# Sample: Public safety, Chicago: report

Project: Sample: Public safety, Chicago  
Goal: Describe the project Sample: Public safety, Chicago: run the analyses that tell the most about its data, draw every result, and write a cited report.  
Run: 2e6054ca-62f3-479f-b398-915fe26200ce (running, 19 agent run(s))  
Generated: 2026-10-19T04:36:58Z, version 1  
Model cost: USD 0.0000 over 0 model call(s)  

## Data used

| Data | Kind | Rows | Time span | Summary | Source |
| --- | --- | --- | --- | --- | --- |
| Incidents | vector, Point | 1,680 | 2025-10-01 to 2026-09-30 | 1,680 point features; dated by 'reported_at' from 2025-10-01 to 2026-09-30; 'category' has 7 values (top: theft 539, battery 286, criminal damage 207); 'location_type' has 6 values (top: street 626, residence 351, business 274); 'severity' 1 to 5 (mean 2.587); extent -87.6800,41.8566 to -87.6078,41.9016. Dated point events by category: where and when Incidents happen. | [2] |
| Street light outages | vector, Point | 420 | 2025-10-01 to 2026-09-30 | 420 point features; dated by 'reported_at' from 2025-10-01 to 2026-09-30; 'cause' has 4 values (top: lamp failure 209, cable fault 100, vandalism 64); 'days_out' 1 to 60 (mean 6.95); extent -87.6803,41.8565 to -87.6125,41.9013. Dated point events by cause: where and when Street light outages happen. | [3] |
| Patrol beats | vector, Polygon | 16 |  | 16 polygon (area) features; 'district' has 1 values (top: District 12 16); 'population' 11,440 to 33,436 (mean 2.514e+04); 'officers_assigned' 6 to 12 (mean 9.75); named by 'name' (e.g. North River Beat, Market West Beat, Station Quarter Beat); total area 30.02 km2; extent -87.6804,41.8563 to -87.6076,41.9017. Areas (Patrol beats) with population to compare and map. | [145] |
| Police stations | vector, Point | 3 |  | 3 point features; 'open_24h' has 2 values (top: False 2, True 1); 'officers' 97 to 139 (mean 124.7); named by 'name' (e.g. Central District Station, Westside Station, Riverside Station); extent -87.6671,41.8744 to -87.6210,41.8881. Point locations of Police stations by open_24h. | [146] |
| CCTV cameras | vector, Point | 60 |  | 60 point features; 'ptz' has 2 values (top: False 41, True 19); 'status' has 2 values (top: live 45, fault 15); 'installed_year' 2,012 to 2,025 (mean 2018); extent -87.6796,41.8566 to -87.6139,41.9010. Point locations of CCTV cameras by ptz. | [147] |
| Transit stops | vector, Point | 80 |  | 80 point features; 'mode' has 2 values (top: bus 74, rail 6); 'daily_boardings' 48 to 4,542 (mean 311.9); named by 'name' (e.g. Central Transit Center, Stop 002, Stop 003); extent -87.6794,41.8569 to -87.6138,41.9013. Point locations of Transit stops by mode. | [148] |
| Licensed premises | vector, Point | 120 |  | 120 point features; 'type' has 4 values (top: restaurant 57, bar 38, liquor store 13); 'closing_hour' 0 to 23 (mean 5.775); 'capacity' 32 to 350 (mean 124.8); extent -87.6794,41.8569 to -87.6140,41.9008. Point locations of Licensed premises by type. | [149] |
| Roads | vector, LineString | 1,033 |  | 1,033 line features; 'class' has 4 values (top: local 814, other 181, motorway 25); 'speed_limit_mph' 25 to 35 (mean 25.37); 'lanes' 2 to 4 (mean 2.074); named by 'name' (e.g. Randolph St, W Arcade Pl, Harrison St); total length 509.7 km; extent -87.6804,41.8563 to -87.6077,41.9016. Lines (Roads) such as routes or networks. | [150] |
| Calls for service by beat and month | table | 384 | 2024-10-01 to 2026-09-01 | Table of 384 rows and 6 columns without geometry; dated by 'month' from 2024-10-01 to 2026-09-01; 'calls' 61 to 814 (mean 226.5); 'priority_1_calls' 9 to 139 (mean 35.84); 'median_response_min' 5.7 to 13.9 (mean 9.779). A table of Calls for service by beat and month without locations. | [4] |
| Beat staffing by shift | table | 48 |  | Table of 48 rows and 5 columns without geometry; 'shift' has 3 values (top: day 16, evening 16, night 16); 'officers' 2 to 6 (mean 3.75); 'vehicles' 1 to 3 (mean 1.979); 'overtime_hours' 0 to 59 (mean 28.85). A table of Beat staffing by shift without locations. | [151] |
| Quarterly crime and safety briefing | document |  |  | 1-page md document 'Quarterly crime and safety briefing, third quarter 2026'; language en; mentions 7 place(s): Central Transit Center (2), Market West Beat (2), N Elizabeth St (2), Kingsway Beat (2), 41.87815, -87.63532 (1). A document about Central Transit Center, Market West Beat, N Elizabeth St; it names event terms Thefts, Transit, street. | [152] |
| Hot spot patrol plan | document |  |  | 1-page pdf document 'Hot spot patrol plan'; language en; mentions 6 place(s): Central District Station (1), Westside Station (1), Riverside Station (1), Central Transit Center (1), Market West Beat (1). A document about Central District Station, Westside Station, Riverside Station; it names organizations Focus; event terms theft, Patrols, Transit. | [153] |
| Sample: Public safety, Chicago | record |  |  |  | [154] |
| Hot spot analysis: CCTV cameras | layer, Polygon | 36 |  |  | [98] |
| Hot spot analysis: Licensed premises | layer, Polygon | 112 |  |  | [155] |
| Emerging hot spots: Incidents | layer, Polygon | 270 |  |  | [156] |
| Emerging hot spots: Street light outages | layer, Polygon | 47 |  |  | [157] |
| Find point clusters: Licensed premises | layer, Point | 120 |  |  | [158] |
| Find point clusters: cluster hulls | layer, Polygon | 3 |  |  | [81] |
| Find point clusters: Incidents | layer, Point | 1,680 |  |  | [159] |
| Find point clusters: cluster hulls | layer, Polygon | 3 |  |  | [160] |
| Find point clusters: Street light outages | layer, Point | 420 |  |  | [161] |
| Find point clusters: cluster hulls | layer, Polygon | 1 |  |  | [162] |
| Summarize within: Patrol beats | layer, Polygon | 16 |  |  | [163] |
| Summarize within: Patrol beats | layer, Polygon | 16 |  |  | [164] |
| Summarize within: Patrol beats | layer, Polygon | 16 |  |  | [88] |
| Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) | layer, Polygon | 16 |  |  | [8] |
| Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09) | layer, Polygon | 7 |  |  | [10] |
| Calculate density: Licensed premises | layer, Polygon | 164 |  |  | [165] |
| Calculate density: Incidents | layer, Polygon | 2,874 |  |  | [166] |
| Calculate density: Street light outages | layer, Polygon | 848 |  |  | [93] |
| Hot spot analysis: Incidents | layer, Polygon | 2,912 |  |  | [80] |
| Repeat and near repeat classification: Incidents | layer, Point | 1,680 |  |  | [167] |
| Dense areas of street light outages | layer, MultiPolygon | 5 |  |  | [110] |
| Hot cells inside each cluster of licensed premises | layer, Polygon | 3 |  |  | [82] |
| Change beside the counts per patrol beat | layer, Polygon | 16 |  |  | [89] |
| Change of incidents where density is high | layer, Polygon | 16 |  |  | [94] |
| Hot cells of CCTV cameras per patrol beat | layer, Polygon | 16 |  |  | [99] |
| Nearest police station to each cluster of licensed premises | layer, Polygon | 3 |  |  | [104] |
| Change of street light outages per patrol beat | layer, Polygon | 16 |  |  | [113] |

## Timeline

![Timeline: events per month by dataset](charts/39-timeline-events-per-month-by-dataset.png)

*Figure 1. Timeline: events per month by dataset [1]. Per month, 2024-10-01 to 2026-09-01. Source: Incidents, Street light outages, Calls for service by beat and month (2,484 features). The data is in the appendix.*

![Timeline lanes: events per month by dataset](charts/40-timeline-lanes-events-per-month-by-dataset.png)

*Figure 2. Timeline lanes: events per month by dataset [5]. Per month, 2024-10-01 to 2026-09-01. Source: Incidents, Street light outages, Calls for service by beat and month (2,484 features). The data is in the appendix.*

![Timeline: change from one month to the next](charts/41-timeline-change-from-one-month-to-the-next.png)

*Figure 3. Timeline: change from one month to the next [6]. Per month, 2024-11-01 to 2026-09-01. Source: Incidents, Street light outages, Calls for service by beat and month (2,484 features). The data is in the appendix.*

The timeline counts 2,484 dated events in Incidents, Street light outages, Calls for service by beat and month per month, from October 2024 to September 2026 [1][2][3][4]. By dataset: Incidents 1,680; Street light outages 420; Calls for service by beat and month 384 [1][5]. The trend is +11.8 events per month; the second half of the months has 2,292 against 192 in the first (+1093.8%) [1]. Month over month, the largest rise was from September 2025 to October 2025 (16 to 163, +147) and the largest fall was from January 2026 to February 2026 (177 to 150, -27) [1][6]. The count rose from 33 in week of 2026-06-22 to 72 in week of 2026-06-29 (+118.2%), beyond what chance would give (p = 0.000178) [7][8]. Between the two, the largest rise was in Market West Beat (7 to 16, +9) and the largest fall in Granary Beat (2 to 0, -2) [7][8]. The count fell from 17 in week of 2026-02-02 to 4 in week of 2026-02-09 (-76.5%), beyond what chance would give (p = 0.0072) [9][10]. Between the two, the largest rise was in Parkview Beat (0 to 2, +2) and the largest fall in Market West Beat (9 to 2, -7) [9][10].

**Busiest months**

- July 2026: 235 events [1]
- September 2026: 234 events [1]
- August 2026: 228 events [1]

**Quietest months**

- October 2024: 16 events [1]
- November 2024: 16 events [1]
- December 2024: 16 events [1]

## Overview

Overview: the top 3 results in the report's order, each in one distinct color, over Patrol beats as context [11].

![Overview: the top 3 results](maps/01-overview-the-top-3-results.png)

*Figure 4. Overview: the top 3 results [11]*

## Spatial structure

Incidents are clustered (nearest-neighbor ratio 0.73, z = -21.0; features lie 48.8 m apart on average against 66.5 m if random) [12]. Incidents are dated by 'reported_at' from 2025-10-01 to 2026-09-30 at a week grain [12]. Over time the pattern changes: the earlier half is clustered (ratio 0.7893) and the later half clustered (ratio 0.7746); the center moved 132 m [12]. Street light outages are clustered (nearest-neighbor ratio 0.88, z = -4.9; features lie 113 m apart on average against 129 m if random) [13]. Street light outages are dated by 'reported_at' from 2025-10-01 to 2026-09-30 at a week grain [13]. Over time the pattern changes: the earlier half is clustered (ratio 0.8542) and the later half clustered (ratio 0.8877); the center moved 263 m [13]. Patrol beats are 16 areas of 1.876 km2 on average, covering 99 percent of their extent, with no overlap [14]. CCTV cameras are spread at random (nearest-neighbor ratio 0.93, z = -1.0; features lie 311 m apart on average against 334 m if random) [15]. Transit stops are spread at random (nearest-neighbor ratio 0.93, z = -1.2; features lie 268 m apart on average against 289 m if random) [16]. Licensed premises are clustered (nearest-neighbor ratio 0.85, z = -3.2; features lie 199 m apart on average against 234 m if random) [17]. Roads form a connected network: 1033 lines with 120 junctions and 2864 crossings in 98 connected parts, 509.7 km in all [18]. 100 percent of CCTV cameras lie inside Patrol beats (60 of 60) [14][15]. 100 percent of Incidents lie inside Patrol beats (1680 of 1680) [12][14]. 100 percent of Licensed premises lie inside Patrol beats (120 of 120) [14][17]. 100 percent of Police stations lie inside Patrol beats (3 of 3) [14][19]. 100 percent of Street light outages lie inside Patrol beats (420 of 420) [13][14]. 100 percent of Transit stops lie inside Patrol beats (80 of 80) [14][16]. 99 percent of Roads lie inside Patrol beats (1019 of 1033) [14][18]. CCTV cameras lie 2.33 m from the nearest of Roads (median); 100 percent lie within 100 m [15][18]. Street light outages lie 3.89 m from the nearest of Roads (median); 96 percent lie within 100 m [13][18]. Transit stops lie 4.41 m from the nearest of Roads (median); 100 percent lie within 100 m [16][18].

| Result | Value |
| --- | --- |
| layers profiled | 8 |
| clustered point layers | 3 |
| random point layers | 2 |

**Findings**

- **Evidenced:** Incidents are clustered (nearest-neighbor ratio 0.73, z = -21.0; features lie 48.8 m apart on average against 66.5 m if random) [12].
- **Evidenced:** Incidents are dated by 'reported_at' from 2025-10-01 to 2026-09-30 at a week grain [12].
- **Evidenced:** Over time the pattern changes: the earlier half is clustered (ratio 0.7893) and the later half clustered (ratio 0.7746); the center moved 132 m [12].
- **Evidenced:** Street light outages are clustered (nearest-neighbor ratio 0.88, z = -4.9; features lie 113 m apart on average against 129 m if random) [13].
- **Evidenced:** Street light outages are dated by 'reported_at' from 2025-10-01 to 2026-09-30 at a week grain [13].
- **Evidenced:** Over time the pattern changes: the earlier half is clustered (ratio 0.8542) and the later half clustered (ratio 0.8877); the center moved 263 m [13].
- **Evidenced:** Patrol beats are 16 areas of 1.876 km2 on average, covering 99 percent of their extent, with no overlap [14].
- **Evidenced:** CCTV cameras are spread at random (nearest-neighbor ratio 0.93, z = -1.0; features lie 311 m apart on average against 334 m if random) [15].
- **Evidenced:** Transit stops are spread at random (nearest-neighbor ratio 0.93, z = -1.2; features lie 268 m apart on average against 289 m if random) [16].
- **Evidenced:** Licensed premises are clustered (nearest-neighbor ratio 0.85, z = -3.2; features lie 199 m apart on average against 234 m if random) [17].
- **Evidenced:** Roads form a connected network: 1033 lines with 120 junctions and 2864 crossings in 98 connected parts, 509.7 km in all [18].
- **Evidenced:** 100 percent of CCTV cameras lie inside Patrol beats (60 of 60) [14][15].
- **Evidenced:** 100 percent of Incidents lie inside Patrol beats (1680 of 1680) [12][14].
- **Evidenced:** 100 percent of Licensed premises lie inside Patrol beats (120 of 120) [14][17].
- **Evidenced:** 100 percent of Police stations lie inside Patrol beats (3 of 3) [14][19].
- **Evidenced:** 100 percent of Street light outages lie inside Patrol beats (420 of 420) [13][14].
- **Evidenced:** 100 percent of Transit stops lie inside Patrol beats (80 of 80) [14][16].
- **Evidenced:** 99 percent of Roads lie inside Patrol beats (1019 of 1033) [14][18].
- **Evidenced:** CCTV cameras lie 2.33 m from the nearest of Roads (median); 100 percent lie within 100 m [15][18].
- **Evidenced:** Street light outages lie 3.89 m from the nearest of Roads (median); 96 percent lie within 100 m [13][18].
- **Evidenced:** Transit stops lie 4.41 m from the nearest of Roads (median); 100 percent lie within 100 m [16][18].

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [20]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [20].

**Why it was run on this data.** 60 points: Getis-Ord Gi* tests whether the concentrations in CCTV cameras are statistically significant [21].

**What it found.** The hot spot analysis tool, `gp_hot_spots`, utilized the CCTV cameras layer [15] and returned a result containing 36 features [22].

| Result | Value |
| --- | --- |
| result, bins | 36 |
| result, count | 36 |
| result, elapsed ms | 3 |
| result, cell size m | 1,000 |
| result, incidents used | 60 |
| result, distance band m | 2,000 |
| billing, cost | 0.0512 |
| billing, list price | 0.0512 |
| billing, upstream cost | 0 |

![Hot spots: CCTV cameras](maps/02-hot-spots-cctv-cameras.png)

*Figure 5. Hot spots: CCTV cameras [23]*

**What the map shows.** The map draws Hot spot analysis: CCTV cameras by gi_bin in 7 classes: Hot spot - 99% confidence, Hot spot - 95% confidence, Hot spot - 90% confidence, Not significant, Cold spot - 90% confidence, Cold spot - 95% confidence and 1 more [23]. The map draws Patrol beats with one symbol [23]. It draws 52 features [23]. It covers longitude -87.715 to -87.578 and latitude 41.838 to 41.917, at about 1:47,468 [23].

**Findings**

- **Evidenced:** Hot spots on CCTV cameras: Hot spot analysis: 1 layer saved: Hot spot analysis: CCTV cameras (36 features) [15][22].
- **Evidenced:** It reported result, bins 36; result, count 36; result, elapsed ms 3; result, cell size m 1,000; result, incidents used 60; result, distance band m 2,000 [22].

*Tool gp_hot_spots; parameters {"project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "CCTV cameras (ad88aaeb)"}, "aggregation": "hexagon", "cell_size_m": 1000.0, "distance_band_m": 2000.0}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [20]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [20].

**Why it was run on this data.** 120 points: Getis-Ord Gi* tests whether the concentrations in Licensed premises are statistically significant [24]. Measured: Licensed premises are clustered (nearest-neighbor ratio 0.85, z = -3.2), median spacing 121 m [24]. Cells of 500 m and a band of 1000 m from the measured spacing [24].

**What it found.** Hot spots on Licensed premises: Hot spot analysis: 1 layer saved: Hot spot analysis: Licensed premises (112 features) [17][25]. It reported result, bins 112; result, count 112; result, elapsed ms 5; result, cell size m 500; result, incidents used 120; result, distance band m 1,000 [25].

| Result | Value |
| --- | --- |
| result, bins | 112 |
| result, count | 112 |
| result, elapsed ms | 5 |
| result, cell size m | 500 |
| result, incidents used | 120 |
| result, distance band m | 1,000 |
| billing, cost | 0.0524 |
| billing, list price | 0.0524 |
| billing, upstream cost | 0 |

![Hot spots: licensed premises](maps/03-hot-spots-licensed-premises.png)

*Figure 6. Hot spots: licensed premises [26]*

**What the map shows.** The map draws Hot spot analysis: Licensed premises by gi_bin in 7 classes: Hot spot - 99% confidence, Hot spot - 95% confidence, Hot spot - 90% confidence, Not significant, Cold spot - 90% confidence, Cold spot - 95% confidence and 1 more [26]. The map draws Patrol beats with one symbol [26]. It draws 128 features [26]. It covers longitude -87.705 to -87.593 and latitude 41.847 to 41.911, at about 1:38,555 [26].

**Findings**

- **Evidenced:** Hot spots on Licensed premises: Hot spot analysis: 1 layer saved: Hot spot analysis: Licensed premises (112 features) [17][25].
- **Evidenced:** It reported result, bins 112; result, count 112; result, elapsed ms 5; result, cell size m 500; result, incidents used 120; result, distance band m 1,000 [25].

*A model draft was discarded because a sentence did not cite a source of this section.*

*Tool gp_hot_spots; parameters {"project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Licensed premises (055b5c2c)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 1000.0}*

## 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 [27]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [27].

**Why it was run on this data.** Incidents carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week [28]. Measured: the pattern of Incidents changes over time (earlier half clustered, later half clustered; the center moved 132 m) [28].

**What it found.** The space time hot spots analysis was executed using the Incidents layer [12] and resulted in 270 features saved as "Emerging hot spots: Incidents" [29]. The process was efficient, concluding in an elapsed time of 46 milliseconds [29]. Furthermore, the analysis utilized 1,680 features and incurred a total cost of 0.184 [29].

| Result | Value |
| --- | --- |
| result, count | 270 |
| result, elapsed ms | 46 |
| result, resolution | 9 |
| result, features used | 1,680 |
| result, time neighbors | 0 |
| billing, cost | 0.184 |
| billing, list price | 0.184 |
| billing, upstream cost | 0 |

![Emerging hot spots: incidents](maps/04-emerging-hot-spots-incidents.png)

*Figure 7. Emerging hot spots: incidents [30]*

**What the map shows.** The map draws Emerging hot spots: Incidents by category in 2 classes: Sporadic hot spot, No pattern detected [30]. The map draws Patrol beats with one symbol [30]. It draws 286 features [30]. It covers longitude -87.702 to -87.587 and latitude 41.846 to 41.91, at about 1:39,916 [30].

**Findings**

- **Evidenced:** Space time hot spots on Incidents: Emerging hot spots: 1 layer saved: Emerging hot spots: Incidents (270 features) [12][29].
- **Evidenced:** It reported result, count 270; result, elapsed ms 46; result, resolution 9; result, features used 1,680; result, time neighbors 0; billing, cost 0.184 [29].

*Tool gp_space_time_hot_spots; parameters {"project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Incidents (36ad5f7c)"}, "interval": "week", "bin_shape": "h3", "resolution": 9, "time_field": "reported_at"}*

## 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 [27]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [27].

**Why it was run on this data.** Street light outages carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week [31]. Measured: the pattern of Street light outages changes over time (earlier half clustered, later half clustered; the center moved 263 m) [31].

**What it found.** Space time hot spots on Street light outages: Emerging hot spots: 1 layer saved: Emerging hot spots: Street light outages (47 features) [13][32]. It reported result, count 47; result, elapsed ms 10; result, resolution 8; result, features used 420; result, time neighbors 0; billing, cost 0.121 [32].

| Result | Value |
| --- | --- |
| result, count | 47 |
| result, elapsed ms | 10 |
| result, resolution | 8 |
| result, features used | 420 |
| result, time neighbors | 0 |
| billing, cost | 0.121 |
| billing, list price | 0.121 |
| billing, upstream cost | 0 |

![Emerging hot spots: street light outages](maps/05-emerging-hot-spots-street-light-outages.png)

*Figure 8. Emerging hot spots: street light outages [33]*

**What the map shows.** The map draws Emerging hot spots: Street light outages by category in 2 classes: Sporadic hot spot, No pattern detected [33]. The map draws Patrol beats with one symbol [33]. It draws 63 features [33]. It covers longitude -87.716 to -87.578 and latitude 41.839 to 41.916, at about 1:47,799 [33].

**Findings**

- **Evidenced:** Space time hot spots on Street light outages: Emerging hot spots: 1 layer saved: Emerging hot spots: Street light outages (47 features) [13][32].
- **Evidenced:** It reported result, count 47; result, elapsed ms 10; result, resolution 8; result, features used 420; result, time neighbors 0; billing, cost 0.121 [32].

*Tool gp_space_time_hot_spots; parameters {"project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Street light outages (a7218535)"}, "interval": "week", "bin_shape": "h3", "resolution": 8, "time_field": "reported_at"}*

## Find point clusters

**What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [34]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [34].

**Why it was run on this data.** DBSCAN finds dense groups of Licensed premises and separates noise [35]. Measured: Licensed premises are clustered (nearest-neighbor ratio 0.85, z = -3.2), median spacing 121 m [35]. DBSCAN at 200 m (twice the measured spacing) [35].

**What it found.** Find point clusters on Licensed premises: Find point clusters: 2 layers saved: Find point clusters: Licensed premises (120 features); Find point clusters: cluster hulls (3 features) [17][36]. It reported result, count 120; result, noise 61; result, distance m 200; result, elapsed ms 499; result, min points 3; billing, cost 0.0524 [36].

| Result | Value |
| --- | --- |
| result, count | 120 |
| result, noise | 61 |
| result, distance m | 200 |
| result, elapsed ms | 499 |
| result, min points | 3 |
| billing, cost | 0.0524 |
| billing, list price | 0.0524 |
| billing, upstream cost | 0 |

![Clusters: licensed premises](maps/06-clusters-licensed-premises.png)

*Figure 9. Clusters: licensed premises [37]*

**What the map shows.** The map draws Patrol beats with one symbol [37]. The map draws Find point clusters: cluster hulls with one symbol [37]. The map draws Find point clusters: Licensed premises by cluster_id in 4 classes: Cluster 0, Cluster 1, Cluster 2, Noise [37]. It draws 139 features [37]. It covers longitude -87.695 to -87.598 and latitude 41.851 to 41.906, at about 1:33,333 [37].

**Findings**

- **Evidenced:** Find point clusters on Licensed premises: Find point clusters: 2 layers saved: Find point clusters: Licensed premises (120 features); Find point clusters: cluster hulls (3 features) [17][36].
- **Evidenced:** It reported result, count 120; result, noise 61; result, distance m 200; result, elapsed ms 499; result, min points 3; billing, cost 0.0524 [36].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Licensed premises (055b5c2c)"}, "distance_m": 200.0, "min_points": 3}*

## Find point clusters

**What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [34]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [34].

**Why it was run on this data.** DBSCAN finds dense groups of Incidents and separates noise [38]. Measured: Incidents are clustered (nearest-neighbor ratio 0.73, z = -21.0), median spacing 34.3 m [38]. DBSCAN at 50 m (twice the measured spacing) [38].

**What it found.** Find point clusters on Incidents: Find point clusters: 2 layers saved: Find point clusters: Incidents (1680 features); Find point clusters: cluster hulls (3 features) [12][39]. It reported result, count 1,680; result, noise 1,609; result, distance m 50; result, elapsed ms 19; result, min points 20; billing, cost 0.0836 [39].

| Result | Value |
| --- | --- |
| result, count | 1,680 |
| result, noise | 1,609 |
| result, distance m | 50 |
| result, elapsed ms | 19 |
| result, min points | 20 |
| billing, cost | 0.0836 |
| billing, list price | 0.0836 |
| billing, upstream cost | 0 |

![Clusters: incidents](maps/07-clusters-incidents.png)

*Figure 10. Clusters: incidents [40]*

**What the map shows.** The map draws Find point clusters: cluster hulls with one symbol [40]. The map draws Find point clusters: Incidents by cluster_id in 4 classes: Cluster 0, Cluster 1, Cluster 2, Noise [40]. It draws 1,683 features [40]. It covers longitude -87.693 to -87.595 and latitude 41.85 to 41.907, at about 1:34,033 [40].

**Findings**

- **Evidenced:** Find point clusters on Incidents: Find point clusters: 2 layers saved: Find point clusters: Incidents (1680 features); Find point clusters: cluster hulls (3 features) [12][39].
- **Evidenced:** It reported result, count 1,680; result, noise 1,609; result, distance m 50; result, elapsed ms 19; result, min points 20; billing, cost 0.0836 [39].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Incidents (36ad5f7c)"}, "distance_m": 50.0, "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 [34]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [34].

**Why it was run on this data.** DBSCAN finds dense groups of Street light outages and separates noise [41]. Measured: Street light outages are clustered (nearest-neighbor ratio 0.88, z = -4.9), median spacing 74.9 m [41]. DBSCAN at 100 m (twice the measured spacing) [41].

**What it found.** The `gp_find_point_clusters` tool was utilized against the Street light outages layer [13]. This analysis successfully identified 420 features [42], classifying 410 of the input data as noise [42]. The cluster process was configured with a distance threshold of 100 meters and required a minimum of 10 points [42].

| Result | Value |
| --- | --- |
| result, count | 420 |
| result, noise | 410 |
| result, distance m | 100 |
| result, elapsed ms | 5 |
| result, min points | 10 |
| billing, cost | 0.0584 |
| billing, list price | 0.0584 |
| billing, upstream cost | 0 |

![Clusters: street light outages](maps/08-clusters-street-light-outages.png)

*Figure 11. Clusters: street light outages [43]*

**What the map shows.** The map draws Patrol beats with one symbol [43]. The map draws Find point clusters: cluster hulls with one symbol [43]. The map draws Find point clusters: Street light outages by cluster_id in 2 classes: Cluster 0, Noise [43]. It draws 437 features [43]. It covers longitude -87.695 to -87.597 and latitude 41.85 to 41.906, at about 1:33,798 [43].

**Findings**

- **Evidenced:** Find point clusters on Street light outages: Find point clusters: 2 layers saved: Find point clusters: Street light outages (420 features); Find point clusters: cluster hulls (1 feature) [13][42].
- **Evidenced:** It reported result, count 420; result, noise 410; result, distance m 100; result, elapsed ms 5; result, min points 10; billing, cost 0.0584 [42].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Street light outages (a7218535)"}, "distance_m": 100.0, "min_points": 10}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [44]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [44].

**Why it was run on this data.** Count CCTV cameras inside each feature of Patrol beats by ptz; the counts map as a choropleth (natural breaks, sequential colors) [45].

**What it found.** Summarize within on CCTV cameras, Patrol beats: Summarize within: 1 layer saved: Summarize within: Patrol beats (16 features) [14][15][46]. It reported result, count 16; result, elapsed ms 0; billing, cost 0.01154; billing, list price 0.01154; billing, upstream cost 0 [46].

| Result | Value |
| --- | --- |
| result, count | 16 |
| result, elapsed ms | 0 |
| billing, cost | 0.01154 |
| billing, list price | 0.01154 |
| billing, upstream cost | 0 |

![Counts: CCTV cameras, by patrol beat](maps/09-counts-cctv-cameras-by-patrol-beat.png)

*Figure 12. Counts: CCTV cameras, by patrol beat [47]*

**What the map shows.** The map draws Summarize within: Patrol beats by count in 5 classes: 1 to 3, 3 to 4, 4 to 5, 5 to 9.5, 9.5 to 13 [47]. It draws 16 features [47]. It covers longitude -87.694 to -87.594 and latitude 41.85 to 41.907, at about 1:34,270 [47].

**Findings**

- **Evidenced:** Summarize within on CCTV cameras, Patrol beats: Summarize within: 1 layer saved: Summarize within: Patrol beats (16 features) [14][15][46].
- **Evidenced:** It reported result, count 16; result, elapsed ms 0; billing, cost 0.01154; billing, list price 0.01154; billing, upstream cost 0 [46].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "installed_year"}], "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "CCTV cameras (ad88aaeb)"}, "group_by": "ptz", "polygons": {"layer": "Patrol beats (8c67e28f)"}}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [44]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [44].

**Why it was run on this data.** Count CCTV cameras inside each feature of Patrol beats by ptz; the counts map as a choropleth (natural breaks, sequential colors) [45].

**What it found.** The analysis used the "Summarize within: Patrol beats" function, which processed 16 features [48].

| Result | Value |
| --- | --- |
| result, count | 16 |
| result, elapsed ms | 0 |
| billing, cost | 0.01274 |
| billing, list price | 0.01274 |
| billing, upstream cost | 0 |

![Counts: licensed premises, by patrol beat](maps/10-counts-licensed-premises-by-patrol-beat.png)

*Figure 13. Counts: licensed premises, by patrol beat [49]*

**What the map shows.** The map draws Summarize within: Patrol beats by count in 5 classes: 1 to 4, 4 to 7, 7 to 23, 23 to 27, 27 to 31 [49]. It draws 16 features [49]. It covers longitude -87.694 to -87.594 and latitude 41.85 to 41.907, at about 1:34,270 [49].

**Findings**

- **Evidenced:** Summarize within on Licensed premises, Patrol beats: Summarize within: 1 layer saved: Summarize within: Patrol beats (16 features) [14][17][48].
- **Evidenced:** It reported result, count 16; result, elapsed ms 0; billing, cost 0.01274; billing, list price 0.01274; billing, upstream cost 0 [48].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "closing_hour"}], "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Licensed premises (055b5c2c)"}, "group_by": "type", "polygons": {"layer": "Patrol beats (8c67e28f)"}}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [44]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [44].

**Why it was run on this data.** Count CCTV cameras inside each feature of Patrol beats by ptz; the counts map as a choropleth (natural breaks, sequential colors) [45].

**What it found.** Summarize within on Incidents, Patrol beats: Summarize within: 1 layer saved: Summarize within: Patrol beats (16 features) [12][14][50]. It reported result, count 16; result, elapsed ms 2; billing, cost 0.04394; billing, list price 0.04394; billing, upstream cost 0 [50].

| Result | Value |
| --- | --- |
| result, count | 16 |
| result, elapsed ms | 2 |
| billing, cost | 0.04394 |
| billing, list price | 0.04394 |
| billing, upstream cost | 0 |

![Counts: incidents, by patrol beat](maps/11-counts-incidents-by-patrol-beat.png)

*Figure 14. Counts: incidents, by patrol beat [51]*

**What the map shows.** The map draws Summarize within: Patrol beats by count in 5 classes: 18 to 53, 53 to 117, 117 to 154, 154 to 304, 304 to 332 [51]. It draws 16 features [51]. It covers longitude -87.694 to -87.594 and latitude 41.85 to 41.907, at about 1:34,270 [51].

**Findings**

- **Evidenced:** Summarize within on Incidents, Patrol beats: Summarize within: 1 layer saved: Summarize within: Patrol beats (16 features) [12][14][50].
- **Evidenced:** It reported result, count 16; result, elapsed ms 2; billing, cost 0.04394; billing, list price 0.04394; billing, upstream cost 0 [50].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "severity"}], "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Incidents (36ad5f7c)"}, "group_by": "category", "polygons": {"layer": "Patrol beats (8c67e28f)"}}*

## 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 [52]. In ArcGIS it is Summarize Percent Change (Crime Analysis) or Change Point Detection [52].

**Why it was run on this data.** Incidents carry dates in 'reported_at' over 52 weeks: the count per week, week over week, with the places of largest rise and fall per feature of Patrol beats and whether the change is beyond chance [53].

**What it found.** Period change on Patrol beats, Incidents produced Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) (16 features) [7][8][12][14]. Incidents: count per week shows a trend of +0.36 per week; the second half of its weeks has 979 against 676 in the first (+44.8%) [55]. The chart Incidents: week of 2026-06-22 to week of 2026-06-29 by place is drawn from 30 rows [56]. The chart Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) is drawn from 5 rows [57].

![Period change: incidents, by patrol beat](maps/12-period-change-incidents-by-patrol-beat.png)

*Figure 15. Period change: incidents, by patrol beat [54]*

**What the map shows.** The map draws Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) by change in 4 classes: < -0.5 Std; Dev, -0.5 to 0.5 Std; Dev, 0.5 to 1.5 Std; Dev, > 1.5 Std; Dev [54]. It draws 16 features [54]. It covers longitude -87.696 to -87.592 and latitude 41.85 to 41.907, at about 1:35,725 [54].

![Incidents: count per week](charts/01-incidents-count-per-week.png)

*Figure 16. Incidents: count per week [55]. Per week, 2025-09-29 to 2026-09-28. Source: Incidents (1,680 features). The data is in the appendix.*

![Incidents: week of 2026-06-22 to week of 2026-06-29 by place](charts/02-incidents-week-of-2026-06-22-to-week-of-2026-06-29-by-place.png)

*Figure 17. Incidents: week of 2026-06-22 to week of 2026-06-29 by place [56]. Source: Incidents (1,680 features). The data is in the appendix.*

![Period change: Incidents (week of 2026-06-22 to week of 2026-06-29)](charts/22-period-change-incidents-week-of-2026-06-22-to-week-of-2026-0.png)

*Figure 18. Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) [57]. Source: Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) (16 features). The data is in the appendix.*

**Findings**

- **Evidenced:** Period change on Patrol beats, Incidents produced Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) (16 features) [7][8][12][14].
- **Evidenced:** Incidents: count per week shows a trend of +0.36 per week; the second half of its weeks has 979 against 676 in the first (+44.8%) [55].
- **Evidenced:** The chart Incidents: week of 2026-06-22 to week of 2026-06-29 by place is drawn from 30 rows [56].
- **Evidenced:** The chart Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) is drawn from 5 rows [57].

*Tool gp_period_change; parameters {"by": "area", "areas": {"layer": "Patrol beats (8c67e28f)"}, "period": "week", "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Incidents (36ad5f7c)"}, "area_field": "name", "time_field": "reported_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 [52]. In ArcGIS it is Summarize Percent Change (Crime Analysis) or Change Point Detection [52].

**Why it was run on this data.** Incidents carry dates in 'reported_at' over 52 weeks: the count per week, week over week, with the places of largest rise and fall per feature of Patrol beats and whether the change is beyond chance [53].

**What it found.** Period change on Patrol beats, Street light outages produced Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09) (7 features) [9][10][13][14]. Street light outages: count per week shows a trend of -0.0337 per week; the second half of its weeks has 183 against 224 in the first (-18.3%) [59]. The chart Street light outages: week of 2026-02-02 to week of 2026-02-09 by place is drawn from 14 rows [60]. The chart Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09) is drawn from 5 rows [61].

![Period change: street light outages, by patrol beat](maps/13-period-change-street-light-outages-by-patrol-beat.png)

*Figure 19. Period change: street light outages, by patrol beat [58]*

**What the map shows.** The map draws Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09) by change in 4 classes: < -1.5 Std; Dev, -1.5 to -0.5 Std; Dev, -0.5 to 0.5 Std; Dev, > 0.5 Std; Dev [58]. It draws 7 features [58]. It covers longitude -87.7 to -87.597 and latitude 41.85 to 41.907, at about 1:35,479 [58].

![Street light outages: count per week](charts/03-street-light-outages-count-per-week.png)

*Figure 20. Street light outages: count per week [59]. Per week, 2025-09-29 to 2026-09-28. Source: Street light outages (420 features). The data is in the appendix.*

![Street light outages: week of 2026-02-02 to week of 2026-02-09 by place](charts/04-street-light-outages-week-of-2026-02-02-to-week-of-2026-02-0.png)

*Figure 21. Street light outages: week of 2026-02-02 to week of 2026-02-09 by place [60]. Source: Street light outages (420 features). The data is in the appendix.*

![Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09)](charts/23-period-change-street-light-outages-week-of-2026-02-02-to-wee.png)

*Figure 22. Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09) [61]. Source: Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09) (7 features). The data is in the appendix.*

**Findings**

- **Evidenced:** Period change on Patrol beats, Street light outages produced Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09) (7 features) [9][10][13][14].
- **Evidenced:** Street light outages: count per week shows a trend of -0.0337 per week; the second half of its weeks has 183 against 224 in the first (-18.3%) [59].
- **Evidenced:** The chart Street light outages: week of 2026-02-02 to week of 2026-02-09 by place is drawn from 14 rows [60].
- **Evidenced:** The chart Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09) is drawn from 5 rows [61].

*Tool gp_period_change; parameters {"by": "area", "areas": {"layer": "Patrol beats (8c67e28f)"}, "period": "week", "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Street light outages (a7218535)"}, "area_field": "name", "time_field": "reported_at"}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [62]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [62].

**Why it was run on this data.** 120 points in Licensed premises: a density surface shows where they concentrate [63]. Measured: Licensed premises are clustered (nearest-neighbor ratio 0.85, z = -3.2), median spacing 121 m [63].

**What it found.** Calculate density on Licensed premises: Calculate density: 1 layer saved: Calculate density: Licensed premises (164 features) [17][64]. It reported result, count 164; result, elapsed ms 4; result, bandwidth m 1,000; result, cell size m 500; result, excluded null 0; result, max density per km2 42.62 [64].

| Result | Value |
| --- | --- |
| result, count | 164 |
| result, elapsed ms | 4 |
| result, bandwidth m | 1,000 |
| result, cell size m | 500 |
| result, excluded null | 0 |
| result, max density per km2 | 42.62 |
| billing, cost | 0.0524 |
| billing, list price | 0.0524 |
| billing, upstream cost | 0 |

![Density: licensed premises](maps/14-density-licensed-premises.png)

*Figure 23. Density: licensed premises [65]*

**What the map shows.** The map draws Calculate density: Licensed premises by density_per_km2 in 6 classes: 1.26 to 2.87, 2.87 to 5.35, 5.35 to 15.5, 15.5 to 25.5, 25.5 to 35.5, 35.5 to 42.6 [65]. The map draws Patrol beats with one symbol [65]. It draws 180 features [65]. It covers longitude -87.716 to -87.577 and latitude 41.839 to 41.918, at about 1:47,798 [65].

**Findings**

- **Evidenced:** Calculate density on Licensed premises: Calculate density: 1 layer saved: Calculate density: Licensed premises (164 features) [17][64].
- **Evidenced:** It reported result, count 164; result, elapsed ms 4; result, bandwidth m 1,000; result, cell size m 500; result, excluded null 0; result, max density per km2 42.62 [64].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Licensed premises (055b5c2c)"}, "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 [62]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [62].

**Why it was run on this data.** 1680 points in Incidents: a density surface shows where they concentrate [66]. Measured: Incidents are clustered (nearest-neighbor ratio 0.73, z = -21.0), median spacing 34.3 m [66].

**What it found.** Calculate density on Incidents: Calculate density: 1 layer saved: Calculate density: Incidents (2874 features) [12][67]. It reported result, count 2,874; result, elapsed ms 50; result, bandwidth m 200; result, cell size m 100; result, excluded null 0; result, max density per km2 1,347 [67].

| Result | Value |
| --- | --- |
| result, count | 2,874 |
| result, elapsed ms | 50 |
| result, bandwidth m | 200 |
| result, cell size m | 100 |
| result, excluded null | 0 |
| result, max density per km2 | 1,347 |
| billing, cost | 0.0836 |
| billing, list price | 0.0836 |
| billing, upstream cost | 0 |

![Density: incidents](maps/15-density-incidents.png)

*Figure 24. Density: incidents [68]*

**What the map shows.** The map draws Calculate density: Incidents by density_per_km2 in 6 classes: 38.9 to 95.2, 95.2 to 203, 203 to 404, 404 to 714, 714 to 1,139, 1,139 to 1,347 [68]. The map draws Patrol beats with one symbol [68]. It draws 2,890 features [68]. It covers longitude -87.697 to -87.591 and latitude 41.848 to 41.909, at about 1:36,730 [68].

**Findings**

- **Evidenced:** Calculate density on Incidents: Calculate density: 1 layer saved: Calculate density: Incidents (2874 features) [12][67].
- **Evidenced:** It reported result, count 2,874; result, elapsed ms 50; result, bandwidth m 200; result, cell size m 100; result, excluded null 0; result, max density per km2 1,347 [67].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Incidents (36ad5f7c)"}, "bandwidth_m": 200.0, "cell_size_m": 100.0}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [62]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [62].

**Why it was run on this data.** 420 points in Street light outages: a density surface shows where they concentrate [69]. Measured: Street light outages are clustered (nearest-neighbor ratio 0.88, z = -4.9), median spacing 74.9 m [69].

**What it found.** Calculate density on Street light outages: Calculate density: 1 layer saved: Calculate density: Street light outages (848 features) [13][70]. It reported result, count 848; result, elapsed ms 15; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 107.5 [70].

| Result | Value |
| --- | --- |
| result, count | 848 |
| result, elapsed ms | 15 |
| result, bandwidth m | 500 |
| result, cell size m | 200 |
| result, excluded null | 0 |
| result, max density per km2 | 107.5 |
| billing, cost | 0.0584 |
| billing, list price | 0.0584 |
| billing, upstream cost | 0 |

![Density: street light outages](maps/16-density-street-light-outages.png)

*Figure 25. Density: street light outages [71]*

**What the map shows.** The map draws Calculate density: Street light outages by density_per_km2 in 6 classes: 4.4 to 10, 10 to 20.1, 20.1 to 37.8, 37.8 to 56.8, 56.8 to 80.7, 80.7 to 108 [71]. The map draws Patrol beats with one symbol [71]. The map draws Dense areas of street light outages with one symbol [71]. It draws 869 features [71]. It covers longitude -87.706 to -87.588 and latitude 41.845 to 41.912, at about 1:40,754 [71].

**Findings**

- **Evidenced:** Calculate density on Street light outages: Calculate density: 1 layer saved: Calculate density: Street light outages (848 features) [13][70].
- **Evidenced:** It reported result, count 848; result, elapsed ms 15; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 107.5 [70].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Street light outages (a7218535)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [20]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [20].

**Why it was run on this data.** 1680 points: Getis-Ord Gi* tests whether the concentrations in Incidents are statistically significant [72]. Measured: Incidents are clustered (nearest-neighbor ratio 0.73, z = -21.0), median spacing 34.3 m [72]. Cells of 100 m and a band of 200 m from the measured spacing [72].

**What it found.** Hot spots on Incidents: Hot spot analysis: 1 layer saved: Hot spot analysis: Incidents (2912 features) [12][73]. It reported result, bins 2,912; result, count 2,912; result, elapsed ms 100; result, cell size m 100; result, incidents used 1,680; result, distance band m 200 [73].

| Result | Value |
| --- | --- |
| result, bins | 2,912 |
| result, count | 2,912 |
| result, elapsed ms | 100 |
| result, cell size m | 100 |
| result, incidents used | 1,680 |
| result, distance band m | 200 |
| billing, cost | 0.0836 |
| billing, list price | 0.0836 |
| billing, upstream cost | 0 |

![Hot spots: incidents](maps/17-hot-spots-incidents.png)

*Figure 26. Hot spots: incidents [74]*

**What the map shows.** The map draws Hot spot analysis: Incidents by gi_bin in 7 classes: Hot spot - 99% confidence, Hot spot - 95% confidence, Hot spot - 90% confidence, Not significant, Cold spot - 90% confidence, Cold spot - 95% confidence and 1 more [74]. The map draws Patrol beats with one symbol [74]. It draws 2,928 features [74]. It covers longitude -87.695 to -87.593 and latitude 41.849 to 41.907, at about 1:35,233 [74].

**Findings**

- **Evidenced:** Hot spots on Incidents: Hot spot analysis: 1 layer saved: Hot spot analysis: Incidents (2912 features) [12][73].
- **Evidenced:** It reported result, bins 2,912; result, count 2,912; result, elapsed ms 100; result, cell size m 100; result, incidents used 1,680; result, distance band m 200 [73].

*Tool gp_hot_spots; parameters {"project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Incidents (36ad5f7c)"}, "aggregation": "hexagon", "cell_size_m": 100.0, "distance_band_m": 200.0}*

## Near repeat

**What this analysis is.** Repeat and near repeat classification: Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table [75]. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator [75].

**Why it was run on this data.** Near repeat analysis tests whether Incidents close in space also follow each other within 30 days [76].

**What it found.** The gp\_near\_repeat tool processed the Incidents data [12] and generated results covering 30 days [77]. The tool identified 1,680 results [77], involving 152 chains [77]. This analysis calculated pairs linked involving 1,357 pairs [77] over a distance of 100 m [77]. The total cost for this process was 0.1004 [77].

| Result | Value |
| --- | --- |
| result, days | 30 |
| result, count | 1,680 |
| result, chains | 152 |
| result, distance m | 100 |
| result, elapsed ms | 39 |
| result, pairs linked | 1,357 |
| billing, cost | 0.1004 |
| billing, list price | 0.1004 |
| billing, upstream cost | 0 |

![Near repeat: incidents](maps/18-near-repeat-incidents.png)

*Figure 27. Near repeat: incidents [78]*

**What the map shows.** The map draws Repeat and near repeat classification: Incidents by nr_class in 4 classes: Originator, Repeat, Near repeat, Isolated [78]. It draws 1,680 features [78]. It covers longitude -87.695 to -87.593 and latitude 41.85 to 41.907, at about 1:35,233 [78].

**Findings**

- **Evidenced:** Near repeat on Incidents: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Incidents (1680 features) [12][77].
- **Evidenced:** It reported result, days 30; result, count 1,680; result, chains 152; result, distance m 100; result, elapsed ms 39; result, pairs linked 1,357 [77].

*Tool gp_near_repeat; parameters {"days": 30, "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Incidents (36ad5f7c)"}, "distance_m": 100.0, "time_field": "reported_at"}*

## What follows from the results

Hot cells inside each cluster of licensed premises, from Hot spot analysis: Incidents and Find point clusters: cluster hulls: 1 of 3 clusters hold hot spot cells; cluster 0 holds the most (53) [79][73][36]. Change beside the counts per patrol beat, from Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) and Summarize within: Patrol beats: the busiest area, Market West Beat (332 in all), changed by +9 [87][7][50]. Change of incidents where density is high, from Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) and Calculate density: Street light outages: the largest rise (+9, Market West Beat) is where density averages 58.82 per square kilometer [92][7][70]. Hot cells of CCTV cameras per patrol beat, from Hot spot analysis: CCTV cameras: 9 of 16 areas hold hot spot cells; Southgate Beat holds the most (5) [97][22]. Nearest police station to each cluster of licensed premises, from Find point clusters: cluster hulls: the 3 clusters lie 836 to 972 m from the nearest facility (Central District Station and others) [103][36]. Dense areas of street light outages, from Calculate density: Street light outages: the densest of five classes joins 170 cells into one area [109][70]. Change of street light outages per patrol beat, from Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09): Parkview Beat rose the most (+2) and Market West Beat fell the most (-7) [112][9].

**Findings**

- **Evidenced:** Hot cells inside each cluster of licensed premises, from Hot spot analysis: Incidents and Find point clusters: cluster hulls: 1 of 3 clusters hold hot spot cells; cluster 0 holds the most (53) [79][73][36].
- **Evidenced:** Change beside the counts per patrol beat, from Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) and Summarize within: Patrol beats: the busiest area, Market West Beat (332 in all), changed by +9 [87][7][50].
- **Evidenced:** Change of incidents where density is high, from Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) and Calculate density: Street light outages: the largest rise (+9, Market West Beat) is where density averages 58.82 per square kilometer [92][7][70].
- **Evidenced:** Hot cells of CCTV cameras per patrol beat, from Hot spot analysis: CCTV cameras: 9 of 16 areas hold hot spot cells; Southgate Beat holds the most (5) [97][22].
- **Evidenced:** Nearest police station to each cluster of licensed premises, from Find point clusters: cluster hulls: the 3 clusters lie 836 to 972 m from the nearest facility (Central District Station and others) [103][36].
- **Evidenced:** Dense areas of street light outages, from Calculate density: Street light outages: the densest of five classes joins 170 cells into one area [109][70].
- **Evidenced:** Change of street light outages per patrol beat, from Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09): Parkview Beat rose the most (+2) and Market West Beat fell the most (-7) [112][9].

## Hot cells inside each cluster of licensed premises

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [44]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [44].

**Why it was run on this data.** Count CCTV cameras inside each feature of Patrol beats by ptz; the counts map as a choropleth (natural breaks, sequential colors) [45].

**What it found.** The `gp_summarize_within` analysis saved one layer called Hot cells inside each cluster of licensed premises with 3 features [79].

| Result | Value |
| --- | --- |
| result, count | 3 |
| result, elapsed ms | 6 |
| billing, cost | 0.0683 |
| billing, list price | 0.0683 |
| billing, upstream cost | 0 |

![Hot cells inside each cluster of licensed premises](maps/19-hot-cells-inside-each-cluster-of-licensed-premises.png)

*Figure 28. Hot cells inside each cluster of licensed premises [83]*

**What the map shows.** The map draws Hot cells inside each cluster of licensed premises by count in 2 classes: 2 to 5, 5 to 86 [83]. The map draws Patrol beats with one symbol [83]. It draws 19 features [83]. It covers longitude -87.667 to -87.631 and latitude 41.875 to 41.896, at about 1:12,455 [83].

![Hot cells inside each cluster of licensed premises](charts/29-hot-cells-inside-each-cluster-of-licensed-premises.png)

*Figure 29. Hot cells inside each cluster of licensed premises [84]. Source: Hot cells inside each cluster of licensed premises (3 features). The data is in the appendix.*

**Findings**

- **Evidenced:** Summarize within on Hot spot analysis: Incidents, Find point clusters: cluster hulls produced Hot cells inside each cluster of licensed premises (3 features) [79][80][81][82].
- **Evidenced:** It reported result, count 3; result, elapsed ms 6; billing, cost 0.0683; billing, list price 0.0683; billing, upstream cost 0 [79].
- **Evidenced:** The chart Hot cells inside each cluster of licensed premises is drawn from 3 rows [84].

*Tool gp_summarize_within; parameters {"project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Hot spot analysis: Incidents (8382f930)"}, "group_by": "gi_bin", "polygons": {"layer": "Find point clusters: cluster hulls (4dfef6fb)"}, "output_name": "Hot cells inside each cluster of licensed premises"}*

## Change beside the counts per patrol beat

**What this analysis is.** Spatial join: Attach attributes (or aggregates) of matching features from another layer, by location, distance, time and attributes [85]. In ArcGIS it is Join Features or Spatial Join; in QGIS, native:joinattributesbylocation or native:joinbylocationsummary [85].

**Why it was run on this data.** It was run toward the goal "Describe the project Sample: Public safety, Chicago: run the analyses that tell the most about its data, draw every result, and write a cited report" [86].

**What it found.** Spatial join on Summarize within: Patrol beats, Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) produced Change beside the counts per patrol beat (16 features) [8][87][88][89]. It reported result, count 16; result, elapsed ms 1; billing, cost 0.01066; billing, list price 0.01066; billing, upstream cost 0 [87]. The chart Change beside the counts per patrol beat is drawn from 5 rows [91].

| Result | Value |
| --- | --- |
| result, count | 16 |
| result, elapsed ms | 1 |
| billing, cost | 0.01066 |
| billing, list price | 0.01066 |
| billing, upstream cost | 0 |

![Change beside the counts per patrol beat](maps/20-change-beside-the-counts-per-patrol-beat.png)

*Figure 30. Change beside the counts per patrol beat [90]*

**What the map shows.** The map draws Change beside the counts per patrol beat by count in 5 classes: 18 to 53, 53 to 117, 117 to 154, 154 to 304, 304 to 332 [90]. It draws 16 features [90]. It covers longitude -87.694 to -87.594 and latitude 41.85 to 41.907, at about 1:34,270 [90].

![Change beside the counts per patrol beat](charts/30-change-beside-the-counts-per-patrol-beat.png)

*Figure 31. Change beside the counts per patrol beat [91]. Source: Change beside the counts per patrol beat (16 features). The data is in the appendix.*

**Findings**

- **Evidenced:** Spatial join on Summarize within: Patrol beats, Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) produced Change beside the counts per patrol beat (16 features) [8][87][88][89].
- **Evidenced:** It reported result, count 16; result, elapsed ms 1; billing, cost 0.01066; billing, list price 0.01066; billing, upstream cost 0 [87].
- **Evidenced:** The chart Change beside the counts per patrol beat is drawn from 5 rows [91].

*Tool gp_spatial_join; parameters {"join": {"layer": "Summarize within: Patrol beats (31bedf68)"}, "fields": ["count"], "target": {"layer": "Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) (1e0726ac)"}, "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "predicate": "largest_overlap", "output_name": "Change beside the counts per patrol beat"}*

## Change of incidents 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 [85]. In ArcGIS it is Join Features or Spatial Join; in QGIS, native:joinattributesbylocation or native:joinbylocationsummary [85].

**Why it was run on this data.** It was run toward the goal "Describe the project Sample: Public safety, Chicago: run the analyses that tell the most about its data, draw every result, and write a cited report" [86].

**What it found.** The spatial join utilized the Period change: Incidents data [8] and calculated density data from Street light outages [93].

| Result | Value |
| --- | --- |
| result, count | 16 |
| result, elapsed ms | 1 |
| billing, cost | 0.0273 |
| billing, list price | 0.0273 |
| billing, upstream cost | 0 |

![Change of incidents where density is high](maps/21-change-of-incidents-where-density-is-high.png)

*Figure 32. Change of incidents where density is high [95]*

**What the map shows.** The map draws Change of incidents where density is high by join_count in 5 classes: 38 to 46, 46 to 57, 57 to 75, 75 to 90, 90 to 94 [95]. It draws 16 features [95]. It covers longitude -87.694 to -87.594 and latitude 41.85 to 41.907, at about 1:34,270 [95].

![Change of incidents where density is high](charts/31-change-of-incidents-where-density-is-high.png)

*Figure 33. Change of incidents where density is high [96]. Source: Change of incidents where density is high (16 features). The data is in the appendix.*

**Findings**

- **Evidenced:** Spatial join on Calculate density: Street light outages, Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) produced Change of incidents where density is high (16 features) [8][92][93][94].
- **Evidenced:** It reported result, count 16; result, elapsed ms 1; billing, cost 0.0273; billing, list price 0.0273; billing, upstream cost 0 [92].
- **Evidenced:** The chart Change of incidents where density is high is drawn from 5 rows [96].

*Tool gp_spatial_join; parameters {"join": {"layer": "Calculate density: Street light outages (22316e02)"}, "stats": [{"agg": "mean", "field": "density_per_km2"}], "target": {"layer": "Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) (1e0726ac)"}, "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "predicate": "intersects", "output_name": "Change of incidents where density is high"}*

## Hot cells of CCTV cameras per patrol beat

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [44]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [44].

**Why it was run on this data.** Count CCTV cameras inside each feature of Patrol beats by ptz; the counts map as a choropleth (natural breaks, sequential colors) [45].

**What it found.** Summarize within on Hot spot analysis: CCTV cameras, Patrol beats produced Hot cells of CCTV cameras per patrol beat (16 features) [14][97][98][99]. It reported result, count 16; result, elapsed ms 2; billing, cost 0.01104; billing, list price 0.01104; billing, upstream cost 0 [97]. The chart Hot cells of CCTV cameras per patrol beat is drawn from 5 rows [101].

| Result | Value |
| --- | --- |
| result, count | 16 |
| result, elapsed ms | 2 |
| billing, cost | 0.01104 |
| billing, list price | 0.01104 |
| billing, upstream cost | 0 |

![Hot cells of CCTV cameras per patrol beat](maps/22-hot-cells-of-cctv-cameras-per-patrol-beat.png)

*Figure 34. Hot cells of CCTV cameras per patrol beat [100]*

**What the map shows.** The map draws Hot cells of CCTV cameras per patrol beat by count in 4 classes: 2 to 4, 4 to 5, 5 to 6, 6 to 7 [100]. It draws 16 features [100]. It covers longitude -87.694 to -87.594 and latitude 41.85 to 41.907, at about 1:34,270 [100].

![Hot cells of CCTV cameras per patrol beat](charts/32-hot-cells-of-cctv-cameras-per-patrol-beat.png)

*Figure 35. Hot cells of CCTV cameras per patrol beat [101]. Source: Hot cells of CCTV cameras per patrol beat (16 features). The data is in the appendix.*

**Findings**

- **Evidenced:** Summarize within on Hot spot analysis: CCTV cameras, Patrol beats produced Hot cells of CCTV cameras per patrol beat (16 features) [14][97][98][99].
- **Evidenced:** It reported result, count 16; result, elapsed ms 2; billing, cost 0.01104; billing, list price 0.01104; billing, upstream cost 0 [97].
- **Evidenced:** The chart Hot cells of CCTV cameras per patrol beat is drawn from 5 rows [101].

*Tool gp_summarize_within; parameters {"project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Hot spot analysis: CCTV cameras (2cc32b98)"}, "group_by": "gi_bin", "polygons": {"layer": "Patrol beats (8c67e28f)"}, "output_name": "Hot cells of CCTV cameras per patrol beat"}*

## Nearest police station to each cluster of licensed premises

**What this analysis is.** Find nearest: The k nearest features for each input, with geodesic distance and bearing [102]. In ArcGIS it is Find Nearest or Near; in QGIS, native:joinbynearest or native:nearestneighbouranalysis [102].

**Why it was run on this data.** It was run toward the goal "Describe the project Sample: Public safety, Chicago: run the analyses that tell the most about its data, draw every result, and write a cited report" [86].

**What it found.** Find nearest on Police stations, Find point clusters: cluster hulls produced Nearest police station to each cluster of licensed premises (3 features) [19][81][103][104]. It reported result, count 3; result, elapsed ms 3; billing, cost 0.01012; billing, list price 0.01012; billing, upstream cost 0 [103]. The chart Nearest police station to each cluster of licensed premises is drawn from 3 rows [106].

| Result | Value |
| --- | --- |
| result, count | 3 |
| result, elapsed ms | 3 |
| billing, cost | 0.01012 |
| billing, list price | 0.01012 |
| billing, upstream cost | 0 |

![Nearest police station to each cluster of licensed premises](maps/23-nearest-police-station-to-each-cluster-of-licensed-premises.png)

*Figure 36. Nearest police station to each cluster of licensed premises [105]*

**What the map shows.** The map draws Nearest police station to each cluster of licensed premises by distance_m in 2 classes: 836 to 881, 881 to 972 [105]. The map draws Patrol beats with one symbol [105]. It draws 19 features [105]. It covers longitude -87.667 to -87.631 and latitude 41.875 to 41.896, at about 1:12,455 [105].

![Nearest police station to each cluster of licensed premises](charts/33-nearest-police-station-to-each-cluster-of-licensed-premises.png)

*Figure 37. Nearest police station to each cluster of licensed premises [106]. Source: Nearest police station to each cluster of licensed premises (3 features). The data is in the appendix.*

**Findings**

- **Evidenced:** Find nearest on Police stations, Find point clusters: cluster hulls produced Nearest police station to each cluster of licensed premises (3 features) [19][81][103][104].
- **Evidenced:** It reported result, count 3; result, elapsed ms 3; billing, cost 0.01012; billing, list price 0.01012; billing, upstream cost 0 [103].
- **Evidenced:** The chart Nearest police station to each cluster of licensed premises is drawn from 3 rows [106].

*Tool gp_find_nearest; parameters {"k": 1, "to": {"layer": "Police stations (6b7fdf5e)"}, "from": {"layer": "Find point clusters: cluster hulls (4dfef6fb)"}, "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "output_name": "Nearest police station to each cluster of licensed premises"}*

## Dense areas of street light outages

**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 [107]. Every step's map outputs are saved as styled layers [107].

**Why it was run on this data.** Patrol beats holds population in 'population': CCTV cameras per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors) [108].

**What it found.** Pipeline on Calculate density: Street light outages produced Dense areas of street light outages (5 features) [93][109][110]. It reported result, count 5; result, elapsed ms 13; billing, cost 0.03697; billing, list price 0.03697; billing, upstream cost 0; list price 0.03697 [109]. The chart Dense areas of street light outages is drawn from 5 rows [111].

| Result | Value |
| --- | --- |
| result, count | 5 |
| result, elapsed ms | 13 |
| billing, cost | 0.03697 |
| billing, list price | 0.03697 |
| billing, upstream cost | 0 |
| list price | 0.03697 |

**What the map shows.** Drawn with calculate density on the map Density: street light outages [71].

![Dense areas of street light outages](charts/34-dense-areas-of-street-light-outages.png)

*Figure 38. Dense areas of street light outages [111]. Source: Dense areas of street light outages (5 features). The data is in the appendix.*

**Findings**

- **Evidenced:** Pipeline on Calculate density: Street light outages produced Dense areas of street light outages (5 features) [93][109][110].
- **Evidenced:** It reported result, count 5; result, elapsed ms 13; billing, cost 0.03697; billing, list price 0.03697; billing, upstream cost 0; list price 0.03697 [109].
- **Evidenced:** The chart Dense areas of street light outages is drawn from 5 rows [111].

*Tool gp_pipeline; parameters {"save": "final", "steps": [{"id": "classes", "params": {"field": "density_per_km2", "method": "quantile", "classes": 5, "features": {"layer": "Calculate density: Street light outages (22316e02)"}}, "operation": "classify_field"}, {"id": "areas", "params": {"by": ["class"], "features": "$classes"}, "operation": "dissolve"}], "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "output_name": "Dense *

## Change of street light outages per patrol beat

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [44]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [44].

**Why it was run on this data.** Count CCTV cameras inside each feature of Patrol beats by ptz; the counts map as a choropleth (natural breaks, sequential colors) [45].

**What it found.** Summarize within on Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09), Patrol beats produced Change of street light outages per patrol beat (16 features) [10][14][112][113]. It reported result, count 16; result, elapsed ms 1; billing, cost 0.01048; billing, list price 0.01048; billing, upstream cost 0 [112]. The chart Change of street light outages per patrol beat is drawn from 5 rows [115].

| Result | Value |
| --- | --- |
| result, count | 16 |
| result, elapsed ms | 1 |
| billing, cost | 0.01048 |
| billing, list price | 0.01048 |
| billing, upstream cost | 0 |

![Change of street light outages per patrol beat](maps/24-change-of-street-light-outages-per-patrol-beat.png)

*Figure 39. Change of street light outages per patrol beat [114]*

**What the map shows.** The map draws Change of street light outages per patrol beat by sum_change in 4 classes: -7 to -3, -3 to -2, -2 to -1, -1 to 2 [114]. It draws 16 features [114]. It covers longitude -87.694 to -87.594 and latitude 41.85 to 41.907, at about 1:34,270 [114].

![Change of street light outages per patrol beat](charts/35-change-of-street-light-outages-per-patrol-beat.png)

*Figure 40. Change of street light outages per patrol beat [115]. Source: Change of street light outages per patrol beat (16 features). The data is in the appendix.*

**Findings**

- **Evidenced:** Summarize within on Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09), Patrol beats produced Change of street light outages per patrol beat (16 features) [10][14][112][113].
- **Evidenced:** It reported result, count 16; result, elapsed ms 1; billing, cost 0.01048; billing, list price 0.01048; billing, upstream cost 0 [112].
- **Evidenced:** The chart Change of street light outages per patrol beat is drawn from 5 rows [115].

*Tool gp_summarize_within; parameters {"stats": [{"agg": "sum", "field": "change"}], "project": "fe9df6b9-8a15-4264-946b-4b37b8dbb89d", "features": {"layer": "Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09) (b942fd09)"}, "polygons": {"layer": "Patrol beats (8c67e28f)"}, "output_name": "Change of street light outages per patrol beat"}*

## Maps and charts

The run also made 25 chart(s) [116][117][118][119][120][121][122][123][124][125][126][127][128][129][130][131][132][133][134][135][136][137][138][139][140].

![Incidents over time (reported_at)](charts/05-incidents-over-time-reported-at.png)

*Figure 41. Incidents over time (reported_at) [116]. Per week, 2025-09-29 to 2026-09-28. Source: Incidents (1,680 features). The data is in the appendix.*

![Street light outages over time (reported_at)](charts/06-street-light-outages-over-time-reported-at.png)

*Figure 42. Street light outages over time (reported_at) [117]. Per week, 2025-09-29 to 2026-09-28. Source: Street light outages (420 features). The data is in the appendix.*

![CCTV cameras over time (installed_year)](charts/07-cctv-cameras-over-time-installed-year.png)

*Figure 43. CCTV cameras over time (installed_year) [118]. Source: CCTV cameras (60 features). The data is in the appendix.*

![Calls for service by beat and month over time (month)](charts/08-calls-for-service-by-beat-and-month-over-time-month.png)

*Figure 44. Calls for service by beat and month over time (month) [119]. Per month, 2024-10-01 to 2026-09-01. Source: Calls for service by beat and month (384 features). The data is in the appendix.*

![Hot spot analysis: CCTV cameras](charts/09-hot-spot-analysis-cctv-cameras.png)

*Figure 45. Hot spot analysis: CCTV cameras [120]. Source: Hot spot analysis: CCTV cameras (36 features). The data is in the appendix.*

![Hot spot analysis: Licensed premises](charts/10-hot-spot-analysis-licensed-premises.png)

*Figure 46. Hot spot analysis: Licensed premises [121]. Source: Hot spot analysis: Licensed premises (112 features). The data is in the appendix.*

![Emerging hot spots: Incidents](charts/11-emerging-hot-spots-incidents.png)

*Figure 47. Emerging hot spots: Incidents [122]. Source: Emerging hot spots: Incidents (270 features). The data is in the appendix.*

![Emerging hot spots: Street light outages](charts/12-emerging-hot-spots-street-light-outages.png)

*Figure 48. Emerging hot spots: Street light outages [123]. Source: Emerging hot spots: Street light outages (47 features). The data is in the appendix.*

![Find point clusters: Licensed premises](charts/13-find-point-clusters-licensed-premises.png)

*Figure 49. Find point clusters: Licensed premises [124]. Source: Find point clusters: Licensed premises (120 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/14-find-point-clusters-cluster-hulls.png)

*Figure 50. Find point clusters: cluster hulls [125]. Source: Find point clusters: cluster hulls (3 features). The data is in the appendix.*

![Find point clusters: Incidents](charts/15-find-point-clusters-incidents.png)

*Figure 51. Find point clusters: Incidents [126]. Period: 2025-10-01 to 2026-09-30 (reported_at). Source: Find point clusters: Incidents (1,680 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/16-find-point-clusters-cluster-hulls.png)

*Figure 52. Find point clusters: cluster hulls [127]. Source: Find point clusters: cluster hulls (3 features). The data is in the appendix.*

![Find point clusters: Street light outages](charts/17-find-point-clusters-street-light-outages.png)

*Figure 53. Find point clusters: Street light outages [128]. Period: 2025-10-06 to 2026-10-20 (repaired_on). Source: Find point clusters: Street light outages (420 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/18-find-point-clusters-cluster-hulls.png)

*Figure 54. Find point clusters: cluster hulls [129]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.*

![Summarize within: Patrol beats](charts/19-summarize-within-patrol-beats.png)

*Figure 55. Summarize within: Patrol beats [130]. Source: Summarize within: Patrol beats (16 features). The data is in the appendix.*

![Summarize within: Patrol beats](charts/20-summarize-within-patrol-beats.png)

*Figure 56. Summarize within: Patrol beats [131]. Source: Summarize within: Patrol beats (16 features). The data is in the appendix.*

![Summarize within: Patrol beats](charts/21-summarize-within-patrol-beats.png)

*Figure 57. Summarize within: Patrol beats [132]. Source: Summarize within: Patrol beats (16 features). The data is in the appendix.*

![Calculate density: Licensed premises](charts/24-calculate-density-licensed-premises.png)

*Figure 58. Calculate density: Licensed premises [133]. Source: Calculate density: Licensed premises (164 features). The data is in the appendix.*

![Calculate density: Incidents](charts/25-calculate-density-incidents.png)

*Figure 59. Calculate density: Incidents [134]. Source: Calculate density: Incidents (2,874 features). The data is in the appendix.*

![Calculate density: Street light outages](charts/26-calculate-density-street-light-outages.png)

*Figure 60. Calculate density: Street light outages [135]. Source: Calculate density: Street light outages (848 features). The data is in the appendix.*

![Hot spot analysis: Incidents](charts/27-hot-spot-analysis-incidents.png)

*Figure 61. Hot spot analysis: Incidents [136]. Source: Hot spot analysis: Incidents (2,912 features). The data is in the appendix.*

![Repeat and near repeat classification: Incidents](charts/28-repeat-and-near-repeat-classification-incidents.png)

*Figure 62. Repeat and near repeat classification: Incidents [137]. Per week, 2025-09-29 to 2026-09-28. Source: Repeat and near repeat classification: Incidents (1,680 features). The data is in the appendix.*

![Repeat and near repeat classification: Incidents over time](charts/36-repeat-and-near-repeat-classification-incidents-over-time.png)

*Figure 63. Repeat and near repeat classification: Incidents over time [138]. Per week, 2025-09-29 to 2026-09-28. Source: Repeat and near repeat classification: Incidents (1,680 features). The data is in the appendix.*

![Find point clusters: Street light outages over time](charts/37-find-point-clusters-street-light-outages-over-time.png)

*Figure 64. Find point clusters: Street light outages over time [139]. Per month, 2025-10-01 to 2026-10-01. Source: Find point clusters: Street light outages (420 features). The data is in the appendix.*

![Find point clusters: Incidents over time](charts/38-find-point-clusters-incidents-over-time.png)

*Figure 65. Find point clusters: Incidents over time [140]. Per week, 2025-09-29 to 2026-09-28. Source: Find point clusters: Incidents (1,680 features). The data is in the appendix.*

## Documents

**What the documents name.** Quarterly crime and safety briefing, third quarter 2026 (1 page, 1 page(s) from the text layer) names 12 event terms and 11 places [141]. Event terms in Quarterly crime and safety briefing, third quarter 2026: Thefts (page 1, line 7, 2 mentions), Transit (page 1, line 7, 2 mentions), street (page 1, line 9, 2 mentions), demonstration (page 1, line 3) [142]. Places named in Quarterly crime and safety briefing, third quarter 2026: Central Transit Center (page 1, line 7, 2 mentions), Market West Beat (page 1, line 9, 2 mentions), N Elizabeth St (page 1, line 11, 2 mentions), Kingsway Beat (page 1, line 11, 2 mentions) [142]. Hot spot patrol plan (1 page, 1 page(s) from the text layer) names 1 organization, 8 event terms and 6 places [143]. Organizations named in Hot spot patrol plan: Focus (page 1, line 7) [144]. Event terms in Hot spot patrol plan: theft (page 1, line 11, 2 mentions), patrol (page 1, line 1), Patrols (page 1, line 3), Transit (page 1, line 8) [144]. Places named in Hot spot patrol plan: Central District Station (page 1, line 3), Westside Station (page 1, line 3), Riverside Station (page 1, line 3), Central Transit Center (page 1, line 8) [144].

| Result | Value |
| --- | --- |
| Documents | 2 |
| Entities | 38 |
| Organizations | 1 |
| Event terms | 20 |
| Places | 17 |

**Findings**

- **Evidenced:** Quarterly crime and safety briefing, third quarter 2026 (1 page, 1 page(s) from the text layer) names 12 event terms and 11 places.
- **Evidenced:** Event terms in Quarterly crime and safety briefing, third quarter 2026: Thefts (page 1, line 7, 2 mentions), Transit (page 1, line 7, 2 mentions), street (page 1, line 9, 2 mentions), demonstration (page 1, line 3).
- **Evidenced:** Places named in Quarterly crime and safety briefing, third quarter 2026: Central Transit Center (page 1, line 7, 2 mentions), Market West Beat (page 1, line 9, 2 mentions), N Elizabeth St (page 1, line 11, 2 mentions), Kingsway Beat (page 1, line 11, 2 mentions).
- **Evidenced:** Hot spot patrol plan (1 page, 1 page(s) from the text layer) names 1 organization, 8 event terms and 6 places.
- **Evidenced:** Organizations named in Hot spot patrol plan: Focus (page 1, line 7).
- **Evidenced:** Event terms in Hot spot patrol plan: theft (page 1, line 11, 2 mentions), patrol (page 1, line 1), Patrols (page 1, line 3), Transit (page 1, line 8).
- **Evidenced:** Places named in Hot spot patrol plan: Central District Station (page 1, line 3), Westside Station (page 1, line 3), Riverside Station (page 1, line 3), Central Transit Center (page 1, line 8).

## Changes since the last run

94 number(s) changed since the report of 2026-10-19T04:03:32Z.

| Analysis | Measure | Before | After | Change |
| --- | --- | --- | --- | --- |
| Hot spots | result, bins | 35 | 36 | 1 |
| Hot spots | result, count | 35 | 36 | 1 |
| Hot spots | result, elapsed ms | 2 | 3 | 1 |
| Hot spots | result, incidents used | 80 | 60 | -20 |
| Hot spots | result, distance band m | 1,000 | 2,000 | 1,000 |
| Hot spots | billing, cost | 0.0516 | 0.0512 | -0.0004 |
| Hot spots | billing, list price | 0.0516 | 0.0512 | -0.0004 |
| Hot spots | result, bins | 35 | 112 | 77 |
| Hot spots | result, count | 35 | 112 | 77 |
| Hot spots | result, elapsed ms | 2 | 5 | 3 |
| Hot spots | result, cell size m | 1,000 | 500 | -500 |
| Hot spots | result, incidents used | 80 | 120 | 40 |
| Hot spots | billing, cost | 0.0516 | 0.0524 | 0.0008 |
| Hot spots | billing, list price | 0.0516 | 0.0524 | 0.0008 |
| Space time hot spots | result, elapsed ms | 44 | 46 | 2 |
| Space time hot spots | result, count | 270 | 47 | -223 |
| Space time hot spots | result, elapsed ms | 44 | 10 | -34 |
| Space time hot spots | result, resolution | 9 | 8 | -1 |
| Space time hot spots | result, features used | 1,680 | 420 | -1,260 |
| Space time hot spots | billing, cost | 0.184 | 0.121 | -0.063 |
| Space time hot spots | billing, list price | 0.184 | 0.121 | -0.063 |
| Find point clusters | result, count | 1,680 | 120 | -1,560 |
| Find point clusters | result, noise | 1,609 | 61 | -1,548 |
| Find point clusters | result, distance m | 50 | 200 | 150 |
| Find point clusters | result, elapsed ms | 118 | 499 | 381 |
| Find point clusters | result, min points | 20 | 3 | -17 |
| Find point clusters | billing, cost | 0.0836 | 0.0524 | -0.0312 |
| Find point clusters | billing, list price | 0.0836 | 0.0524 | -0.0312 |
| Find point clusters | result, elapsed ms | 118 | 19 | -99 |
| Find point clusters | result, count | 1,680 | 420 | -1,260 |
| Find point clusters | result, noise | 1,609 | 410 | -1,199 |
| Find point clusters | result, distance m | 50 | 100 | 50 |
| Find point clusters | result, elapsed ms | 118 | 5 | -113 |
| Find point clusters | result, min points | 20 | 10 | -10 |
| Find point clusters | billing, cost | 0.0836 | 0.0584 | -0.0252 |
| Find point clusters | billing, list price | 0.0836 | 0.0584 | -0.0252 |
| Summarize within | result, elapsed ms | 1 | 0 | -1 |
| Summarize within | billing, cost | 0.01048 | 0.01154 | 0.00106 |
| Summarize within | billing, list price | 0.01048 | 0.01154 | 0.00106 |
| Summarize within | result, elapsed ms | 1 | 0 | -1 |
| Summarize within | billing, cost | 0.01048 | 0.01274 | 0.00226 |
| Summarize within | billing, list price | 0.01048 | 0.01274 | 0.00226 |
| Summarize within | result, elapsed ms | 1 | 2 | 1 |
| Summarize within | billing, cost | 0.01048 | 0.04394 | 0.03346 |
| Summarize within | billing, list price | 0.01048 | 0.04394 | 0.03346 |
| Calculate density | result, count | 2,874 | 164 | -2,710 |
| Calculate density | result, elapsed ms | 162 | 4 | -158 |
| Calculate density | result, bandwidth m | 200 | 1,000 | 800 |
| Calculate density | result, cell size m | 100 | 500 | 400 |
| Calculate density | result, max density per km2 | 1,347 | 42.62 | -1,305 |
| Calculate density | billing, cost | 0.0836 | 0.0524 | -0.0312 |
| Calculate density | billing, list price | 0.0836 | 0.0524 | -0.0312 |
| Calculate density | result, elapsed ms | 162 | 50 | -112 |
| Calculate density | result, count | 2,874 | 848 | -2,026 |
| Calculate density | result, elapsed ms | 162 | 15 | -147 |
| Calculate density | result, bandwidth m | 200 | 500 | 300 |
| Calculate density | result, cell size m | 100 | 200 | 100 |
| Calculate density | result, max density per km2 | 1,347 | 107.5 | -1,240 |
| Calculate density | billing, cost | 0.0836 | 0.0584 | -0.0252 |
| Calculate density | billing, list price | 0.0836 | 0.0584 | -0.0252 |
| Hot spots | result, bins | 35 | 2,912 | 2,877 |
| Hot spots | result, count | 35 | 2,912 | 2,877 |
| Hot spots | result, elapsed ms | 2 | 100 | 98 |
| Hot spots | result, cell size m | 1,000 | 100 | -900 |
| Hot spots | result, incidents used | 80 | 1,680 | 1,600 |
| Hot spots | result, distance band m | 1,000 | 200 | -800 |
| Hot spots | billing, cost | 0.0516 | 0.0836 | 0.032 |
| Hot spots | billing, list price | 0.0516 | 0.0836 | 0.032 |
| Near repeat | result, count | 420 | 1,680 | 1,260 |
| Near repeat | result, chains | 53 | 152 | 99 |
| Near repeat | result, distance m | 200 | 100 | -100 |
| Near repeat | result, elapsed ms | 10 | 39 | 29 |
| Near repeat | result, pairs linked | 185 | 1,357 | 1,172 |
| Near repeat | billing, cost | 0.0626 | 0.1004 | 0.0378 |
| Near repeat | billing, list price | 0.0626 | 0.1004 | 0.0378 |
| Hot cells inside each cluster of licensed premises | result, count | 16 | 3 | -13 |
| Hot cells inside each cluster of licensed premises | result, elapsed ms | 1 | 6 | 5 |
| Hot cells inside each cluster of licensed premises | billing, cost | 0.01048 | 0.0683 | 0.05782 |
| Hot cells inside each cluster of licensed premises | billing, list price | 0.01048 | 0.0683 | 0.05782 |
| Change beside the counts per patrol beat | billing, cost | 0.0273 | 0.01066 | -0.01664 |
| Change beside the counts per patrol beat | billing, list price | 0.0273 | 0.01066 | -0.01664 |
| Hot cells of CCTV cameras per patrol beat | result, elapsed ms | 1 | 2 | 1 |
| Hot cells of CCTV cameras per patrol beat | billing, cost | 0.01048 | 0.01104 | 0.00056 |
| Hot cells of CCTV cameras per patrol beat | billing, list price | 0.01048 | 0.01104 | 0.00056 |
| Nearest police station to each cluster of licensed premises | result, elapsed ms | 2 | 3 | 1 |
| Dense areas of street light outages | result, count | 936 | 5 | -931 |
| Dense areas of street light outages | result, elapsed ms | 22 | 13 | -9 |
| Dense areas of street light outages | billing, cost | 0.102 | 0.03697 | -0.06503 |
| Dense areas of street light outages | billing, list price | 0.102 | 0.03697 | -0.06503 |
| Dense areas of street light outages | list price | 0.102 | 0.03697 | -0.06503 |
| Documents | Documents | 17 | 2 | -15 |
| Documents | Entities | 17 | 38 | 21 |
| Documents | Organizations | 17 | 1 | -16 |
| Documents | Event terms | 17 | 20 | 3 |

New findings:

- Change beside the counts per patrol beat, from Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) and Summarize within: Patrol beats: the busiest area, Market West Beat (332 in all), changed by +9 [87][7][50].
- Change of incidents where density is high, from Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) and Calculate density: Street light outages: the largest rise (+9, Market West Beat) is where density averages 58.82 per square kilometer [92][7][70].
- Change of street light outages per patrol beat, from Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09): Parkview Beat rose the most (+2) and Market West Beat fell the most (-7) [112][9].
- Dense areas of street light outages, from Calculate density: Street light outages: the densest of five classes joins 170 cells into one area [109][70].
- Find nearest on Police stations, Find point clusters: cluster hulls produced Nearest police station to each cluster of licensed premises (3 features).
- Hot cells inside each cluster of licensed premises, from Hot spot analysis: Incidents and Find point clusters: cluster hulls: 1 of 3 clusters hold hot spot cells; cluster 0 holds the most (53) [79][73][36].
- Hot cells of CCTV cameras per patrol beat, from Hot spot analysis: CCTV cameras: 9 of 16 areas hold hot spot cells; Southgate Beat holds the most (5) [97][22].
- Hot spots on Incidents: Hot spot analysis: 1 layer saved: Hot spot analysis: Incidents (2912 features).
- Hot spots on Licensed premises: Hot spot analysis: 1 layer saved: Hot spot analysis: Licensed premises (112 features).
- It reported result, bins 112; result, count 112; result, elapsed ms 5; result, cell size m 500; result, incidents used 120; result, distance band m 1,000.
- It reported result, bins 2,912; result, count 2,912; result, elapsed ms 100; result, cell size m 100; result, incidents used 1,680; result, distance band m 200.
- It reported result, bins 36; result, count 36; result, elapsed ms 3; result, cell size m 1,000; result, incidents used 60; result, distance band m 2,000.
- It reported result, count 1,680; result, noise 1,609; result, distance m 50; result, elapsed ms 19; result, min points 20; billing, cost 0.0836.
- It reported result, count 120; result, noise 61; result, distance m 200; result, elapsed ms 499; result, min points 3; billing, cost 0.0524.
- It reported result, count 164; result, elapsed ms 4; result, bandwidth m 1,000; result, cell size m 500; result, excluded null 0; result, max density per km2 42.62.
- It reported result, count 16; result, elapsed ms 0; billing, cost 0.01274; billing, list price 0.01274; billing, upstream cost 0.
- It reported result, count 16; result, elapsed ms 2; billing, cost 0.04394; billing, list price 0.04394; billing, upstream cost 0.
- It reported result, count 2,874; result, elapsed ms 50; result, bandwidth m 200; result, cell size m 100; result, excluded null 0; result, max density per km2 1,347.
- It reported result, count 270; result, elapsed ms 46; result, resolution 9; result, features used 1,680; result, time neighbors 0; billing, cost 0.184.
- It reported result, count 3; result, elapsed ms 3; billing, cost 0.01012; billing, list price 0.01012; billing, upstream cost 0.
- It reported result, count 3; result, elapsed ms 6; billing, cost 0.0683; billing, list price 0.0683; billing, upstream cost 0.
- It reported result, count 420; result, noise 410; result, distance m 100; result, elapsed ms 5; result, min points 10; billing, cost 0.0584.
- It reported result, count 5; result, elapsed ms 13; billing, cost 0.03697; billing, list price 0.03697; billing, upstream cost 0; list price 0.03697.
- It reported result, count 848; result, elapsed ms 15; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 107.5.
- It reported result, days 30; result, count 1,680; result, chains 152; result, distance m 100; result, elapsed ms 39; result, pairs linked 1,357.
- Near repeat on Incidents: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Incidents (1680 features).
- Nearest police station to each cluster of licensed premises, from Find point clusters: cluster hulls: the 3 clusters lie 836 to 972 m from the nearest facility (Central District Station and others) [103][36].
- Summarize within on Hot spot analysis: Incidents, Find point clusters: cluster hulls produced Hot cells inside each cluster of licensed premises (3 features).
- Summarize within on Incidents, Patrol beats: Summarize within: 1 layer saved: Summarize within: Patrol beats (16 features).
- Summarize within on Licensed premises, Patrol beats: Summarize within: 1 layer saved: Summarize within: Patrol beats (16 features).
- The chart Hot cells inside each cluster of licensed premises is drawn from 3 rows.
- The chart Nearest police station to each cluster of licensed premises is drawn from 3 rows.

## Sources

[1] Rendering: Chart: Timeline: events per month by dataset. 72 rows (id d013059e-6933-47a5-bb09-9a0dc42d4902)  
[2] Dataset: Incidents. vector, 1680 rows (id 8e5ad24c-d6a6-4a58-8281-8b363a095f60)  
[3] Dataset: Street light outages. vector, 420 rows (id abe096b9-4f2d-4a2c-8093-abb51b141f33)  
[4] Dataset: Calls for service by beat and month. table, 384 rows (id 924696a2-7623-45fc-aa81-6181d9eba827)  
[5] Rendering: Chart: Timeline lanes: events per month by dataset. 72 rows (id fb1cdee2-f533-45f4-983f-0cd951892613)  
[6] Rendering: Chart: Timeline: change from one month to the next. 23 rows (id 2d043b97-3b7e-4081-a3cc-a2d4f2bb2246)  
[7] Tool result: gp_period_change, step 0. The count rose from 33 in week of 2026-06-22 to 72 in week of 2026-06-29 (+118.2%); a change this large is unlikely to be chance (p = 0.000178). (id 2042e52a-03d6-42f1-8365-22ab6777dc1e:0)  
[8] Layer: Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) (id 1e0726ac-a223-423b-b680-55434fcfa5e6)  
[9] Tool result: gp_period_change, step 1. The count fell from 17 in week of 2026-02-02 to 4 in week of 2026-02-09 (-76.5%); a change this large is unlikely to be chance (p = 0.0072). (id 2042e52a-03d6-42f1-8365-22ab6777dc1e:1)  
[10] Layer: Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09) (id b942fd09-4b01-4ffe-a2ab-c88503e90ef1)  
[11] Rendering: Map: Overview: the top 3 results. view longitude -87.71811 to -87.57468, latitude 41.83798 to 41.91748 (id 79e66837-1f85-4f45-956b-0a34bfd5fe00)  
[12] Layer: Incidents. spatial profile (id 36ad5f7c-eab7-4ac7-8256-9737c4a1b044)  
[13] Layer: Street light outages. spatial profile (id a7218535-327c-4a76-b5d4-8caa09f44155)  
[14] Layer: Patrol beats. spatial profile (id 8c67e28f-6678-462f-a450-e83d278138e8)  
[15] Layer: CCTV cameras. spatial profile (id ad88aaeb-e7ac-4b9c-aa1c-c4626ae498b8)  
[16] Layer: Transit stops. spatial profile (id 313ae694-78aa-4830-955c-22be11a8a8a3)  
[17] Layer: Licensed premises. spatial profile (id 055b5c2c-2a86-4379-95be-6f38f708b65b)  
[18] Layer: Roads. spatial profile (id f63e5a45-031b-44a5-bf1e-7b9d17cb2378)  
[19] Layer: Police stations. spatial profile (id 6b7fdf5e-7de9-4f14-995d-0c918b2e91ec)  
[20] 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)  
[21] Plan: Hot spots of CCTV cameras (the Start plan's candidate c_f0a51c3ce192). 60 points: Getis-Ord Gi* tests whether the concentrations in CCTV cameras are statistically significant. (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_f0a51c3ce192)  
[22] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: CCTV cameras (36 features). (id 321ba186-8b6e-4c4b-bb58-94f915d8b6aa:0)  
[23] Rendering: Map: Hot spots: CCTV cameras. view longitude -87.7152 to -87.57762, latitude 41.83848 to 41.91701 (id a5a339e4-5577-4333-9b86-911f2f84f49a)  
[24] Plan: Hot spots of Licensed premises (the Start plan's candidate c_91bb663e526f). 120 points: Getis-Ord Gi* tests whether the concentrations in Licensed premises are statistically significant. Measured: Licensed premises are clustered (nearest-neighbor ratio 0.85, z = -3.2), median spacing 121 m. Cells of 500 m and a band of 1000 m from the measured spacing. (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_91bb663e526f)  
[25] Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Licensed premises (112 features). (id 321ba186-8b6e-4c4b-bb58-94f915d8b6aa:1)  
[26] Rendering: Map: Hot spots: licensed premises. view longitude -87.70465 to -87.5929, latitude 41.84743 to 41.91121 (id 2145f368-15a9-49e0-ad22-77165edbf2be)  
[27] 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)  
[28] Plan: Space time hot spots of Incidents (the Start plan's candidate c_1c60b58dbb29). Incidents carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. Measured: the pattern of Incidents changes over time (earlier half clustered, later half clustered; the center moved 132 m). (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_1c60b58dbb29)  
[29] Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Incidents (270 features). (id 961133c9-b424-4eba-880c-76b21b589fcb:0)  
[30] Rendering: Map: Emerging hot spots: incidents. view longitude -87.70246 to -87.58676, latitude 41.84569 to 41.90983 (id 6f2b1b3c-6588-4aa1-b6cf-4e9a3a111f44)  
[31] Plan: Space time hot spots of Street light outages (the Start plan's candidate c_2469aa67bba0). Street light outages carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. Measured: the pattern of Street light outages changes over time (earlier half clustered, later half clustered; the center moved 263 m). (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_2469aa67bba0)  
[32] Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: Street light outages (47 features). (id 961133c9-b424-4eba-880c-76b21b589fcb:1)  
[33] Rendering: Map: Emerging hot spots: street light outages. view longitude -87.71613 to -87.57759, latitude 41.83894 to 41.91574 (id 91191d14-e6a0-40e1-a6fa-dd44826aaf6e)  
[34] 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)  
[35] Plan: Cluster points of Licensed premises (the Start plan's candidate c_bca1140666e1). DBSCAN finds dense groups of Licensed premises and separates noise. Measured: Licensed premises are clustered (nearest-neighbor ratio 0.85, z = -3.2), median spacing 121 m. DBSCAN at 200 m (twice the measured spacing). (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_bca1140666e1)  
[36] Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Licensed premises (120 features); Find point clusters: cluster hulls (3 features). (id 215b8c93-8d01-4390-b188-f5c767207b46:0)  
[37] Rendering: Map: Clusters: licensed premises. view longitude -87.69497 to -87.59835, latitude 41.85068 to 41.90582 (id 2605dc65-0adc-4643-9694-8530ea34521a)  
[38] Plan: Cluster points of Incidents (the Start plan's candidate c_0aed3f12db5c). DBSCAN finds dense groups of Incidents and separates noise. Measured: Incidents are clustered (nearest-neighbor ratio 0.73, z = -21.0), median spacing 34.3 m. DBSCAN at 50 m (twice the measured spacing). (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_0aed3f12db5c)  
[39] Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Incidents (1680 features); Find point clusters: cluster hulls (3 features). (id 215b8c93-8d01-4390-b188-f5c767207b46:1)  
[40] Rendering: Map: Clusters: incidents. view longitude -87.69327 to -87.59462, latitude 41.8503 to 41.9066 (id d9cb38e3-2bbf-4bd6-8736-3a3f54e2766c)  
[41] Plan: Cluster points of Street light outages (the Start plan's candidate c_eac5e908317b). DBSCAN finds dense groups of Street light outages and separates noise. Measured: Street light outages are clustered (nearest-neighbor ratio 0.88, z = -4.9), median spacing 74.9 m. DBSCAN at 100 m (twice the measured spacing). (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_eac5e908317b)  
[42] Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Street light outages (420 features); Find point clusters: cluster hulls (1 feature). (id 215b8c93-8d01-4390-b188-f5c767207b46:2)  
[43] Rendering: Map: Clusters: street light outages. view longitude -87.6954 to -87.59744, latitude 41.85029 to 41.9062 (id e91b963b-96c8-45a2-9a13-6a160f41445f)  
[44] 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)  
[45] Plan: CCTV cameras per feature of Patrol beats (the Start plan's candidate c_c58f45b29b6e). Count CCTV cameras inside each feature of Patrol beats by ptz; the counts map as a choropleth (natural breaks, sequential colors). (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_c58f45b29b6e)  
[46] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Patrol beats (16 features). (id 5e359a15-f5e2-429c-a252-d2486c76e559:0)  
[47] Rendering: Map: Counts: CCTV cameras, by patrol beat. view longitude -87.69371 to -87.59438, latitude 41.85001 to 41.9067 (id 1d6099da-be90-4b9c-9991-ddaea0c2a814)  
[48] Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Patrol beats (16 features). (id 5e359a15-f5e2-429c-a252-d2486c76e559:1)  
[49] Rendering: Map: Counts: licensed premises, by patrol beat. view longitude -87.69371 to -87.59438, latitude 41.85001 to 41.9067 (id f3ebc560-673b-4e0e-bfeb-f244b6e425c8)  
[50] Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Patrol beats (16 features). (id 5e359a15-f5e2-429c-a252-d2486c76e559:2)  
[51] Rendering: Map: Counts: incidents, by patrol beat. view longitude -87.69371 to -87.59438, latitude 41.85001 to 41.9067 (id f6217787-f7ba-4030-aa6d-63161fb23a9e)  
[52] 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)  
[53] Plan: Incidents: change per week per feature of Patrol beats (the Start plan's candidate c_55dcc49526ff). Incidents carry dates in 'reported_at' over 52 weeks: the count per week, week over week, with the places of largest rise and fall per feature of Patrol beats and whether the change is beyond chance. (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_55dcc49526ff)  
[54] Rendering: Map: Period change: incidents, by patrol beat. view longitude -87.69576 to -87.59221, latitude 41.8496 to 41.907 (id f8fa38e0-6b2b-4027-9d48-4238d9d56b8b)  
[55] Rendering: Chart: Incidents: count per week. 53 rows (id 669d11fe-6a6b-4977-827a-e6583fbb9ec8)  
[56] Rendering: Chart: Incidents: week of 2026-06-22 to week of 2026-06-29 by place. 30 rows (id d57784f7-9e7d-4cb3-a1b4-5c224a8d2ffd)  
[57] Rendering: Chart: Period change: Incidents (week of 2026-06-22 to week of 2026-06-29). 5 rows (id 8f97238b-666e-416b-87f0-c34f89646566)  
[58] Rendering: Map: Period change: street light outages, by patrol beat. view longitude -87.69969 to -87.59686, latitude 41.84969 to 41.90669 (id 064b0349-aa03-4072-ac98-9eb86c884bd9)  
[59] Rendering: Chart: Street light outages: count per week. 53 rows (id 73202d54-d8bb-4119-8b31-815538262a83)  
[60] Rendering: Chart: Street light outages: week of 2026-02-02 to week of 2026-02-09 by place. 14 rows (id 56c46fab-d5ad-44bb-b563-5bba1cc24883)  
[61] Rendering: Chart: Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09). 5 rows (id d58ef4fc-c705-4cc6-bbc5-aca3bf3f48b6)  
[62] 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)  
[63] Plan: Kernel density of Licensed premises (the Start plan's candidate c_2b247b24a505). 120 points in Licensed premises: a density surface shows where they concentrate. Measured: Licensed premises are clustered (nearest-neighbor ratio 0.85, z = -3.2), median spacing 121 m. (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_2b247b24a505)  
[64] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Licensed premises (164 features). (id 8475a7ee-691d-4552-8a24-41bb69bf46a4:0)  
[65] Rendering: Map: Density: licensed premises. view longitude -87.71552 to -87.57698, latitude 41.83894 to 41.91801 (id 2d0ffd6b-492c-46b6-9708-edf0e8f348fa)  
[66] Plan: Kernel density of Incidents (the Start plan's candidate c_9007d02c4a41). 1680 points in Incidents: a density surface shows where they concentrate. Measured: Incidents are clustered (nearest-neighbor ratio 0.73, z = -21.0), median spacing 34.3 m. (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_9007d02c4a41)  
[67] Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Incidents (2874 features). (id 8475a7ee-691d-4552-8a24-41bb69bf46a4:1)  
[68] Rendering: Map: Density: incidents. view longitude -87.69708 to -87.59062, latitude 41.848 to 41.90876 (id 0aa6b6e6-e52d-4399-b08c-8e798eb73c93)  
[69] Plan: Kernel density of Street light outages (the Start plan's candidate c_bd626b9123bb). 420 points in Street light outages: a density surface shows where they concentrate. Measured: Street light outages are clustered (nearest-neighbor ratio 0.88, z = -4.9), median spacing 74.9 m. (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_bd626b9123bb)  
[70] Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: Street light outages (848 features). (id 8475a7ee-691d-4552-8a24-41bb69bf46a4:2)  
[71] Rendering: Map: Density: street light outages. view longitude -87.70567 to -87.58754, latitude 41.84454 to 41.91196 (id 27e532e4-8298-4330-ad1a-0d9375f48b07)  
[72] Plan: Hot spots of Incidents (the Start plan's candidate c_c312b5d076a7). 1680 points: Getis-Ord Gi* tests whether the concentrations in Incidents are statistically significant. Measured: Incidents are clustered (nearest-neighbor ratio 0.73, z = -21.0), median spacing 34.3 m. Cells of 100 m and a band of 200 m from the measured spacing. (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_c312b5d076a7)  
[73] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Incidents (2912 features). (id bc2d155b-8245-44bd-bc02-ae3dd8ce99bd:0)  
[74] Rendering: Map: Hot spots: incidents. view longitude -87.69527 to -87.59314, latitude 41.84909 to 41.90737 (id 207cb3df-c55c-401c-8124-cbf80367ad53)  
[75] Catalog: Repeat and near repeat classification (operation catalog, gp_near_repeat). Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table. ArcGIS: Repeat and Near Repeat Classification, Near Repeat Calculator. (id gp_near_repeat)  
[76] Plan: Near repeat of Incidents (the Start plan's candidate c_cabb2c797127). Near repeat analysis tests whether Incidents close in space also follow each other within 30 days. (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_cabb2c797127)  
[77] Tool result: gp_near_repeat, step 0. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Incidents (1680 features). (id e703fd06-1a0c-47e1-8d41-9a7f7ca7a096:0)  
[78] Rendering: Map: Near repeat: incidents. view longitude -87.69493 to -87.59281, latitude 41.85009 to 41.9067 (id 28e59ede-c587-4b24-bb28-3230a99a4d59)  
[79] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Hot cells inside each cluster of licensed premises (3 features). (id 47c5f0de-dfdf-494b-911d-90b10a714d4d:0)  
[80] Layer: Hot spot analysis: Incidents. 2,912 features (id 8382f930-c9d0-432b-82b7-fabc618137e9)  
[81] Layer: Find point clusters: cluster hulls. 3 features (id 4dfef6fb-4663-43a7-86b3-cb7ca742b344)  
[82] Layer: Hot cells inside each cluster of licensed premises. 3 features (id ca4b4f64-91e3-4c8b-b95e-1027e417c18a)  
[83] Rendering: Map: Hot cells inside each cluster of licensed premises. view longitude -87.66722 to -87.63111, latitude 41.87536 to 41.89597 (id 1f5b237f-456e-4271-ac5a-8b7c49ea1203)  
[84] Rendering: Chart: Hot cells inside each cluster of licensed premises. 3 rows (id dc8f6491-2889-4c96-8892-a5a92ddccff9)  
[85] 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)  
[86] Plan: The run's goal. Describe the project Sample: Public safety, Chicago: run the analyses that tell the most about its data, draw every result, and write a cited report. (id 2e6054ca-62f3-479f-b398-915fe26200ce:goal)  
[87] Tool result: gp_spatial_join, step 0. Spatial join: 1 layer saved: Change beside the counts per patrol beat (16 features). (id b47bd286-7bf7-47ee-8462-aba2f7f03009:0)  
[88] Layer: Summarize within: Patrol beats. 16 features (id 31bedf68-b7b3-41b2-abda-6a464b2f0776)  
[89] Layer: Change beside the counts per patrol beat. 16 features (id a982a2d6-956f-4dfe-b821-16eefe49fc59)  
[90] Rendering: Map: Change beside the counts per patrol beat. view longitude -87.69371 to -87.59438, latitude 41.85001 to 41.9067 (id 72abfc34-1aa3-41c0-918f-e1038e14d6e4)  
[91] Rendering: Chart: Change beside the counts per patrol beat. 5 rows (id 0db6652b-8019-4841-a5c5-bb77cb37c605)  
[92] Tool result: gp_spatial_join, step 0. Spatial join: 1 layer saved: Change of incidents where density is high (16 features). (id a507fc16-21fd-45b9-aeb6-d8c679c59bfa:0)  
[93] Layer: Calculate density: Street light outages. 848 features (id 22316e02-9759-4f61-b1eb-206b935b14bb)  
[94] Layer: Change of incidents where density is high. 16 features (id b18abe82-9e7c-4939-aba0-31147b337e8d)  
[95] Rendering: Map: Change of incidents where density is high. view longitude -87.69371 to -87.59438, latitude 41.85001 to 41.9067 (id 4c6d9fb2-ffd0-4f64-8535-c3988767f184)  
[96] Rendering: Chart: Change of incidents where density is high. 5 rows (id 92ea2477-a522-47ee-89b1-746ee6856ca5)  
[97] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Hot cells of CCTV cameras per patrol beat (16 features). (id 614dc9e6-1702-4222-aaee-d9710ae1d2fa:0)  
[98] Layer: Hot spot analysis: CCTV cameras. 36 features (id 2cc32b98-9097-49ba-8818-944110cdf58f)  
[99] Layer: Hot cells of CCTV cameras per patrol beat. 16 features (id d17a96b4-151e-4595-be18-86aaab10e46f)  
[100] Rendering: Map: Hot cells of CCTV cameras per patrol beat. view longitude -87.69371 to -87.59438, latitude 41.85001 to 41.9067 (id b8be83a4-10bf-4533-a453-099468da0894)  
[101] Rendering: Chart: Hot cells of CCTV cameras per patrol beat. 5 rows (id 35fadd76-8342-4832-bb6b-54b406cc2355)  
[102] 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)  
[103] Tool result: gp_find_nearest, step 0. Find nearest: 1 layer saved: Nearest police station to each cluster of licensed premises (3 features). (id c991d762-1f84-44c6-96ae-3710f29ec64c:0)  
[104] Layer: Nearest police station to each cluster of licensed premises. 3 features (id e1507503-e9d4-495b-92ae-125ebdaa2cf0)  
[105] Rendering: Map: Nearest police station to each cluster of licensed premises. view longitude -87.66722 to -87.63111, latitude 41.87536 to 41.89597 (id 85be50e0-3982-4731-8c0e-823fa9294a88)  
[106] Rendering: Chart: Nearest police station to each cluster of licensed premises. 3 rows (id 2dec7314-090d-4c50-abac-7f19aa87b8c3)  
[107] 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)  
[108] Plan: CCTV cameras per 10,000 people per feature of Patrol beats (the Start plan's candidate c_c2ae8a2714b2). Patrol beats holds population in 'population': CCTV cameras per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors). (id f922208b-bbd9-4f8c-a605-3fb31853d7fd:c_c2ae8a2714b2)  
[109] Tool result: gp_pipeline, step 0. Pipeline of 2 steps (classify_field > dissolve): 1 layer saved. (id 6ca7bfd5-79a6-41c1-bf7d-8e6d94107d57:0)  
[110] Layer: Dense areas of street light outages. 5 features (id ac9b6845-0f71-4373-bd6d-610425383c47)  
[111] Rendering: Chart: Dense areas of street light outages. 5 rows (id eb14b23d-e657-47db-bbb9-6dc855c8b8cc)  
[112] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Change of street light outages per patrol beat (16 features). (id cc3dd633-eef0-4eb3-8158-2f9fd13fb200:0)  
[113] Layer: Change of street light outages per patrol beat. 16 features (id 3aa058c3-6af3-4495-9b14-12531d1ab9a2)  
[114] Rendering: Map: Change of street light outages per patrol beat. view longitude -87.69371 to -87.59438, latitude 41.85001 to 41.9067 (id 8c09d493-6848-4496-8154-9e4918bed43f)  
[115] Rendering: Chart: Change of street light outages per patrol beat. 5 rows (id f5280b27-6c71-4ddf-ba62-2c3527a68ab7)  
[116] Rendering: Chart: Incidents over time (reported_at). 318 rows (id 77127ac6-8e99-4791-97cd-fae719d89262)  
[117] Rendering: Chart: Street light outages over time (reported_at). 212 rows (id f9621103-3d0a-49ab-b90b-f7b40f93745b)  
[118] Rendering: Chart: CCTV cameras over time (installed_year). 7 rows (id d430a879-effd-4bd0-81f7-baa28a4e5360)  
[119] Rendering: Chart: Calls for service by beat and month over time (month). 24 rows (id 7c56f65f-9b6b-4cdd-a312-17f1982d8de0)  
[120] Rendering: Chart: Hot spot analysis: CCTV cameras. 12 rows (id 64aa9539-922f-4609-8b9a-6ef06a308a05)  
[121] Rendering: Chart: Hot spot analysis: Licensed premises. 15 rows (id 4a5bc439-21a2-432b-9b8c-8615b719b8f0)  
[122] Rendering: Chart: Emerging hot spots: Incidents. 10 rows (id 07721714-421e-4a56-9250-c62be37e8e3e)  
[123] Rendering: Chart: Emerging hot spots: Street light outages. 7 rows (id 890e75d2-c676-4d56-aa47-5dc8fc6b1f37)  
[124] Rendering: Chart: Find point clusters: Licensed premises. 4 rows (id eab8321b-459d-4b21-8ac3-03b4c9716a0b)  
[125] Rendering: Chart: Find point clusters: cluster hulls. 3 rows (id 597c1cc7-6d4d-45ea-805f-e76406a7301d)  
[126] Rendering: Chart: Find point clusters: Incidents. 4 rows (id 834a7e88-1f92-40c3-876e-07b230849ef1)  
[127] Rendering: Chart: Find point clusters: cluster hulls. 3 rows (id b1d73a1a-bd54-4f9a-829e-a64da853e8b8)  
[128] Rendering: Chart: Find point clusters: Street light outages. 2 rows (id da93f938-3c5c-4e4b-a1e9-332a9c4d97c4)  
[129] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id d0a69a7c-4f2e-46e8-8fbb-3db7d1804d97)  
[130] Rendering: Chart: Summarize within: Patrol beats. 5 rows (id cf0bb795-0579-4a40-a979-e2f725bdfd8a)  
[131] Rendering: Chart: Summarize within: Patrol beats. 5 rows (id cf1142c2-7076-48aa-b438-d97d2f7f83d6)  
[132] Rendering: Chart: Summarize within: Patrol beats. 5 rows (id 85f26d21-089a-4d33-bb0c-b985b8e6ddae)  
[133] Rendering: Chart: Calculate density: Licensed premises. 9 rows (id 3365b9ed-e2bc-4612-b00e-b147032aaa0b)  
[134] Rendering: Chart: Calculate density: Incidents. 13 rows (id 130ec3b4-ddc0-4f62-8c53-493253574a12)  
[135] Rendering: Chart: Calculate density: Street light outages. 11 rows (id 5d19b0ed-c618-4e8e-9536-3f3a0821dcd8)  
[136] Rendering: Chart: Hot spot analysis: Incidents. 48 rows (id dc40d842-a071-4cf4-87b5-9dbd4aff8223)  
[137] Rendering: Chart: Repeat and near repeat classification: Incidents. 106 rows (id 2774b60c-5a11-46d6-a8e1-f56f2a252051)  
[138] Rendering: Chart: Repeat and near repeat classification: Incidents over time. 106 rows (id d3456b40-f8d2-4674-9fe4-b420ca81087b)  
[139] Rendering: Chart: Find point clusters: Street light outages over time. 52 rows (id b7008422-a117-4748-8bb2-7dd55cd29bdd)  
[140] Rendering: Chart: Find point clusters: Incidents over time. 106 rows (id fcaf3d1b-6848-404c-9e97-b57b023c1cbd)  
[141] Document: Quarterly crime and safety briefing, third quarter 2026. 1 page(s), 1 page(s) from the text layer (id 7f7de84b-c518-4d25-b279-e98be530690e)  
[142] Document page: Quarterly crime and safety briefing, third quarter 2026, page 1. Thefts and robberies remain concentrated around the Central Transit Center at 41.87815, -87.63532, mostly on weekday commuting hours. (id 7f7de84b-c518-4d25-b279-e98be530690e:1)  
[143] Document: Hot spot patrol plan. 1 page(s), 1 page(s) from the text layer (id 0bf0414d-d633-4517-a876-66ca320180f3)  
[144] Document page: Hot spot patrol plan, page 1. Focus (id 0bf0414d-d633-4517-a876-66ca320180f3:1)  
[145] Dataset: Patrol beats. vector, 16 rows (id ba848777-0930-4af3-b069-03edd17a2c3a)  
[146] Dataset: Police stations. vector, 3 rows (id d3ceed49-4119-4de8-9218-65b32992e120)  
[147] Dataset: CCTV cameras. vector, 60 rows (id 13e9e089-c520-4be6-9034-c0ecaf7790ab)  
[148] Dataset: Transit stops. vector, 80 rows (id cc2e635f-1052-402a-82aa-95b2db052e76)  
[149] Dataset: Licensed premises. vector, 120 rows (id 1a088947-ca4e-4632-b13d-975d48c05e97)  
[150] Dataset: Roads. vector, 1033 rows (id e72c7c55-1cdd-49b2-b75d-3d2ec18b9c1e)  
[151] Dataset: Beat staffing by shift. table, 48 rows (id 6abb3c2e-68b3-4a9c-b2a7-ce316203ebe8)  
[152] Dataset: Quarterly crime and safety briefing. document, ? rows (id 535a8782-a933-42ff-af25-92ec818afdd6)  
[153] Dataset: Hot spot patrol plan. document, ? rows (id 70e64e98-d266-4079-a9a1-7d54ac2c3e96)  
[154] Record: Sample: Public safety, Chicago (id fe9df6b9-8a15-4264-946b-4b37b8dbb89d)  
[155] Layer: Hot spot analysis: Licensed premises. 112 features (id 90322b1c-a218-4ac5-8478-7f0de2a327dd)  
[156] Layer: Emerging hot spots: Incidents. 270 features (id ddbdee29-f892-4397-9f92-4fb6062a0a16)  
[157] Layer: Emerging hot spots: Street light outages. 47 features (id 377fb9d7-1ae0-4b9d-b202-467adca71899)  
[158] Layer: Find point clusters: Licensed premises. 120 features (id 0f39a418-fa9c-4906-bbe4-7afb6c00ba8c)  
[159] Layer: Find point clusters: Incidents. 1,680 features (id 75dd415e-0b2c-4a18-b79c-cc2338385dd5)  
[160] Layer: Find point clusters: cluster hulls. 3 features (id 99ba9a84-3453-4bca-a43e-5861ef41c540)  
[161] Layer: Find point clusters: Street light outages. 420 features (id 4ac9e9ca-a714-4ec7-9df8-4e3869dbcc58)  
[162] Layer: Find point clusters: cluster hulls. 1 features (id 6d783f7e-8f24-4458-8abc-6e3126e9630a)  
[163] Layer: Summarize within: Patrol beats. 16 features (id 430dcae6-3721-44c7-9264-40595ff75b4e)  
[164] Layer: Summarize within: Patrol beats. 16 features (id fabe7842-f3f5-4d5e-a46f-c3232381dc5f)  
[165] Layer: Calculate density: Licensed premises. 164 features (id 76319405-6afb-4505-946b-c33b783be8eb)  
[166] Layer: Calculate density: Incidents. 2,874 features (id 8d228b68-9a3d-43fd-96a3-e37297b29f9a)  
[167] Layer: Repeat and near repeat classification: Incidents. 1,680 features (id aa6cf905-4fec-4559-9917-c9810ef2a94b)  

## Appendix: the data behind the charts

### Figure 1. Timeline: events per month by dataset [1]

| period | series | value |
| --- | --- | --- |
| 2024-10-01 | Incidents | 0 |
| 2024-10-01 | Street light outages | 0 |
| 2024-10-01 | Calls for service by beat and month | 16 |
| 2024-11-01 | Incidents | 0 |
| 2024-11-01 | Street light outages | 0 |
| 2024-11-01 | Calls for service by beat and month | 16 |
| 2024-12-01 | Incidents | 0 |
| 2024-12-01 | Street light outages | 0 |
| 2024-12-01 | Calls for service by beat and month | 16 |
| 2025-01-01 | Incidents | 0 |
| 2025-01-01 | Street light outages | 0 |
| 2025-01-01 | Calls for service by beat and month | 16 |
| 2025-02-01 | Incidents | 0 |
| 2025-02-01 | Street light outages | 0 |
| 2025-02-01 | Calls for service by beat and month | 16 |
| 2025-03-01 | Incidents | 0 |
| 2025-03-01 | Street light outages | 0 |
| 2025-03-01 | Calls for service by beat and month | 16 |
| 2025-04-01 | Incidents | 0 |
| 2025-04-01 | Street light outages | 0 |
| 2025-04-01 | Calls for service by beat and month | 16 |
| 2025-05-01 | Incidents | 0 |
| 2025-05-01 | Street light outages | 0 |
| 2025-05-01 | Calls for service by beat and month | 16 |
| 2025-06-01 | Incidents | 0 |
| 2025-06-01 | Street light outages | 0 |
| 2025-06-01 | Calls for service by beat and month | 16 |
| 2025-07-01 | Incidents | 0 |
| 2025-07-01 | Street light outages | 0 |
| 2025-07-01 | Calls for service by beat and month | 16 |
| 2025-08-01 | Incidents | 0 |
| 2025-08-01 | Street light outages | 0 |
| 2025-08-01 | Calls for service by beat and month | 16 |
| 2025-09-01 | Incidents | 0 |
| 2025-09-01 | Street light outages | 0 |
| 2025-09-01 | Calls for service by beat and month | 16 |
| 2025-10-01 | Incidents | 114 |
| 2025-10-01 | Street light outages | 33 |
| 2025-10-01 | Calls for service by beat and month | 16 |
| 2025-11-01 | Incidents | 125 |
| 2025-11-01 | Street light outages | 26 |
| 2025-11-01 | Calls for service by beat and month | 16 |
| 2025-12-01 | Incidents | 119 |
| 2025-12-01 | Street light outages | 40 |
| 2025-12-01 | Calls for service by beat and month | 16 |
| 2026-01-01 | Incidents | 112 |
| 2026-01-01 | Street light outages | 49 |
| 2026-01-01 | Calls for service by beat and month | 16 |
| 2026-02-01 | Incidents | 90 |
| 2026-02-01 | Street light outages | 44 |
| 2026-02-01 | Calls for service by beat and month | 16 |
| 2026-03-01 | Incidents | 126 |
| 2026-03-01 | Street light outages | 37 |
| 2026-03-01 | Calls for service by beat and month | 16 |
| 2026-04-01 | Incidents | 133 |
| 2026-04-01 | Street light outages | 37 |
| 2026-04-01 | Calls for service by beat and month | 16 |
| 2026-05-01 | Incidents | 162 |
| 2026-05-01 | Street light outages | 28 |
| 2026-05-01 | Calls for service by beat and month | 16 |
| 2026-06-01 | Incidents | 143 |
| 2026-06-01 | Street light outages | 33 |
| 2026-06-01 | Calls for service by beat and month | 16 |
| 2026-07-01 | Incidents | 195 |
| 2026-07-01 | Street light outages | 24 |
| 2026-07-01 | Calls for service by beat and month | 16 |
| 2026-08-01 | Incidents | 179 |
| 2026-08-01 | Street light outages | 33 |
| 2026-08-01 | Calls for service by beat and month | 16 |
| 2026-09-01 | Incidents | 182 |
| 2026-09-01 | Street light outages | 36 |
| 2026-09-01 | Calls for service by beat and month | 16 |

### Figure 2. Timeline lanes: events per month by dataset [5]

| lane | period | period_end | value |
| --- | --- | --- | --- |
| Incidents | 2024-10-01 | 2024-11-01 | 0 |
| Incidents | 2024-11-01 | 2024-12-01 | 0 |
| Incidents | 2024-12-01 | 2025-01-01 | 0 |
| Incidents | 2025-01-01 | 2025-02-01 | 0 |
| Incidents | 2025-02-01 | 2025-03-01 | 0 |
| Incidents | 2025-03-01 | 2025-04-01 | 0 |
| Incidents | 2025-04-01 | 2025-05-01 | 0 |
| Incidents | 2025-05-01 | 2025-06-01 | 0 |
| Incidents | 2025-06-01 | 2025-07-01 | 0 |
| Incidents | 2025-07-01 | 2025-08-01 | 0 |
| Incidents | 2025-08-01 | 2025-09-01 | 0 |
| Incidents | 2025-09-01 | 2025-10-01 | 0 |
| Incidents | 2025-10-01 | 2025-11-01 | 114 |
| Incidents | 2025-11-01 | 2025-12-01 | 125 |
| Incidents | 2025-12-01 | 2026-01-01 | 119 |
| Incidents | 2026-01-01 | 2026-02-01 | 112 |
| Incidents | 2026-02-01 | 2026-03-01 | 90 |
| Incidents | 2026-03-01 | 2026-04-01 | 126 |
| Incidents | 2026-04-01 | 2026-05-01 | 133 |
| Incidents | 2026-05-01 | 2026-06-01 | 162 |
| Incidents | 2026-06-01 | 2026-07-01 | 143 |
| Incidents | 2026-07-01 | 2026-08-01 | 195 |
| Incidents | 2026-08-01 | 2026-09-01 | 179 |
| Incidents | 2026-09-01 | 2026-10-01 | 182 |
| Street light outages | 2024-10-01 | 2024-11-01 | 0 |
| Street light outages | 2024-11-01 | 2024-12-01 | 0 |
| Street light outages | 2024-12-01 | 2025-01-01 | 0 |
| Street light outages | 2025-01-01 | 2025-02-01 | 0 |
| Street light outages | 2025-02-01 | 2025-03-01 | 0 |
| Street light outages | 2025-03-01 | 2025-04-01 | 0 |
| Street light outages | 2025-04-01 | 2025-05-01 | 0 |
| Street light outages | 2025-05-01 | 2025-06-01 | 0 |
| Street light outages | 2025-06-01 | 2025-07-01 | 0 |
| Street light outages | 2025-07-01 | 2025-08-01 | 0 |
| Street light outages | 2025-08-01 | 2025-09-01 | 0 |
| Street light outages | 2025-09-01 | 2025-10-01 | 0 |
| Street light outages | 2025-10-01 | 2025-11-01 | 33 |
| Street light outages | 2025-11-01 | 2025-12-01 | 26 |
| Street light outages | 2025-12-01 | 2026-01-01 | 40 |
| Street light outages | 2026-01-01 | 2026-02-01 | 49 |
| Street light outages | 2026-02-01 | 2026-03-01 | 44 |
| Street light outages | 2026-03-01 | 2026-04-01 | 37 |
| Street light outages | 2026-04-01 | 2026-05-01 | 37 |
| Street light outages | 2026-05-01 | 2026-06-01 | 28 |
| Street light outages | 2026-06-01 | 2026-07-01 | 33 |
| Street light outages | 2026-07-01 | 2026-08-01 | 24 |
| Street light outages | 2026-08-01 | 2026-09-01 | 33 |
| Street light outages | 2026-09-01 | 2026-10-01 | 36 |
| Calls for service by beat and month | 2024-10-01 | 2024-11-01 | 16 |
| Calls for service by beat and month | 2024-11-01 | 2024-12-01 | 16 |
| Calls for service by beat and month | 2024-12-01 | 2025-01-01 | 16 |
| Calls for service by beat and month | 2025-01-01 | 2025-02-01 | 16 |
| Calls for service by beat and month | 2025-02-01 | 2025-03-01 | 16 |
| Calls for service by beat and month | 2025-03-01 | 2025-04-01 | 16 |
| Calls for service by beat and month | 2025-04-01 | 2025-05-01 | 16 |
| Calls for service by beat and month | 2025-05-01 | 2025-06-01 | 16 |
| Calls for service by beat and month | 2025-06-01 | 2025-07-01 | 16 |
| Calls for service by beat and month | 2025-07-01 | 2025-08-01 | 16 |
| Calls for service by beat and month | 2025-08-01 | 2025-09-01 | 16 |
| Calls for service by beat and month | 2025-09-01 | 2025-10-01 | 16 |
| Calls for service by beat and month | 2025-10-01 | 2025-11-01 | 16 |
| Calls for service by beat and month | 2025-11-01 | 2025-12-01 | 16 |
| Calls for service by beat and month | 2025-12-01 | 2026-01-01 | 16 |
| Calls for service by beat and month | 2026-01-01 | 2026-02-01 | 16 |
| Calls for service by beat and month | 2026-02-01 | 2026-03-01 | 16 |
| Calls for service by beat and month | 2026-03-01 | 2026-04-01 | 16 |
| Calls for service by beat and month | 2026-04-01 | 2026-05-01 | 16 |
| Calls for service by beat and month | 2026-05-01 | 2026-06-01 | 16 |
| Calls for service by beat and month | 2026-06-01 | 2026-07-01 | 16 |
| Calls for service by beat and month | 2026-07-01 | 2026-08-01 | 16 |
| Calls for service by beat and month | 2026-08-01 | 2026-09-01 | 16 |
| Calls for service by beat and month | 2026-09-01 | 2026-10-01 | 16 |

### Figure 3. Timeline: change from one month to the next [6]

| period | period_end | value | previous | change |
| --- | --- | --- | --- | --- |
| 2024-11-01 | 2024-12-01 | 16 | 16 | 0 |
| 2024-12-01 | 2025-01-01 | 16 | 16 | 0 |
| 2025-01-01 | 2025-02-01 | 16 | 16 | 0 |
| 2025-02-01 | 2025-03-01 | 16 | 16 | 0 |
| 2025-03-01 | 2025-04-01 | 16 | 16 | 0 |
| 2025-04-01 | 2025-05-01 | 16 | 16 | 0 |
| 2025-05-01 | 2025-06-01 | 16 | 16 | 0 |
| 2025-06-01 | 2025-07-01 | 16 | 16 | 0 |
| 2025-07-01 | 2025-08-01 | 16 | 16 | 0 |
| 2025-08-01 | 2025-09-01 | 16 | 16 | 0 |
| 2025-09-01 | 2025-10-01 | 16 | 16 | 0 |
| 2025-10-01 | 2025-11-01 | 163 | 16 | 147 |
| 2025-11-01 | 2025-12-01 | 167 | 163 | 4 |
| 2025-12-01 | 2026-01-01 | 175 | 167 | 8 |
| 2026-01-01 | 2026-02-01 | 177 | 175 | 2 |
| 2026-02-01 | 2026-03-01 | 150 | 177 | -27 |
| 2026-03-01 | 2026-04-01 | 179 | 150 | 29 |
| 2026-04-01 | 2026-05-01 | 186 | 179 | 7 |
| 2026-05-01 | 2026-06-01 | 206 | 186 | 20 |
| 2026-06-01 | 2026-07-01 | 192 | 206 | -14 |
| 2026-07-01 | 2026-08-01 | 235 | 192 | 43 |
| 2026-08-01 | 2026-09-01 | 228 | 235 | -7 |
| 2026-09-01 | 2026-10-01 | 234 | 228 | 6 |

### Figure 16. Incidents: count per week [55]

| period | value |
| --- | --- |
| 2025-09-29 | 16 |
| 2025-10-06 | 25 |
| 2025-10-13 | 29 |
| 2025-10-20 | 19 |
| 2025-10-27 | 31 |
| 2025-11-03 | 37 |
| 2025-11-10 | 31 |
| 2025-11-17 | 31 |
| 2025-11-24 | 20 |
| 2025-12-01 | 22 |
| 2025-12-08 | 32 |
| 2025-12-15 | 28 |
| 2025-12-22 | 24 |
| 2025-12-29 | 32 |
| 2026-01-05 | 22 |
| 2026-01-12 | 24 |
| 2026-01-19 | 26 |
| 2026-01-26 | 24 |
| 2026-02-02 | 18 |
| 2026-02-09 | 18 |
| 2026-02-16 | 26 |
| 2026-02-23 | 29 |
| 2026-03-02 | 16 |
| 2026-03-09 | 23 |
| 2026-03-16 | 34 |
| 2026-03-23 | 39 |
| 2026-03-30 | 25 |
| 2026-04-06 | 41 |
| 2026-04-13 | 22 |
| 2026-04-20 | 36 |
| 2026-04-27 | 36 |
| 2026-05-04 | 36 |
| 2026-05-11 | 32 |
| 2026-05-18 | 40 |
| 2026-05-25 | 37 |
| 2026-06-01 | 22 |
| 2026-06-08 | 37 |
| 2026-06-15 | 42 |
| 2026-06-22 | 33 |
| 2026-06-29 | 72 |
| 2026-07-06 | 34 |
| 2026-07-13 | 36 |
| 2026-07-20 | 36 |
| 2026-07-27 | 40 |
| 2026-08-03 | 31 |
| 2026-08-10 | 24 |
| 2026-08-17 | 46 |
| 2026-08-24 | 55 |
| 2026-08-31 | 41 |
| 2026-09-07 | 41 |
| 2026-09-14 | 49 |
| 2026-09-21 | 47 |
| 2026-09-28 | 13 |

### Figure 17. Incidents: week of 2026-06-22 to week of 2026-06-29 by place [56]

| group | period | value |
| --- | --- | --- |
| Market West Beat | week of 2026-06-22 | 7 |
| Market West Beat | week of 2026-06-29 | 16 |
| Kingsway Beat | week of 2026-06-22 | 6 |
| Kingsway Beat | week of 2026-06-29 | 13 |
| Southgate Beat | week of 2026-06-22 | 2 |
| Southgate Beat | week of 2026-06-29 | 9 |
| Elm Park Beat | week of 2026-06-22 | 5 |
| Elm Park Beat | week of 2026-06-29 | 9 |
| North River Beat | week of 2026-06-22 | 5 |
| North River Beat | week of 2026-06-29 | 9 |
| Granary Beat | week of 2026-06-22 | 2 |
| Granary Beat | week of 2026-06-29 | 0 |
| Midtown Beat | week of 2026-06-22 | 1 |
| Midtown Beat | week of 2026-06-29 | 3 |
| Old Mill Beat | week of 2026-06-22 | 1 |
| Old Mill Beat | week of 2026-06-29 | 3 |
| Westbrook Beat | week of 2026-06-22 | 0 |
| Westbrook Beat | week of 2026-06-29 | 2 |
| Brickworks Beat | week of 2026-06-22 | 0 |
| Brickworks Beat | week of 2026-06-29 | 1 |
| Cedar Heights Beat | week of 2026-06-22 | 0 |
| Cedar Heights Beat | week of 2026-06-29 | 1 |
| Foundry Beat | week of 2026-06-22 | 0 |
| Foundry Beat | week of 2026-06-29 | 1 |
| Parkview Beat | week of 2026-06-22 | 2 |
| Parkview Beat | week of 2026-06-29 | 1 |
| Station Quarter Beat | week of 2026-06-22 | 1 |
| Station Quarter Beat | week of 2026-06-29 | 2 |
| Union Yards Beat | week of 2026-06-22 | 0 |
| Union Yards Beat | week of 2026-06-29 | 1 |

### Figure 18. Period change: Incidents (week of 2026-06-22 to week of 2026-06-29) [57]

| bin_start | bin_end | count |
| --- | --- | --- |
| -1.414 | -0.7092 | 1 |
| -0.7092 | -0.00428 | 1 |
| -0.00428 | 0.7007 | 2 |
| 0.7007 | 1.406 | 8 |
| 1.406 | 2.111 | 4 |

### Figure 20. Street light outages: count per week [59]

| period | value |
| --- | --- |
| 2025-09-29 | 4 |
| 2025-10-06 | 6 |
| 2025-10-13 | 6 |
| 2025-10-20 | 10 |
| 2025-10-27 | 10 |
| 2025-11-03 | 7 |
| 2025-11-10 | 7 |
| 2025-11-17 | 4 |
| 2025-11-24 | 5 |
| 2025-12-01 | 14 |
| 2025-12-08 | 11 |
| 2025-12-15 | 7 |
| 2025-12-22 | 7 |
| 2025-12-29 | 11 |
| 2026-01-05 | 9 |
| 2026-01-12 | 9 |
| 2026-01-19 | 16 |
| 2026-01-26 | 6 |
| 2026-02-02 | 17 |
| 2026-02-09 | 4 |
| 2026-02-16 | 12 |
| 2026-02-23 | 11 |
| 2026-03-02 | 4 |
| 2026-03-09 | 9 |
| 2026-03-16 | 6 |
| 2026-03-23 | 12 |
| 2026-03-30 | 13 |
| 2026-04-06 | 9 |
| 2026-04-13 | 9 |
| 2026-04-20 | 7 |
| 2026-04-27 | 7 |
| 2026-05-04 | 3 |
| 2026-05-11 | 8 |
| 2026-05-18 | 8 |
| 2026-05-25 | 6 |
| 2026-06-01 | 12 |
| 2026-06-08 | 7 |
| 2026-06-15 | 3 |
| 2026-06-22 | 10 |
| 2026-06-29 | 5 |
| 2026-07-06 | 6 |
| 2026-07-13 | 6 |
| 2026-07-20 | 7 |
| 2026-07-27 | 6 |
| 2026-08-03 | 7 |
| 2026-08-10 | 9 |
| 2026-08-17 | 6 |
| 2026-08-24 | 5 |
| 2026-08-31 | 10 |
| 2026-09-07 | 8 |
| 2026-09-14 | 8 |
| 2026-09-21 | 9 |
| 2026-09-28 | 2 |

### Figure 21. Street light outages: week of 2026-02-02 to week of 2026-02-09 by place [60]

| group | period | value |
| --- | --- | --- |
| Market West Beat | week of 2026-02-02 | 9 |
| Market West Beat | week of 2026-02-09 | 2 |
| North River Beat | week of 2026-02-02 | 3 |
| North River Beat | week of 2026-02-09 | 0 |
| Parkview Beat | week of 2026-02-02 | 0 |
| Parkview Beat | week of 2026-02-09 | 2 |
| Southgate Beat | week of 2026-02-02 | 2 |
| Southgate Beat | week of 2026-02-09 | 0 |
| Canal Row Beat | week of 2026-02-02 | 1 |
| Canal Row Beat | week of 2026-02-09 | 0 |
| Cedar Heights Beat | week of 2026-02-02 | 1 |
| Cedar Heights Beat | week of 2026-02-09 | 0 |
| Westbrook Beat | week of 2026-02-02 | 1 |
| Westbrook Beat | week of 2026-02-09 | 0 |

### Figure 22. Period change: Street light outages (week of 2026-02-02 to week of 2026-02-09) [61]

| bin_start | bin_end | count |
| --- | --- | --- |
| -2.111 | -1.406 | 3 |
| -1.406 | -0.7007 | 3 |
| -0.7007 | 0.00428 | 0 |
| 0.00428 | 0.7092 | 0 |
| 0.7092 | 1.414 | 1 |

### Figure 29. Hot cells inside each cluster of licensed premises [84]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.3333 | 33.3% |
| 1 | 1 | 0.3333 | 33.3% |
| 2 | 1 | 0.3333 | 33.3% |

### Figure 31. Change beside the counts per patrol beat [91]

| bin_start | bin_end | count |
| --- | --- | --- |
| -1.414 | -0.7092 | 1 |
| -0.7092 | -0.00428 | 1 |
| -0.00428 | 0.7007 | 2 |
| 0.7007 | 1.406 | 8 |
| 1.406 | 2.111 | 4 |

### Figure 33. Change of incidents where density is high [96]

| bin_start | bin_end | count |
| --- | --- | --- |
| -1.414 | -0.7092 | 1 |
| -0.7092 | -0.00428 | 1 |
| -0.00428 | 0.7007 | 2 |
| 0.7007 | 1.406 | 8 |
| 1.406 | 2.111 | 4 |

### Figure 35. Hot cells of CCTV cameras per patrol beat [101]

| bin_start | bin_end | count |
| --- | --- | --- |
| 2 | 3 | 1 |
| 3 | 4 | 0 |
| 4 | 5 | 2 |
| 5 | 6 | 4 |
| 6 | 7 | 9 |

### Figure 37. Nearest police station to each cluster of licensed premises [106]

| rank | name |
| --- | --- |
| 1 | Central District Station |
| 1 | Central District Station |
| 1 | Central District Station |

### Figure 38. Dense areas of street light outages [111]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 0.8 | 1 |
| 0.8 | 1.6 | 1 |
| 1.6 | 2.4 | 1 |
| 2.4 | 3.2 | 1 |
| 3.2 | 4 | 1 |

### Figure 40. Change of street light outages per patrol beat [115]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 0.2 | 9 |
| 0.2 | 0.4 | 0 |
| 0.4 | 0.6 | 0 |
| 0.6 | 0.8 | 0 |
| 0.8 | 1 | 7 |

### Figure 41. Incidents over time (reported_at) [116]

| period | series | value |
| --- | --- | --- |
| 2025-09-29 | theft | 8 |
| 2025-09-29 | battery | 4 |
| 2025-09-29 | criminal damage | 0 |
| 2025-09-29 | burglary | 1 |
| 2025-09-29 | motor vehicle theft | 0 |
| 2025-09-29 | Other | 3 |
| 2025-10-06 | theft | 8 |
| 2025-10-06 | battery | 1 |
| 2025-10-06 | criminal damage | 3 |
| 2025-10-06 | burglary | 9 |
| 2025-10-06 | motor vehicle theft | 0 |
| 2025-10-06 | Other | 4 |
| 2025-10-13 | theft | 10 |
| 2025-10-13 | battery | 5 |
| 2025-10-13 | criminal damage | 6 |
| 2025-10-13 | burglary | 1 |
| 2025-10-13 | motor vehicle theft | 3 |
| 2025-10-13 | Other | 4 |
| 2025-10-20 | theft | 8 |
| 2025-10-20 | battery | 2 |
| 2025-10-20 | criminal damage | 3 |
| 2025-10-20 | burglary | 2 |
| 2025-10-20 | motor vehicle theft | 2 |
| 2025-10-20 | Other | 2 |
| 2025-10-27 | theft | 11 |
| 2025-10-27 | battery | 5 |
| 2025-10-27 | criminal damage | 3 |
| 2025-10-27 | burglary | 5 |
| 2025-10-27 | motor vehicle theft | 1 |
| 2025-10-27 | Other | 6 |
| 2025-11-03 | theft | 12 |
| 2025-11-03 | battery | 7 |
| 2025-11-03 | criminal damage | 1 |
| 2025-11-03 | burglary | 7 |
| 2025-11-03 | motor vehicle theft | 1 |
| 2025-11-03 | Other | 9 |
| 2025-11-10 | theft | 7 |
| 2025-11-10 | battery | 8 |
| 2025-11-10 | criminal damage | 5 |
| 2025-11-10 | burglary | 2 |
| 2025-11-10 | motor vehicle theft | 4 |
| 2025-11-10 | Other | 5 |
| 2025-11-17 | theft | 9 |
| 2025-11-17 | battery | 4 |
| 2025-11-17 | criminal damage | 5 |
| 2025-11-17 | burglary | 3 |
| 2025-11-17 | motor vehicle theft | 4 |
| 2025-11-17 | Other | 6 |
| 2025-11-24 | theft | 5 |
| 2025-11-24 | battery | 3 |
| 2025-11-24 | criminal damage | 2 |
| 2025-11-24 | burglary | 2 |
| 2025-11-24 | motor vehicle theft | 4 |
| 2025-11-24 | Other | 4 |
| 2025-12-01 | theft | 6 |
| 2025-12-01 | battery | 4 |
| 2025-12-01 | criminal damage | 4 |
| 2025-12-01 | burglary | 3 |
| 2025-12-01 | motor vehicle theft | 0 |
| 2025-12-01 | Other | 5 |
| 2025-12-08 | theft | 8 |
| 2025-12-08 | battery | 3 |
| 2025-12-08 | criminal damage | 8 |
| 2025-12-08 | burglary | 4 |
| 2025-12-08 | motor vehicle theft | 2 |
| 2025-12-08 | Other | 7 |
| 2025-12-15 | theft | 8 |
| 2025-12-15 | battery | 5 |
| 2025-12-15 | criminal damage | 6 |
| 2025-12-15 | burglary | 2 |
| 2025-12-15 | motor vehicle theft | 2 |
| 2025-12-15 | Other | 5 |
| 2025-12-22 | theft | 8 |
| 2025-12-22 | battery | 2 |
| 2025-12-22 | criminal damage | 3 |
| 2025-12-22 | burglary | 5 |
| 2025-12-22 | motor vehicle theft | 2 |
| 2025-12-22 | Other | 4 |
| 2025-12-29 | theft | 11 |
| 2025-12-29 | battery | 2 |
| 2025-12-29 | criminal damage | 6 |
| 2025-12-29 | burglary | 4 |
| 2025-12-29 | motor vehicle theft | 1 |
| 2025-12-29 | Other | 8 |
| 2026-01-05 | theft | 7 |
| 2026-01-05 | battery | 5 |
| 2026-01-05 | criminal damage | 3 |
| 2026-01-05 | burglary | 0 |
| 2026-01-05 | motor vehicle theft | 4 |
| 2026-01-05 | Other | 3 |
| 2026-01-12 | theft | 10 |
| 2026-01-12 | battery | 3 |
| 2026-01-12 | criminal damage | 0 |
| 2026-01-12 | burglary | 5 |
| 2026-01-12 | motor vehicle theft | 3 |
| 2026-01-12 | Other | 3 |
| 2026-01-19 | theft | 10 |
| 2026-01-19 | battery | 1 |
| 2026-01-19 | criminal damage | 1 |
| 2026-01-19 | burglary | 4 |
| 2026-01-19 | motor vehicle theft | 2 |
| 2026-01-19 | Other | 8 |
| 2026-01-26 | theft | 12 |
| 2026-01-26 | battery | 1 |
| 2026-01-26 | criminal damage | 3 |
| 2026-01-26 | burglary | 3 |
| 2026-01-26 | motor vehicle theft | 0 |
| 2026-01-26 | Other | 5 |
| 2026-02-02 | theft | 9 |
| 2026-02-02 | battery | 1 |
| 2026-02-02 | criminal damage | 1 |
| 2026-02-02 | burglary | 1 |
| 2026-02-02 | motor vehicle theft | 4 |
| 2026-02-02 | Other | 2 |
| 2026-02-09 | theft | 6 |
| 2026-02-09 | battery | 2 |
| 2026-02-09 | criminal damage | 2 |
| 2026-02-09 | burglary | 3 |
| 2026-02-09 | motor vehicle theft | 3 |
| 2026-02-09 | Other | 2 |
| 2026-02-16 | theft | 9 |
| 2026-02-16 | battery | 3 |
| 2026-02-16 | criminal damage | 2 |
| 2026-02-16 | burglary | 7 |
| 2026-02-16 | motor vehicle theft | 3 |
| 2026-02-16 | Other | 2 |
| 2026-02-23 | theft | 13 |
| 2026-02-23 | battery | 4 |
| 2026-02-23 | criminal damage | 1 |
| 2026-02-23 | burglary | 0 |
| 2026-02-23 | motor vehicle theft | 6 |
| 2026-02-23 | Other | 5 |
| 2026-03-02 | theft | 5 |
| 2026-03-02 | battery | 1 |
| 2026-03-02 | criminal damage | 1 |
| 2026-03-02 | burglary | 4 |
| 2026-03-02 | motor vehicle theft | 3 |
| 2026-03-02 | Other | 2 |
| 2026-03-09 | theft | 7 |
| 2026-03-09 | battery | 4 |
| 2026-03-09 | criminal damage | 2 |
| 2026-03-09 | burglary | 1 |
| 2026-03-09 | motor vehicle theft | 6 |
| 2026-03-09 | Other | 3 |
| 2026-03-16 | theft | 7 |
| 2026-03-16 | battery | 8 |
| 2026-03-16 | criminal damage | 6 |
| 2026-03-16 | burglary | 7 |
| 2026-03-16 | motor vehicle theft | 1 |
| 2026-03-16 | Other | 5 |
| 2026-03-23 | theft | 9 |
| 2026-03-23 | battery | 4 |
| 2026-03-23 | criminal damage | 5 |
| 2026-03-23 | burglary | 5 |
| 2026-03-23 | motor vehicle theft | 7 |
| 2026-03-23 | Other | 9 |
| 2026-03-30 | theft | 6 |
| 2026-03-30 | battery | 8 |
| 2026-03-30 | criminal damage | 2 |
| 2026-03-30 | burglary | 2 |
| 2026-03-30 | motor vehicle theft | 3 |
| 2026-03-30 | Other | 4 |
| 2026-04-06 | theft | 11 |
| 2026-04-06 | battery | 7 |
| 2026-04-06 | criminal damage | 5 |
| 2026-04-06 | burglary | 8 |
| 2026-04-06 | motor vehicle theft | 5 |
| 2026-04-06 | Other | 5 |
| 2026-04-13 | theft | 8 |
| 2026-04-13 | battery | 7 |
| 2026-04-13 | criminal damage | 1 |
| 2026-04-13 | burglary | 2 |
| 2026-04-13 | motor vehicle theft | 2 |
| 2026-04-13 | Other | 2 |
| 2026-04-20 | theft | 9 |
| 2026-04-20 | battery | 9 |
| 2026-04-20 | criminal damage | 5 |
| 2026-04-20 | burglary | 5 |
| 2026-04-20 | motor vehicle theft | 3 |
| 2026-04-20 | Other | 5 |
| 2026-04-27 | theft | 9 |
| 2026-04-27 | battery | 10 |
| 2026-04-27 | criminal damage | 6 |
| 2026-04-27 | burglary | 4 |
| 2026-04-27 | motor vehicle theft | 2 |
| 2026-04-27 | Other | 5 |
| 2026-05-04 | theft | 14 |
| 2026-05-04 | battery | 2 |
| 2026-05-04 | criminal damage | 7 |
| 2026-05-04 | burglary | 4 |
| 2026-05-04 | motor vehicle theft | 4 |
| 2026-05-04 | Other | 5 |
| 2026-05-11 | theft | 11 |
| 2026-05-11 | battery | 4 |
| 2026-05-11 | criminal damage | 2 |
| 2026-05-11 | burglary | 7 |
| 2026-05-11 | motor vehicle theft | 5 |
| 2026-05-11 | Other | 3 |
| 2026-05-18 | theft | 14 |
| 2026-05-18 | battery | 7 |
| 2026-05-18 | criminal damage | 7 |
| 2026-05-18 | burglary | 3 |
| 2026-05-18 | motor vehicle theft | 3 |
| 2026-05-18 | Other | 6 |
| 2026-05-25 | theft | 14 |
| 2026-05-25 | battery | 5 |
| 2026-05-25 | criminal damage | 7 |
| 2026-05-25 | burglary | 3 |
| 2026-05-25 | motor vehicle theft | 3 |
| 2026-05-25 | Other | 5 |
| 2026-06-01 | theft | 6 |
| 2026-06-01 | battery | 3 |
| 2026-06-01 | criminal damage | 6 |
| 2026-06-01 | burglary | 2 |
| 2026-06-01 | motor vehicle theft | 3 |
| 2026-06-01 | Other | 2 |
| 2026-06-08 | theft | 6 |
| 2026-06-08 | battery | 7 |
| 2026-06-08 | criminal damage | 7 |
| 2026-06-08 | burglary | 7 |
| 2026-06-08 | motor vehicle theft | 5 |
| 2026-06-08 | Other | 5 |
| 2026-06-15 | theft | 8 |
| 2026-06-15 | battery | 14 |
| 2026-06-15 | criminal damage | 4 |
| 2026-06-15 | burglary | 4 |
| 2026-06-15 | motor vehicle theft | 6 |
| 2026-06-15 | Other | 6 |
| 2026-06-22 | theft | 12 |
| 2026-06-22 | battery | 6 |
| 2026-06-22 | criminal damage | 10 |
| 2026-06-22 | burglary | 1 |
| 2026-06-22 | motor vehicle theft | 2 |
| 2026-06-22 | Other | 2 |
| 2026-06-29 | theft | 20 |
| 2026-06-29 | battery | 21 |
| 2026-06-29 | criminal damage | 9 |
| 2026-06-29 | burglary | 6 |
| 2026-06-29 | motor vehicle theft | 7 |
| 2026-06-29 | Other | 9 |
| 2026-07-06 | theft | 12 |
| 2026-07-06 | battery | 7 |
| 2026-07-06 | criminal damage | 1 |
| 2026-07-06 | burglary | 7 |
| 2026-07-06 | motor vehicle theft | 3 |
| 2026-07-06 | Other | 4 |
| 2026-07-13 | theft | 9 |
| 2026-07-13 | battery | 4 |
| 2026-07-13 | criminal damage | 4 |
| 2026-07-13 | burglary | 4 |
| 2026-07-13 | motor vehicle theft | 7 |
| 2026-07-13 | Other | 8 |
| 2026-07-20 | theft | 7 |
| 2026-07-20 | battery | 9 |
| 2026-07-20 | criminal damage | 6 |
| 2026-07-20 | burglary | 2 |
| 2026-07-20 | motor vehicle theft | 3 |
| 2026-07-20 | Other | 9 |
| 2026-07-27 | theft | 15 |
| 2026-07-27 | battery | 5 |
| 2026-07-27 | criminal damage | 6 |
| 2026-07-27 | burglary | 3 |
| 2026-07-27 | motor vehicle theft | 7 |
| 2026-07-27 | Other | 4 |
| 2026-08-03 | theft | 11 |
| 2026-08-03 | battery | 7 |
| 2026-08-03 | criminal damage | 4 |
| 2026-08-03 | burglary | 1 |
| 2026-08-03 | motor vehicle theft | 4 |
| 2026-08-03 | Other | 4 |
| 2026-08-10 | theft | 11 |
| 2026-08-10 | battery | 2 |
| 2026-08-10 | criminal damage | 3 |
| 2026-08-10 | burglary | 1 |
| 2026-08-10 | motor vehicle theft | 4 |
| 2026-08-10 | Other | 3 |
| 2026-08-17 | theft | 18 |
| 2026-08-17 | battery | 6 |
| 2026-08-17 | criminal damage | 7 |
| 2026-08-17 | burglary | 5 |
| 2026-08-17 | motor vehicle theft | 6 |
| 2026-08-17 | Other | 4 |
| 2026-08-24 | theft | 18 |
| 2026-08-24 | battery | 7 |
| 2026-08-24 | criminal damage | 5 |
| 2026-08-24 | burglary | 5 |
| 2026-08-24 | motor vehicle theft | 8 |
| 2026-08-24 | Other | 12 |
| 2026-08-31 | theft | 15 |
| 2026-08-31 | battery | 8 |
| 2026-08-31 | criminal damage | 2 |
| 2026-08-31 | burglary | 3 |
| 2026-08-31 | motor vehicle theft | 6 |
| 2026-08-31 | Other | 7 |
| 2026-09-07 | theft | 19 |
| 2026-09-07 | battery | 3 |
| 2026-09-07 | criminal damage | 3 |
| 2026-09-07 | burglary | 5 |
| 2026-09-07 | motor vehicle theft | 2 |
| 2026-09-07 | Other | 9 |
| 2026-09-14 | theft | 15 |
| 2026-09-14 | battery | 12 |
| 2026-09-14 | criminal damage | 4 |
| 2026-09-14 | burglary | 7 |
| 2026-09-14 | motor vehicle theft | 4 |
| 2026-09-14 | Other | 7 |
| 2026-09-21 | theft | 17 |
| 2026-09-21 | battery | 9 |
| 2026-09-21 | criminal damage | 1 |
| 2026-09-21 | burglary | 5 |
| 2026-09-21 | motor vehicle theft | 7 |
| 2026-09-21 | Other | 8 |
| 2026-09-28 | theft | 1 |
| 2026-09-28 | battery | 5 |
| 2026-09-28 | criminal damage | 1 |
| 2026-09-28 | burglary | 0 |
| 2026-09-28 | motor vehicle theft | 1 |
| 2026-09-28 | Other | 5 |

### Figure 42. Street light outages over time (reported_at) [117]

| period | series | value |
| --- | --- | --- |
| 2025-09-29 | lamp failure | 2 |
| 2025-09-29 | cable fault | 1 |
| 2025-09-29 | vandalism | 1 |
| 2025-09-29 | collision | 0 |
| 2025-10-06 | lamp failure | 4 |
| 2025-10-06 | cable fault | 0 |
| 2025-10-06 | vandalism | 2 |
| 2025-10-06 | collision | 0 |
| 2025-10-13 | lamp failure | 4 |
| 2025-10-13 | cable fault | 0 |
| 2025-10-13 | vandalism | 2 |
| 2025-10-13 | collision | 0 |
| 2025-10-20 | lamp failure | 6 |
| 2025-10-20 | cable fault | 3 |
| 2025-10-20 | vandalism | 0 |
| 2025-10-20 | collision | 1 |
| 2025-10-27 | lamp failure | 3 |
| 2025-10-27 | cable fault | 3 |
| 2025-10-27 | vandalism | 3 |
| 2025-10-27 | collision | 1 |
| 2025-11-03 | lamp failure | 4 |
| 2025-11-03 | cable fault | 2 |
| 2025-11-03 | vandalism | 1 |
| 2025-11-03 | collision | 0 |
| 2025-11-10 | lamp failure | 5 |
| 2025-11-10 | cable fault | 1 |
| 2025-11-10 | vandalism | 1 |
| 2025-11-10 | collision | 0 |
| 2025-11-17 | lamp failure | 2 |
| 2025-11-17 | cable fault | 1 |
| 2025-11-17 | vandalism | 1 |
| 2025-11-17 | collision | 0 |
| 2025-11-24 | lamp failure | 2 |
| 2025-11-24 | cable fault | 0 |
| 2025-11-24 | vandalism | 2 |
| 2025-11-24 | collision | 1 |
| 2025-12-01 | lamp failure | 6 |
| 2025-12-01 | cable fault | 4 |
| 2025-12-01 | vandalism | 2 |
| 2025-12-01 | collision | 2 |
| 2025-12-08 | lamp failure | 6 |
| 2025-12-08 | cable fault | 1 |
| 2025-12-08 | vandalism | 2 |
| 2025-12-08 | collision | 2 |
| 2025-12-15 | lamp failure | 2 |
| 2025-12-15 | cable fault | 2 |
| 2025-12-15 | vandalism | 2 |
| 2025-12-15 | collision | 1 |
| 2025-12-22 | lamp failure | 2 |
| 2025-12-22 | cable fault | 4 |
| 2025-12-22 | vandalism | 0 |
| 2025-12-22 | collision | 1 |
| 2025-12-29 | lamp failure | 7 |
| 2025-12-29 | cable fault | 3 |
| 2025-12-29 | vandalism | 0 |
| 2025-12-29 | collision | 1 |
| 2026-01-05 | lamp failure | 5 |
| 2026-01-05 | cable fault | 3 |
| 2026-01-05 | vandalism | 1 |
| 2026-01-05 | collision | 0 |
| 2026-01-12 | lamp failure | 5 |
| 2026-01-12 | cable fault | 1 |
| 2026-01-12 | vandalism | 1 |
| 2026-01-12 | collision | 2 |
| 2026-01-19 | lamp failure | 6 |
| 2026-01-19 | cable fault | 6 |
| 2026-01-19 | vandalism | 3 |
| 2026-01-19 | collision | 1 |
| 2026-01-26 | lamp failure | 1 |
| 2026-01-26 | cable fault | 2 |
| 2026-01-26 | vandalism | 1 |
| 2026-01-26 | collision | 2 |
| 2026-02-02 | lamp failure | 8 |
| 2026-02-02 | cable fault | 1 |
| 2026-02-02 | vandalism | 5 |
| 2026-02-02 | collision | 3 |
| 2026-02-09 | lamp failure | 2 |
| 2026-02-09 | cable fault | 1 |
| 2026-02-09 | vandalism | 0 |
| 2026-02-09 | collision | 1 |
| 2026-02-16 | lamp failure | 4 |
| 2026-02-16 | cable fault | 1 |
| 2026-02-16 | vandalism | 4 |
| 2026-02-16 | collision | 3 |
| 2026-02-23 | lamp failure | 6 |
| 2026-02-23 | cable fault | 2 |
| 2026-02-23 | vandalism | 1 |
| 2026-02-23 | collision | 2 |
| 2026-03-02 | lamp failure | 1 |
| 2026-03-02 | cable fault | 3 |
| 2026-03-02 | vandalism | 0 |
| 2026-03-02 | collision | 0 |
| 2026-03-09 | lamp failure | 4 |
| 2026-03-09 | cable fault | 4 |
| 2026-03-09 | vandalism | 0 |
| 2026-03-09 | collision | 1 |
| 2026-03-16 | lamp failure | 3 |
| 2026-03-16 | cable fault | 2 |
| 2026-03-16 | vandalism | 1 |
| 2026-03-16 | collision | 0 |
| 2026-03-23 | lamp failure | 8 |
| 2026-03-23 | cable fault | 1 |
| 2026-03-23 | vandalism | 3 |
| 2026-03-23 | collision | 0 |
| 2026-03-30 | lamp failure | 10 |
| 2026-03-30 | cable fault | 1 |
| 2026-03-30 | vandalism | 1 |
| 2026-03-30 | collision | 1 |
| 2026-04-06 | lamp failure | 3 |
| 2026-04-06 | cable fault | 3 |
| 2026-04-06 | vandalism | 2 |
| 2026-04-06 | collision | 1 |
| 2026-04-13 | lamp failure | 6 |
| 2026-04-13 | cable fault | 2 |
| 2026-04-13 | vandalism | 1 |
| 2026-04-13 | collision | 0 |
| 2026-04-20 | lamp failure | 4 |
| 2026-04-20 | cable fault | 1 |
| 2026-04-20 | vandalism | 2 |
| 2026-04-20 | collision | 0 |
| 2026-04-27 | lamp failure | 3 |
| 2026-04-27 | cable fault | 3 |
| 2026-04-27 | vandalism | 1 |
| 2026-04-27 | collision | 0 |
| 2026-05-04 | lamp failure | 0 |
| 2026-05-04 | cable fault | 1 |
| 2026-05-04 | vandalism | 0 |
| 2026-05-04 | collision | 2 |
| 2026-05-11 | lamp failure | 5 |
| 2026-05-11 | cable fault | 1 |
| 2026-05-11 | vandalism | 2 |
| 2026-05-11 | collision | 0 |
| 2026-05-18 | lamp failure | 5 |
| 2026-05-18 | cable fault | 2 |
| 2026-05-18 | vandalism | 0 |
| 2026-05-18 | collision | 1 |
| 2026-05-25 | lamp failure | 5 |
| 2026-05-25 | cable fault | 1 |
| 2026-05-25 | vandalism | 0 |
| 2026-05-25 | collision | 0 |
| 2026-06-01 | lamp failure | 9 |
| 2026-06-01 | cable fault | 1 |
| 2026-06-01 | vandalism | 1 |
| 2026-06-01 | collision | 1 |
| 2026-06-08 | lamp failure | 4 |
| 2026-06-08 | cable fault | 0 |
| 2026-06-08 | vandalism | 3 |
| 2026-06-08 | collision | 0 |
| 2026-06-15 | lamp failure | 0 |
| 2026-06-15 | cable fault | 1 |
| 2026-06-15 | vandalism | 1 |
| 2026-06-15 | collision | 1 |
| 2026-06-22 | lamp failure | 4 |
| 2026-06-22 | cable fault | 3 |
| 2026-06-22 | vandalism | 1 |
| 2026-06-22 | collision | 2 |
| 2026-06-29 | lamp failure | 2 |
| 2026-06-29 | cable fault | 1 |
| 2026-06-29 | vandalism | 1 |
| 2026-06-29 | collision | 1 |
| 2026-07-06 | lamp failure | 4 |
| 2026-07-06 | cable fault | 1 |
| 2026-07-06 | vandalism | 0 |
| 2026-07-06 | collision | 1 |
| 2026-07-13 | lamp failure | 4 |
| 2026-07-13 | cable fault | 1 |
| 2026-07-13 | vandalism | 1 |
| 2026-07-13 | collision | 0 |
| 2026-07-20 | lamp failure | 1 |
| 2026-07-20 | cable fault | 4 |
| 2026-07-20 | vandalism | 0 |
| 2026-07-20 | collision | 2 |
| 2026-07-27 | lamp failure | 4 |
| 2026-07-27 | cable fault | 0 |
| 2026-07-27 | vandalism | 1 |
| 2026-07-27 | collision | 1 |
| 2026-08-03 | lamp failure | 5 |
| 2026-08-03 | cable fault | 1 |
| 2026-08-03 | vandalism | 0 |
| 2026-08-03 | collision | 1 |
| 2026-08-10 | lamp failure | 3 |
| 2026-08-10 | cable fault | 6 |
| 2026-08-10 | vandalism | 0 |
| 2026-08-10 | collision | 0 |
| 2026-08-17 | lamp failure | 2 |
| 2026-08-17 | cable fault | 2 |
| 2026-08-17 | vandalism | 1 |
| 2026-08-17 | collision | 1 |
| 2026-08-24 | lamp failure | 3 |
| 2026-08-24 | cable fault | 0 |
| 2026-08-24 | vandalism | 1 |
| 2026-08-24 | collision | 1 |
| 2026-08-31 | lamp failure | 4 |
| 2026-08-31 | cable fault | 5 |
| 2026-08-31 | vandalism | 0 |
| 2026-08-31 | collision | 1 |
| 2026-09-07 | lamp failure | 2 |
| 2026-09-07 | cable fault | 3 |
| 2026-09-07 | vandalism | 2 |
| 2026-09-07 | collision | 1 |
| 2026-09-14 | lamp failure | 4 |
| 2026-09-14 | cable fault | 1 |
| 2026-09-14 | vandalism | 2 |
| 2026-09-14 | collision | 1 |
| 2026-09-21 | lamp failure | 4 |
| 2026-09-21 | cable fault | 3 |
| 2026-09-21 | vandalism | 1 |
| 2026-09-21 | collision | 1 |
| 2026-09-28 | lamp failure | 1 |
| 2026-09-28 | cable fault | 0 |
| 2026-09-28 | vandalism | 0 |
| 2026-09-28 | collision | 1 |

### Figure 43. CCTV cameras over time (installed_year) [118]

| bin_start | bin_end | count |
| --- | --- | --- |
| 2,012 | 2,014 | 9 |
| 2,014 | 2,016 | 7 |
| 2,016 | 2,018 | 8 |
| 2,018 | 2,019 | 11 |
| 2,019 | 2,021 | 11 |
| 2,021 | 2,023 | 8 |
| 2,023 | 2,025 | 6 |

### Figure 44. Calls for service by beat and month over time (month) [119]

| period | value |
| --- | --- |
| 2024-10-01 | 3,466 |
| 2024-11-01 | 3,080 |
| 2024-12-01 | 2,721 |
| 2025-01-01 | 2,613 |
| 2025-02-01 | 2,623 |
| 2025-03-01 | 2,843 |
| 2025-04-01 | 3,288 |
| 2025-05-01 | 3,726 |
| 2025-06-01 | 4,161 |
| 2025-07-01 | 4,458 |
| 2025-08-01 | 4,448 |
| 2025-09-01 | 4,171 |
| 2025-10-01 | 3,824 |
| 2025-11-01 | 3,418 |
| 2025-12-01 | 2,939 |
| 2026-01-01 | 2,808 |
| 2026-02-01 | 2,892 |
| 2026-03-01 | 3,079 |
| 2026-04-01 | 3,632 |
| 2026-05-01 | 4,128 |
| 2026-06-01 | 4,507 |
| 2026-07-01 | 4,792 |
| 2026-08-01 | 4,812 |
| 2026-09-01 | 4,551 |

### Figure 45. Hot spot analysis: CCTV cameras [120]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -2 | -1.5 | cold_90 | Cold spot, 90% confidence | 1 |
| -2 | -1.5 | not_significant | Not significant | 1 |
| -1.5 | -1 | not_significant | Not significant | 1 |
| -1 | -0.5 | not_significant | Not significant | 10 |
| -0.5 | 0 | not_significant | Not significant | 4 |
| 0 | 0.5 | not_significant | Not significant | 5 |
| 0.5 | 1 | not_significant | Not significant | 2 |
| 1 | 1.5 | not_significant | Not significant | 4 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 1 |
| 1.5 | 2 | hot_95 | Hot spot, 95% confidence | 1 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 5 |
| 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 46. Hot spot analysis: Licensed premises [121]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1.5 | -1 | not_significant | Not significant | 12 |
| -1 | -0.5 | not_significant | Not significant | 49 |
| -0.5 | 0 | not_significant | Not significant | 28 |
| 0 | 0.5 | not_significant | Not significant | 2 |
| 0.5 | 1 | not_significant | Not significant | 2 |
| 1 | 1.5 | not_significant | Not significant | 2 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 2 |
| 1.5 | 2 | not_significant | Not significant | 1 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 2 |
| 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 3 |
| 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 | 2 |
| 5 | 5.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 5.5 | 6 | hot_99 | Hot spot, 99% confidence | 3 |

### Figure 47. Emerging hot spots: Incidents [122]

| bin_start | bin_end | count |
| --- | --- | --- |
| -0.659 | -0.1378 | 163 |
| -0.1378 | 0.3834 | 72 |
| 0.3834 | 0.9046 | 2 |
| 0.9046 | 1.426 | 0 |
| 1.426 | 1.947 | 19 |
| 1.947 | 2.468 | 6 |
| 2.468 | 2.989 | 0 |
| 2.989 | 3.511 | 0 |
| 3.511 | 4.032 | 6 |
| 4.032 | 4.553 | 2 |

### Figure 48. Emerging hot spots: Street light outages [123]

| bin_start | bin_end | count |
| --- | --- | --- |
| -0.707 | -0.2919 | 28 |
| -0.2919 | 0.1233 | 0 |
| 0.1233 | 0.5384 | 14 |
| 0.5384 | 0.9536 | 0 |
| 0.9536 | 1.369 | 4 |
| 1.369 | 1.784 | 0 |
| 1.784 | 2.199 | 1 |

### Figure 49. Find point clusters: Licensed premises [124]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 61 | 0.5083 | 50.8% |
| 0 | 53 | 0.4417 | 44.2% |
| 1 | 3 | 0.025 | 2.5% |
| 2 | 3 | 0.025 | 2.5% |

### Figure 50. Find point clusters: cluster hulls [125]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.3333 | 33.3% |
| 1 | 1 | 0.3333 | 33.3% |
| 2 | 1 | 0.3333 | 33.3% |

### Figure 51. Find point clusters: Incidents [126]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 1,609 | 0.9577 | 95.8% |
| 0 | 30 | 0.01786 | 1.8% |
| 1 | 21 | 0.0125 | 1.2% |
| 2 | 20 | 0.01191 | 1.2% |

### Figure 52. Find point clusters: cluster hulls [127]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.3333 | 33.3% |
| 1 | 1 | 0.3333 | 33.3% |
| 2 | 1 | 0.3333 | 33.3% |

### Figure 53. Find point clusters: Street light outages [128]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 410 | 0.9762 | 97.6% |
| 0 | 10 | 0.02381 | 2.4% |

### Figure 54. Find point clusters: cluster hulls [129]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 1 | 100.0% |

### Figure 55. Summarize within: Patrol beats [130]

| bin_start | bin_end | count |
| --- | --- | --- |
| 1 | 3.4 | 11 |
| 3.4 | 5.8 | 3 |
| 5.8 | 8.2 | 1 |
| 8.2 | 10.6 | 0 |
| 10.6 | 13 | 1 |

### Figure 56. Summarize within: Patrol beats [131]

| bin_start | bin_end | count |
| --- | --- | --- |
| 1 | 7 | 10 |
| 7 | 13 | 4 |
| 13 | 19 | 0 |
| 19 | 25 | 1 |
| 25 | 31 | 1 |

### Figure 57. Summarize within: Patrol beats [132]

| bin_start | bin_end | count |
| --- | --- | --- |
| 18 | 80.8 | 10 |
| 80.8 | 143.6 | 2 |
| 143.6 | 206.4 | 2 |
| 206.4 | 269.2 | 0 |
| 269.2 | 332 | 2 |

### Figure 58. Calculate density: Licensed premises [133]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 1.556 | 15 |
| 1.556 | 3.111 | 22 |
| 3.111 | 4.667 | 13 |
| 4.667 | 6.222 | 26 |
| 6.222 | 7.778 | 14 |
| 7.778 | 9.333 | 26 |
| 9.333 | 10.89 | 10 |
| 10.89 | 12.44 | 23 |
| 12.44 | 14 | 15 |

### Figure 59. Calculate density: Incidents [134]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 4.846 | 172 |
| 4.846 | 9.692 | 242 |
| 9.692 | 14.54 | 246 |
| 14.54 | 19.38 | 260 |
| 19.38 | 24.23 | 260 |
| 24.23 | 29.08 | 240 |
| 29.08 | 33.92 | 204 |
| 33.92 | 38.77 | 261 |
| 38.77 | 43.62 | 264 |
| 43.62 | 48.46 | 266 |
| 48.46 | 53.31 | 262 |
| 53.31 | 58.15 | 153 |
| 58.15 | 63 | 44 |

### Figure 60. Calculate density: Street light outages [135]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 2.909 | 59 |
| 2.909 | 5.818 | 84 |
| 5.818 | 8.727 | 82 |
| 8.727 | 11.64 | 85 |
| 11.64 | 14.55 | 79 |
| 14.55 | 17.45 | 85 |
| 17.45 | 20.36 | 86 |
| 20.36 | 23.27 | 87 |
| 23.27 | 26.18 | 87 |
| 26.18 | 29.09 | 80 |
| 29.09 | 32 | 34 |

### Figure 61. Hot spot analysis: Incidents [136]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1.5 | -1 | not_significant | Not significant | 685 |
| -1 | -0.5 | not_significant | Not significant | 757 |
| -0.5 | 0 | not_significant | Not significant | 714 |
| 0 | 0.5 | not_significant | Not significant | 318 |
| 0.5 | 1 | not_significant | Not significant | 104 |
| 1 | 1.5 | not_significant | Not significant | 125 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 13 |
| 1.5 | 2 | not_significant | Not significant | 27 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 35 |
| 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 26 |
| 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 8 |
| 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 8 |
| 4 | 4.5 | hot_99 | Hot spot, 99% confidence | 5 |
| 4.5 | 5 | hot_99 | Hot spot, 99% confidence | 7 |
| 5 | 5.5 | hot_99 | Hot spot, 99% confidence | 2 |
| 5.5 | 6 | hot_99 | Hot spot, 99% confidence | 4 |
| 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 | 5 |
| 7.5 | 8 | hot_99 | Hot spot, 99% confidence | 3 |
| 8 | 8.5 | hot_99 | Hot spot, 99% confidence | 2 |
| 8.5 | 9 | hot_99 | Hot spot, 99% confidence | 4 |
| 9 | 9.5 | hot_99 | Hot spot, 99% confidence | 2 |
| 9.5 | 10 | hot_99 | Hot spot, 99% confidence | 2 |
| 10.5 | 11 | hot_99 | Hot spot, 99% confidence | 1 |
| 11 | 11.5 | hot_99 | Hot spot, 99% confidence | 3 |
| 11.5 | 12 | hot_99 | Hot spot, 99% confidence | 3 |
| 12 | 12.5 | hot_99 | Hot spot, 99% confidence | 4 |
| 12.5 | 13 | hot_99 | Hot spot, 99% confidence | 4 |
| 13 | 13.5 | hot_99 | Hot spot, 99% confidence | 3 |
| 13.5 | 14 | hot_99 | Hot spot, 99% confidence | 2 |
| 14.5 | 15 | hot_99 | Hot spot, 99% confidence | 3 |
| 15 | 15.5 | hot_99 | Hot spot, 99% confidence | 2 |
| 15.5 | 16 | hot_99 | Hot spot, 99% confidence | 1 |
| 16 | 16.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 16.5 | 17 | hot_99 | Hot spot, 99% confidence | 2 |
| 17 | 17.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 18 | 18.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 19 | 19.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 20.5 | 21 | hot_99 | Hot spot, 99% confidence | 5 |
| 21 | 21.5 | hot_99 | Hot spot, 99% confidence | 2 |
| 21.5 | 22 | hot_99 | Hot spot, 99% confidence | 2 |
| 22 | 22.5 | hot_99 | Hot spot, 99% confidence | 2 |
| 22.5 | 23 | hot_99 | Hot spot, 99% confidence | 1 |
| 23.5 | 24 | hot_99 | Hot spot, 99% confidence | 1 |
| 24 | 24.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 24.5 | 25 | hot_99 | Hot spot, 99% confidence | 1 |
| 26 | 26.5 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 62. Repeat and near repeat classification: Incidents [137]

| period | series | value |
| --- | --- | --- |
| 2025-09-29 | no arrest | 13 |
| 2025-09-29 | arrest | 3 |
| 2025-10-06 | no arrest | 20 |
| 2025-10-06 | arrest | 5 |
| 2025-10-13 | no arrest | 24 |
| 2025-10-13 | arrest | 5 |
| 2025-10-20 | no arrest | 19 |
| 2025-10-20 | arrest | 0 |
| 2025-10-27 | no arrest | 25 |
| 2025-10-27 | arrest | 6 |
| 2025-11-03 | no arrest | 31 |
| 2025-11-03 | arrest | 6 |
| 2025-11-10 | no arrest | 28 |
| 2025-11-10 | arrest | 3 |
| 2025-11-17 | no arrest | 24 |
| 2025-11-17 | arrest | 7 |
| 2025-11-24 | no arrest | 16 |
| 2025-11-24 | arrest | 4 |
| 2025-12-01 | no arrest | 21 |
| 2025-12-01 | arrest | 1 |
| 2025-12-08 | no arrest | 29 |
| 2025-12-08 | arrest | 3 |
| 2025-12-15 | no arrest | 26 |
| 2025-12-15 | arrest | 2 |
| 2025-12-22 | no arrest | 23 |
| 2025-12-22 | arrest | 1 |
| 2025-12-29 | no arrest | 27 |
| 2025-12-29 | arrest | 5 |
| 2026-01-05 | no arrest | 19 |
| 2026-01-05 | arrest | 3 |
| 2026-01-12 | no arrest | 17 |
| 2026-01-12 | arrest | 7 |
| 2026-01-19 | no arrest | 22 |
| 2026-01-19 | arrest | 4 |
| 2026-01-26 | no arrest | 22 |
| 2026-01-26 | arrest | 2 |
| 2026-02-02 | no arrest | 17 |
| 2026-02-02 | arrest | 1 |
| 2026-02-09 | no arrest | 16 |
| 2026-02-09 | arrest | 2 |
| 2026-02-16 | no arrest | 25 |
| 2026-02-16 | arrest | 1 |
| 2026-02-23 | no arrest | 29 |
| 2026-02-23 | arrest | 0 |
| 2026-03-02 | no arrest | 13 |
| 2026-03-02 | arrest | 3 |
| 2026-03-09 | no arrest | 22 |
| 2026-03-09 | arrest | 1 |
| 2026-03-16 | no arrest | 31 |
| 2026-03-16 | arrest | 3 |
| 2026-03-23 | no arrest | 32 |
| 2026-03-23 | arrest | 7 |
| 2026-03-30 | no arrest | 20 |
| 2026-03-30 | arrest | 5 |
| 2026-04-06 | no arrest | 37 |
| 2026-04-06 | arrest | 4 |
| 2026-04-13 | no arrest | 19 |
| 2026-04-13 | arrest | 3 |
| 2026-04-20 | no arrest | 34 |
| 2026-04-20 | arrest | 2 |
| 2026-04-27 | no arrest | 33 |
| 2026-04-27 | arrest | 3 |
| 2026-05-04 | no arrest | 31 |
| 2026-05-04 | arrest | 5 |
| 2026-05-11 | no arrest | 25 |
| 2026-05-11 | arrest | 7 |
| 2026-05-18 | no arrest | 39 |
| 2026-05-18 | arrest | 1 |
| 2026-05-25 | no arrest | 34 |
| 2026-05-25 | arrest | 3 |
| 2026-06-01 | no arrest | 21 |
| 2026-06-01 | arrest | 1 |
| 2026-06-08 | no arrest | 33 |
| 2026-06-08 | arrest | 4 |
| 2026-06-15 | no arrest | 34 |
| 2026-06-15 | arrest | 8 |
| 2026-06-22 | no arrest | 28 |
| 2026-06-22 | arrest | 5 |
| 2026-06-29 | no arrest | 58 |
| 2026-06-29 | arrest | 14 |
| 2026-07-06 | no arrest | 29 |
| 2026-07-06 | arrest | 5 |
| 2026-07-13 | no arrest | 36 |
| 2026-07-13 | arrest | 0 |
| 2026-07-20 | no arrest | 27 |
| 2026-07-20 | arrest | 9 |
| 2026-07-27 | no arrest | 36 |
| 2026-07-27 | arrest | 4 |
| 2026-08-03 | no arrest | 27 |
| 2026-08-03 | arrest | 4 |
| 2026-08-10 | no arrest | 21 |
| 2026-08-10 | arrest | 3 |
| 2026-08-17 | no arrest | 43 |
| 2026-08-17 | arrest | 3 |
| 2026-08-24 | no arrest | 48 |
| 2026-08-24 | arrest | 7 |
| 2026-08-31 | no arrest | 38 |
| 2026-08-31 | arrest | 3 |
| 2026-09-07 | no arrest | 35 |
| 2026-09-07 | arrest | 6 |
| 2026-09-14 | no arrest | 43 |
| 2026-09-14 | arrest | 6 |
| 2026-09-21 | no arrest | 38 |
| 2026-09-21 | arrest | 9 |
| 2026-09-28 | no arrest | 11 |
| 2026-09-28 | arrest | 2 |

### Figure 63. Repeat and near repeat classification: Incidents over time [138]

| period | series | value |
| --- | --- | --- |
| 2025-09-29 | no arrest | 13 |
| 2025-09-29 | arrest | 3 |
| 2025-10-06 | no arrest | 20 |
| 2025-10-06 | arrest | 5 |
| 2025-10-13 | no arrest | 24 |
| 2025-10-13 | arrest | 5 |
| 2025-10-20 | no arrest | 19 |
| 2025-10-20 | arrest | 0 |
| 2025-10-27 | no arrest | 25 |
| 2025-10-27 | arrest | 6 |
| 2025-11-03 | no arrest | 31 |
| 2025-11-03 | arrest | 6 |
| 2025-11-10 | no arrest | 28 |
| 2025-11-10 | arrest | 3 |
| 2025-11-17 | no arrest | 24 |
| 2025-11-17 | arrest | 7 |
| 2025-11-24 | no arrest | 16 |
| 2025-11-24 | arrest | 4 |
| 2025-12-01 | no arrest | 21 |
| 2025-12-01 | arrest | 1 |
| 2025-12-08 | no arrest | 29 |
| 2025-12-08 | arrest | 3 |
| 2025-12-15 | no arrest | 26 |
| 2025-12-15 | arrest | 2 |
| 2025-12-22 | no arrest | 23 |
| 2025-12-22 | arrest | 1 |
| 2025-12-29 | no arrest | 27 |
| 2025-12-29 | arrest | 5 |
| 2026-01-05 | no arrest | 19 |
| 2026-01-05 | arrest | 3 |
| 2026-01-12 | no arrest | 17 |
| 2026-01-12 | arrest | 7 |
| 2026-01-19 | no arrest | 22 |
| 2026-01-19 | arrest | 4 |
| 2026-01-26 | no arrest | 22 |
| 2026-01-26 | arrest | 2 |
| 2026-02-02 | no arrest | 17 |
| 2026-02-02 | arrest | 1 |
| 2026-02-09 | no arrest | 16 |
| 2026-02-09 | arrest | 2 |
| 2026-02-16 | no arrest | 25 |
| 2026-02-16 | arrest | 1 |
| 2026-02-23 | no arrest | 29 |
| 2026-02-23 | arrest | 0 |
| 2026-03-02 | no arrest | 13 |
| 2026-03-02 | arrest | 3 |
| 2026-03-09 | no arrest | 22 |
| 2026-03-09 | arrest | 1 |
| 2026-03-16 | no arrest | 31 |
| 2026-03-16 | arrest | 3 |
| 2026-03-23 | no arrest | 32 |
| 2026-03-23 | arrest | 7 |
| 2026-03-30 | no arrest | 20 |
| 2026-03-30 | arrest | 5 |
| 2026-04-06 | no arrest | 37 |
| 2026-04-06 | arrest | 4 |
| 2026-04-13 | no arrest | 19 |
| 2026-04-13 | arrest | 3 |
| 2026-04-20 | no arrest | 34 |
| 2026-04-20 | arrest | 2 |
| 2026-04-27 | no arrest | 33 |
| 2026-04-27 | arrest | 3 |
| 2026-05-04 | no arrest | 31 |
| 2026-05-04 | arrest | 5 |
| 2026-05-11 | no arrest | 25 |
| 2026-05-11 | arrest | 7 |
| 2026-05-18 | no arrest | 39 |
| 2026-05-18 | arrest | 1 |
| 2026-05-25 | no arrest | 34 |
| 2026-05-25 | arrest | 3 |
| 2026-06-01 | no arrest | 21 |
| 2026-06-01 | arrest | 1 |
| 2026-06-08 | no arrest | 33 |
| 2026-06-08 | arrest | 4 |
| 2026-06-15 | no arrest | 34 |
| 2026-06-15 | arrest | 8 |
| 2026-06-22 | no arrest | 28 |
| 2026-06-22 | arrest | 5 |
| 2026-06-29 | no arrest | 58 |
| 2026-06-29 | arrest | 14 |
| 2026-07-06 | no arrest | 29 |
| 2026-07-06 | arrest | 5 |
| 2026-07-13 | no arrest | 36 |
| 2026-07-13 | arrest | 0 |
| 2026-07-20 | no arrest | 27 |
| 2026-07-20 | arrest | 9 |
| 2026-07-27 | no arrest | 36 |
| 2026-07-27 | arrest | 4 |
| 2026-08-03 | no arrest | 27 |
| 2026-08-03 | arrest | 4 |
| 2026-08-10 | no arrest | 21 |
| 2026-08-10 | arrest | 3 |
| 2026-08-17 | no arrest | 43 |
| 2026-08-17 | arrest | 3 |
| 2026-08-24 | no arrest | 48 |
| 2026-08-24 | arrest | 7 |
| 2026-08-31 | no arrest | 38 |
| 2026-08-31 | arrest | 3 |
| 2026-09-07 | no arrest | 35 |
| 2026-09-07 | arrest | 6 |
| 2026-09-14 | no arrest | 43 |
| 2026-09-14 | arrest | 6 |
| 2026-09-21 | no arrest | 38 |
| 2026-09-21 | arrest | 9 |
| 2026-09-28 | no arrest | 11 |
| 2026-09-28 | arrest | 2 |

### Figure 64. Find point clusters: Street light outages over time [139]

| period | series | value |
| --- | --- | --- |
| 2025-10-01 | lamp failure | 15 |
| 2025-10-01 | cable fault | 3 |
| 2025-10-01 | vandalism | 6 |
| 2025-10-01 | collision | 1 |
| 2025-11-01 | lamp failure | 16 |
| 2025-11-01 | cable fault | 8 |
| 2025-11-01 | vandalism | 5 |
| 2025-11-01 | collision | 1 |
| 2025-12-01 | lamp failure | 17 |
| 2025-12-01 | cable fault | 11 |
| 2025-12-01 | vandalism | 8 |
| 2025-12-01 | collision | 7 |
| 2026-01-01 | lamp failure | 22 |
| 2026-01-01 | cable fault | 13 |
| 2026-01-01 | vandalism | 6 |
| 2026-01-01 | collision | 4 |
| 2026-02-01 | lamp failure | 19 |
| 2026-02-01 | cable fault | 6 |
| 2026-02-01 | vandalism | 9 |
| 2026-02-01 | collision | 9 |
| 2026-03-01 | lamp failure | 15 |
| 2026-03-01 | cable fault | 11 |
| 2026-03-01 | vandalism | 2 |
| 2026-03-01 | collision | 3 |
| 2026-04-01 | lamp failure | 23 |
| 2026-04-01 | cable fault | 6 |
| 2026-04-01 | vandalism | 8 |
| 2026-04-01 | collision | 2 |
| 2026-05-01 | lamp failure | 17 |
| 2026-05-01 | cable fault | 8 |
| 2026-05-01 | vandalism | 3 |
| 2026-05-01 | collision | 3 |
| 2026-06-01 | lamp failure | 20 |
| 2026-06-01 | cable fault | 4 |
| 2026-06-01 | vandalism | 5 |
| 2026-06-01 | collision | 3 |
| 2026-07-01 | lamp failure | 13 |
| 2026-07-01 | cable fault | 8 |
| 2026-07-01 | vandalism | 3 |
| 2026-07-01 | collision | 4 |
| 2026-08-01 | lamp failure | 13 |
| 2026-08-01 | cable fault | 8 |
| 2026-08-01 | vandalism | 3 |
| 2026-08-01 | collision | 4 |
| 2026-09-01 | lamp failure | 15 |
| 2026-09-01 | cable fault | 11 |
| 2026-09-01 | vandalism | 5 |
| 2026-09-01 | collision | 4 |
| 2026-10-01 | lamp failure | 4 |
| 2026-10-01 | cable fault | 3 |
| 2026-10-01 | vandalism | 1 |
| 2026-10-01 | collision | 2 |

### Figure 65. Find point clusters: Incidents over time [140]

| period | series | value |
| --- | --- | --- |
| 2025-09-29 | no arrest | 13 |
| 2025-09-29 | arrest | 3 |
| 2025-10-06 | no arrest | 20 |
| 2025-10-06 | arrest | 5 |
| 2025-10-13 | no arrest | 24 |
| 2025-10-13 | arrest | 5 |
| 2025-10-20 | no arrest | 19 |
| 2025-10-20 | arrest | 0 |
| 2025-10-27 | no arrest | 25 |
| 2025-10-27 | arrest | 6 |
| 2025-11-03 | no arrest | 31 |
| 2025-11-03 | arrest | 6 |
| 2025-11-10 | no arrest | 28 |
| 2025-11-10 | arrest | 3 |
| 2025-11-17 | no arrest | 24 |
| 2025-11-17 | arrest | 7 |
| 2025-11-24 | no arrest | 16 |
| 2025-11-24 | arrest | 4 |
| 2025-12-01 | no arrest | 21 |
| 2025-12-01 | arrest | 1 |
| 2025-12-08 | no arrest | 29 |
| 2025-12-08 | arrest | 3 |
| 2025-12-15 | no arrest | 26 |
| 2025-12-15 | arrest | 2 |
| 2025-12-22 | no arrest | 23 |
| 2025-12-22 | arrest | 1 |
| 2025-12-29 | no arrest | 27 |
| 2025-12-29 | arrest | 5 |
| 2026-01-05 | no arrest | 19 |
| 2026-01-05 | arrest | 3 |
| 2026-01-12 | no arrest | 17 |
| 2026-01-12 | arrest | 7 |
| 2026-01-19 | no arrest | 22 |
| 2026-01-19 | arrest | 4 |
| 2026-01-26 | no arrest | 22 |
| 2026-01-26 | arrest | 2 |
| 2026-02-02 | no arrest | 17 |
| 2026-02-02 | arrest | 1 |
| 2026-02-09 | no arrest | 16 |
| 2026-02-09 | arrest | 2 |
| 2026-02-16 | no arrest | 25 |
| 2026-02-16 | arrest | 1 |
| 2026-02-23 | no arrest | 29 |
| 2026-02-23 | arrest | 0 |
| 2026-03-02 | no arrest | 13 |
| 2026-03-02 | arrest | 3 |
| 2026-03-09 | no arrest | 22 |
| 2026-03-09 | arrest | 1 |
| 2026-03-16 | no arrest | 31 |
| 2026-03-16 | arrest | 3 |
| 2026-03-23 | no arrest | 32 |
| 2026-03-23 | arrest | 7 |
| 2026-03-30 | no arrest | 20 |
| 2026-03-30 | arrest | 5 |
| 2026-04-06 | no arrest | 37 |
| 2026-04-06 | arrest | 4 |
| 2026-04-13 | no arrest | 19 |
| 2026-04-13 | arrest | 3 |
| 2026-04-20 | no arrest | 34 |
| 2026-04-20 | arrest | 2 |
| 2026-04-27 | no arrest | 33 |
| 2026-04-27 | arrest | 3 |
| 2026-05-04 | no arrest | 31 |
| 2026-05-04 | arrest | 5 |
| 2026-05-11 | no arrest | 25 |
| 2026-05-11 | arrest | 7 |
| 2026-05-18 | no arrest | 39 |
| 2026-05-18 | arrest | 1 |
| 2026-05-25 | no arrest | 34 |
| 2026-05-25 | arrest | 3 |
| 2026-06-01 | no arrest | 21 |
| 2026-06-01 | arrest | 1 |
| 2026-06-08 | no arrest | 33 |
| 2026-06-08 | arrest | 4 |
| 2026-06-15 | no arrest | 34 |
| 2026-06-15 | arrest | 8 |
| 2026-06-22 | no arrest | 28 |
| 2026-06-22 | arrest | 5 |
| 2026-06-29 | no arrest | 58 |
| 2026-06-29 | arrest | 14 |
| 2026-07-06 | no arrest | 29 |
| 2026-07-06 | arrest | 5 |
| 2026-07-13 | no arrest | 36 |
| 2026-07-13 | arrest | 0 |
| 2026-07-20 | no arrest | 27 |
| 2026-07-20 | arrest | 9 |
| 2026-07-27 | no arrest | 36 |
| 2026-07-27 | arrest | 4 |
| 2026-08-03 | no arrest | 27 |
| 2026-08-03 | arrest | 4 |
| 2026-08-10 | no arrest | 21 |
| 2026-08-10 | arrest | 3 |
| 2026-08-17 | no arrest | 43 |
| 2026-08-17 | arrest | 3 |
| 2026-08-24 | no arrest | 48 |
| 2026-08-24 | arrest | 7 |
| 2026-08-31 | no arrest | 38 |
| 2026-08-31 | arrest | 3 |
| 2026-09-07 | no arrest | 35 |
| 2026-09-07 | arrest | 6 |
| 2026-09-14 | no arrest | 43 |
| 2026-09-14 | arrest | 6 |
| 2026-09-21 | no arrest | 38 |
| 2026-09-21 | arrest | 9 |
| 2026-09-28 | no arrest | 11 |
| 2026-09-28 | arrest | 2 |


*GeoBit Large Spatial Model. Findings are labeled Evidenced (computed by a tool), Inferred (a reading or reasoning) or Unverified (no evidence yet).*