# Sample: Logistics, Madrid: report

Project: Sample: Logistics, Madrid  
Goal: Describe the project Sample: Logistics, Madrid: run the analyses that tell the most about its data, draw every result, and write a cited report.  
Run: a282190f-59a4-4a74-8816-0f88c1c06122 (running, 11 agent run(s))  
Generated: 2026-10-18T05:22:18Z, version 1  
Model cost: USD 0.0000 over 0 model call(s)  

## Data used

| Data | Kind | Rows | Time span | Summary | Source |
| --- | --- | --- | --- | --- | --- |
| Delivery zones | vector, Polygon | 10 |  | 10 polygon (area) features; 'parcels_per_day' 1,219 to 4,832 (mean 2062); 'households' 26,833 to 106,313 (mean 4.537e+04); named by 'name' (e.g. Barrio Norte, Las Flores, El Molino); total area 56.04 km2; extent -3.7513,40.3850 to -3.6564,40.4486. Areas (Delivery zones) with parcels_per_day to compare and map. | [110] |
| Depots | vector, Point | 3 |  | 3 point features; 'vans' 23 to 32 (mean 28.33); 'cargo_bikes' 4 to 14 (mean 8.333); 'dock_doors' 8 to 15 (mean 12.67); named by 'name' (e.g. Depot Norte, Depot Sur, Depot Este); extent -3.7276,40.3903 to -3.6643,40.4387. Point locations of Depots. | [111] |
| Routes | vector, LineString | 36 |  | 36 line features; 'vehicle' has 3 values (top: van 23, cargo bike 10, electric van 3); 'distance_km' 9.14 to 35.39 (mean 19.79); 'planned_min' 142 to 378 (mean 237.6); total length 712.7 km; extent -3.7501,40.3857 to -3.6573,40.4480. Lines (Routes) such as routes or networks. | [112] |
| Delivery stops | vector, Point | 2,000 | 2026-07-27 to 2026-09-19 | 2,000 point features; dated by 'delivered_at' from 2026-07-27 to 2026-09-19; 'status' has 2 values (top: delivered 1877, failed 123); 'delay_min' -10 to 59 (mean 10.29); 'parcels' 1 to 3 (mean 1.583); extent -3.7499,40.3856 to -3.6567,40.4475. Dated point events by status: where and when Delivery stops happen. | [2] |
| Parcel lockers | vector, Point | 45 |  | 45 point features; 'host' has 3 values (top: supermarket 16, metro 15, gas station 14); 'compartments' 40 to 90 (mean 60); 'utilization_pct' 35.3 to 97.3 (mean 60.95); extent -3.7492,40.3915 to -3.6578,40.4475. Point locations of Parcel lockers by host. | [113] |
| Loading bays | vector, Point | 60 |  | 60 point features; 'enforced' has 2 values (top: True 43, False 17); 'max_minutes' 15 to 30 (mean 21.83); extent -3.7497,40.3880 to -3.6596,40.4477. Point locations of Loading bays by enforced. | [114] |
| Roadworks | vector, LineString | 12 | 2026-06-27 to 2026-09-03 | 12 line features; dated by 'start_date' from 2026-06-27 to 2026-09-03; 'type' has 3 values (top: water main 6, resurfacing 3, metro 3); 'lanes_closed' 1 to 2 (mean 1.333); total length 4.26 km; extent -3.7212,40.3883 to -3.6585,40.4349. Lines (Roadworks) such as routes or networks. | [3] |
| Route performance by day | table | 1,728 | 2026-07-27 to 2026-09-19 | Table of 1,728 rows and 6 columns without geometry; dated by 'date' from 2026-07-27 to 2026-09-19; 'on_time_pct' 59.4 to 99.7 (mean 85.51); 'avg_delay_min' 0 to 24.5 (mean 8.212); 'failed_stops' 0 to 7 (mean 1.513). A table of Route performance by day without locations. | [4] |
| Vehicle fleet | table | 60 |  | Table of 60 rows and 6 columns without geometry; 'type' has 3 values (top: van 32, electric van 21, cargo bike 7); 'capacity_parcels' 35 to 160 (mean 104.4); 'range_km' 60 to 320 (mean 206.7); 'model_year' 2,017 to 2,026 (mean 2022). A table of Vehicle fleet without locations. | [115] |
| Operations review | document |  |  | 1-page md document 'Operations review: August and September 2026'; language en; mentions 5 place(s): Depot Este (2), El Molino (1), 40.41805, -3.70570 (1), Depot Sur (1), La Estacion (1). A document about Depot Este, El Molino, 40.41805, -3.70570. | [116] |
| Sample: Logistics, Madrid | record |  |  |  | [117] |
| Emerging hot spots: Delivery stops | layer, Polygon | 88 |  |  | [118] |
| Repeat and near repeat classification: Delivery stops | layer, Point | 2,000 |  |  | [119] |
| Summarize within: Delivery zones | layer, Polygon | 10 |  |  | [120] |
| Summarize within: Delivery zones | layer, Polygon | 10 |  |  | [121] |
| Summarize within: Delivery zones | layer, Polygon | 10 |  |  | [122] |
| Hot spot analysis: Delivery stops | layer, Polygon | 254 |  |  | [123] |
| Hot spot analysis: Parcel lockers | layer, Polygon | 19 |  |  | [124] |
| Hot spot analysis: Loading bays | layer, Polygon | 19 |  |  | [125] |
| Calculate density: Delivery stops | layer, Polygon | 1,654 |  |  | [126] |
| Calculate density: Parcel lockers | layer, Polygon | 99 |  |  | [127] |
| Calculate density: Loading bays | layer, Polygon | 101 |  |  | [128] |
| Find point clusters: Delivery stops | layer, Point | 2,000 |  |  | [129] |
| Find point clusters: cluster hulls | layer, Polygon | 2 |  |  | [130] |
| Find point clusters: Parcel lockers | layer, Point | 45 |  |  | [131] |
| Find point clusters: cluster hulls | layer, Polygon | 1 |  |  | [132] |
| Find point clusters: Loading bays | layer, Point | 60 |  |  | [133] |
| Find point clusters: cluster hulls | layer, Polygon | 6 |  |  | [134] |
| Find outliers: Delivery zones | layer, Polygon | 10 |  |  | [135] |
| Areas and lengths: Routes | layer, LineString | 36 |  |  | [136] |
| Areas and lengths: Roadworks | layer, LineString | 12 |  |  | [137] |
| Line density: Routes | layer, Polygon | 480 |  |  | [138] |

## Timeline

![Timeline: events per week by dataset](charts/28-timeline-events-per-week-by-dataset.png)

*Figure 1. Timeline: events per week by dataset [1]. Per week, 2026-06-22 to 2026-09-14. Source: Delivery stops, Roadworks, Route performance by day (3,740 features). The data is in the appendix.*

The timeline counts 3,740 dated events in Delivery stops, Roadworks, Route performance by day per week, from week of 2026-06-22 to week of 2026-09-14 [1][2][3][4]. By dataset: Delivery stops 2,000; Route performance by day 1,728; Roadworks 12 [1]. The trend is +51.2 events per week; the second half of the weeks has 2,801 against 472 in the first (+493.4%) [1]. 1 of the 13 weeks had no events [1]. Change point detection found no shift in the counts [5].

**Busiest weeks**

- week of 2026-08-24: 491 events [1]
- week of 2026-09-07: 471 events [1]
- week of 2026-08-03: 467 events [1]

**Quietest weeks with events**

- week of 2026-06-22: 1 event [1]
- week of 2026-07-13: 1 event [1]
- week of 2026-07-06: 2 events [1]

## 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 [6]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [6].

**Why it was run on this data.** Delivery stops carry dates in 'delivered_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week [7].

**What it found.** Space time hot spots on Delivery stops: Emerging hot spots: 1 layer saved: Emerging hot spots: Delivery stops (88 features) [8][9]. It reported result, count 88; result, elapsed ms 15; result, resolution 8; result, features used 2,000; result, time neighbors 0; billing, cost 0.2 [8].

| Result | Value |
| --- | --- |
| result, count | 88 |
| result, elapsed ms | 15 |
| result, resolution | 8 |
| result, features used | 2,000 |
| result, time neighbors | 0 |
| billing, cost | 0.2 |
| billing, list price | 0.2 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Delivery stops: Emerging hot spots: 1 layer saved: Emerging hot spots: Delivery stops (88 features) [8][9].
- **Evidenced:** It reported result, count 88; result, elapsed ms 15; result, resolution 8; result, features used 2,000; result, time neighbors 0; billing, cost 0.2 [8].

*Tool gp_space_time_hot_spots; parameters {"project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Delivery stops (ba8073d9)"}, "interval": "week", "bin_shape": "h3", "resolution": 8, "time_field": "delivered_at"}*

## 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 [10]. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator [10].

**Why it was run on this data.** Near repeat analysis tests whether Delivery stops close in space also follow each other within 5 days [11].

**What it found.** Near repeat on Delivery stops: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Delivery stops (2000 features) [9][12]. It reported result, days 5; result, count 2,000; result, chains 276; result, distance m 200; result, elapsed ms 48; result, pairs linked 1,743 [12].

| Result | Value |
| --- | --- |
| result, days | 5 |
| result, count | 2,000 |
| result, chains | 276 |
| result, distance m | 200 |
| result, elapsed ms | 48 |
| result, pairs linked | 1,743 |
| billing, cost | 0.11 |
| billing, list price | 0.11 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Near repeat on Delivery stops: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Delivery stops (2000 features) [9][12].
- **Evidenced:** It reported result, days 5; result, count 2,000; result, chains 276; result, distance m 200; result, elapsed ms 48; result, pairs linked 1,743 [12].

*Tool gp_near_repeat; parameters {"days": 5, "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Delivery stops (ba8073d9)"}, "distance_m": 200.0, "time_field": "delivered_at"}*

## Change point detection

**What this analysis is.** Change point detection: When did the level, count, trend or variability change [13]? For the whole study area or for every bin [13]. In ArcGIS it is Change Point Detection [13].

**Why it was run on this data.** Change point detection finds when the weekly count of Delivery stops shifted [14].

**What it found.** Change point detection on Delivery stops: Change point detection: done; no map output [5][9]. It reported result, n changes 0; result, elapsed ms 4; result, events used 2,000; billing, cost 0.11; billing, list price 0.11; billing, upstream cost 0 [5].

| Result | Value |
| --- | --- |
| result, n changes | 0 |
| result, elapsed ms | 4 |
| result, events used | 2,000 |
| billing, cost | 0.11 |
| billing, list price | 0.11 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Change point detection on Delivery stops: Change point detection: done; no map output [5][9].
- **Evidenced:** It reported result, n changes 0; result, elapsed ms 4; result, events used 2,000; billing, cost 0.11; billing, list price 0.11; billing, upstream cost 0 [5].

*Tool gp_change_point_detection; parameters {"method": "count", "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Delivery stops (ba8073d9)"}, "interval": "week", "time_field": "delivered_at"}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [15]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [15].

**Why it was run on this data.** Count Depots inside each feature of Delivery zones; the counts map as a choropleth (natural breaks, sequential colors) [16].

**What it found.** Summarize within on Depots, Delivery zones: Summarize within: 1 layer saved: Summarize within: Delivery zones (10 features) [17][18][19]. It reported result, count 10; result, elapsed ms 0; billing, cost 0.01028; billing, list price 0.01028; billing, upstream cost 0 [17].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 0 |
| billing, cost | 0.01028 |
| billing, list price | 0.01028 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Depots, Delivery zones: Summarize within: 1 layer saved: Summarize within: Delivery zones (10 features) [17][18][19].
- **Evidenced:** It reported result, count 10; result, elapsed ms 0; billing, cost 0.01028; billing, list price 0.01028; billing, upstream cost 0 [17].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "vans"}], "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Depots (84d3272c)"}, "polygons": {"layer": "Delivery zones (bfcf8b53)"}}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [15]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [15].

**Why it was run on this data.** Count Depots inside each feature of Delivery zones; the counts map as a choropleth (natural breaks, sequential colors) [16].

**What it found.** Summarize within on Delivery stops, Delivery zones: Summarize within: 1 layer saved: Summarize within: Delivery zones (10 features) [9][19][20]. It reported result, count 10; result, elapsed ms 4; billing, cost 0.05022; billing, list price 0.05022; billing, upstream cost 0 [20].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 4 |
| billing, cost | 0.05022 |
| billing, list price | 0.05022 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Delivery stops, Delivery zones: Summarize within: 1 layer saved: Summarize within: Delivery zones (10 features) [9][19][20].
- **Evidenced:** It reported result, count 10; result, elapsed ms 4; billing, cost 0.05022; billing, list price 0.05022; billing, upstream cost 0 [20].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "delay_min"}], "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Delivery stops (ba8073d9)"}, "group_by": "status", "polygons": {"layer": "Delivery zones (bfcf8b53)"}}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [15]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [15].

**Why it was run on this data.** Count Depots inside each feature of Delivery zones; the counts map as a choropleth (natural breaks, sequential colors) [16].

**What it found.** Summarize within on Parcel lockers, Delivery zones: Summarize within: 1 layer saved: Summarize within: Delivery zones (10 features) [19][21][22]. It reported result, count 10; result, elapsed ms 0; billing, cost 0.01112; billing, list price 0.01112; billing, upstream cost 0 [21].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 0 |
| billing, cost | 0.01112 |
| billing, list price | 0.01112 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Parcel lockers, Delivery zones: Summarize within: 1 layer saved: Summarize within: Delivery zones (10 features) [19][21][22].
- **Evidenced:** It reported result, count 10; result, elapsed ms 0; billing, cost 0.01112; billing, list price 0.01112; billing, upstream cost 0 [21].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "compartments"}], "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Parcel lockers (4e9e11a9)"}, "group_by": "host", "polygons": {"layer": "Delivery zones (bfcf8b53)"}}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [23]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [23].

**Why it was run on this data.** 2000 points: Getis-Ord Gi* tests whether the concentrations in Delivery stops are statistically significant [24].

**What it found.** The hot spot analysis was conducted using the Delivery stops layer [9] and the gp_hot_spots tool [25]. The process produced 254 features and calculated a count of 254 results [25]. For this analysis, 2,000 incidents were utilized, and the resulting cell size was 500 m [25]. The total billing cost for the analysis was 0.09 [25].

| Result | Value |
| --- | --- |
| result, bins | 254 |
| result, count | 254 |
| result, elapsed ms | 23 |
| result, cell size m | 500 |
| result, incidents used | 2,000 |
| result, distance band m | 500 |
| billing, cost | 0.09 |
| billing, list price | 0.09 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Delivery stops: Hot spot analysis: 1 layer saved: Hot spot analysis: Delivery stops (254 features) [9][25].
- **Evidenced:** It reported result, bins 254; result, count 254; result, elapsed ms 23; result, cell size m 500; result, incidents used 2,000; result, distance band m 500 [25].

*Tool gp_hot_spots; parameters {"project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Delivery stops (ba8073d9)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 500.0}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [23]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [23].

**Why it was run on this data.** 45 points: Getis-Ord Gi* tests whether the concentrations in Parcel lockers are statistically significant [26].

**What it found.** The hot spot analysis was conducted using the Parcel lockers layer [22]. The analysis produced 1 layer, titled "Hot spot analysis: Parcel lockers (19 features)" [27]. Key results showed the analysis generated 19 bins containing 19 features [27]. The operation took 2 milliseconds to complete, and the resulting cell size and distance band were both 2,000 meters [27].

| Result | Value |
| --- | --- |
| result, bins | 19 |
| result, count | 19 |
| result, elapsed ms | 2 |
| result, cell size m | 2,000 |
| result, incidents used | 45 |
| result, distance band m | 2,000 |
| billing, cost | 0.0509 |
| billing, list price | 0.0509 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Parcel lockers: Hot spot analysis: 1 layer saved: Hot spot analysis: Parcel lockers (19 features) [22][27].
- **Evidenced:** It reported result, bins 19; result, count 19; result, elapsed ms 2; result, cell size m 2,000; result, incidents used 45; result, distance band m 2,000 [27].

*Tool gp_hot_spots; parameters {"project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Parcel lockers (4e9e11a9)"}, "aggregation": "hexagon", "cell_size_m": 2000.0, "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 [23]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [23].

**Why it was run on this data.** 60 points: Getis-Ord Gi* tests whether the concentrations in Loading bays are statistically significant [28].

**What it found.** Hot spots on Loading bays: Hot spot analysis: 1 layer saved: Hot spot analysis: Loading bays (19 features) [29][30]. It reported result, bins 19; result, count 19; result, elapsed ms 2; result, cell size m 2,000; result, incidents used 60; result, distance band m 2,000 [29].

| Result | Value |
| --- | --- |
| result, bins | 19 |
| result, count | 19 |
| result, elapsed ms | 2 |
| result, cell size m | 2,000 |
| result, incidents used | 60 |
| result, distance band m | 2,000 |
| billing, cost | 0.0512 |
| billing, list price | 0.0512 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Loading bays: Hot spot analysis: 1 layer saved: Hot spot analysis: Loading bays (19 features) [29][30].
- **Evidenced:** It reported result, bins 19; result, count 19; result, elapsed ms 2; result, cell size m 2,000; result, incidents used 60; result, distance band m 2,000 [29].

*Tool gp_hot_spots; parameters {"project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Loading bays (16574f8d)"}, "aggregation": "hexagon", "cell_size_m": 2000.0, "distance_band_m": 2000.0}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [31]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [31].

**Why it was run on this data.** 2000 points in Delivery stops: a density surface shows where they concentrate [32].

**What it found.** Calculate density on Delivery stops: Calculate density: 1 layer saved: Calculate density: Delivery stops (1654 features) [9][33]. It reported result, count 1,654; result, elapsed ms 74; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 396 [33].

| Result | Value |
| --- | --- |
| result, count | 1,654 |
| result, elapsed ms | 74 |
| result, bandwidth m | 500 |
| result, cell size m | 200 |
| result, excluded null | 0 |
| result, max density per km2 | 396 |
| billing, cost | 0.09 |
| billing, list price | 0.09 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Delivery stops: Calculate density: 1 layer saved: Calculate density: Delivery stops (1654 features) [9][33].
- **Evidenced:** It reported result, count 1,654; result, elapsed ms 74; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 396 [33].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Delivery stops (ba8073d9)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [31]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [31].

**Why it was run on this data.** 45 points in Parcel lockers: a density surface shows where they concentrate [34].

**What it found.** The calculation of density using the `gp_calculate_density` tool analyzed the Parcel lockers layer [22] and processed 99 features [35]. The process required an elapsed time of 5 ms [35], identifying a maximum density of 1.381 per km² [35].

| Result | Value |
| --- | --- |
| result, count | 99 |
| result, elapsed ms | 5 |
| result, bandwidth m | 2,000 |
| result, cell size m | 1,000 |
| result, excluded null | 0 |
| result, max density per km2 | 1.381 |
| billing, cost | 0.0509 |
| billing, list price | 0.0509 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Parcel lockers: Calculate density: 1 layer saved: Calculate density: Parcel lockers (99 features) [22][35].
- **Evidenced:** It reported result, count 99; result, elapsed ms 5; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 1.381 [35].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Parcel lockers (4e9e11a9)"}, "bandwidth_m": 2000.0, "cell_size_m": 1000.0}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [31]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [31].

**Why it was run on this data.** 60 points in Loading bays: a density surface shows where they concentrate [36].

**What it found.** The density calculation used the Loading bays dataset [30]. This process analyzed 101 features and reported a total bandwidth of 2,000 meters [37]. The maximum density calculated across the layers was 1.929 per km² [37].

| Result | Value |
| --- | --- |
| result, count | 101 |
| result, elapsed ms | 5 |
| result, bandwidth m | 2,000 |
| result, cell size m | 1,000 |
| result, excluded null | 0 |
| result, max density per km2 | 1.929 |
| billing, cost | 0.0512 |
| billing, list price | 0.0512 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Loading bays: Calculate density: 1 layer saved: Calculate density: Loading bays (101 features) [30][37].
- **Evidenced:** It reported result, count 101; result, elapsed ms 5; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 1.929 [37].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Loading bays (16574f8d)"}, "bandwidth_m": 2000.0, "cell_size_m": 1000.0}*

## Find point clusters

**What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [38]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [38].

**Why it was run on this data.** DBSCAN finds dense groups of Delivery stops and separates noise [39].

**What it found.** Find point clusters on Delivery stops: Find point clusters: 2 layers saved: Find point clusters: Delivery stops (2000 features); Find point clusters: cluster hulls (2 features) [9][40]. It reported result, count 2,000; result, noise 1,570; result, distance m 200; result, elapsed ms 214; result, min points 20; billing, cost 0.09 [40].

| Result | Value |
| --- | --- |
| result, count | 2,000 |
| result, noise | 1,570 |
| result, distance m | 200 |
| result, elapsed ms | 214 |
| result, min points | 20 |
| billing, cost | 0.09 |
| billing, list price | 0.09 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Delivery stops: Find point clusters: 2 layers saved: Find point clusters: Delivery stops (2000 features); Find point clusters: cluster hulls (2 features) [9][40].
- **Evidenced:** It reported result, count 2,000; result, noise 1,570; result, distance m 200; result, elapsed ms 214; result, min points 20; billing, cost 0.09 [40].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Delivery stops (ba8073d9)"}, "distance_m": 200.0, "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 [38]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [38].

**Why it was run on this data.** DBSCAN finds dense groups of Parcel lockers and separates noise [41].

**What it found.** Find point clusters on Parcel lockers: Find point clusters: 2 layers saved: Find point clusters: Parcel lockers (45 features); Find point clusters: cluster hulls (1 feature) [22][42]. It reported result, count 45; result, noise 1; result, distance m 2,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0509 [42].

| Result | Value |
| --- | --- |
| result, count | 45 |
| result, noise | 1 |
| result, distance m | 2,000 |
| result, elapsed ms | 2 |
| result, min points | 3 |
| billing, cost | 0.0509 |
| billing, list price | 0.0509 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Parcel lockers: Find point clusters: 2 layers saved: Find point clusters: Parcel lockers (45 features); Find point clusters: cluster hulls (1 feature) [22][42].
- **Evidenced:** It reported result, count 45; result, noise 1; result, distance m 2,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0509 [42].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Parcel lockers (4e9e11a9)"}, "distance_m": 2000.0, "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 [38]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [38].

**Why it was run on this data.** DBSCAN finds dense groups of Loading bays and separates noise [43].

**What it found.** Find point clusters on Loading bays: Find point clusters: 2 layers saved: Find point clusters: Loading bays (60 features); Find point clusters: cluster hulls (6 features) [30][44]. It reported result, count 60; result, noise 7; result, distance m 1,000; result, elapsed ms 4; result, min points 3; billing, cost 0.0512 [44].

| Result | Value |
| --- | --- |
| result, count | 60 |
| result, noise | 7 |
| result, distance m | 1,000 |
| result, elapsed ms | 4 |
| result, min points | 3 |
| billing, cost | 0.0512 |
| billing, list price | 0.0512 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Loading bays: Find point clusters: 2 layers saved: Find point clusters: Loading bays (60 features); Find point clusters: cluster hulls (6 features) [30][44].
- **Evidenced:** It reported result, count 60; result, noise 7; result, distance m 1,000; result, elapsed ms 4; result, min points 3; billing, cost 0.0512 [44].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Loading bays (16574f8d)"}, "distance_m": 1000.0, "min_points": 3}*

## Outliers

**What this analysis is.** Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers [45]. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) [45].

**Why it was run on this data.** Local Moran's I maps clusters and outliers of 'parcels_per_day' [46].

**What it found.** Outliers on Delivery zones: Find outliers: 1 layer saved: Find outliers: Delivery zones (10 features) [19][47]. It reported result, count 10; result, elapsed ms 4; result, permutations 199; result, excluded null 0; result, distance band m 2,661; billing, cost 0.0503 [47].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 4 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 2,661 |
| billing, cost | 0.0503 |
| billing, list price | 0.0503 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Delivery zones: Find outliers: 1 layer saved: Find outliers: Delivery zones (10 features) [19][47].
- **Evidenced:** It reported result, count 10; result, elapsed ms 4; result, permutations 199; result, excluded null 0; result, distance band m 2,661; billing, cost 0.0503 [47].

*Tool gp_outliers; parameters {"field": "parcels_per_day", "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Delivery zones (bfcf8b53)"}, "aggregation": "hexagon"}*

## Spatial autocorrelation

**What this analysis is.** Spatial autocorrelation: Global Moran’s I: is the pattern clustered, dispersed or random [48]. In ArcGIS it is Spatial Autocorrelation (Global Moran’s I) [48].

**Why it was run on this data.** Global Moran's I says whether similar values of 'parcels_per_day' sit next to each other [49].

**What it found.** Spatial autocorrelation on Delivery zones: Spatial autocorrelation: done; no map output [19][50]. It reported result, n 10; result, p value 0.1412; result, z score 1.471; result, morans i 0.09356; result, variance 0.01935; result, elapsed ms 2 [50].

| Result | Value |
| --- | --- |
| result, n | 10 |
| result, p value | 0.1412 |
| result, z score | 1.471 |
| result, morans i | 0.09356 |
| result, variance | 0.01935 |
| result, elapsed ms | 2 |
| result, expected i | -0.1111 |
| result, excluded null | 0 |
| result, distance band m | 2,661 |
| result, p value normality | 0.421 |
| result, z score normality | 0.8046 |
| result, variance normality | 0.06471 |
| billing, cost | 0.02 |
| billing, list price | 0.02 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Spatial autocorrelation on Delivery zones: Spatial autocorrelation: done; no map output [19][50].
- **Evidenced:** It reported result, n 10; result, p value 0.1412; result, z score 1.471; result, morans i 0.09356; result, variance 0.01935; result, elapsed ms 2 [50].

*Tool gp_spatial_autocorrelation; parameters {"field": "parcels_per_day", "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Delivery zones (bfcf8b53)"}}*

## Measure

**What this analysis is.** Areas and lengths: Geodesic area, perimeter and length of each feature [51]. In ArcGIS it is Areas And Lengths or Calculate Geometry; in QGIS, qgis:exportaddgeometrycolumns [51].

**Why it was run on this data.** Lengths of the 36 lines of Routes (total and per feature) [52].

**What it found.** Measure on Routes: Areas and lengths: 1 layer saved: Areas and lengths: Routes (36 features) [53][54]. It reported result, count 36; result, elapsed ms 7; billing, cost 0.01; billing, list price 0.01; billing, upstream cost 0 [53].

| Result | Value |
| --- | --- |
| result, count | 36 |
| result, elapsed ms | 7 |
| billing, cost | 0.01 |
| billing, list price | 0.01 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Measure on Routes: Areas and lengths: 1 layer saved: Areas and lengths: Routes (36 features) [53][54].
- **Evidenced:** It reported result, count 36; result, elapsed ms 7; billing, cost 0.01; billing, list price 0.01; billing, upstream cost 0 [53].

*Tool gp_measure; parameters {"units": "metric", "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Routes (93de6e09)"}}*

## Measure

**What this analysis is.** Areas and lengths: Geodesic area, perimeter and length of each feature [51]. In ArcGIS it is Areas And Lengths or Calculate Geometry; in QGIS, qgis:exportaddgeometrycolumns [51].

**Why it was run on this data.** Lengths of the 12 lines of Roadworks (total and per feature) [55].

**What it found.** The `gp_measure` tool was utilized with the Roadworks layer as input [57]. The tool saved the areas and lengths for Roadworks, which contained 12 features [56]. The process resulted in a count of 12 features and took 0 elapsed ms [56].

| Result | Value |
| --- | --- |
| result, count | 12 |
| result, elapsed ms | 0 |
| billing, cost | 0.01 |
| billing, list price | 0.01 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Measure on Roadworks: Areas and lengths: 1 layer saved: Areas and lengths: Roadworks (12 features) [56][57].
- **Evidenced:** It reported result, count 12; result, elapsed ms 0; billing, cost 0.01; billing, list price 0.01; billing, upstream cost 0 [56].

*Tool gp_measure; parameters {"units": "metric", "project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Roadworks (50ecfc27)"}}*

## Line density

**What this analysis is.** Line density: Length of lines per unit area around each cell (km per square km), optionally weighted by a field [58]. In ArcGIS it is Line Density; in QGIS, qgis:linedensity [58].

**Why it was run on this data.** Line density shows where Routes are densest [59].

**What it found.** Line density on Routes: Line density: 1 layer saved: Line density: Routes (480 features) [54][60]. It reported result, count 480; result, radius m 2,000; result, elapsed ms 69; result, cell size m 500; result, total length km 712.7; result, max density km per km2 20.21 [60].

| Result | Value |
| --- | --- |
| result, count | 480 |
| result, radius m | 2,000 |
| result, elapsed ms | 69 |
| result, cell size m | 500 |
| result, total length km | 712.7 |
| result, max density km per km2 | 20.21 |
| billing, cost | 0.05072 |
| billing, list price | 0.05072 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Line density on Routes: Line density: 1 layer saved: Line density: Routes (480 features) [54][60].
- **Evidenced:** It reported result, count 480; result, radius m 2,000; result, elapsed ms 69; result, cell size m 500; result, total length km 712.7; result, max density km per km2 20.21 [60].

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

*Tool gp_line_density; parameters {"project": "55917da2-e619-41aa-b6a1-e521a75b705c", "features": {"layer": "Routes (93de6e09)"}, "radius_m": 2000.0, "cell_size_m": 500.0}*

## Maps and charts

The run also made 22 map image(s) [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82] and 27 chart(s) [83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109].

![Emerging hot spots: Delivery stops](maps/01-emerging-hot-spots-delivery-stops.png)

*Figure 2. Emerging hot spots: Delivery stops [61]*

![Repeat and near repeat classification: Delivery stops](maps/02-repeat-and-near-repeat-classification-delivery-stops.png)

*Figure 3. Repeat and near repeat classification: Delivery stops [62]*

![Summarize within: Delivery zones](maps/03-summarize-within-delivery-zones.png)

*Figure 4. Summarize within: Delivery zones [63]*

![Summarize within: Delivery zones](maps/04-summarize-within-delivery-zones.png)

*Figure 5. Summarize within: Delivery zones [64]*

![Summarize within: Delivery zones](maps/05-summarize-within-delivery-zones.png)

*Figure 6. Summarize within: Delivery zones [65]*

![Hot spot analysis: Delivery stops](maps/06-hot-spot-analysis-delivery-stops.png)

*Figure 7. Hot spot analysis: Delivery stops [66]*

![Hot spot analysis: Parcel lockers](maps/07-hot-spot-analysis-parcel-lockers.png)

*Figure 8. Hot spot analysis: Parcel lockers [67]*

![Hot spot analysis: Loading bays](maps/08-hot-spot-analysis-loading-bays.png)

*Figure 9. Hot spot analysis: Loading bays [68]*

![Calculate density: Delivery stops](maps/09-calculate-density-delivery-stops.png)

*Figure 10. Calculate density: Delivery stops [69]*

![Calculate density: Parcel lockers](maps/10-calculate-density-parcel-lockers.png)

*Figure 11. Calculate density: Parcel lockers [70]*

![Calculate density: Loading bays](maps/11-calculate-density-loading-bays.png)

*Figure 12. Calculate density: Loading bays [71]*

![Find point clusters: Delivery stops](maps/12-find-point-clusters-delivery-stops.png)

*Figure 13. Find point clusters: Delivery stops [72]*

![Find point clusters: cluster hulls](maps/13-find-point-clusters-cluster-hulls.png)

*Figure 14. Find point clusters: cluster hulls [73]*

![Find point clusters: Parcel lockers](maps/14-find-point-clusters-parcel-lockers.png)

*Figure 15. Find point clusters: Parcel lockers [74]*

![Find point clusters: cluster hulls](maps/15-find-point-clusters-cluster-hulls.png)

*Figure 16. Find point clusters: cluster hulls [75]*

![Find point clusters: Loading bays](maps/16-find-point-clusters-loading-bays.png)

*Figure 17. Find point clusters: Loading bays [76]*

![Find point clusters: cluster hulls](maps/17-find-point-clusters-cluster-hulls.png)

*Figure 18. Find point clusters: cluster hulls [77]*

![Delivery zones by parcels_per_day](maps/18-delivery-zones-by-parcels-per-day.png)

*Figure 19. Delivery zones by parcels_per_day [78]*

![Find outliers: Delivery zones](maps/19-find-outliers-delivery-zones.png)

*Figure 20. Find outliers: Delivery zones [79]*

![Areas and lengths: Routes](maps/20-areas-and-lengths-routes.png)

*Figure 21. Areas and lengths: Routes [80]*

![Areas and lengths: Roadworks](maps/21-areas-and-lengths-roadworks.png)

*Figure 22. Areas and lengths: Roadworks [81]*

![Line density: Routes](maps/22-line-density-routes.png)

*Figure 23. Line density: Routes [82]*

**What the map shows.** The map shows the emerging hot spots for delivery stops, categorizing intensity [61]

*Inferred: read from the image by gemma4:e4b, not evidence.*

![Delivery zones over time (parcels_per_day)](charts/01-delivery-zones-over-time-parcels-per-day.png)

*Figure 24. Delivery zones over time (parcels_per_day) [83]. Source: Delivery zones (10 features). The data is in the appendix.*

![Roadworks over time (start_date)](charts/02-roadworks-over-time-start-date.png)

*Figure 25. Roadworks over time (start_date) [84]. Per week, 2026-06-22 to 2026-08-31. Source: Roadworks (12 features). The data is in the appendix.*

![Route performance by day over time (date)](charts/03-route-performance-by-day-over-time-date.png)

*Figure 26. Route performance by day over time (date) [85]. Per week, 2026-07-27 to 2026-09-14. Source: Route performance by day (1,728 features). The data is in the appendix.*

![Vehicle fleet over time (model_year)](charts/04-vehicle-fleet-over-time-model-year.png)

*Figure 27. Vehicle fleet over time (model_year) [86]. Source: Vehicle fleet (60 features). The data is in the appendix.*

![Emerging hot spots: Delivery stops](charts/05-emerging-hot-spots-delivery-stops.png)

*Figure 28. Emerging hot spots: Delivery stops [87]. Source: Emerging hot spots: Delivery stops (88 features). The data is in the appendix.*

![Repeat and near repeat classification: Delivery stops](charts/06-repeat-and-near-repeat-classification-delivery-stops.png)

*Figure 29. Repeat and near repeat classification: Delivery stops [88]. Per week, 2026-07-27 to 2026-09-14. Source: Repeat and near repeat classification: Delivery stops (2,000 features). The data is in the appendix.*

![Summarize within: Delivery zones](charts/07-summarize-within-delivery-zones.png)

*Figure 30. Summarize within: Delivery zones [89]. Source: Summarize within: Delivery zones (10 features). The data is in the appendix.*

![Summarize within: Delivery zones](charts/08-summarize-within-delivery-zones.png)

*Figure 31. Summarize within: Delivery zones [90]. Source: Summarize within: Delivery zones (10 features). The data is in the appendix.*

![Summarize within: Delivery zones](charts/09-summarize-within-delivery-zones.png)

*Figure 32. Summarize within: Delivery zones [91]. Source: Summarize within: Delivery zones (10 features). The data is in the appendix.*

![Hot spot analysis: Delivery stops](charts/10-hot-spot-analysis-delivery-stops.png)

*Figure 33. Hot spot analysis: Delivery stops [92]. Source: Hot spot analysis: Delivery stops (254 features). The data is in the appendix.*

![Hot spot analysis: Parcel lockers](charts/11-hot-spot-analysis-parcel-lockers.png)

*Figure 34. Hot spot analysis: Parcel lockers [93]. Source: Hot spot analysis: Parcel lockers (19 features). The data is in the appendix.*

![Hot spot analysis: Loading bays](charts/12-hot-spot-analysis-loading-bays.png)

*Figure 35. Hot spot analysis: Loading bays [94]. Source: Hot spot analysis: Loading bays (19 features). The data is in the appendix.*

![Calculate density: Delivery stops](charts/13-calculate-density-delivery-stops.png)

*Figure 36. Calculate density: Delivery stops [95]. Source: Calculate density: Delivery stops (1,654 features). The data is in the appendix.*

![Calculate density: Parcel lockers](charts/14-calculate-density-parcel-lockers.png)

*Figure 37. Calculate density: Parcel lockers [96]. Source: Calculate density: Parcel lockers (99 features). The data is in the appendix.*

![Calculate density: Loading bays](charts/15-calculate-density-loading-bays.png)

*Figure 38. Calculate density: Loading bays [97]. Source: Calculate density: Loading bays (101 features). The data is in the appendix.*

![Find point clusters: Delivery stops](charts/16-find-point-clusters-delivery-stops.png)

*Figure 39. Find point clusters: Delivery stops [98]. Period: 2026-07-27 to 2026-09-19 (delivered_at). Source: Find point clusters: Delivery stops (2,000 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/17-find-point-clusters-cluster-hulls.png)

*Figure 40. Find point clusters: cluster hulls [99]. Source: Find point clusters: cluster hulls (2 features). The data is in the appendix.*

![Find point clusters: Parcel lockers](charts/18-find-point-clusters-parcel-lockers.png)

*Figure 41. Find point clusters: Parcel lockers [100]. Source: Find point clusters: Parcel lockers (45 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/19-find-point-clusters-cluster-hulls.png)

*Figure 42. Find point clusters: cluster hulls [101]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.*

![Find point clusters: Loading bays](charts/20-find-point-clusters-loading-bays.png)

*Figure 43. Find point clusters: Loading bays [102]. Source: Find point clusters: Loading bays (60 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/21-find-point-clusters-cluster-hulls.png)

*Figure 44. Find point clusters: cluster hulls [103]. Source: Find point clusters: cluster hulls (6 features). The data is in the appendix.*

![Find outliers: Delivery zones](charts/22-find-outliers-delivery-zones.png)

*Figure 45. Find outliers: Delivery zones [104]. Source: Find outliers: Delivery zones (10 features). The data is in the appendix.*

![Areas and lengths: Routes](charts/23-areas-and-lengths-routes.png)

*Figure 46. Areas and lengths: Routes [105]. Source: Areas and lengths: Routes (36 features). The data is in the appendix.*

![Areas and lengths: Roadworks](charts/24-areas-and-lengths-roadworks.png)

*Figure 47. Areas and lengths: Roadworks [106]. Per week, 2026-08-03 to 2026-11-16. Source: Areas and lengths: Roadworks (12 features). The data is in the appendix.*

![Areas and lengths: Roadworks over time](charts/25-areas-and-lengths-roadworks-over-time.png)

*Figure 48. Areas and lengths: Roadworks over time [107]. Per week, 2026-08-03 to 2026-11-16. Source: Areas and lengths: Roadworks (12 features). The data is in the appendix.*

![Find point clusters: Delivery stops over time](charts/26-find-point-clusters-delivery-stops-over-time.png)

*Figure 49. Find point clusters: Delivery stops over time [108]. Per week, 2026-07-27 to 2026-09-14. Source: Find point clusters: Delivery stops (2,000 features). The data is in the appendix.*

![Repeat and near repeat classification: Delivery stops over time](charts/27-repeat-and-near-repeat-classification-delivery-stops-over-ti.png)

*Figure 50. Repeat and near repeat classification: Delivery stops over time [109]. Per week, 2026-07-27 to 2026-09-14. Source: Repeat and near repeat classification: Delivery stops (2,000 features). The data is in the appendix.*

## Changes since the last run

This is the first report of this project.

## Sources

[1] Rendering: Chart: Timeline: events per week by dataset. 39 rows (id 1521b52f-2ba2-4bbc-a8f6-ccfe5e7fdf7f)  
[2] Dataset: Delivery stops. vector, 2000 rows (id b701b76f-6822-4c92-82fe-b4acdce6c331)  
[3] Dataset: Roadworks. vector, 12 rows (id 6fda682d-6a14-4d92-9cdd-cdcabec36863)  
[4] Dataset: Route performance by day. table, 1728 rows (id 93163425-d292-4c3d-9bd8-2e8681a816c2)  
[5] Tool result: gp_change_point_detection, step 2. Change point detection: done; no map output. (id 0bc1c879-b643-4a1a-9035-72f62d929ae4:2)  
[6] 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)  
[7] Plan: Space time hot spots of Delivery stops (the Start plan's candidate c_24901805ebc9). Delivery stops carry dates in 'delivered_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_24901805ebc9)  
[8] Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Delivery stops (88 features). (id 0bc1c879-b643-4a1a-9035-72f62d929ae4:0)  
[9] Layer: Delivery stops. 2,000 features (id ba8073d9-f217-4175-acc5-437aabf37b16)  
[10] 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)  
[11] Plan: Near repeat of Delivery stops (the Start plan's candidate c_07c2356dde76). Near repeat analysis tests whether Delivery stops close in space also follow each other within 5 days. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_07c2356dde76)  
[12] Tool result: gp_near_repeat, step 1. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Delivery stops (2000 features). (id 0bc1c879-b643-4a1a-9035-72f62d929ae4:1)  
[13] Catalog: Change point detection (operation catalog, gp_change_point_detection). When did the level, count, trend or variability change? For the whole study area or for every bin. ArcGIS: Change Point Detection. (id gp_change_point_detection)  
[14] Plan: Change points of Delivery stops (the Start plan's candidate c_e6c80aa728e2). Change point detection finds when the weekly count of Delivery stops shifted. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_e6c80aa728e2)  
[15] 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)  
[16] Plan: Depots per feature of Delivery zones (the Start plan's candidate c_63970c31d3b8). Count Depots inside each feature of Delivery zones; the counts map as a choropleth (natural breaks, sequential colors). (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_63970c31d3b8)  
[17] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Delivery zones (10 features). (id 06830d85-af54-42ad-b4e3-68502ee43e45:0)  
[18] Layer: Depots. 3 features (id 84d3272c-9902-4e4b-a3a0-899c3b10edb7)  
[19] Layer: Delivery zones. 10 features (id bfcf8b53-48d2-44c7-b00e-a89315aee1b5)  
[20] Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Delivery zones (10 features). (id 06830d85-af54-42ad-b4e3-68502ee43e45:1)  
[21] Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Delivery zones (10 features). (id 06830d85-af54-42ad-b4e3-68502ee43e45:2)  
[22] Layer: Parcel lockers. 45 features (id 4e9e11a9-8154-47b8-a5ce-2ba3dbf5f3e6)  
[23] 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)  
[24] Plan: Hot spots of Delivery stops (the Start plan's candidate c_90d2c6f9911f). 2000 points: Getis-Ord Gi* tests whether the concentrations in Delivery stops are statistically significant. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_90d2c6f9911f)  
[25] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Delivery stops (254 features). (id baba02b7-1f72-470f-802a-5d9cf0a96e0e:0)  
[26] Plan: Hot spots of Parcel lockers (the Start plan's candidate c_7f159ca7fe80). 45 points: Getis-Ord Gi* tests whether the concentrations in Parcel lockers are statistically significant. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_7f159ca7fe80)  
[27] Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Parcel lockers (19 features). (id baba02b7-1f72-470f-802a-5d9cf0a96e0e:1)  
[28] Plan: Hot spots of Loading bays (the Start plan's candidate c_be4560f0ca20). 60 points: Getis-Ord Gi* tests whether the concentrations in Loading bays are statistically significant. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_be4560f0ca20)  
[29] Tool result: gp_hot_spots, step 2. Hot spot analysis: 1 layer saved: Hot spot analysis: Loading bays (19 features). (id baba02b7-1f72-470f-802a-5d9cf0a96e0e:2)  
[30] Layer: Loading bays. 60 features (id 16574f8d-1ab3-403e-bf7c-ef2918a3168a)  
[31] 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)  
[32] Plan: Kernel density of Delivery stops (the Start plan's candidate c_b00e122abf70). 2000 points in Delivery stops: a density surface shows where they concentrate. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_b00e122abf70)  
[33] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Delivery stops (1654 features). (id 5426b1e3-5d44-4200-ad65-fcd6b9219354:0)  
[34] Plan: Kernel density of Parcel lockers (the Start plan's candidate c_d0abfcdb6ebd). 45 points in Parcel lockers: a density surface shows where they concentrate. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_d0abfcdb6ebd)  
[35] Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Parcel lockers (99 features). (id 5426b1e3-5d44-4200-ad65-fcd6b9219354:1)  
[36] Plan: Kernel density of Loading bays (the Start plan's candidate c_f4d285023245). 60 points in Loading bays: a density surface shows where they concentrate. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_f4d285023245)  
[37] Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: Loading bays (101 features). (id 5426b1e3-5d44-4200-ad65-fcd6b9219354:2)  
[38] 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)  
[39] Plan: Cluster points of Delivery stops (the Start plan's candidate c_ecc06546584b). DBSCAN finds dense groups of Delivery stops and separates noise. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_ecc06546584b)  
[40] Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Delivery stops (2000 features); Find point clusters: cluster hulls (2 features). (id 95a73131-a24c-4f4f-9468-8e9fb0e8dd34:0)  
[41] Plan: Cluster points of Parcel lockers (the Start plan's candidate c_ea1cd80d3915). DBSCAN finds dense groups of Parcel lockers and separates noise. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_ea1cd80d3915)  
[42] Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Parcel lockers (45 features); Find point clusters: cluster hulls (1 feature). (id 95a73131-a24c-4f4f-9468-8e9fb0e8dd34:1)  
[43] Plan: Cluster points of Loading bays (the Start plan's candidate c_d272c4a82a27). DBSCAN finds dense groups of Loading bays and separates noise. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_d272c4a82a27)  
[44] Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Loading bays (60 features); Find point clusters: cluster hulls (6 features). (id 95a73131-a24c-4f4f-9468-8e9fb0e8dd34:2)  
[45] Catalog: Find outliers (operation catalog, gp_outliers). Anselin local Moran’s I: HH, LL clusters and HL, LH outliers. ArcGIS: Find Outliers, Cluster and Outlier Analysis (Anselin Local Moran’s I). (id gp_outliers)  
[46] Plan: Local morans i of Delivery zones (the Start plan's candidate c_0bac5b3b8830). Local Moran's I maps clusters and outliers of 'parcels_per_day'. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_0bac5b3b8830)  
[47] Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: Delivery zones (10 features). (id 4c008862-b527-4c01-a253-53d590dcfc30:0)  
[48] Catalog: Spatial autocorrelation (operation catalog, gp_spatial_autocorrelation). Global Moran’s I: is the pattern clustered, dispersed or random. ArcGIS: Spatial Autocorrelation (Global Moran’s I). (id gp_spatial_autocorrelation)  
[49] Plan: Global morans i of Delivery zones (the Start plan's candidate c_49e20ce52312). Global Moran's I says whether similar values of 'parcels_per_day' sit next to each other. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_49e20ce52312)  
[50] Tool result: gp_spatial_autocorrelation, step 1. Spatial autocorrelation: done; no map output. (id 4c008862-b527-4c01-a253-53d590dcfc30:1)  
[51] Catalog: Areas and lengths (operation catalog, gp_measure). Geodesic area, perimeter and length of each feature. ArcGIS: Areas And Lengths, Calculate Geometry. QGIS: qgis:exportaddgeometrycolumns. (id gp_measure)  
[52] Plan: Length stats of Routes (the Start plan's candidate c_85d40397c2bc). Lengths of the 36 lines of Routes (total and per feature). (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_85d40397c2bc)  
[53] Tool result: gp_measure, step 0. Areas and lengths: 1 layer saved: Areas and lengths: Routes (36 features). (id 91b1b73d-cdc4-44e3-a35d-b8f649d4d58d:0)  
[54] Layer: Routes. 36 features (id 93de6e09-79b3-40e9-8b57-4b7906bd7eef)  
[55] Plan: Length stats of Roadworks (the Start plan's candidate c_a7200769644e). Lengths of the 12 lines of Roadworks (total and per feature). (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_a7200769644e)  
[56] Tool result: gp_measure, step 1. Areas and lengths: 1 layer saved: Areas and lengths: Roadworks (12 features). (id 91b1b73d-cdc4-44e3-a35d-b8f649d4d58d:1)  
[57] Layer: Roadworks. 12 features (id 50ecfc27-2fb7-43a7-a97c-62847d89632e)  
[58] Catalog: Line density (operation catalog, gp_line_density). Length of lines per unit area around each cell (km per square km), optionally weighted by a field. ArcGIS: Line Density. QGIS: qgis:linedensity. (id gp_line_density)  
[59] Plan: Line density of Routes (the Start plan's candidate c_42f6ef8679a2). Line density shows where Routes are densest. (id 99a0ba18-4245-45ac-a729-8f0ed736c8b0:c_42f6ef8679a2)  
[60] Tool result: gp_line_density, step 2. Line density: 1 layer saved: Line density: Routes (480 features). (id 91b1b73d-cdc4-44e3-a35d-b8f649d4d58d:2)  
[61] Rendering: Map: Emerging hot spots: Delivery stops (id f1d06252-1aff-40b3-8003-f6b25228a418)  
[62] Rendering: Map: Repeat and near repeat classification: Delivery stops (id 56dbe991-5135-48c1-bc6c-7d3b6a07d808)  
[63] Rendering: Map: Summarize within: Delivery zones (id 858fe721-2926-4049-9403-b702f7c1d708)  
[64] Rendering: Map: Summarize within: Delivery zones (id 2824f9fd-2587-4fb5-a77e-a3aa3900d97b)  
[65] Rendering: Map: Summarize within: Delivery zones (id 27fa5b0d-6371-4492-98c5-31025c68e575)  
[66] Rendering: Map: Hot spot analysis: Delivery stops (id 2bacb0be-de03-4c38-bf8f-0ae5de932ab2)  
[67] Rendering: Map: Hot spot analysis: Parcel lockers (id 518d4c2b-80c3-42d9-bc31-0561cedde38a)  
[68] Rendering: Map: Hot spot analysis: Loading bays (id 3bd8a3a6-8e83-4ea2-b732-5258f2917d5b)  
[69] Rendering: Map: Calculate density: Delivery stops (id e5664686-25ef-412b-ba05-b9ff052753ba)  
[70] Rendering: Map: Calculate density: Parcel lockers (id 9153e49e-e4a6-473d-9b9a-0c99aa981559)  
[71] Rendering: Map: Calculate density: Loading bays (id 089aef48-4d04-4e2b-adf3-1b5f5ba6339b)  
[72] Rendering: Map: Find point clusters: Delivery stops (id 7e2737d2-52c9-41a9-ba06-a22cd7165f20)  
[73] Rendering: Map: Find point clusters: cluster hulls (id 4f3cc889-cd88-4ebf-a131-f8a38db74f3a)  
[74] Rendering: Map: Find point clusters: Parcel lockers (id 980074f9-34f5-41ca-b730-bf16feeff400)  
[75] Rendering: Map: Find point clusters: cluster hulls (id 7a66f851-f7b6-4f69-9d38-1eabeffea2a6)  
[76] Rendering: Map: Find point clusters: Loading bays (id ce8ded9c-4170-4c0f-89c3-9e2f0139076b)  
[77] Rendering: Map: Find point clusters: cluster hulls (id 3a1bb395-8af7-4e6b-92e6-5049fe664a97)  
[78] Rendering: Map: Delivery zones by parcels_per_day (id 69ec72f2-551e-4c36-9de4-64b8694296ea)  
[79] Rendering: Map: Find outliers: Delivery zones (id 784a0e29-5811-4741-945d-714d4fcd467c)  
[80] Rendering: Map: Areas and lengths: Routes (id 79d6c4d8-5815-4392-acf3-1f691e6cd92c)  
[81] Rendering: Map: Areas and lengths: Roadworks (id 639ec0fd-16fb-4b55-80ad-9ccaac010750)  
[82] Rendering: Map: Line density: Routes (id c697b1c6-e2a2-4247-a4b7-0e7df7e97f00)  
[83] Rendering: Chart: Delivery zones over time (parcels_per_day). 5 rows (id 3c505c95-3e52-4c5e-b196-a568f2171052)  
[84] Rendering: Chart: Roadworks over time (start_date). 33 rows (id b39779d1-1e2b-44ac-aabb-76eb01c77d35)  
[85] Rendering: Chart: Route performance by day over time (date). 8 rows (id b65bccae-a1a1-4e72-ad21-f8677654af9e)  
[86] Rendering: Chart: Vehicle fleet over time (model_year). 7 rows (id 931218d3-0ce6-4d63-b966-4d7ecca4f72b)  
[87] Rendering: Chart: Emerging hot spots: Delivery stops. 8 rows (id 001edf48-ce03-4110-b451-7e47cefb2ce2)  
[88] Rendering: Chart: Repeat and near repeat classification: Delivery stops. 16 rows (id 2613f13d-0205-403c-968f-467d6b8f18f2)  
[89] Rendering: Chart: Summarize within: Delivery zones. 5 rows (id 40091302-ced6-4853-a28e-c0ab84bba36e)  
[90] Rendering: Chart: Summarize within: Delivery zones. 5 rows (id c26a03d2-ecac-406f-b1c6-cb323c8972c6)  
[91] Rendering: Chart: Summarize within: Delivery zones. 5 rows (id 2e9694e1-5c83-4efa-9ca3-1f148701edd0)  
[92] Rendering: Chart: Hot spot analysis: Delivery stops. 12 rows (id 6f99017b-7c08-476b-b981-9fa925479410)  
[93] Rendering: Chart: Hot spot analysis: Parcel lockers. 6 rows (id 20343f2f-b6eb-4043-a48d-0151b407e909)  
[94] Rendering: Chart: Hot spot analysis: Loading bays. 6 rows (id c1c9a919-b186-4232-8d8b-b7389924e9cd)  
[95] Rendering: Chart: Calculate density: Delivery stops. 12 rows (id 0ba97a70-8aad-48cf-87c2-76cf7c7397ac)  
[96] Rendering: Chart: Calculate density: Parcel lockers. 8 rows (id 497850ae-e8d8-417c-a07d-29a5ca46284e)  
[97] Rendering: Chart: Calculate density: Loading bays. 8 rows (id 35ec39ed-b26e-42b1-bc59-ed94b99f9552)  
[98] Rendering: Chart: Find point clusters: Delivery stops. 3 rows (id 38539886-bc2b-42a5-a72d-5568be187825)  
[99] Rendering: Chart: Find point clusters: cluster hulls. 2 rows (id 338c3b9e-35ed-4db2-8bc0-a3a861c4c0a5)  
[100] Rendering: Chart: Find point clusters: Parcel lockers. 2 rows (id 07017722-d2cb-4819-a0c1-8678ecaa6069)  
[101] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id df1f8b27-67ba-452d-9903-5fb39e94536e)  
[102] Rendering: Chart: Find point clusters: Loading bays. 7 rows (id 55877d31-670a-4414-b3d9-a5a94c031ea2)  
[103] Rendering: Chart: Find point clusters: cluster hulls. 6 rows (id e2598c9e-3c2b-436e-aee2-463caf5d80d5)  
[104] Rendering: Chart: Find outliers: Delivery zones. 10 rows (id 91935639-db85-4e93-abb1-ebacc5cbe28f)  
[105] Rendering: Chart: Areas and lengths: Routes. 7 rows (id 35bb3975-be91-41d8-b635-b9396a602106)  
[106] Rendering: Chart: Areas and lengths: Roadworks. 48 rows (id 0a975f43-6acf-4887-afda-2a66d1ef8d59)  
[107] Rendering: Chart: Areas and lengths: Roadworks over time. 48 rows (id 9f61cad0-7d51-4311-a916-62ea61f27aa8)  
[108] Rendering: Chart: Find point clusters: Delivery stops over time. 16 rows (id 71c46175-748d-4398-9f7d-e07e43524c4c)  
[109] Rendering: Chart: Repeat and near repeat classification: Delivery stops over time. 16 rows (id 7f6f8dcd-9739-475e-8a73-e687a2eed73e)  
[110] Dataset: Delivery zones. vector, 10 rows (id ad6ace2b-b87b-4426-b59c-fe04636a404c)  
[111] Dataset: Depots. vector, 3 rows (id 01a5d86e-7877-46c0-b8d7-c7125ff945d6)  
[112] Dataset: Routes. vector, 36 rows (id 6b02543e-134e-4798-8c55-7433740f3a99)  
[113] Dataset: Parcel lockers. vector, 45 rows (id bec44bb9-e98a-4cba-ad40-4738c680b667)  
[114] Dataset: Loading bays. vector, 60 rows (id c8f85255-787a-4380-8e60-7b2d51723499)  
[115] Dataset: Vehicle fleet. table, 60 rows (id 247c930d-b071-44b8-abcc-a946270b0521)  
[116] Dataset: Operations review. document, ? rows (id db8eab40-ac85-4720-ba18-144f3cb5e953)  
[117] Record: Sample: Logistics, Madrid (id 55917da2-e619-41aa-b6a1-e521a75b705c)  
[118] Layer: Emerging hot spots: Delivery stops. 88 features (id 3eda3d63-4109-4ada-a0a5-a28cb0adffff)  
[119] Layer: Repeat and near repeat classification: Delivery stops. 2,000 features (id 16311f39-dfc9-4fec-9ba4-5ab359b9d89b)  
[120] Layer: Summarize within: Delivery zones. 10 features (id eaca5331-e8c9-435a-bd13-b434e2db618e)  
[121] Layer: Summarize within: Delivery zones. 10 features (id ddf33542-ad9c-48ca-92aa-bb183fc2175b)  
[122] Layer: Summarize within: Delivery zones. 10 features (id 7f9be9fb-5f32-4d00-b86c-2ccbc578ecc5)  
[123] Layer: Hot spot analysis: Delivery stops. 254 features (id 277a1b72-5c43-4bb2-8a19-af8ab6d8cc80)  
[124] Layer: Hot spot analysis: Parcel lockers. 19 features (id 169714e0-714b-4884-8a02-53783c9c7f03)  
[125] Layer: Hot spot analysis: Loading bays. 19 features (id 4caff2b6-ee3f-4415-bdc9-5fa9437fd063)  
[126] Layer: Calculate density: Delivery stops. 1,654 features (id 99dbaab0-d5d0-4b45-987b-d2c43d1d09c8)  
[127] Layer: Calculate density: Parcel lockers. 99 features (id 472257d0-327b-4c9e-a4a8-480595dc3856)  
[128] Layer: Calculate density: Loading bays. 101 features (id b6afbc2f-7942-4c9b-94f7-38c291b44345)  
[129] Layer: Find point clusters: Delivery stops. 2,000 features (id cc731b64-afbe-4a38-943f-995b6107d190)  
[130] Layer: Find point clusters: cluster hulls. 2 features (id 436a36cd-df6c-4e32-a0cc-26d50a8c6585)  
[131] Layer: Find point clusters: Parcel lockers. 45 features (id 5e40b4a8-a526-40ac-b338-c5f4ebf27f56)  
[132] Layer: Find point clusters: cluster hulls. 1 features (id 5ab85912-f47a-4fb3-b798-991d03f36a1b)  
[133] Layer: Find point clusters: Loading bays. 60 features (id 90a837e8-38e5-46ee-a786-10b7fcae3bc0)  
[134] Layer: Find point clusters: cluster hulls. 6 features (id 4e2df776-301c-492e-bb6b-6813e216076d)  
[135] Layer: Find outliers: Delivery zones. 10 features (id 5e638f0b-04fd-4df9-a5c9-b3d338aebaea)  
[136] Layer: Areas and lengths: Routes. 36 features (id 8b07f80e-053c-4638-867e-d4f875f2ab43)  
[137] Layer: Areas and lengths: Roadworks. 12 features (id 2ce6988d-7414-430d-b0b9-ede487fe0343)  
[138] Layer: Line density: Routes. 480 features (id 90fa509b-1446-4abc-b818-41008be35963)  

## Appendix: the data behind the charts

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

| period | series | value |
| --- | --- | --- |
| 2026-06-22 | Delivery stops | 0 |
| 2026-06-22 | Route performance by day | 0 |
| 2026-06-22 | Roadworks | 1 |
| 2026-06-29 | Delivery stops | 0 |
| 2026-06-29 | Route performance by day | 0 |
| 2026-06-29 | Roadworks | 0 |
| 2026-07-06 | Delivery stops | 0 |
| 2026-07-06 | Route performance by day | 0 |
| 2026-07-06 | Roadworks | 2 |
| 2026-07-13 | Delivery stops | 0 |
| 2026-07-13 | Route performance by day | 0 |
| 2026-07-13 | Roadworks | 1 |
| 2026-07-20 | Delivery stops | 0 |
| 2026-07-20 | Route performance by day | 0 |
| 2026-07-20 | Roadworks | 3 |
| 2026-07-27 | Delivery stops | 248 |
| 2026-07-27 | Route performance by day | 216 |
| 2026-07-27 | Roadworks | 1 |
| 2026-08-03 | Delivery stops | 251 |
| 2026-08-03 | Route performance by day | 216 |
| 2026-08-03 | Roadworks | 0 |
| 2026-08-10 | Delivery stops | 242 |
| 2026-08-10 | Route performance by day | 216 |
| 2026-08-10 | Roadworks | 1 |
| 2026-08-17 | Delivery stops | 248 |
| 2026-08-17 | Route performance by day | 216 |
| 2026-08-17 | Roadworks | 0 |
| 2026-08-24 | Delivery stops | 274 |
| 2026-08-24 | Route performance by day | 216 |
| 2026-08-24 | Roadworks | 1 |
| 2026-08-31 | Delivery stops | 238 |
| 2026-08-31 | Route performance by day | 216 |
| 2026-08-31 | Roadworks | 2 |
| 2026-09-07 | Delivery stops | 255 |
| 2026-09-07 | Route performance by day | 216 |
| 2026-09-07 | Roadworks | 0 |
| 2026-09-14 | Delivery stops | 244 |
| 2026-09-14 | Route performance by day | 216 |
| 2026-09-14 | Roadworks | 0 |

### Figure 24. Delivery zones over time (parcels_per_day) [83]

| bin_start | bin_end | count |
| --- | --- | --- |
| 1,219 | 1,942 | 6 |
| 1,942 | 2,664 | 3 |
| 2,664 | 3,387 | 0 |
| 3,387 | 4,109 | 0 |
| 4,109 | 4,832 | 1 |

### Figure 25. Roadworks over time (start_date) [84]

| period | series | value |
| --- | --- | --- |
| 2026-06-22 | water main | 1 |
| 2026-06-22 | metro | 0 |
| 2026-06-22 | resurfacing | 0 |
| 2026-06-29 | water main | 0 |
| 2026-06-29 | metro | 0 |
| 2026-06-29 | resurfacing | 0 |
| 2026-07-06 | water main | 1 |
| 2026-07-06 | metro | 0 |
| 2026-07-06 | resurfacing | 1 |
| 2026-07-13 | water main | 1 |
| 2026-07-13 | metro | 0 |
| 2026-07-13 | resurfacing | 0 |
| 2026-07-20 | water main | 0 |
| 2026-07-20 | metro | 2 |
| 2026-07-20 | resurfacing | 1 |
| 2026-07-27 | water main | 1 |
| 2026-07-27 | metro | 0 |
| 2026-07-27 | resurfacing | 0 |
| 2026-08-03 | water main | 0 |
| 2026-08-03 | metro | 0 |
| 2026-08-03 | resurfacing | 0 |
| 2026-08-10 | water main | 1 |
| 2026-08-10 | metro | 0 |
| 2026-08-10 | resurfacing | 0 |
| 2026-08-17 | water main | 0 |
| 2026-08-17 | metro | 0 |
| 2026-08-17 | resurfacing | 0 |
| 2026-08-24 | water main | 1 |
| 2026-08-24 | metro | 0 |
| 2026-08-24 | resurfacing | 0 |
| 2026-08-31 | water main | 0 |
| 2026-08-31 | metro | 1 |
| 2026-08-31 | resurfacing | 1 |

### Figure 26. Route performance by day over time (date) [85]

| period | value |
| --- | --- |
| 2026-07-27 | 216 |
| 2026-08-03 | 216 |
| 2026-08-10 | 216 |
| 2026-08-17 | 216 |
| 2026-08-24 | 216 |
| 2026-08-31 | 216 |
| 2026-09-07 | 216 |
| 2026-09-14 | 216 |

### Figure 27. Vehicle fleet over time (model_year) [86]

| bin_start | bin_end | count |
| --- | --- | --- |
| 35 | 52.86 | 13 |
| 52.86 | 70.71 | 0 |
| 70.71 | 88.57 | 0 |
| 88.57 | 106.4 | 13 |
| 106.4 | 124.3 | 20 |
| 124.3 | 142.1 | 0 |
| 142.1 | 160 | 14 |

### Figure 28. Emerging hot spots: Delivery stops [87]

| bin_start | bin_end | count |
| --- | --- | --- |
| -1.381 | -0.5457 | 20 |
| -0.5457 | 0.2895 | 28 |
| 0.2895 | 1.125 | 17 |
| 1.125 | 1.96 | 11 |
| 1.96 | 2.795 | 3 |
| 2.795 | 3.631 | 5 |
| 3.631 | 4.466 | 1 |
| 4.466 | 5.301 | 3 |

### Figure 29. Repeat and near repeat classification: Delivery stops [88]

| period | series | value |
| --- | --- | --- |
| 2026-07-27 | delivered | 238 |
| 2026-07-27 | failed | 10 |
| 2026-08-03 | delivered | 227 |
| 2026-08-03 | failed | 24 |
| 2026-08-10 | delivered | 229 |
| 2026-08-10 | failed | 13 |
| 2026-08-17 | delivered | 233 |
| 2026-08-17 | failed | 15 |
| 2026-08-24 | delivered | 253 |
| 2026-08-24 | failed | 21 |
| 2026-08-31 | delivered | 222 |
| 2026-08-31 | failed | 16 |
| 2026-09-07 | delivered | 241 |
| 2026-09-07 | failed | 14 |
| 2026-09-14 | delivered | 234 |
| 2026-09-14 | failed | 10 |

### Figure 30. Summarize within: Delivery zones [89]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 0.2 | 7 |
| 0.2 | 0.4 | 0 |
| 0.4 | 0.6 | 0 |
| 0.6 | 0.8 | 0 |
| 0.8 | 1 | 3 |

### Figure 31. Summarize within: Delivery zones [90]

| bin_start | bin_end | count |
| --- | --- | --- |
| 62 | 145.2 | 6 |
| 145.2 | 228.4 | 1 |
| 228.4 | 311.6 | 1 |
| 311.6 | 394.8 | 0 |
| 394.8 | 478 | 2 |

### Figure 32. Summarize within: Delivery zones [91]

| bin_start | bin_end | count |
| --- | --- | --- |
| 1 | 3.2 | 5 |
| 3.2 | 5.4 | 2 |
| 5.4 | 7.6 | 1 |
| 7.6 | 9.8 | 1 |
| 9.8 | 12 | 1 |

### Figure 33. Hot spot analysis: Delivery stops [92]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 50 |
| -0.5 | 0 | not_significant | Not significant | 127 |
| 0 | 0.5 | not_significant | Not significant | 41 |
| 0.5 | 1 | not_significant | Not significant | 11 |
| 1 | 1.5 | not_significant | Not significant | 13 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 4 |
| 1.5 | 2 | not_significant | Not significant | 1 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 3 |
| 4 | 4.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 5.5 | 6 | hot_99 | Hot spot, 99% confidence | 1 |
| 6.5 | 7 | hot_99 | Hot spot, 99% confidence | 1 |
| 7.5 | 8 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 34. Hot spot analysis: Parcel lockers [93]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -2 | -1.5 | not_significant | Not significant | 2 |
| -1 | -0.5 | not_significant | Not significant | 6 |
| -0.5 | 0 | not_significant | Not significant | 1 |
| 0 | 0.5 | not_significant | Not significant | 5 |
| 1 | 1.5 | not_significant | Not significant | 3 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 2 |

### Figure 35. Hot spot analysis: Loading bays [94]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1.5 | -1 | not_significant | Not significant | 3 |
| -1 | -0.5 | not_significant | Not significant | 5 |
| -0.5 | 0 | not_significant | Not significant | 2 |
| 0 | 0.5 | not_significant | Not significant | 3 |
| 0.5 | 1 | not_significant | Not significant | 4 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 2 |

### Figure 36. Calculate density: Delivery stops [95]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 3.667 | 129 |
| 3.667 | 7.333 | 155 |
| 7.333 | 11 | 113 |
| 11 | 14.67 | 149 |
| 14.67 | 18.33 | 156 |
| 18.33 | 22 | 115 |
| 22 | 25.67 | 154 |
| 25.67 | 29.33 | 152 |
| 29.33 | 33 | 116 |
| 33 | 36.67 | 154 |
| 36.67 | 40.33 | 156 |
| 40.33 | 44 | 105 |

### Figure 37. Calculate density: Parcel lockers [96]

| bin_start | bin_end | count |
| --- | --- | --- |
| 2.197e-05 | 0.1726 | 32 |
| 0.1726 | 0.3451 | 15 |
| 0.3451 | 0.5177 | 11 |
| 0.5177 | 0.6903 | 15 |
| 0.6903 | 0.8628 | 9 |
| 0.8628 | 1.035 | 6 |
| 1.035 | 1.208 | 8 |
| 1.208 | 1.381 | 3 |

### Figure 38. Calculate density: Loading bays [97]

| bin_start | bin_end | count |
| --- | --- | --- |
| 3.822e-07 | 0.2412 | 35 |
| 0.2412 | 0.4823 | 15 |
| 0.4823 | 0.7235 | 12 |
| 0.7235 | 0.9647 | 13 |
| 0.9647 | 1.206 | 11 |
| 1.206 | 1.447 | 6 |
| 1.447 | 1.688 | 6 |
| 1.688 | 1.929 | 3 |

### Figure 39. Find point clusters: Delivery stops [98]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 1,570 | 0.785 | 78.5% |
| 0 | 402 | 0.201 | 20.1% |
| 1 | 28 | 0.014 | 1.4% |

### Figure 40. Find point clusters: cluster hulls [99]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.5 | 50.0% |
| 1 | 1 | 0.5 | 50.0% |

### Figure 41. Find point clusters: Parcel lockers [100]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 44 | 0.9778 | 97.8% |
| Noise | 1 | 0.02222 | 2.2% |

### Figure 42. Find point clusters: cluster hulls [101]

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

### Figure 43. Find point clusters: Loading bays [102]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 2 | 16 | 0.2667 | 26.7% |
| 1 | 15 | 0.25 | 25.0% |
| 0 | 12 | 0.2 | 20.0% |
| Noise | 7 | 0.1167 | 11.7% |
| 5 | 4 | 0.06667 | 6.7% |
| 3 | 3 | 0.05 | 5.0% |
| 4 | 3 | 0.05 | 5.0% |

### Figure 44. Find point clusters: cluster hulls [103]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.1667 | 16.7% |
| 1 | 1 | 0.1667 | 16.7% |
| 2 | 1 | 0.1667 | 16.7% |
| 3 | 1 | 0.1667 | 16.7% |
| 4 | 1 | 0.1667 | 16.7% |
| 5 | 1 | 0.1667 | 16.7% |

### Figure 45. Find outliers: Delivery zones [104]

| value | lag | type |
| --- | --- | --- |
| -0.2612 | -0.2991 | NS |
| -0.3242 | -0.2814 | NS |
| 0.0467 | -0.2927 | NS |
| 0.117 | 0.7266 | NS |
| 2.862 | 0.117 | NS |
| -0.8708 | -0.1672 | NS |
| -0.0732 | -0.3384 | NS |
| -0.6094 | -0.1465 | NS |
| -0.2323 | -0.6321 | NS |
| -0.6549 | -0.2323 | NS |

### Figure 46. Areas and lengths: Routes [105]

| bin_start | bin_end | count |
| --- | --- | --- |
| 9,145 | 1.29e+04 | 6 |
| 1.29e+04 | 1.665e+04 | 2 |
| 1.665e+04 | 2.04e+04 | 11 |
| 2.04e+04 | 2.415e+04 | 10 |
| 2.415e+04 | 2.79e+04 | 4 |
| 2.79e+04 | 3.165e+04 | 2 |
| 3.165e+04 | 3.54e+04 | 1 |

### Figure 47. Areas and lengths: Roadworks [106]

| period | series | value |
| --- | --- | --- |
| 2026-08-03 | water main | 1 |
| 2026-08-03 | metro | 0 |
| 2026-08-03 | resurfacing | 0 |
| 2026-08-10 | water main | 0 |
| 2026-08-10 | metro | 0 |
| 2026-08-10 | resurfacing | 0 |
| 2026-08-17 | water main | 1 |
| 2026-08-17 | metro | 0 |
| 2026-08-17 | resurfacing | 1 |
| 2026-08-24 | water main | 1 |
| 2026-08-24 | metro | 0 |
| 2026-08-24 | resurfacing | 0 |
| 2026-08-31 | water main | 0 |
| 2026-08-31 | metro | 1 |
| 2026-08-31 | resurfacing | 0 |
| 2026-09-07 | water main | 0 |
| 2026-09-07 | metro | 0 |
| 2026-09-07 | resurfacing | 0 |
| 2026-09-14 | water main | 0 |
| 2026-09-14 | metro | 0 |
| 2026-09-14 | resurfacing | 0 |
| 2026-09-21 | water main | 0 |
| 2026-09-21 | metro | 0 |
| 2026-09-21 | resurfacing | 0 |
| 2026-09-28 | water main | 1 |
| 2026-09-28 | metro | 0 |
| 2026-09-28 | resurfacing | 1 |
| 2026-10-05 | water main | 0 |
| 2026-10-05 | metro | 0 |
| 2026-10-05 | resurfacing | 0 |
| 2026-10-12 | water main | 1 |
| 2026-10-12 | metro | 1 |
| 2026-10-12 | resurfacing | 0 |
| 2026-10-19 | water main | 0 |
| 2026-10-19 | metro | 0 |
| 2026-10-19 | resurfacing | 0 |
| 2026-10-26 | water main | 0 |
| 2026-10-26 | metro | 0 |
| 2026-10-26 | resurfacing | 0 |
| 2026-11-02 | water main | 0 |
| 2026-11-02 | metro | 0 |
| 2026-11-02 | resurfacing | 1 |
| 2026-11-09 | water main | 0 |
| 2026-11-09 | metro | 0 |
| 2026-11-09 | resurfacing | 0 |
| 2026-11-16 | water main | 1 |
| 2026-11-16 | metro | 1 |
| 2026-11-16 | resurfacing | 0 |

### Figure 48. Areas and lengths: Roadworks over time [107]

| period | series | value |
| --- | --- | --- |
| 2026-08-03 | water main | 1 |
| 2026-08-03 | metro | 0 |
| 2026-08-03 | resurfacing | 0 |
| 2026-08-10 | water main | 0 |
| 2026-08-10 | metro | 0 |
| 2026-08-10 | resurfacing | 0 |
| 2026-08-17 | water main | 1 |
| 2026-08-17 | metro | 0 |
| 2026-08-17 | resurfacing | 1 |
| 2026-08-24 | water main | 1 |
| 2026-08-24 | metro | 0 |
| 2026-08-24 | resurfacing | 0 |
| 2026-08-31 | water main | 0 |
| 2026-08-31 | metro | 1 |
| 2026-08-31 | resurfacing | 0 |
| 2026-09-07 | water main | 0 |
| 2026-09-07 | metro | 0 |
| 2026-09-07 | resurfacing | 0 |
| 2026-09-14 | water main | 0 |
| 2026-09-14 | metro | 0 |
| 2026-09-14 | resurfacing | 0 |
| 2026-09-21 | water main | 0 |
| 2026-09-21 | metro | 0 |
| 2026-09-21 | resurfacing | 0 |
| 2026-09-28 | water main | 1 |
| 2026-09-28 | metro | 0 |
| 2026-09-28 | resurfacing | 1 |
| 2026-10-05 | water main | 0 |
| 2026-10-05 | metro | 0 |
| 2026-10-05 | resurfacing | 0 |
| 2026-10-12 | water main | 1 |
| 2026-10-12 | metro | 1 |
| 2026-10-12 | resurfacing | 0 |
| 2026-10-19 | water main | 0 |
| 2026-10-19 | metro | 0 |
| 2026-10-19 | resurfacing | 0 |
| 2026-10-26 | water main | 0 |
| 2026-10-26 | metro | 0 |
| 2026-10-26 | resurfacing | 0 |
| 2026-11-02 | water main | 0 |
| 2026-11-02 | metro | 0 |
| 2026-11-02 | resurfacing | 1 |
| 2026-11-09 | water main | 0 |
| 2026-11-09 | metro | 0 |
| 2026-11-09 | resurfacing | 0 |
| 2026-11-16 | water main | 1 |
| 2026-11-16 | metro | 1 |
| 2026-11-16 | resurfacing | 0 |

### Figure 49. Find point clusters: Delivery stops over time [108]

| period | series | value |
| --- | --- | --- |
| 2026-07-27 | delivered | 238 |
| 2026-07-27 | failed | 10 |
| 2026-08-03 | delivered | 227 |
| 2026-08-03 | failed | 24 |
| 2026-08-10 | delivered | 229 |
| 2026-08-10 | failed | 13 |
| 2026-08-17 | delivered | 233 |
| 2026-08-17 | failed | 15 |
| 2026-08-24 | delivered | 253 |
| 2026-08-24 | failed | 21 |
| 2026-08-31 | delivered | 222 |
| 2026-08-31 | failed | 16 |
| 2026-09-07 | delivered | 241 |
| 2026-09-07 | failed | 14 |
| 2026-09-14 | delivered | 234 |
| 2026-09-14 | failed | 10 |

### Figure 50. Repeat and near repeat classification: Delivery stops over time [109]

| period | series | value |
| --- | --- | --- |
| 2026-07-27 | delivered | 238 |
| 2026-07-27 | failed | 10 |
| 2026-08-03 | delivered | 227 |
| 2026-08-03 | failed | 24 |
| 2026-08-10 | delivered | 229 |
| 2026-08-10 | failed | 13 |
| 2026-08-17 | delivered | 233 |
| 2026-08-17 | failed | 15 |
| 2026-08-24 | delivered | 253 |
| 2026-08-24 | failed | 21 |
| 2026-08-31 | delivered | 222 |
| 2026-08-31 | failed | 16 |
| 2026-09-07 | delivered | 241 |
| 2026-09-07 | failed | 14 |
| 2026-09-14 | delivered | 234 |
| 2026-09-14 | failed | 10 |


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