an earlier step's result) or "$id.key", and layer references. Every step's map outputs are saved as styled layers. Never calls Mapbox. (id gp_pipeline) [250] Plan: Towers per 10,000 people per feature of Neighborhoods (the Start plan's candidate c_fffb26c34f8a). Neighborhoods holds population in 'population': Towers per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors). (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_fffb26c34f8a) [251] Tool result: gp_pipeline, step 0. Pipeline of 2 steps (classify_field > dissolve): 1 layer saved. (id 10dc1e9a-829a-440a-9928-c9bafc5cfcfc:0) [252] Layer: Dense areas of coverage complaints. 5 features (id f989a643-ec4f-442c-97be-8a0fddd52a26) [253] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Change of coverage complaints per neighborhood (16 features). (id 10890110-b724-4750-94f1-10fd8c977156:0) [254] Layer: Change of coverage complaints per neighborhood. 16 features (id d32232a5-46f8-49d5-aa5b-9c9e6b54114b) [255] Plan: Dropped calls per 10,000 people per feature of Neighborhoods (the Start plan's candidate c_86bee1bc8eec). Neighborhoods holds population in 'population': Dropped calls per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors). (id a00dee40-3e85-4150-a19d-0a3dc31dc346:c_86bee1bc8eec) [256] Tool result: gp_pipeline, step 0. Pipeline of 2 steps (merge_layers > find_point_clusters): 2 layers saved. (id 1fed814d-2dfa-4cf1-9e16-0e0d10059eb1:0) [257] Layer: Find point clusters: cluster hulls. 3 features (id ea31f3f4-b400-4d67-9bc9-8c4cf2d61283) [258] Layer: Clusters of all inputs together: dropped calls and coverage complaints. 2,220 features (id ee796301-5d0a-41a0-bc40-b37c75f8316e) [259] Rendering: Chart: Clusters of all inputs together: dropped calls and coverage complaints over time. 32 rows (id 0a6729a9-a79e-4eb0-8e24-006b330cef61) [260] Tool result: gp_pipeline, step 0. Pipeline of 2 steps (buffer > summarize_within): 1 layer saved. (id f9f27f84-f111-4a4c-bbb8-c78cea2257df:0) [261] Layer: Towers within 250 m of each cluster of dropped calls. 2 features (id aa01efd8-91d6-4e2e-bfe4-e13b7860bd90) [262] Rendering: Chart: Dropped calls over time (occurred_at). 32 rows (id 0681173b-b38f-4b01-a4d7-2ab74ab91700) [263] Rendering: Chart: Coverage complaints over time (date). 96 rows (id 6f41bbcd-2031-4ed1-99c1-1ad010efb552) [264] Rendering: Chart: Cell KPIs by day over time (date). 8 rows (id d725991f-1d8a-4f5c-864a-5486cdb95bbb) [265] Rendering: Chart: Site leases over time (lease_end_year). 5 rows (id 58f93069-51bd-440b-a9e4-b70009ecd554) [266] Rendering: Chart: Hot spot analysis: Dropped calls. 52 rows (id 03c9261a-d273-4086-abb3-efd4f10fdf1b) [267] Rendering: Chart: Hot spot analysis: Coverage complaints. 24 rows (id a9cc4c22-20b0-4f47-858f-699f70dae447) [268] Rendering: Chart: Hot spot analysis: Small cells. 6 rows (id c67b881d-0bfc-4658-b18c-594033e9650c) [269] Rendering: Chart: Emerging hot spots: Coverage complaints. 8 rows (id 5ea8ca11-6b11-4e39-bf0a-8997736de7a3) [270] Rendering: Chart: Emerging hot spots: Dropped calls. 8 rows (id 6303d20b-1860-4709-a0c8-0c0298c5c5a2) [271] Rendering: Chart: Summarize within: Neighborhoods. 5 rows (id 80783649-c91f-4c92-9276-3d11f50436cb) [272] Rendering: Chart: Summarize within: Neighborhoods. 5 rows (id 6fc5004d-264e-49e5-ad6c-d4d543dc52fd) [273] Rendering: Chart: Summarize within: Neighborhoods. 5 rows (id 8facccf9-0a3f-4519-8e85-fd26bf2db6dd) [274] Rendering: Chart: Calculate density: Dropped calls. 12 rows (id 1c010436-0bb1-4b53-89b8-9698a11dd46c) [275] Rendering: Chart: Calculate density: Coverage complaints. 10 rows (id f20038e2-e629-4efb-a84a-c466fc2dd82e) [276] Rendering: Chart: Calculate density: Small cells. 8 rows (id f55923d1-2540-4ccd-8136-64b2079373d5) [277] Rendering: Chart: Find point clusters: Dropped calls. 3 rows (id 37e96529-f2a3-44ba-862a-6d13759196cc) [278] Rendering: Chart: Find point clusters: cluster hulls. 2 rows (id bbc68612-5048-4a74-99bd-d51836232dfc) [279] Rendering: Chart: Find point clusters: Coverage complaints. 2 rows (id ffe9bca7-c681-47f9-9f55-c9cb4efc025a) [280] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id 6a62a92f-bead-4280-bda5-84067ec03d9b) [281] Rendering: Chart: Find outliers: Neighborhoods. 16 rows (id 68e28373-d8c2-4e4a-926a-4ae3bc9e8b75) [282] Rendering: Chart: Find point clusters: Coverage complaints over time. 96 rows (id 34abe3a8-df38-4071-8fd0-3bd1fd8b5620) [283] Rendering: Chart: Find point clusters: Dropped calls over time. 32 rows (id fe5f81a0-7b11-4ccf-9ac2-ff9d18c9874b) [284] Rendering: Map: Terrain analysis (continued 23). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 7ba2e232-611a-4a54-a5b2-59ca9e6394ea) [285] Rendering: Map: Terrain analysis (continued 24). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 91a5bfd8-7f1c-49ee-aa82-92bb8b7006af) [286] Rendering: Map: Terrain analysis (continued 25). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id bfd30168-038c-4ea7-95c3-e4de3a96656d) [287] Rendering: Map: Terrain analysis (continued 26). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 2fdd9996-023b-4d2b-8dd6-518f43e65e90) [288] Rendering: Map: Terrain analysis (continued 27). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 9f967a9d-7f16-4b64-87c5-b25cf48385a5) [289] Rendering: Map: Terrain analysis (continued 28). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id a5090724-5011-4f0c-8d3a-1015010cbddf) [290] Rendering: Map: Terrain analysis (continued 29). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 941a89ab-4148-444d-8ac2-d58aa7b4c7fc) [291] Rendering: Map: Terrain analysis (continued 30). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 7010e697-e94e-475d-b5b8-cbb9ea19ae0e) [292] Rendering: Map: Terrain analysis (continued 31). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 6052254a-99c3-44e1-aead-2e2d7eb8c113) [293] Rendering: Map: Terrain analysis (continued 32). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id f331279f-2646-46b8-b180-f6a801b825f8) [294] Rendering: Map: Terrain analysis (continued 33). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id afb22b11-87dd-424e-b451-05d1b841f806) [295] Rendering: Map: Terrain analysis (continued 34). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 9ffe9b08-f2f8-430b-989a-39cd2b625e33) [296] Rendering: Map: Terrain analysis (continued 35). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id ffc6e4c3-c85c-462c-8979-46fd13c3a992) [297] Rendering: Map: Terrain analysis (continued 36). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 5185d8c1-a699-46cd-959e-19be9ecb8208) [298] Rendering: Map: Terrain analysis (continued 37). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id b1b46527-eb1a-440b-aa0f-d980eb201b38) [299] Rendering: Map: Terrain analysis (continued 38). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id b55b0ffe-007f-4985-b99d-725489b399c0) [300] Rendering: Map: Terrain analysis (continued 39). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 894cb6f5-aac8-4566-9903-e1f7d6c859b3) [301] Rendering: Map: Terrain analysis (continued 40). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 16f16b10-7162-4331-a6f7-673a54b64d43) [302] Rendering: Map: Terrain analysis (continued 41). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 6193886d-7ee4-450b-bbef-c6bc65b6f4b7) [303] Rendering: Map: Slope: valley terrain model (DEM). view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 9d1771b1-dda8-4425-b2f4-624be210443c) [304] Rendering: Map: Hot spots: dropped calls. view longitude -75.64245 to -75.521, latitude 6.19662 to 6.28916 (id ba1b1bc8-89c3-4d28-ba5e-4818ae1fac72) [305] Rendering: Map: Hot spots: coverage complaints. view longitude -75.6474 to -75.51542, latitude 6.19269 to 6.29326 (id 5e496c34-3a8e-4b72-839d-6216c7e20584) [306] Rendering: Map: Hot spots: small cells. view longitude -75.66626 to -75.50711, latitude 6.17736 to 6.29862 (id a44b5e5c-f7c0-4792-bd5c-081b9430d44c) [307] Rendering: Map: Emerging hot spots: coverage complaints and dropped calls. view longitude -75.65596 to -75.50436, latitude 6.18436 to 6.29656 (id 380f8a67-2099-4242-a0e6-676ad2a3d706) [308] Rendering: Map: Counts: towers, by neighborhood. view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id a03d1c9b-c252-4d57-9d06-80c0a21d1faa) [309] Rendering: Map: Counts: small cells, by neighborhood. view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 6304a730-e2b7-42d0-9619-8c47de65575a) [310] Rendering: Map: Counts: dropped calls, by neighborhood. view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id f1d9bd1c-d035-40ff-beb6-d78a53faeb5c) [311] Rendering: Map: Period change: dropped calls, by neighborhood. view longitude -75.64277 to -75.51963, latitude 6.19749 to 6.28863 (id eb007c4e-5f1b-4ef3-9e68-783b13fe65c7) [312] Rendering: Map: Period change: coverage complaints, by neighborhood. view longitude -75.63751 to -75.52498, latitude 6.2049 to 6.28818 (id 3a2a010d-def6-40d4-84aa-7e5fac94898c) [313] Rendering: Map: Seasonality: dropped calls, by neighborhood. view longitude -75.64277 to -75.51963, latitude 6.19749 to 6.28863 (id d59703ba-960c-4164-ac85-b8e9bb5f0559) [314] Rendering: Map: Density: dropped calls. view longitude -75.64705 to -75.51598, latitude 6.19306 to 6.29293 (id 80eac121-f07a-42c1-8bc2-c1ffe0d6f498) [315] Rendering: Map: Density: coverage complaints. view longitude -75.65486 to -75.50637, latitude 6.18703 to 6.30018 (id 98b0b8c4-c591-4d09-84d6-457a4f91f94b) [316] Rendering: Map: Density: small cells. view longitude -75.66793 to -75.49012, latitude 6.17641 to 6.3119 (id f02d869a-03a0-4bf9-a8c3-ccae27642b47) [317] Rendering: Map: Clusters: dropped calls. view longitude -75.6395 to -75.523, latitude 6.1989 to 6.28767 (id 7b5220df-dadc-42a5-9f23-3e8009c2eddb) [318] Rendering: Map: Clusters: coverage complaints. view longitude -75.63988 to -75.52338, latitude 6.19877 to 6.28754 (id 7648442d-ff4b-4d97-a890-fc939e3d2d29) [319] Rendering: Map: Outliers: neighborhoods. view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id aa89b4f2-ae09-4e89-80cb-75ab51db9ae1) [320] Rendering: Map: Change beside the counts per neighborhood. view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 114e78e7-68ad-4862-91ef-02eff7d20604) [321] Rendering: Map: Change of dropped calls where density is high. view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id ec3911c3-7eae-4b38-9527-253d456903d7) [322] Rendering: Map: Hot cells of dropped calls per neighborhood. view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 79259c3e-1feb-42e5-9349-debbff5e6940) [323] Rendering: Map: Nearest tower to each cluster of dropped calls. view longitude -75.55941 to -75.5498, latitude 6.24305 to 6.25037 (id 7f662cc4-1d6c-483c-b75a-559a63a39fda) [324] Rendering: Map: Change of coverage complaints per neighborhood. view longitude -75.64065 to -75.5217, latitude 6.19781 to 6.28844 (id 89d884a1-9030-4a7b-97a0-db01ee450ab4) [325] Rendering: Map: Clusters of all inputs together: dropped calls and coverage complaints. view longitude -75.6395 to -75.523, latitude 6.19877 to 6.28754 (id 8ef52cb6-2830-4fd8-9733-b7fc8ab4e04f) [326] Rendering: Map: Towers within 250 m of each cluster of dropped calls. view longitude -75.56316 to -75.54608, latitude 6.24013 to 6.25314 (id 432f6a00-2da5-49a3-9d7a-8e2e691de44e) [327] Document: Network performance review, August and September 2026. 1 page(s), 1 page(s) from the text layer (id 2ab288be-688c-4831-8e20-a7f05cb66642) [328] Document page: Network performance review, August and September 2026, page 1. Sample document: synthetic data.* Most coverage drops sit on the eastern slopes in Los Olivos, behind the ridge, around 6.24785, -75.55517; no valley tower sees that slope. (id 2ab288be-688c-4831-8e20-a7f05cb66642:1) [329] Dataset: Neighborhoods. vector, 16 rows (id b54822d8-c1c1-46ca-9ffb-512aad30de12) [330] Dataset: Towers. vector, 14 rows (id 444f52fe-8be9-413e-8a92-2dea9c3c3a3a) [331] Dataset: Fiber routes. vector, 19 rows (id 9b993085-682e-439d-9202-d4e2c5530087) [332] Dataset: Small cells. vector, 40 rows (id a9c84a25-e7ab-4869-b005-982f041f3080) [333] Dataset: Valley terrain model (DEM). raster, ? rows (id f309cad9-7065-421d-b65f-542f4ad825d1) [334] Dataset: Site leases. table, 14 rows (id 6809dc14-fdde-4af8-9d7f-19c5b2ec1403) [335] Dataset: Network performance review. document, ? rows (id 8d46bafb-b62e-4cfe-826f-6e98f9ba3477) [336] Record: Sample: Telecom, Medellin (id bceeca13-52a5-4344-bf84-77a2f9f8e186) [337] Layer: Hot spot analysis: Coverage complaints. 305 features (id 538a875c-9598-4717-a3ca-5b58d23a31fc) [338] Layer: Hot spot analysis: Small cells. 21 features (id 5f9e6840-03d3-4652-b348-bae22eed19fd) [339] Layer: Emerging hot spots: Coverage complaints. 94 features (id 4989d796-f544-4fad-90b1-baf86f137f51) [340] Layer: Emerging hot spots: Dropped calls. 109 features (id afa274cb-c4df-48de-aa11-71ef9b6da39d) [341] Layer: Summarize within: Neighborhoods. 16 features (id 7156bcb4-52eb-4982-bb91-f6bb82c6cd3a) [342] Layer: Summarize within: Neighborhoods. 16 features (id 9c7cbc96-f424-400e-a849-c04ea07500ff) [343] Layer: Calculate density: Dropped calls. 2,034 features (id b06077fa-d8fe-4c22-a048-2be63772bc6a) [344] Layer: Calculate density: Small cells. 117 features (id f41e0de7-a388-4cc6-b181-c1181ef5abb5) [345] Layer: Find point clusters: Dropped calls. 1,800 features (id 0ca0a340-90e6-4a5c-9e43-388a97d02b94) [346] Layer: Find point clusters: cluster hulls. 1 features (id bd39a5f0-c5ae-4773-adc2-68414f3ad727) [347] Layer: Find point clusters: Coverage complaints. 420 features (id f7460ebf-7b40-4ee2-8594-1d26f54dc5f3) [348] Layer: Find outliers: Neighborhoods. 16 features (id bea1ed6b-133f-4ba2-aa1d-595e35262e30) ## Appendix: the data behind the charts ### Figure 1. Timeline: events per week by dataset [1] | period | series | value | | --- | --- | --- | | 2026-02-23 | Dropped calls | 0 | | 2026-02-23 | Cell KPIs by day | 0 | | 2026-02-23 | Coverage complaints | 4 | | 2026-03-02 | Dropped calls | 0 | | 2026-03-02 | Cell KPIs by day | 0 | | 2026-03-02 | Coverage complaints | 11 | | 2026-03-09 | Dropped calls | 0 | | 2026-03-09 | Cell KPIs by day | 0 | | 2026-03-09 | Coverage complaints | 14 | | 2026-03-16 | Dropped calls | 0 | | 2026-03-16 | Cell KPIs by day | 0 | | 2026-03-16 | Coverage complaints | 12 | | 2026-03-23 | Dropped calls | 0 | | 2026-03-23 | Cell KPIs by day | 0 | | 2026-03-23 | Coverage complaints | 14 | | 2026-03-30 | Dropped calls | 0 | | 2026-03-30 | Cell KPIs by day | 0 | | 2026-03-30 | Coverage complaints | 13 | | 2026-04-06 | Dropped calls | 0 | | 2026-04-06 | Cell KPIs by day | 0 | | 2026-04-06 | Coverage complaints | 16 | | 2026-04-13 | Dropped calls | 0 | | 2026-04-13 | Cell KPIs by day | 0 | | 2026-04-13 | Coverage complaints | 11 | | 2026-04-20 | Dropped calls | 0 | | 2026-04-20 | Cell KPIs by day | 0 | | 2026-04-20 | Coverage complaints | 15 | | 2026-04-27 | Dropped calls | 0 | | 2026-04-27 | Cell KPIs by day | 0 | | 2026-04-27 | Coverage complaints | 10 | | 2026-05-04 | Dropped calls | 0 | | 2026-05-04 | Cell KPIs by day | 0 | | 2026-05-04 | Coverage complaints | 11 | | 2026-05-11 | Dropped calls | 0 | | 2026-05-11 | Cell KPIs by day | 0 | | 2026-05-11 | Coverage complaints | 18 | | 2026-05-18 | Dropped calls | 0 | | 2026-05-18 | Cell KPIs by day | 0 | | 2026-05-18 | Coverage complaints | 17 | | 2026-05-25 | Dropped calls | 0 | | 2026-05-25 | Cell KPIs by day | 0 | | 2026-05-25 | Coverage complaints | 11 | | 2026-06-01 | Dropped calls | 0 | | 2026-06-01 | Cell KPIs by day | 0 | | 2026-06-01 | Coverage complaints | 8 | | 2026-06-08 | Dropped calls | 0 | | 2026-06-08 | Cell KPIs by day | 0 | | 2026-06-08 | Coverage complaints | 13 | | 2026-06-15 | Dropped calls | 0 | | 2026-06-15 | Cell KPIs by day | 0 | | 2026-06-15 | Coverage complaints | 16 | | 2026-06-22 | Dropped calls | 0 | | 2026-06-22 | Cell KPIs by day | 0 | | 2026-06-22 | Coverage complaints | 15 | | 2026-06-29 | Dropped calls | 0 | | 2026-06-29 | Cell KPIs by day | 0 | | 2026-06-29 | Coverage complaints | 11 | | 2026-07-06 | Dropped calls | 0 | | 2026-07-06 | Cell KPIs by day | 0 | | 2026-07-06 | Coverage complaints | 18 | | 2026-07-13 | Dropped calls | 0 | | 2026-07-13 | Cell KPIs by day | 0 | | 2026-07-13 | Coverage complaints | 13 | | 2026-07-20 | Dropped calls | 0 | | 2026-07-20 | Cell KPIs by day | 0 | | 2026-07-20 | Coverage complaints | 13 | | 2026-07-27 | Dropped calls | 0 | | 2026-07-27 | Cell KPIs by day | 0 | | 2026-07-27 | Coverage complaints | 16 | | 2026-08-03 | Dropped calls | 239 | | 2026-08-03 | Cell KPIs by day | 98 | | 2026-08-03 | Coverage complaints | 14 | | 2026-08-10 | Dropped calls | 232 | | 2026-08-10 | Cell KPIs by day | 98 | | 2026-08-10 | Coverage complaints | 9 | | 2026-08-17 | Dropped calls | 218 | | 2026-08-17 | Cell KPIs by day | 98 | | 2026-08-17 | Coverage complaints | 17 | | 2026-08-24 | Dropped calls | 200 | | 2026-08-24 | Cell KPIs by day | 98 | | 2026-08-24 | Coverage complaints | 19 | | 2026-08-31 | Dropped calls | 242 | | 2026-08-31 | Cell KPIs by day | 98 | | 2026-08-31 | Coverage complaints | 15 | | 2026-09-07 | Dropped calls | 224 | | 2026-09-07 | Cell KPIs by day | 98 | | 2026-09-07 | Coverage complaints | 10 | | 2026-09-14 | Dropped calls | 218 | | 2026-09-14 | Cell KPIs by day | 98 | | 2026-09-14 | Coverage complaints | 16 | | 2026-09-21 | Dropped calls | 227 | | 2026-09-21 | Cell KPIs by day | 98 | | 2026-09-21 | Coverage complaints | 13 | | 2026-09-28 | Dropped calls | 0 | | 2026-09-28 | Cell KPIs by day | 0 | | 2026-09-28 | Coverage complaints | 7 | ### Figure 2. Timeline lanes: events per week by dataset [5] | lane | period | period_end | value | | --- | --- | --- | --- | | Dropped calls | 2026-02-23 | 2026-03-02 | 0 | | Dropped calls | 2026-03-02 | 2026-03-09 | 0 | | Dropped calls | 2026-03-09 | 2026-03-16 | 0 | | Dropped calls | 2026-03-16 | 2026-03-23 | 0 | | Dropped calls | 2026-03-23 | 2026-03-30 | 0 | | Dropped calls | 2026-03-30 | 2026-04-06 | 0 | | Dropped calls | 2026-04-06 | 2026-04-13 | 0 | | Dropped calls | 2026-04-13 | 2026-04-20 | 0 | | Dropped calls | 2026-04-20 | 2026-04-27 | 0 | | Dropped calls | 2026-04-27 | 2026-05-04 | 0 | | Dropped calls | 2026-05-04 | 2026-05-11 | 0 | | Dropped calls | 2026-05-11 | 2026-05-18 | 0 | | Dropped calls | 2026-05-18 | 2026-05-25 | 0 | | Dropped calls | 2026-05-25 | 2026-06-01 | 0 | | Dropped calls | 2026-06-01 | 2026-06-08 | 0 | | Dropped calls | 2026-06-08 | 2026-06-15 | 0 | | Dropped calls | 2026-06-15 | 2026-06-22 | 0 | | Dropped calls | 2026-06-22 | 2026-06-29 | 0 | | Dropped calls | 2026-06-29 | 2026-07-06 | 0 | | Dropped calls | 2026-07-06 | 2026-07-13 | 0 | | Dropped calls | 2026-07-13 | 2026-07-20 | 0 | | Dropped calls | 2026-07-20 | 2026-07-27 | 0 | | Dropped calls | 2026-07-27 | 2026-08-03 | 0 | | Dropped calls | 2026-08-03 | 2026-08-10 | 239 | | Dropped calls | 2026-08-10 | 2026-08-17 | 232 | | Dropped calls | 2026-08-17 | 2026-08-24 | 218 | | Dropped calls | 2026-08-24 | 2026-08-31 | 200 | | Dropped calls | 2026-08-31 | 2026-09-07 | 242 | | Dropped calls | 2026-09-07 | 2026-09-14 | 224 | | Dropped calls | 2026-09-14 | 2026-09-21 | 218 | | Dropped calls | 2026-09-21 | 2026-09-28 | 227 | | Dropped calls | 2026-09-28 | 2026-10-05 | 0 | | Cell KPIs by day | 2026-02-23 | 2026-03-02 | 0 | | Cell KPIs by day | 2026-03-02 | 2026-03-09 | 0 | | Cell KPIs by day | 2026-03-09 | 2026-03-16 | 0 | | Cell KPIs by day | 2026-03-16 | 2026-03-23 | 0 | | Cell KPIs by day | 2026-03-23 | 2026-03-30 | 0 | | Cell KPIs by day | 2026-03-30 | 2026-04-06 | 0 | | Cell KPIs by day | 2026-04-06 | 2026-04-13 | 0 | | Cell KPIs by day | 2026-04-13 | 2026-04-20 | 0 | | Cell KPIs by day | 2026-04-20 | 2026-04-27 | 0 | | Cell KPIs by day | 2026-04-27 | 2026-05-04 | 0 | | Cell KPIs by day | 2026-05-04 | 2026-05-11 | 0 | | Cell KPIs by day | 2026-05-11 | 2026-05-18 | 0 | | Cell KPIs by day | 2026-05-18 | 2026-05-25 | 0 | | Cell KPIs by day | 2026-05-25 | 2026-06-01 | 0 | | Cell KPIs by day | 2026-06-01 | 2026-06-08 | 0 | | Cell KPIs by day | 2026-06-08 | 2026-06-15 | 0 | | Cell KPIs by day | 2026-06-15 | 2026-06-22 | 0 | | Cell KPIs by day | 2026-06-22 | 2026-06-29 | 0 | | Cell KPIs by day | 2026-06-29 | 2026-07-06 | 0 | | Cell KPIs by day | 2026-07-06 | 2026-07-13 | 0 | | Cell KPIs by day | 2026-07-13 | 2026-07-20 | 0 | | Cell KPIs by day | 2026-07-20 | 2026-07-27 | 0 | | Cell KPIs by day | 2026-07-27 | 2026-08-03 | 0 | | Cell KPIs by day | 2026-08-03 | 2026-08-10 | 98 | | Cell KPIs by day | 2026-08-10 | 2026-08-17 | 98 | | Cell KPIs by day | 2026-08-17 | 2026-08-24 | 98 | | Cell KPIs by day | 2026-08-24 | 2026-08-31 | 98 | | Cell KPIs by day | 2026-08-31 | 2026-09-07 | 98 | | Cell KPIs by day | 2026-09-07 | 2026-09-14 | 98 | | Cell KPIs by day | 2026-09-14 | 2026-09-21 | 98 | | Cell KPIs by day | 2026-09-21 | 2026-09-28 | 98 | | Cell KPIs by day | 2026-09-28 | 2026-10-05 | 0 | | Coverage complaints | 2026-02-23 | 2026-03-02 | 4 | | Coverage complaints | 2026-03-02 | 2026-03-09 | 11 | | Coverage complaints | 2026-03-09 | 2026-03-16 | 14 | | Coverage complaints | 2026-03-16 | 2026-03-23 | 12 | | Coverage complaints | 2026-03-23 | 2026-03-30 | 14 | | Coverage complaints | 2026-03-30 | 2026-04-06 | 13 | | Coverage complaints | 2026-04-06 | 2026-04-13 | 16 | | Coverage complaints | 2026-04-13 | 2026-04-20 | 11 | | Coverage complaints | 2026-04-20 | 2026-04-27 | 15 | | Coverage complaints | 2026-04-27 | 2026-05-04 | 10 | | Coverage complaints | 2026-05-04 | 2026-05-11 | 11 | | Coverage complaints | 2026-05-11 | 2026-05-18 | 18 | | Coverage complaints | 2026-05-18 | 2026-05-25 | 17 | | Coverage complaints | 2026-05-25 | 2026-06-01 | 11 | | Coverage complaints | 2026-06-01 | 2026-06-08 | 8 | | Coverage complaints | 2026-06-08 | 2026-06-15 | 13 | | Coverage complaints | 2026-06-15 | 2026-06-22 | 16 | | Coverage complaints | 2026-06-22 | 2026-06-29 | 15 | | Coverage complaints | 2026-06-29 | 2026-07-06 | 11 | | Coverage complaints | 2026-07-06 | 2026-07-13 | 18 | | Coverage complaints | 2026-07-13 | 2026-07-20 | 13 | | Coverage complaints | 2026-07-20 | 2026-07-27 | 13 | | Coverage complaints | 2026-07-27 | 2026-08-03 | 16 | | Coverage complaints | 2026-08-03 | 2026-08-10 | 14 | | Coverage complaints | 2026-08-10 | 2026-08-17 | 9 | | Coverage complaints | 2026-08-17 | 2026-08-24 | 17 | | Coverage complaints | 2026-08-24 | 2026-08-31 | 19 | | Coverage complaints | 2026-08-31 | 2026-09-07 | 15 | | Coverage complaints | 2026-09-07 | 2026-09-14 | 10 | | Coverage complaints | 2026-09-14 | 2026-09-21 | 16 | | Coverage complaints | 2026-09-21 | 2026-09-28 | 13 | | Coverage complaints | 2026-09-28 | 2026-10-05 | 7 | ### Figure 3. Timeline: change from one week to the next [6] | period | period_end | value | previous | change | | --- | --- | --- | --- | --- | | 2026-03-02 | 2026-03-09 | 11 | 4 | 7 | | 2026-03-09 | 2026-03-16 | 14 | 11 | 3 | | 2026-03-16 | 2026-03-23 | 12 | 14 | -2 | | 2026-03-23 | 2026-03-30 | 14 | 12 | 2 | | 2026-03-30 | 2026-04-06 | 13 | 14 | -1 | | 2026-04-06 | 2026-04-13 | 16 | 13 | 3 | | 2026-04-13 | 2026-04-20 | 11 | 16 | -5 | | 2026-04-20 | 2026-04-27 | 15 | 11 | 4 | | 2026-04-27 | 2026-05-04 | 10 | 15 | -5 | | 2026-05-04 | 2026-05-11 | 11 | 10 | 1 | | 2026-05-11 | 2026-05-18 | 18 | 11 | 7 | | 2026-05-18 | 2026-05-25 | 17 | 18 | -1 | | 2026-05-25 | 2026-06-01 | 11 | 17 | -6 | | 2026-06-01 | 2026-06-08 | 8 | 11 | -3 | | 2026-06-08 | 2026-06-15 | 13 | 8 | 5 | | 2026-06-15 | 2026-06-22 | 16 | 13 | 3 | | 2026-06-22 | 2026-06-29 | 15 | 16 | -1 | | 2026-06-29 | 2026-07-06 | 11 | 15 | -4 | | 2026-07-06 | 2026-07-13 | 18 | 11 | 7 | | 2026-07-13 | 2026-07-20 | 13 | 18 | -5 | | 2026-07-20 | 2026-07-27 | 13 | 13 | 0 | | 2026-07-27 | 2026-08-03 | 16 | 13 | 3 | | 2026-08-03 | 2026-08-10 | 351 | 16 | 335 | | 2026-08-10 | 2026-08-17 | 339 | 351 | -12 | | 2026-08-17 | 2026-08-24 | 333 | 339 | -6 | | 2026-08-24 | 2026-08-31 | 317 | 333 | -16 | | 2026-08-31 | 2026-09-07 | 355 | 317 | 38 | | 2026-09-07 | 2026-09-14 | 332 | 355 | -23 | | 2026-09-14 | 2026-09-21 | 332 | 332 | 0 | | 2026-09-21 | 2026-09-28 | 338 | 332 | 6 | | 2026-09-28 | 2026-10-05 | 7 | 338 | -331 | ### Figure 35. Hypsometric curve [131] | from_m | to_m | elev_m | area_km2 | pct | above_pct | | --- | --- | --- | --- | --- | --- | | 1,475 | 1,539 | 1,507 | 5.148 | 7.141 | 100 | | 1,539 | 1,604 | 1,572 | 5.17 | 7.171 | 92.86 | | 1,604 | 1,668 | 1,636 | 5.033 | 6.981 | 85.69 | | 1,668 | 1,732 | 1,700 | 4.794 | 6.65 | 78.71 | | 1,732 | 1,796 | 1,764 | 4.684 | 6.497 | 72.06 | | 1,796 | 1,860 | 1,828 | 4.461 | 6.189 | 65.56 | | 1,860 | 1,925 | 1,892 | 4.115 | 5.708 | 59.37 | | 1,925 | 1,989 | 1,957 | 3.711 | 5.147 | 53.66 | | 1,989 | 2,053 | 2,021 | 3.446 | 4.78 | 48.52 | | 2,053 | 2,117 | 2,085 | 3.645 | 5.056 | 43.73 | | 2,117 | 2,181 | 2,149 | 4.684 | 6.498 | 38.68 | | 2,181 | 2,246 | 2,214 | 5.809 | 8.057 | 32.18 | | 2,246 | 2,310 | 2,278 | 4.987 | 6.918 | 24.12 | | 2,310 | 2,374 | 2,342 | 2.472 | 3.429 | 17.21 | | 2,374 | 2,438 | 2,406 | 2.039 | 2.829 | 13.78 | | 2,438 | 2,502 | 2,470 | 2.014 | 2.794 | 10.95 | | 2,502 | 2,567 | 2,535 | 1.985 | 2.754 | 8.154 | | 2,567 | 2,631 | 2,599 | 1.913 | 2.654 | 5.4 | | 2,631 | 2,695 | 2,663 | 1.373 | 1.904 | 2.745 | | 2,695 | 2,759 | 2,727 | 0.6066 | 0.841 | 0.841 | ### Figure 36. Slope distribution [132] | class | order | from_deg | to_deg | pct | area_km2 | color | | --- | --- | --- | --- | --- | --- | --- | | 0 to 2 degrees | 1 | 0 | 2 | 0.846 | 0.6102 | #1a9850 | | 2 to 5 degrees | 2 | 2 | 5 | 2.352 | 1.696 | #91cf60 | | 5 to 10 degrees | 3 | 5 | 10 | 17.98 | 12.96 | #d9ef8b | | 10 to 15 degrees | 4 | 10 | 15 | 59.98 | 43.24 | #fee08b | | 15 to 30 degrees | 5 | 15 | 30 | 15.13 | 10.91 | #fc8d59 | | Over 30 degrees | 6 | 30 | 90 | 3.705 | 2.671 | #d73027 | ### Figure 37. Aspect rose [133] | sector | order | center_deg | pct | area_km2 | color | | --- | --- | --- | --- | --- | --- | | North | 1 | 0 | 0.111 | 0.0801 | #ff0000 | | Northeast | 2 | 45 | 1.984 | 1.43 | #ffa500 | | East | 3 | 90 | 45.05 | 32.47 | #ffff00 | | Southeast | 4 | 135 | 2.357 | 1.699 | #00ff00 | | South | 5 | 180 | 0.255 | 0.1836 | #00ffff | | Southwest | 6 | 225 | 0.195 | 0.1404 | #00a5ff | | West | 7 | 270 | 49.01 | 35.33 | #0000ff | | Northwest | 8 | 315 | 0.768 | 0.5535 | #ff00ff | ### Figure 38. Elevation along the routes [134] | route | d_km | elev_m | | --- | --- | --- | | the longest axis (best path) | 0 | 2,205 | | the longest axis (best path) | 0.03 | 2,199 | | the longest axis (best path) | 0.06 | 2,194 | | the longest axis (best path) | 0.09 | 2,188 | | the longest axis (best path) | 0.12 | 2,182 | | the longest axis (best path) | 0.15 | 2,176 | | the longest axis (best path) | 0.18 | 2,170 | | the longest axis (best path) | 0.21 | 2,165 | | the longest axis (best path) | 0.24 | 2,159 | | the longest axis (best path) | 0.27 | 2,154 | | the longest axis (best path) | 0.3 | 2,148 | | the longest axis (best path) | 0.33 | 2,143 | | the longest axis (best path) | 0.36 | 2,138 | | the longest axis (best path) | 0.39 | 2,133 | | the longest axis (best path) | 0.42 | 2,128 | | the longest axis (best path) | 0.45 | 2,123 | | the longest axis (best path) | 0.48 | 2,118 | | the longest axis (best path) | 0.51 | 2,113 | | the longest axis (best path) | 0.54 | 2,108 | | the longest axis (best path) | 0.57 | 2,103 | | the longest axis (best path) | 0.6 | 2,097 | | the longest axis (best path) | 0.63 | 2,092 | | the longest axis (best path) | 0.66 | 2,086 | | the longest axis (best path) | 0.69 | 2,080 | | the longest axis (best path) | 0.72 | 2,074 | | the longest axis (best path) | 0.75 | 2,068 | | the longest axis (best path) | 0.78 | 2,062 | | the longest axis (best path) | 0.81 | 2,056 | | the longest axis (best path) | 0.84 | 2,050 | | the longest axis (best path) | 0.87 | 2,044 | | the longest axis (best path) | 0.9 | 2,039 | | the longest axis (best path) | 0.93 | 2,033 | | the longest axis (best path) | 0.96 | 2,027 | | the longest axis (best path) | 0.99 | 2,021 | | the longest axis (best path) | 1.02 | 2,015 | | the longest axis (best path) | 1.05 | 2,009 | | the longest axis (best path) | 1.08 | 2,003 | | the longest axis (best path) | 1.11 | 1,998 | | the longest axis (best path) | 1.14 | 1,992 | | the longest axis (best path) | 1.17 | 1,986 | | the longest axis (best path) | 1.2 | 1,980 | | the longest axis (best path) | 1.23 | 1,974 | | the longest axis (best path) | 1.26 | 1,968 | | the longest axis (best path) | 1.29 | 1,962 | | the longest axis (best path) | 1.32 | 1,957 | | the longest axis (best path) | 1.35 | 1,951 | | the longest axis (best path) | 1.38 | 1,945 | | the longest axis (best path) | 1.41 | 1,939 | | the longest axis (best path) | 1.44 | 1,934 | | the longest axis (best path) | 1.47 | 1,928 | | the longest axis (best path) | 1.5 | 1,923 | | the longest axis (best path) | 1.53 | 1,917 | | the longest axis (best path) | 1.56 | 1,912 | | the longest axis (best path) | 1.59 | 1,906 | | the longest axis (best path) | 1.62 | 1,901 | | the longest axis (best path) | 1.65 | 1,895 | | the longest axis (best path) | 1.68 | 1,890 | | the longest axis (best path) | 1.71 | 1,884 | | the longest axis (best path) | 1.74 | 1,879 | | the longest axis (best path) | 1.77 | 1,873 | | the longest axis (best path) | 1.8 | 1,868 | | the longest axis (best path) | 1.83 | 1,863 | | the longest axis (best path) | 1.86 | 1,857 | | the longest axis (best path) | 1.89 | 1,852 | | the longest axis (best path) | 1.92 | 1,846 | | the longest axis (best path) | 1.95 | 1,841 | | the longest axis (best path) | 1.98 | 1,835 | | the longest axis (best path) | 2.01 | 1,830 | | the longest axis (best path) | 2.04 | 1,824 | | the longest axis (best path) | 2.07 | 1,819 | | the longest axis (best path) | 2.1 | 1,813 | | the longest axis (best path) | 2.13 | 1,808 | | the longest axis (best path) | 2.16 | 1,803 | | the longest axis (best path) | 2.19 | 1,797 | | the longest axis (best path) | 2.22 | 1,792 | | the longest axis (best path) | 2.25 | 1,787 | | the longest axis (best path) | 2.28 | 1,782 | | the longest axis (best path) | 2.31 | 1,778 | | the longest axis (best path) | 2.34 | 1,773 | | the longest axis (best path) | 2.37 | 1,768 | | the longest axis (best path) | 2.4 | 1,763 | | the longest axis (best path) | 2.43 | 1,759 | | the longest axis (best path) | 2.46 | 1,754 | | the longest axis (best path) | 2.49 | 1,749 | | the longest axis (best path) | 2.52 | 1,745 | | the longest axis (best path) | 2.55 | 1,740 | | the longest axis (best path) | 2.58 | 1,735 | | the longest axis (best path) | 2.61 | 1,731 | | the longest axis (best path) | 2.64 | 1,726 | | the longest axis (best path) | 2.67 | 1,721 | | the longest axis (best path) | 2.7 | 1,717 | | the longest axis (best path) | 2.73 | 1,712 | | the longest axis (best path) | 2.76 | 1,707 | | the longest axis (best path) | 2.79 | 1,701 | | the longest axis (best path) | 2.82 | 1,696 | | the longest axis (best path) | 2.85 | 1,690 | | the longest axis (best path) | 2.88 | 1,684 | | the longest axis (best path) | 2.91 | 1,678 | | the longest axis (best path) | 2.94 | 1,672 | | the longest axis (best path) | 2.97 | 1,666 | | the longest axis (best path) | 3 | 1,660 | | the longest axis (best path) | 3.03 | 1,654 | | the longest axis (best path) | 3.06 | 1,647 | | the longest axis (best path) | 3.09 | 1,641 | | the longest axis (best path) | 3.12 | 1,635 | | the longest axis (best path) | 3.15 | 1,630 | | the longest axis (best path) | 3.18 | 1,624 | | the longest axis (best path) | 3.21 | 1,619 | | the longest axis (best path) | 3.24 | 1,613 | | the longest axis (best path) | 3.27 | 1,608 | | the longest axis (best path) | 3.3 | 1,603 | | the longest axis (best path) | 3.33 | 1,598 | | the longest axis (best path) | 3.36 | 1,593 | | the longest axis (best path) | 3.39 | 1,588 | | the longest axis (best path) | 3.42 | 1,583 | | the longest axis (best path) | 3.45 | 1,578 | | the longest axis (best path) | 3.48 | 1,572 | | the longest axis (best path) | 3.51 | 1,567 | | the longest axis (best path) | 3.54 | 1,561 | | the longest axis (best path) | 3.57 | 1,556 | | the longest axis (best path) | 3.6 | 1,550 | | the longest axis (best path) | 3.63 | 1,544 | | the longest axis (best path) | 3.66 | 1,539 | | the longest axis (best path) | 3.69 | 1,533 | | the longest axis (best path) | 3.72 | 1,528 | | the longest axis (best path) | 3.75 | 1,522 | | the longest axis (best path) | 3.78 | 1,517 | | the longest axis (best path) | 3.81 | 1,512 | | the longest axis (best path) | 3.84 | 1,508 | | the longest axis (best path) | 3.87 | 1,503 | | the longest axis (best path) | 3.9 | 1,498 | | the longest axis (best path) | 3.93 | 1,494 | | the longest axis (best path) | 3.96 | 1,490 | | the longest axis (best path) | 3.99 | 1,485 | | the longest axis (best path) | 4.02 | 1,486 | | the longest axis (best path) | 4.05 | 1,493 | | the longest axis (best path) | 4.08 | 1,499 | | the longest axis (best path) | 4.11 | 1,505 | | the longest axis (best path) | 4.14 | 1,511 | | the longest axis (best path) | 4.17 | 1,518 | | the longest axis (best path) | 4.2 | 1,524 | | the longest axis (best path) | 4.23 | 1,530 | | the longest axis (best path) | 4.26 | 1,537 | | the longest axis (best path) | 4.29 | 1,543 | | the longest axis (best path) | 4.32 | 1,550 | | the longest axis (best path) | 4.35 | 1,557 | | the longest axis (best path) | 4.38 | 1,564 | | the longest axis (best path) | 4.41 | 1,571 | | the longest axis (best path) | 4.44 | 1,579 | | the longest axis (best path) | 4.47 | 1,587 | | the longest axis (best path) | 4.5 | 1,595 | | the longest axis (best path) | 4.53 | 1,602 | | the longest axis (best path) | 4.56 | 1,610 | | the longest axis (best path) | 4.59 | 1,618 | | the longest axis (best path) | 4.62 | 1,626 | | the longest axis (best path) | 4.65 | 1,634 | | the longest axis (best path) | 4.68 | 1,641 | | the longest axis (best path) | 4.71 | 1,649 | | the longest axis (best path) | 4.74 | 1,656 | | the longest axis (best path) | 4.77 | 1,663 | | the longest axis (best path) | 4.8 | 1,670 | | the longest axis (best path) | 4.83 | 1,676 | | the longest axis (best path) | 4.86 | 1,683 | | the longest axis (best path) | 4.89 | 1,689 | | the longest axis (best path) | 4.92 | 1,695 | | the longest axis (best path) | 4.95 | 1,701 | | the longest axis (best path) | 4.98 | 1,706 | | the longest axis (best path) | 5.01 | 1,712 | | the longest axis (best path) | 5.04 | 1,717 | | the longest axis (best path) | 5.07 | 1,722 | | the longest axis (best path) | 5.1 | 1,727 | | the longest axis (best path) | 5.13 | 1,733 | | the longest axis (best path) | 5.16 | 1,739 | | the longest axis (best path) | 5.19 | 1,745 | | the longest axis (best path) | 5.22 | 1,751 | | the longest axis (best path) | 5.25 | 1,758 | | the longest axis (best path) | 5.28 | 1,765 | | the longest axis (best path) | 5.31 | 1,772 | | the longest axis (best path) | 5.34 | 1,779 | | the longest axis (best path) | 5.37 | 1,787 | | the longest axis (best path) | 5.4 | 1,795 | | the longest axis (best path) | 5.43 | 1,803 | | the longest axis (best path) | 5.46 | 1,811 | | the longest axis (best path) | 5.49 | 1,819 | | the longest axis (best path) | 5.52 | 1,827 | | the longest axis (best path) | 5.55 | 1,835 | | the longest axis (best path) | 5.58 | 1,843 | | the longest axis (best path) | 5.61 | 1,851 | | the longest axis (best path) | 5.64 | 1,860 | | the longest axis (best path) | 5.67 | 1,868 | | the longest axis (best path) | 5.7 | 1,878 | | the longest axis (best path) | 5.73 | 1,887 | | the longest axis (best path) | 5.76 | 1,898 | | the longest axis (best path) | 5.79 | 1,909 | | the longest axis (best path) | 5.82 | 1,921 | | the longest axis (best path) | 5.85 | 1,934 | | the longest axis (best path) | 5.88 | 1,948 | | the longest axis (best path) | 5.91 | 1,964 | | the longest axis (best path) | 5.94 | 1,980 | | the longest axis (best path) | 5.97 | 1,998 | | the longest axis (best path) | 6 | 2,017 | | the longest axis (best path) | 6.03 | 2,038 | | the longest axis (best path) | 6.06 | 2,059 | | the longest axis (best path) | 6.09 | 2,082 | | the longest axis (best path) | 6.12 | 2,104 | | the longest axis (best path) | 6.15 | 2,128 | | the longest axis (best path) | 6.18 | 2,151 | | the longest axis (best path) | 6.21 | 2,173 | | the longest axis (best path) | 6.24 | 2,195 | | the longest axis (best path) | 6.27 | 2,215 | | the longest axis (best path) | 6.3 | 2,233 | | the longest axis (best path) | 6.33 | 2,249 | | the longest axis (best path) | 6.36 | 2,262 | | the longest axis (best path) | 6.39 | 2,273 | | the longest axis (best path) | 6.42 | 2,280 | | the longest axis (best path) | 6.45 | 2,285 | | the longest axis (best path) | 6.48 | 2,286 | | the longest axis (best path) | 6.51 | 2,285 | | the longest axis (best path) | 6.54 | 2,282 | | the longest axis (best path) | 6.57 | 2,277 | | the longest axis (best path) | 6.6 | 2,271 | | the longest axis (best path) | 6.63 | 2,263 | | the longest axis (best path) | 6.66 | 2,255 | | the longest axis (best path) | 6.69 | 2,247 | | the longest axis (best path) | 6.72 | 2,239 | | the longest axis (best path) | 6.75 | 2,232 | | the longest axis (best path) | 6.78 | 2,225 | | the longest axis (best path) | 6.81 | 2,220 | | the longest axis (best path) | 6.84 | 2,215 | | the longest axis (best path) | 6.87 | 2,212 | | the longest axis (best path) | 6.9 | 2,211 | | the longest axis (best path) | 6.93 | 2,210 | | the longest axis (best path) | 6.96 | 2,211 | | the longest axis (best path) | 6.99 | 2,213 | | the longest axis (best path) | 7.02 | 2,215 | | the longest axis (best path) | 7.05 | 2,219 | | the longest axis (best path) | 7.08 | 2,224 | | the longest axis (best path) | 7.11 | 2,229 | | the longest axis (best path) | 7.14 | 2,235 | | the longest axis (best path) | 7.17 | 2,241 | | the longest axis (best path) | 7.2 | 2,248 | | the longest axis (best path) | 7.23 | 2,256 | | the longest axis (best path) | 7.26 | 2,263 | | the longest axis (best path) | 7.29 | 2,271 | | the longest axis (best path) | 7.32 | 2,279 | | the longest axis (best path) | 7.35 | 2,287 | | the longest axis (best path) | 7.38 | 2,295 | | the longest axis (best path) | 7.41 | 2,303 | | the longest axis (best path) | 7.44 | 2,311 | | the longest axis (best path) | 7.47 | 2,318 | | the longest axis (best path) | 7.5 | 2,326 | | the longest axis (best path) | 7.53 | 2,333 | | the longest axis (best path) | 7.56 | 2,340 | | the longest axis (best path) | 7.59 | 2,347 | | the longest axis (best path) | 7.62 | 2,354 | | the longest axis (best path) | 7.65 | 2,361 | | the longest axis (best path) | 7.68 | 2,368 | | the longest axis (best path) | 7.71 | 2,375 | | the longest axis (best path) | 7.74 | 2,382 | | the longest axis (best path) | 7.77 | 2,389 | | the longest axis (best path) | 7.8 | 2,397 | | the longest axis (best path) | 7.83 | 2,404 | | the longest axis (best path) | 7.86 | 2,411 | | the longest axis (best path) | 7.89 | 2,419 | | the longest axis (best path) | 7.92 | 2,426 | | the longest axis (best path) | 7.95 | 2,434 | | the longest axis (best path) | 7.98 | 2,441 | | the longest axis (best path) | 8.01 | 2,449 | | the longest axis (best path) | 8.04 | 2,456 | | the longest axis (best path) | 8.07 | 2,464 | | the longest axis (best path) | 8.1 | 2,472 | | the longest axis (best path) | 8.13 | 2,479 | | the longest axis (best path) | 8.16 | 2,487 | | the longest axis (best path) | 8.19 | 2,495 | | the longest axis (best path) | 8.22 | 2,503 | | the longest axis (best path) | 8.25 | 2,511 | | the longest axis (best path) | 8.28 | 2,518 | | the longest axis (best path) | 8.31 | 2,526 | | the longest axis (best path) | 8.34 | 2,534 | | the longest axis (best path) | 8.37 | 2,542 | | the longest axis (best path) | 8.4 | 2,550 | | the longest axis (best path) | 8.43 | 2,558 | | the longest axis (best path) | 8.46 | 2,566 | | the longest axis (best path) | 8.49 | 2,574 | | the longest axis (best path) | 8.52 | 2,582 | | the longest axis (best path) | 8.55 | 2,591 | | the longest axis (best path) | 8.58 | 2,599 | | the longest axis (best path) | 8.61 | 2,607 | | the longest axis (best path) | 8.64 | 2,615 | | the longest axis (best path) | 8.67 | 2,623 | | the longest axis (best path) | 8.7 | 2,631 | | the longest axis (best path) | 8.73 | 2,640 | | the longest axis (best path) | 8.76 | 2,648 | | the longest axis (best path) | 8.79 | 2,656 | | the longest axis (best path) | 8.82 | 2,665 | | the longest axis (best path) | 8.85 | 2,673 | | the longest axis (best path) | 8.88 | 2,681 | | the longest axis (best path) | 8.91 | 2,689 | | the two highest summits (best path) | 0 | 2,759 | | the two highest summits (best path) | 0.03 | 2,759 | | the two highest summits (best path) | 0.06 | 2,759 | | the two highest summits (best path) | 0.09 | 2,759 | | the two highest summits (best path) | 0.12 | 2,759 | | the two highest summits (best path) | 0.15 | 2,759 | | the two highest summits (best path) | 0.18 | 2,759 | | the two highest summits (best path) | 0.21 | 2,759 | | the two highest summits (best path) | 0.24 | 2,759 | | the two highest summits (best path) | 0.27 | 2,758 | | the two highest summits (best path) | 0.3 | 2,758 | | the two highest summits (best path) | 0.33 | 2,758 | | the two highest summits (best path) | 0.36 | 2,757 | | the two highest summits (best path) | 0.39 | 2,757 | | the two highest summits (best path) | 0.42 | 2,756 | | the two highest summits (best path) | 0.45 | 2,756 | | the two highest summits (best path) | 0.48 | 2,756 | | the two highest summits (best path) | 0.51 | 2,756 | | the two highest summits (best path) | 0.54 | 2,756 | | the two highest summits (best path) | 0.57 | 2,756 | | the two highest summits (best path) | 0.6 | 2,756 | | the two highest summits (best path) | 0.63 | 2,756 | ### Figure 39. Terrain: Contours (100 m) [135] | bin_start | bin_end | count | | --- | --- | --- | | 1,500 | 1,700 | 4 | | 1,700 | 1,900 | 4 | | 1,900 | 2,100 | 4 | | 2,100 | 2,300 | 5 | | 2,300 | 2,500 | 3 | | 2,500 | 2,700 | 4 | ### Figure 40. Terrain: Ravines [136] | bin_start | bin_end | count | | --- | --- | --- | | 1 | 1.8 | 1 | | 1.8 | 2.6 | 1 | | 2.6 | 3.4 | 1 | | 3.4 | 4.2 | 1 | | 4.2 | 5 | 1 | ### Figure 41. Terrain: Ridges [137] | bin_start | bin_end | count | | --- | --- | --- | | 1 | 6 | 5 | | 6 | 11 | 5 | | 11 | 16 | 5 | | 16 | 21 | 5 | | 21 | 26 | 5 | | 26 | 31 | 5 | | 31 | 36 | 6 | ### Figure 42. Terrain: Observation points [138] | bin_start | bin_end | count | | --- | --- | --- | | 1 | 1.2 | 1 | | 1.2 | 1.4 | 0 | | 1.4 | 1.6 | 0 | | 1.6 | 1.8 | 0 | | 1.8 | 2 | 1 | ### Figure 43. Terrain: Open flat ground [139] | bin_start | bin_end | count | | --- | --- | --- | | 1.08e+04 | 2.988e+04 | 5 | | 2.988e+04 | 4.896e+04 | 2 | | 4.896e+04 | 6.804e+04 | 1 | | 6.804e+04 | 8.712e+04 | 0 | | 8.712e+04 | 1.062e+05 | 1 | ### Figure 44. Terrain: Terrain masking (cover) [140] | bin_start | bin_end | count | | --- | --- | --- | | 3.699e+05 | 5.621e+05 | 2 | | 5.621e+05 | 7.544e+05 | 0 | | 7.544e+05 | 9.466e+05 | 0 | | 9.466e+05 | 1,138,860 | 0 | | 1,138,860 | 1,331,100 | 3 | ### Figure 45. Terrain: Streams (Strahler order) [141] | bin_start | bin_end | count | | --- | --- | --- | | 1 | 2.6 | 2 | | 2.6 | 4.2 | 2 | | 4.2 | 5.8 | 1 | | 5.8 | 7.4 | 2 | | 7.4 | 9 | 2 | ### Figure 46. Terrain: Pour points [142] | bin_start | bin_end | count | | --- | --- | --- | | 1 | 1.4 | 1 | | 1.4 | 1.8 | 0 | | 1.8 | 2.2 | 1 | | 2.2 | 2.6 | 0 | | 2.6 | 3 | 1 | ### Figure 47. Terrain: Watershed boundaries [143] | bin_start | bin_end | count | | --- | --- | --- | | 1.004e+04 | 1.764e+04 | 1 | | 1.764e+04 | 2.524e+04 | 1 | | 2.524e+04 | 3.284e+04 | 0 | | 3.284e+04 | 4.044e+04 | 0 | | 4.044e+04 | 4.805e+04 | 1 | ### Figure 48. Terrain: Pour points (snapped) [144] | bin_start | bin_end | count | | --- | --- | --- | | 9,034,200 | 15,875,640 | 1 | | 15,875,640 | 22,717,080 | 1 | | 22,717,080 | 29,558,520 | 0 | | 29,558,520 | 36,399,960 | 0 | | 36,399,960 | 43,241,400 | 1 | ### Figure 49. Terrain: Streams (Strahler order) [145] | bin_start | bin_end | count | | --- | --- | --- | | 1 | 2.6 | 2 | | 2.6 | 4.2 | 2 | | 4.2 | 5.8 | 1 | | 5.8 | 7.4 | 2 | | 7.4 | 9 | 2 | ### Figure 50. Terrain: Flood-prone areas [146] | bin_start | bin_end | count | | --- | --- | --- | | 7.587e+05 | 7.974e+05 | 1 | | 7.974e+05 | 8.361e+05 | 0 | | 8.361e+05 | 8.748e+05 | 0 | | 8.748e+05 | 9.135e+05 | 0 | | 9.135e+05 | 9.522e+05 | 1 | ### Figure 51. Terrain dismounted: Obstacles [147] | bin_start | bin_end | count | | --- | --- | --- | | 2,498,400 | 2,498,400 | 1 | ### Figure 52. Terrain wheeled: Obstacles [148] | bin_start | bin_end | count | | --- | --- | --- | | 2.16e+04 | 1,103,580 | 9 | | 1,103,580 | 2,185,560 | 0 | | 2,185,560 | 3,267,540 | 0 | | 3,267,540 | 4,349,520 | 0 | | 4,349,520 | 5,431,500 | 1 | ### Figure 53. Terrain tracked: Obstacles [149] | bin_start | bin_end | count | | --- | --- | --- | | 2,498,400 | 2,498,400 | 1 | ### Figure 54. Terrain: Obstacles [150] | bin_start | bin_end | count | | --- | --- | --- | | 2,498,400 | 2,498,400 | 1 | ### Figure 55. Terrain: Mobility corridors [151] | bin_start | bin_end | count | | --- | --- | --- | | 20,304,900 | 25,091,100 | 1 | | 25,091,100 | 29,877,300 | 0 | | 29,877,300 | 34,663,500 | 0 | | 34,663,500 | 39,449,700 | 0 | | 39,449,700 | 44,235,900 | 1 | ### Figure 56. Terrain: Key terrain candidates [152] | bin_start | bin_end | count | | --- | --- | --- | | 1 | 1.2 | 1 | | 1.2 | 1.4 | 0 | | 1.4 | 1.6 | 0 | | 1.6 | 1.8 | 0 | | 1.8 | 2 | 1 | ### Figure 57. Terrain: Corridor constrictions [153] | bin_start | bin_end | count | | --- | --- | --- | | 180 | 246.5 | 1 | | 246.5 | 313 | 1 | | 313 | 379.6 | 0 | | 379.6 | 446.1 | 0 | | 446.1 | 512.6 | 1 | ### Figure 58. Terrain: Avenues of approach [154] | bin_start | bin_end | count | | --- | --- | --- | | 8,241 | 8,583 | 1 | | 8,583 | 8,925 | 0 | | 8,925 | 9,267 | 0 | | 9,267 | 9,609 | 1 | | 9,609 | 9,951 | 1 | ### Figure 59. Terrain heights: Visible area [155] | bin_start | bin_end | count | | --- | --- | --- | | 5.94e+04 | 4,117,320 | 4 | | 4,117,320 | 8,175,240 | 0 | | 8,175,240 | 12,233,160 | 0 | | 12,233,160 | 16,291,080 | 0 | | 16,291,080 | 20,349,000 | 1 | ### Figure 60. Terrain heights: Observers [156] | bin_start | bin_end | count | | --- | --- | --- | | 2,270 | 2,368 | 3 | | 2,368 | 2,466 | 0 | | 2,466 | 2,564 | 0 | | 2,564 | 2,663 | 0 | | 2,663 | 2,761 | 2 | ### Figure 61. Terrain DH-1 DH-2: Sightline [157] | bin_start | bin_end | count | | --- | --- | --- | | 3,840 | 3,840 | 1 | ### Figure 62. Terrain DH-1 DH-3: Sightline [158] | bin_start | bin_end | count | | --- | --- | --- | | 2,694 | 2,694 | 1 | ### Figure 63. Terrain DH-1 DH-3: Ground visible from the observer [159] | bin_start | bin_end | count | | --- | --- | --- | | 329.3 | 799.3 | 1 | | 799.3 | 1,269 | 0 | | 1,269 | 1,739 | 0 | | 1,739 | 2,209 | 0 | | 2,209 | 2,679 | 2 | ### Figure 64. Terrain DH-2 DH-3: Sightline [160] | bin_start | bin_end | count | | --- | --- | --- | | 3,915 | 3,915 | 1 | ### Figure 65. Terrain axis: Least-cost routes [161] | bin_start | bin_end | count | | --- | --- | --- | | 1 | 1 | 1 | ### Figure 66. Terrain summits: Least-cost routes [162] | bin_start | bin_end | count | | --- | --- | --- | | 1 | 1 | 1 | ### Figure 67. Terrain axis: Profile line [163] | bin_start | bin_end | count | | --- | --- | --- | | 2,689 | 2,689 | 1 | ### Figure 68. Terrain summits: Profile line [164] | bin_start | bin_end | count | | --- | --- | --- | | 2,759 | 2,759 | 1 | ### Figure 69. Terrain: Best buildability areas [165] | bin_start | bin_end | count | | --- | --- | --- | | 3,600 | 5.366e+04 | 76 | | 5.366e+04 | 1.037e+05 | 6 | | 1.037e+05 | 1.538e+05 | 1 | | 1.538e+05 | 2.038e+05 | 1 | | 2.038e+05 | 2.539e+05 | 0 | | 2.539e+05 | 3.04e+05 | 0 | | 3.04e+05 | 3.54e+05 | 3 | | 3.54e+05 | 4.041e+05 | 1 | ### Figure 70. Terrain: Best mineability areas [166] | bin_start | bin_end | count | | --- | --- | --- | | 4,500 | 3.492e+05 | 11 | | 3.492e+05 | 6.939e+05 | 0 | | 6.939e+05 | 1,038,600 | 2 | | 1,038,600 | 1,383,300 | 0 | | 1,383,300 | 1,728,000 | 2 | ### Figure 71. Terrain: Landing zone candidates [167] | bin_start | bin_end | count | | --- | --- | --- | | 1 | 2 | 1 | | 2 | 3 | 1 | | 3 | 4 | 1 | | 4 | 5 | 1 | | 5 | 6 | 2 | ### Figure 72. Terrain: Landing points [168] | bin_start | bin_end | count | | --- | --- | --- | | 29.9 | 29.9 | 6 | ### Figure 73. Terrain: Visible area [169] | bin_start | bin_end | count | | --- | --- | --- | | 49,940,100 | 49,940,100 | 1 | ### Figure 74. Terrain: Observers [170] | bin_start | bin_end | count | | --- | --- | --- | | 1,496 | 1,648 | 6 | | 1,648 | 1,800 | 3 | | 1,800 | 1,952 | 2 | | 1,952 | 2,103 | 1 | | 2,103 | 2,255 | 2 | ### Figure 75. Terrain: Dominant heights [171] | bin_start | bin_end | count | | --- | --- | --- | | 1 | 1.8 | 1 | | 1.8 | 2.6 | 1 | | 2.6 | 3.4 | 1 | | 3.4 | 4.2 | 1 | | 4.2 | 5 | 1 | ### Figure 76. Dropped calls: count per week [199] | period | value | | --- | --- | | 2026-08-03 | 239 | | 2026-08-10 | 232 | | 2026-08-17 | 218 | | 2026-08-24 | 200 | | 2026-08-31 | 242 | | 2026-09-07 | 224 | | 2026-09-14 | 218 | | 2026-09-21 | 227 | ### Figure 77. Dropped calls: week of 2026-08-24 to week of 2026-08-31 by place [200] | group | period | value | | --- | --- | --- | | Los Olivos | week of 2026-08-24 | 28 | | Los Olivos | week of 2026-08-31 | 41 | | Los Pinos | week of 2026-08-24 | 3 | | Los Pinos | week of 2026-08-31 | 11 | | Barrio Norte | week of 2026-08-24 | 12 | | Barrio Norte | week of 2026-08-31 | 5 | | La Estacion | week of 2026-08-24 | 12 | | La Estacion | week of 2026-08-31 | 19 | | Los Arcos | week of 2026-08-24 | 20 | | Los Arcos | week of 2026-08-31 | 27 | | La Fuente | week of 2026-08-24 | 2 | | La Fuente | week of 2026-08-31 | 7 | | La Vega | week of 2026-08-24 | 1 | | La Vega | week of 2026-08-31 | 6 | | El Cerro | week of 2026-08-24 | 38 | | El Cerro | week of 2026-08-31 | 34 | | Las Flores | week of 2026-08-24 | 5 | | Las Flores | week of 2026-08-31 | 8 | | El Mercado | week of 2026-08-24 | 8 | | El Mercado | week of 2026-08-31 | 6 | | La Ribera | week of 2026-08-24 | 12 | | La Ribera | week of 2026-08-31 | 14 | | Las Acacias | week of 2026-08-24 | 26 | | Las Acacias | week of 2026-08-31 | 28 | | El Molino | week of 2026-08-24 | 7 | | El Molino | week of 2026-08-31 | 8 | | El Puerto Viejo | week of 2026-08-24 | 9 | | El Puerto Viejo | week of 2026-08-31 | 10 | | La Alameda | week of 2026-08-24 | 8 | | La Alameda | week of 2026-08-31 | 9 | ### Figure 78. Period change: Dropped calls (week of 2026-08-24 to week of 2026-08-31) [201] | bin_start | bin_end | count | | --- | --- | --- | | -1.698 | -0.9305 | 1 | | -0.9305 | -0.1634 | 2 | | -0.1634 | 0.6038 | 6 | | 0.6038 | 1.371 | 3 | | 1.371 | 2.138 | 4 | ### Figure 79. Coverage complaints: count per week [202] | period | value | | --- | --- | | 2026-02-23 | 4 | | 2026-03-02 | 11 | | 2026-03-09 | 14 | | 2026-03-16 | 12 | | 2026-03-23 | 14 | | 2026-03-30 | 13 | | 2026-04-06 | 16 | | 2026-04-13 | 11 | | 2026-04-20 | 15 | | 2026-04-27 | 10 | | 2026-05-04 | 11 | | 2026-05-11 | 18 | | 2026-05-18 | 17 | | 2026-05-25 | 11 | | 2026-06-01 | 8 | | 2026-06-08 | 13 | | 2026-06-15 | 16 | | 2026-06-22 | 15 | | 2026-06-29 | 11 | | 2026-07-06 | 18 | | 2026-07-13 | 13 | | 2026-07-20 | 13 | | 2026-07-27 | 16 | | 2026-08-03 | 14 | | 2026-08-10 | 9 | | 2026-08-17 | 17 | | 2026-08-24 | 19 | | 2026-08-31 | 15 | | 2026-09-07 | 10 | | 2026-09-14 | 16 | | 2026-09-21 | 13 | | 2026-09-28 | 7 | ### Figure 80. Coverage complaints: week of 2026-08-10 to week of 2026-08-17 by place [203] | group | period | value | | --- | --- | --- | | El Puerto Viejo | week of 2026-08-10 | 0 | | El Puerto Viejo | week of 2026-08-17 | 3 | | Los Olivos | week of 2026-08-10 | 1 | | Los Olivos | week of 2026-08-17 | 4 | | El Cerro | week of 2026-08-10 | 2 | | El Cerro | week of 2026-08-17 | 4 | | La Fuente | week of 2026-08-10 | 0 | | La Fuente | week of 2026-08-17 | 1 | | La Ribera | week of 2026-08-10 | 2 | | La Ribera | week of 2026-08-17 | 1 | | Las Acacias | week of 2026-08-10 | 1 | | Las Acacias | week of 2026-08-17 | 0 | | Los Arcos | week of 2026-08-10 | 2 | | Los Arcos | week of 2026-08-17 | 3 | | Los Pinos | week of 2026-08-10 | 1 | | Los Pinos | week of 2026-08-17 | 1 | ### Figure 81. Period change: Coverage complaints (week of 2026-08-10 to week of 2026-08-17) [204] | bin_start | bin_end | count | | --- | --- | --- | | -1 | -0.4536 | 2 | | -0.4536 | 0.09284 | 1 | | 0.09284 | 0.6393 | 1 | | 0.6393 | 1.186 | 2 | | 1.186 | 1.732 | 2 | ### Figure 82. Dropped calls by hour of day [209] | label | count | share | | --- | --- | --- | | 00:00 | 30 | 0.0167 | | 01:00 | 15 | 0.0083 | | 02:00 | 14 | 0.0078 | | 03:00 | 6 | 0.0033 | | 04:00 | 7 | 0.0039 | | 05:00 | 18 | 0.01 | | 06:00 | 48 | 0.0267 | | 07:00 | 96 | 0.0533 | | 08:00 | 113 | 0.0628 | | 09:00 | 107 | 0.0594 | | 10:00 | 100 | 0.0556 | | 11:00 | 99 | 0.055 | | 12:00 | 87 | 0.0483 | | 13:00 | 91 | 0.0506 | | 14:00 | 87 | 0.0483 | | 15:00 | 90 | 0.05 | | 16:00 | 121 | 0.0672 | | 17:00 | 154 | 0.0856 | | 18:00 | 138 | 0.0767 | | 19:00 | 110 | 0.0611 | | 20:00 | 101 | 0.0561 | | 21:00 | 80 | 0.0444 | | 22:00 | 53 | 0.0294 | | 23:00 | 35 | 0.0194 | ### Figure 83. Dropped calls by day of week [210] | label | count | share | | --- | --- | --- | | Monday | 185 | 0.1028 | | Tuesday | 195 | 0.1083 | | Wednesday | 199 | 0.1106 | | Thursday | 241 | 0.1339 | | Friday | 292 | 0.1622 | | Saturday | 330 | 0.1833 | | Sunday | 358 | 0.1989 | ### Figure 84. Seasonality of Dropped calls [211] | bin_start | bin_end | count | | --- | --- | --- | | 31 | 90.2 | 9 | | 90.2 | 149.4 | 3 | | 149.4 | 208.6 | 1 | | 208.6 | 267.8 | 2 | | 267.8 | 327 | 1 | ### Figure 85. Clusters of all inputs together: dropped calls and coverage complaints over time [259] | period | series | value | | --- | --- | --- | | 2026-08-03 | coverage | 92 | | 2026-08-03 | handover | 82 | | 2026-08-03 | congestion | 39 | | 2026-08-03 | interference | 26 | | 2026-08-10 | coverage | 80 | | 2026-08-10 | handover | 79 | | 2026-08-10 | congestion | 48 | | 2026-08-10 | interference | 25 | | 2026-08-17 | coverage | 82 | | 2026-08-17 | handover | 86 | | 2026-08-17 | congestion | 35 | | 2026-08-17 | interference | 15 | | 2026-08-24 | coverage | 68 | | 2026-08-24 | handover | 75 | | 2026-08-24 | congestion | 37 | | 2026-08-24 | interference | 20 | | 2026-08-31 | coverage | 82 | | 2026-08-31 | handover | 80 | | 2026-08-31 | congestion | 51 | | 2026-08-31 | interference | 29 | | 2026-09-07 | coverage | 76 | | 2026-09-07 | handover | 74 | | 2026-09-07 | congestion | 51 | | 2026-09-07 | interference | 23 | | 2026-09-14 | coverage | 81 | | 2026-09-14 | handover | 74 | | 2026-09-14 | congestion | 34 | | 2026-09-14 | interference | 29 | | 2026-09-21 | coverage | 85 | | 2026-09-21 | handover | 67 | | 2026-09-21 | congestion | 46 | | 2026-09-21 | interference | 29 | ### Figure 86. Dropped calls over time (occurred_at) [262] | period | series | value | | --- | --- | --- | | 2026-08-03 | coverage | 92 | | 2026-08-03 | handover | 82 | | 2026-08-03 | congestion | 39 | | 2026-08-03 | interference | 26 | | 2026-08-10 | coverage | 80 | | 2026-08-10 | handover | 79 | | 2026-08-10 | congestion | 48 | | 2026-08-10 | interference | 25 | | 2026-08-17 | coverage | 82 | | 2026-08-17 | handover | 86 | | 2026-08-17 | congestion | 35 | | 2026-08-17 | interference | 15 | | 2026-08-24 | coverage | 68 | | 2026-08-24 | handover | 75 | | 2026-08-24 | congestion | 37 | | 2026-08-24 | interference | 20 | | 2026-08-31 | coverage | 82 | | 2026-08-31 | handover | 80 | | 2026-08-31 | congestion | 51 | | 2026-08-31 | interference | 29 | | 2026-09-07 | coverage | 76 | | 2026-09-07 | handover | 74 | | 2026-09-07 | congestion | 51 | | 2026-09-07 | interference | 23 | | 2026-09-14 | coverage | 81 | | 2026-09-14 | handover | 74 | | 2026-09-14 | congestion | 34 | | 2026-09-14 | interference | 29 | | 2026-09-21 | coverage | 85 | | 2026-09-21 | handover | 67 | | 2026-09-21 | congestion | 46 | | 2026-09-21 | interference | 29 | ### Figure 87. Coverage complaints over time (date) [263] | period | series | value | | --- | --- | --- | | 2026-02-23 | slow data | 2 | | 2026-02-23 | calls drop | 2 | | 2026-02-23 | no signal indoors | 0 | | 2026-03-02 | slow data | 4 | | 2026-03-02 | calls drop | 6 | | 2026-03-02 | no signal indoors | 1 | | 2026-03-09 | slow data | 6 | | 2026-03-09 | calls drop | 4 | | 2026-03-09 | no signal indoors | 4 | | 2026-03-16 | slow data | 3 | | 2026-03-16 | calls drop | 6 | | 2026-03-16 | no signal indoors | 3 | | 2026-03-23 | slow data | 6 | | 2026-03-23 | calls drop | 5 | | 2026-03-23 | no signal indoors | 3 | | 2026-03-30 | slow data | 6 | | 2026-03-30 | calls drop | 4 | | 2026-03-30 | no signal indoors | 3 | | 2026-04-06 | slow data | 3 | | 2026-04-06 | calls drop | 5 | | 2026-04-06 | no signal indoors | 8 | | 2026-04-13 | slow data | 4 | | 2026-04-13 | calls drop | 5 | | 2026-04-13 | no signal indoors | 2 | | 2026-04-20 | slow data | 6 | | 2026-04-20 | calls drop | 3 | | 2026-04-20 | no signal indoors | 6 | | 2026-04-27 | slow data | 5 | | 2026-04-27 | calls drop | 2 | | 2026-04-27 | no signal indoors | 3 | | 2026-05-04 | slow data | 5 | | 2026-05-04 | calls drop | 4 | | 2026-05-04 | no signal indoors | 2 | | 2026-05-11 | slow data | 9 | | 2026-05-11 | calls drop | 5 | | 2026-05-11 | no signal indoors | 4 | | 2026-05-18 | slow data | 7 | | 2026-05-18 | calls drop | 7 | | 2026-05-18 | no signal indoors | 3 | | 2026-05-25 | slow data | 6 | | 2026-05-25 | calls drop | 2 | | 2026-05-25 | no signal indoors | 3 | | 2026-06-01 | slow data | 3 | | 2026-06-01 | calls drop | 2 | | 2026-06-01 | no signal indoors | 3 | | 2026-06-08 | slow data | 3 | | 2026-06-08 | calls drop | 6 | | 2026-06-08 | no signal indoors | 4 | | 2026-06-15 | slow data | 5 | | 2026-06-15 | calls drop | 3 | | 2026-06-15 | no signal indoors | 8 | | 2026-06-22 | slow data | 3 | | 2026-06-22 | calls drop | 6 | | 2026-06-22 | no signal indoors | 6 | | 2026-06-29 | slow data | 4 | | 2026-06-29 | calls drop | 5 | | 2026-06-29 | no signal indoors | 2 | | 2026-07-06 | slow data | 8 | | 2026-07-06 | calls drop | 4 | | 2026-07-06 | no signal indoors | 6 | | 2026-07-13 | slow data | 5 | | 2026-07-13 | calls drop | 6 | | 2026-07-13 | no signal indoors | 2 | | 2026-07-20 | slow data | 5 | | 2026-07-20 | calls drop | 5 | | 2026-07-20 | no signal indoors | 3 | | 2026-07-27 | slow data | 8 | | 2026-07-27 | calls drop | 3 | | 2026-07-27 | no signal indoors | 5 | | 2026-08-03 | slow data | 4 | | 2026-08-03 | calls drop | 5 | | 2026-08-03 | no signal indoors | 5 | | 2026-08-10 | slow data | 4 | | 2026-08-10 | calls drop | 3 | | 2026-08-10 | no signal indoors | 2 | | 2026-08-17 | slow data | 6 | | 2026-08-17 | calls drop | 6 | | 2026-08-17 | no signal indoors | 5 | | 2026-08-24 | slow data | 5 | | 2026-08-24 | calls drop | 6 | | 2026-08-24 | no signal indoors | 8 | | 2026-08-31 | slow data | 5 | | 2026-08-31 | calls drop | 4 | | 2026-08-31 | no signal indoors | 6 | | 2026-09-07 | slow data | 5 | | 2026-09-07 | calls drop | 2 | | 2026-09-07 | no signal indoors | 3 | | 2026-09-14 | slow data | 2 | | 2026-09-14 | calls drop | 7 | | 2026-09-14 | no signal indoors | 7 | | 2026-09-21 | slow data | 6 | | 2026-09-21 | calls drop | 3 | | 2026-09-21 | no signal indoors | 4 | | 2026-09-28 | slow data | 1 | | 2026-09-28 | calls drop | 4 | | 2026-09-28 | no signal indoors | 2 | ### Figure 88. Cell KPIs by day over time (date) [264] | period | value | | --- | --- | | 2026-08-03 | 98 | | 2026-08-10 | 98 | | 2026-08-17 | 98 | | 2026-08-24 | 98 | | 2026-08-31 | 98 | | 2026-09-07 | 98 | | 2026-09-14 | 98 | | 2026-09-21 | 98 | ### Figure 89. Site leases over time (lease_end_year) [265] | bin_start | bin_end | count | | --- | --- | --- | | 2,027 | 2,028 | 5 | | 2,028 | 2,029 | 1 | | 2,029 | 2,031 | 0 | | 2,031 | 2,032 | 1 | | 2,032 | 2,033 | 7 | ### Figure 90. Hot spot analysis: Dropped calls [266] | bin_start | bin_end | band | band_label | count | | --- | --- | --- | --- | --- | | -2 | -1.5 | cold_90 | Cold spot, 90% confidence | 15 | | -2 | -1.5 | not_significant | Not significant | 32 | | -1.5 | -1 | not_significant | Not significant | 451 | | -1 | -0.5 | not_significant | Not significant | 622 | | -0.5 | 0 | not_significant | Not significant | 414 | | 0 | 0.5 | not_significant | Not significant | 88 | | 0.5 | 1 | not_significant | Not significant | 19 | | 1 | 1.5 | not_significant | Not significant | 13 | | 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 7 | | 1.5 | 2 | not_significant | Not significant | 2 | | 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 15 | | 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 5 | | 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 9 | | 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 13 | | 4 | 4.5 | hot_99 | Hot spot, 99% confidence | 3 | | 4.5 | 5 | hot_99 | Hot spot, 99% confidence | 2 | | 5 | 5.5 | hot_99 | Hot spot, 99% confidence | 9 | | 5.5 | 6 | hot_99 | Hot spot, 99% confidence | 5 | | 6 | 6.5 | hot_99 | Hot spot, 99% confidence | 3 | | 6.5 | 7 | hot_99 | Hot spot, 99% confidence | 5 | | 7 | 7.5 | hot_99 | Hot spot, 99% confidence | 4 | | 7.5 | 8 | hot_99 | Hot spot, 99% confidence | 7 | | 8 | 8.5 | hot_99 | Hot spot, 99% confidence | 5 | | 8.5 | 9 | hot_99 | Hot spot, 99% confidence | 2 | | 9 | 9.5 | hot_99 | Hot spot, 99% confidence | 1 | | 10 | 10.5 | hot_99 | Hot spot, 99% confidence | 2 | | 10.5 | 11 | hot_99 | Hot spot, 99% confidence | 3 | | 11 | 11.5 | hot_99 | Hot spot, 99% confidence | 2 | | 11.5 | 12 | hot_99 | Hot spot, 99% confidence | 2 | | 12 | 12.5 | hot_99 | Hot spot, 99% confidence | 3 | | 12.5 | 13 | hot_99 | Hot spot, 99% confidence | 1 | | 13 | 13.5 | hot_99 | Hot spot, 99% confidence | 2 | | 13.5 | 14 | hot_99 | Hot spot, 99% confidence | 1 | | 14 | 14.5 | hot_99 | Hot spot, 99% confidence | 2 | | 14.5 | 15 | hot_99 | Hot spot, 99% confidence | 3 | | 15.5 | 16 | hot_99 | Hot spot, 99% confidence | 1 | | 16 | 16.5 | hot_99 | Hot spot, 99% confidence | 1 | | 17 | 17.5 | hot_99 | Hot spot, 99% confidence | 2 | | 18 | 18.5 | hot_99 | Hot spot, 99% confidence | 2 | | 18.5 | 19 | hot_99 | Hot spot, 99% confidence | 2 | | 19 | 19.5 | hot_99 | Hot spot, 99% confidence | 3 | | 19.5 | 20 | hot_99 | Hot spot, 99% confidence | 1 | | 21 | 21.5 | hot_99 | Hot spot, 99% confidence | 1 | | 21.5 | 22 | hot_99 | Hot spot, 99% confidence | 1 | | 22 | 22.5 | hot_99 | Hot spot, 99% confidence | 1 | | 22.5 | 23 | hot_99 | Hot spot, 99% confidence | 2 | | 23 | 23.5 | hot_99 | Hot spot, 99% confidence | 2 | | 24 | 24.5 | hot_99 | Hot spot, 99% confidence | 2 | | 25.5 | 26 | hot_99 | Hot spot, 99% confidence | 2 | | 26.5 | 27 | hot_99 | Hot spot, 99% confidence | 1 | | 27 | 27.5 | hot_99 | Hot spot, 99% confidence | 1 | | 27.5 | 28 | hot_99 | Hot spot, 99% confidence | 1 | ### Figure 91. Hot spot analysis: Coverage complaints [267] | bin_start | bin_end | band | band_label | count | | --- | --- | --- | --- | --- | | -1.5 | -1 | not_significant | Not significant | 47 | | -1 | -0.5 | not_significant | Not significant | 127 | | -0.5 | 0 | not_significant | Not significant | 71 | | 0 | 0.5 | not_significant | Not significant | 21 | | 0.5 | 1 | not_significant | Not significant | 5 | | 1 | 1.5 | not_significant | Not significant | 3 | | 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 2 | | 1.5 | 2 | hot_95 | Hot spot, 95% confidence | 1 | | 1.5 | 2 | not_significant | Not significant | 1 | | 2.5 | 3 | hot_95 | Hot spot, 95% confidence | 2 | | 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 3 | | 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 1 | | 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 1 | | 4 | 4.5 | hot_99 | Hot spot, 99% confidence | 2 | | 4.5 | 5 | hot_99 | Hot spot, 99% confidence | 1 | | 5 | 5.5 | hot_99 | Hot spot, 99% confidence | 3 | | 5.5 | 6 | hot_99 | Hot spot, 99% confidence | 2 | | 8 | 8.5 | hot_99 | Hot spot, 99% confidence | 2 | | 8.5 | 9 | hot_99 | Hot spot, 99% confidence | 3 | | 9 | 9.5 | hot_99 | Hot spot, 99% confidence | 1 | | 10.5 | 11 | hot_99 | Hot spot, 99% confidence | 3 | | 12.5 | 13 | hot_99 | Hot spot, 99% confidence | 1 | | 13 | 13.5 | hot_99 | Hot spot, 99% confidence | 1 | | 13.5 | 14 | hot_99 | Hot spot, 99% confidence | 1 | ### Figure 92. Hot spot analysis: Small cells [268] | bin_start | bin_end | band | band_label | count | | --- | --- | --- | --- | --- | | -1 | -0.5 | not_significant | Not significant | 5 | | -0.5 | 0 | not_significant | Not significant | 8 | | 0 | 0.5 | not_significant | Not significant | 3 | | 0.5 | 1 | not_significant | Not significant | 2 | | 1 | 1.5 | not_significant | Not significant | 2 | | 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 1 | ### Figure 93. Emerging hot spots: Coverage complaints [269] | bin_start | bin_end | count | | --- | --- | --- | | -0.685 | -0.4285 | 42 | | -0.4285 | -0.172 | 0 | | -0.172 | 0.0845 | 0 | | 0.0845 | 0.341 | 0 | | 0.341 | 0.5975 | 38 | | 0.5975 | 0.854 | 0 | | 0.854 | 1.111 | 0 | | 1.111 | 1.367 | 14 | ### Figure 94. Emerging hot spots: Dropped calls [270] | bin_start | bin_end | count | | --- | --- | --- | | -1.205 | -0.9106 | 66 | | -0.9106 | -0.6162 | 23 | | -0.6162 | -0.3219 | 8 | | -0.3219 | -0.0275 | 6 | | -0.0275 | 0.2669 | 2 | | 0.2669 | 0.5613 | 0 | | 0.5613 | 0.8556 | 1 | | 0.8556 | 1.15 | 3 | ### Figure 95. Summarize within: Neighborhoods [271] | bin_start | bin_end | count | | --- | --- | --- | | 0 | 0.6 | 7 | | 0.6 | 1.2 | 5 | | 1.2 | 1.8 | 0 | | 1.8 | 2.4 | 3 | | 2.4 | 3 | 1 | ### Figure 96. Summarize within: Neighborhoods [272] | bin_start | bin_end | count | | --- | --- | --- | | 0 | 1.8 | 8 | | 1.8 | 3.6 | 5 | | 3.6 | 5.4 | 1 | | 5.4 | 7.2 | 0 | | 7.2 | 9 | 2 | ### Figure 97. Summarize within: Neighborhoods [273] | bin_start | bin_end | count | | --- | --- | --- | | 31 | 90.2 | 9 | | 90.2 | 149.4 | 3 | | 149.4 | 208.6 | 1 | | 208.6 | 267.8 | 2 | | 267.8 | 327 | 1 | ### Figure 98. Calculate density: Dropped calls [274] | bin_start | bin_end | count | | --- | --- | --- | | 0 | 4 | 136 | | 4 | 8 | 175 | | 8 | 12 | 171 | | 12 | 16 | 170 | | 16 | 20 | 170 | | 20 | 24 | 172 | | 24 | 28 | 171 | | 28 | 32 | 169 | | 32 | 36 | 172 | | 36 | 40 | 173 | | 40 | 44 | 169 | | 44 | 48 | 186 | ### Figure 99. Calculate density: Coverage complaints [275] | bin_start | bin_end | count | | --- | --- | --- | | 0 | 2.1 | 47 | | 2.1 | 4.2 | 37 | | 4.2 | 6.3 | 39 | | 6.3 | 8.4 | 36 | | 8.4 | 10.5 | 37 | | 10.5 | 12.6 | 39 | | 12.6 | 14.7 | 39 | | 14.7 | 16.8 | 39 | | 16.8 | 18.9 | 37 | | 18.9 | 21 | 33 | ### Figure 100. Calculate density: Small cells [276] | bin_start | bin_end | count | | --- | --- | --- | | 0 | 1.5 | 10 | | 1.5 | 3 | 8 | | 3 | 4.5 | 19 | | 4.5 | 6 | 11 | | 6 | 7.5 | 24 | | 7.5 | 9 | 10 | | 9 | 10.5 | 22 | | 10.5 | 12 | 13 | ### Figure 101. Find point clusters: Dropped calls [277] | cluster | features | fraction | share | | --- | --- | --- | --- | | Noise | 1,744 | 0.9689 | 96.9% | | 1 | 32 | 0.01778 | 1.8% | | 0 | 24 | 0.01333 | 1.3% | ### Figure 102. Find point clusters: cluster hulls [278] | cluster | features | fraction | share | | --- | --- | --- | --- | | 0 | 1 | 0.5 | 50.0% | | 1 | 1 | 0.5 | 50.0% | ### Figure 103. Find point clusters: Coverage complaints [279] | cluster | features | fraction | share | | --- | --- | --- | --- | | Noise | 320 | 0.7619 | 76.2% | | 0 | 100 | 0.2381 | 23.8% | ### Figure 104. Find point clusters: cluster hulls [280] | cluster | features | fraction | share | | --- | --- | --- | --- | | 0 | 1 | 1 | 100.0% | ### Figure 105. Find outliers: Neighborhoods [281] | value | lag | type | | --- | --- | --- | | 1.492 | 0.8836 | NS | | -1.146 | 0.4296 | NS | | -0.1431 | -0.1232 | NS | | 1.74 | -0.319 | NS | | -0.9566 | 0.0861 | NS | | 0.3185 | 0.7984 | NS | | 1.337 | 1.492 | NS | | -0.6946 | 1.072 | NS | | 1.072 | -0.6946 | NS | | -1.568 | -0.5375 | NS | | 0.4296 | 0.1732 | NS | | 0.5457 | -0.1431 | NS | | -0.5388 | -1.568 | NS | | -0.3985 | -1.568 | NS | | -1.234 | -0.1431 | NS | | -0.2563 | -1.568 | NS | ### Figure 106. Find point clusters: Coverage complaints over time [282] | period | series | value | | --- | --- | --- | | 2026-02-23 | prepaid | 0 | | 2026-02-23 | postpaid | 3 | | 2026-02-23 | business | 1 | | 2026-03-02 | prepaid | 2 | | 2026-03-02 | postpaid | 3 | | 2026-03-02 | business | 6 | | 2026-03-09 | prepaid | 6 | | 2026-03-09 | postpaid | 6 | | 2026-03-09 | business | 2 | | 2026-03-16 | prepaid | 5 | | 2026-03-16 | postpaid | 4 | | 2026-03-16 | business | 3 | | 2026-03-23 | prepaid | 6 | | 2026-03-23 | postpaid | 4 | | 2026-03-23 | business | 4 | | 2026-03-30 | prepaid | 4 | | 2026-03-30 | postpaid | 3 | | 2026-03-30 | business | 6 | | 2026-04-06 | prepaid | 6 | | 2026-04-06 | postpaid | 4 | | 2026-04-06 | business | 6 | | 2026-04-13 | prepaid | 7 | | 2026-04-13 | postpaid | 3 | | 2026-04-13 | business | 1 | | 2026-04-20 | prepaid | 3 | | 2026-04-20 | postpaid | 5 | | 2026-04-20 | business | 7 | | 2026-04-27 | prepaid | 4 | | 2026-04-27 | postpaid | 4 | | 2026-04-27 | business | 2 | | 2026-05-04 | prepaid | 3 | | 2026-05-04 | postpaid | 4 | | 2026-05-04 | business | 4 | | 2026-05-11 | prepaid | 5 | | 2026-05-11 | postpaid | 8 | | 2026-05-11 | business | 5 | | 2026-05-18 | prepaid | 6 | | 2026-05-18 | postpaid | 4 | | 2026-05-18 | business | 7 | | 2026-05-25 | prepaid | 4 | | 2026-05-25 | postpaid | 2 | | 2026-05-25 | business | 5 | | 2026-06-01 | prepaid | 3 | | 2026-06-01 | postpaid | 3 | | 2026-06-01 | business | 2 | | 2026-06-08 | prepaid | 4 | | 2026-06-08 | postpaid | 4 | | 2026-06-08 | business | 5 | | 2026-06-15 | prepaid | 9 | | 2026-06-15 | postpaid | 4 | | 2026-06-15 | business | 3 | | 2026-06-22 | prepaid | 5 | | 2026-06-22 | postpaid | 6 | | 2026-06-22 | business | 4 | | 2026-06-29 | prepaid | 4 | | 2026-06-29 | postpaid | 2 | | 2026-06-29 | business | 5 | | 2026-07-06 | prepaid | 7 | | 2026-07-06 | postpaid | 3 | | 2026-07-06 | business | 8 | | 2026-07-13 | prepaid | 5 | | 2026-07-13 | postpaid | 3 | | 2026-07-13 | business | 5 | | 2026-07-20 | prepaid | 4 | | 2026-07-20 | postpaid | 5 | | 2026-07-20 | business | 4 | | 2026-07-27 | prepaid | 6 | | 2026-07-27 | postpaid | 7 | | 2026-07-27 | business | 3 | | 2026-08-03 | prepaid | 5 | | 2026-08-03 | postpaid | 4 | | 2026-08-03 | business | 5 | | 2026-08-10 | prepaid | 4 | | 2026-08-10 | postpaid | 4 | | 2026-08-10 | business | 1 | | 2026-08-17 | prepaid | 7 | | 2026-08-17 | postpaid | 3 | | 2026-08-17 | business | 7 | | 2026-08-24 | prepaid | 3 | | 2026-08-24 | postpaid | 8 | | 2026-08-24 | business | 8 | | 2026-08-31 | prepaid | 5 | | 2026-08-31 | postpaid | 8 | | 2026-08-31 | business | 2 | | 2026-09-07 | prepaid | 5 | | 2026-09-07 | postpaid | 2 | | 2026-09-07 | business | 3 | | 2026-09-14 | prepaid | 4 | | 2026-09-14 | postpaid | 9 | | 2026-09-14 | business | 3 | | 2026-09-21 | prepaid | 5 | | 2026-09-21 | postpaid | 2 | | 2026-09-21 | business | 6 | | 2026-09-28 | prepaid | 1 | | 2026-09-28 | postpaid | 3 | | 2026-09-28 | business | 3 | ### Figure 107. Find point clusters: Dropped calls over time [283] | period | series | value | | --- | --- | --- | | 2026-08-03 | coverage | 92 | | 2026-08-03 | handover | 82 | | 2026-08-03 | congestion | 39 | | 2026-08-03 | interference | 26 | | 2026-08-10 | coverage | 80 | | 2026-08-10 | handover | 79 | | 2026-08-10 | congestion | 48 | | 2026-08-10 | interference | 25 | | 2026-08-17 | coverage | 82 | | 2026-08-17 | handover | 86 | | 2026-08-17 | congestion | 35 | | 2026-08-17 | interference | 15 | | 2026-08-24 | coverage | 68 | | 2026-08-24 | handover | 75 | | 2026-08-24 | congestion | 37 | | 2026-08-24 | interference | 20 | | 2026-08-31 | coverage | 82 | | 2026-08-31 | handover | 80 | | 2026-08-31 | congestion | 51 | | 2026-08-31 | interference | 29 | | 2026-09-07 | coverage | 76 | | 2026-09-07 | handover | 74 | | 2026-09-07 | congestion | 51 | | 2026-09-07 | interference | 23 | | 2026-09-14 | coverage | 81 | | 2026-09-14 | handover | 74 | | 2026-09-14 | congestion | 34 | | 2026-09-14 | interference | 29 | | 2026-09-21 | coverage | 85 | | 2026-09-21 | handover | 67 | | 2026-09-21 | congestion | 46 | | 2026-09-21 | interference | 29 | *GeoBit Large Spatial Model. Findings are labeled Evidenced (computed by a tool), Inferred (a reading or reasoning) or Unverified (no evidence yet).*