# Sample: Retail, Manchester: report

Project: Sample: Retail, Manchester  
Goal: Describe the project Sample: Retail, Manchester: run the analyses that tell the most about its data, draw every result, and write a cited report.  
Run: fc8e4ab3-4293-4a39-95aa-e6a952c9b855 (running, 11 agent run(s))  
Generated: 2026-10-18T05:14:40Z, version 1  
Model cost: USD 0.0000 over 0 model call(s)  

## Data used

| Data | Kind | Rows | Time span | Summary | Source |
| --- | --- | --- | --- | --- | --- |
| Neighborhoods | vector, Polygon | 32 |  | 32 polygon (area) features; 'population' 3,781 to 26,441 (mean 9305); 'households' 1,591 to 11,295 (mean 3926); named by 'name' (e.g. Northgate, Southgate, Eastfield); total area 42.03 km2; extent -2.2952,53.4527 to -2.1888,53.5073. Areas (Neighborhoods) with population to compare and map. | [86] |
| Stores | vector, Point | 14 |  | 14 point features; 'format' has 3 values (top: express 8, superstore 3, metro 3); 'sales_gbp_week' 51,784 to 524,305 (mean 1.759e+05); 'floor_area_m2' 445 to 4,768 (mean 1625); 'opened_year' 1,996 to 2,024 (mean 2009); named by 'name' (e.g. Eastfield Superstore, Millbrook Superstore, Station Quarter Express); extent -2.2796,53.4607 to -2.2011,53.5019. Point locations of Stores by format. | [87] |
| Competitor stores | vector, Point | 70 |  | 70 point features; 'brand' has 5 values (top: Brand A 16, Brand E 15, Brand D 14); 'format' has 3 values (top: convenience 40, discount 21, supermarket 9); 'est_sales_gbp_week' 33,094 to 394,645 (mean 1.278e+05); extent -2.2910,53.4547 to -2.1909,53.5016. Point locations of Competitor stores by brand. | [88] |
| Trade areas | vector, Polygon | 14 |  | 14 polygon (area) features; 'households' 3,766 to 11,828 (mean 7821); 'spend_potential_gbp_week' 194,055 to 511,896 (mean 3.537e+05); named by 'name' (e.g. Eastfield Superstore trade area, Millbrook Superstore trade area, Station Quarter Express trade area); total area 42.03 km2; extent -2.2952,53.4527 to -2.1888,53.5073. Areas (Trade areas) with households to compare and map. | [89] |
| Loyalty transactions | vector, Point | 2,200 | 2026-04-01 to 2026-09-30 | 2,200 point features; dated by 'purchased_at' from 2026-04-01 to 2026-09-30; 'segment' has 4 values (top: family 786, professional 532, student 464); 'channel' has 3 values (top: in store 1531, click and collect 443, delivery 226); 'basket_gbp' 4.02 to 224.7 (mean 33.82); extent -2.2938,53.4534 to -2.1901,53.5066. Dated point events by segment: where and when Loyalty transactions happen. | [2] |
| Footfall counts by week | vector, Point | 780 | 2026-04-06 to 2026-09-28 | 780 point features; dated by 'date' from 2026-04-06 to 2026-09-28; 'visitors' 2,579 to 23,787 (mean 8873); extent -2.2893,53.4537 to -2.1929,53.5030. Dated point events: where and when Footfall counts by week happen. | [3] |
| Parking lots | vector, Point | 25 |  | 25 point features; 'type' has 2 values (top: multi-story 13, surface 12); 'spaces' 64 to 858 (mean 463.1); 'tariff_gbp_hour' 1.35 to 4.23 (mean 2.639); extent -2.2858,53.4557 to -2.1915,53.5046. Point locations of Parking lots by type. | [90] |
| Main roads | vector, LineString | 18 |  | 18 line features; 'class' has 1 values (top: A road 18); 'aadt' 13,250 to 31,246 (mean 1.988e+04); named by 'name' (e.g. Bury Road, Hyde Road, Bridge Street); total length 98.34 km; extent -2.2939,53.4535 to -2.1904,53.5064. Lines (Main roads) such as routes or networks. | [91] |
| Neighborhood demographics | table | 32 |  | Table of 32 rows and 6 columns without geometry; 'median_income_gbp' 20,009 to 46,150 (mean 3.21e+04); 'pct_age_65_plus' 8.1 to 23.4 (mean 16); 'pct_students' 2.1 to 36.8 (mean 20.3). A table of Neighborhood demographics without locations. | [92] |
| Weekly store sales | table | 364 | 2026-04-06 to 2026-09-28 | Table of 364 rows and 5 columns without geometry; dated by 'week_start' from 2026-04-06 to 2026-09-28; 'sales_gbp' 47,580 to 616,246 (mean 1.887e+05); 'transactions' 1,412 to 23,295 (mean 6598); 'avg_basket_gbp' 24.07 to 34 (mean 28.99). A table of Weekly store sales without locations. | [4] |
| Store network expansion memo | document |  |  | 1-page md document 'Store network expansion memo'; language en; mentions 5 place(s): Eastfield Superstore (1), Millbrook Superstore (1), Orchard Park (1), 53.46088, -2.20544 (1), Oakwood Superstore trade area (1). A document about Eastfield Superstore, Millbrook Superstore, Orchard Park. | [93] |
| Sample: Retail, Manchester | record |  |  |  | [94] |
| Hot spot analysis: Competitor stores | layer, Polygon | 43 |  |  | [95] |
| Hot spot analysis: Footfall counts by week | layer, Polygon | 130 |  |  | [96] |
| Emerging hot spots: Footfall counts by week | layer, Polygon | 25 |  |  | [97] |
| Emerging hot spots: Loyalty transactions | layer, Polygon | 395 |  |  | [98] |
| Calculate density: Competitor stores | layer, Polygon | 322 |  |  | [99] |
| Calculate density: Footfall counts by week | layer, Polygon | 428 |  |  | [100] |
| Calculate density: Loyalty transactions | layer, Polygon | 3,864 |  |  | [101] |
| Summarize within: Trade areas | layer, Polygon | 14 |  |  | [102] |
| Summarize within: Trade areas | layer, Polygon | 14 |  |  | [103] |
| Summarize within: Trade areas | layer, Polygon | 14 |  |  | [104] |
| Find point clusters: Stores | layer, Point | 14 |  |  | [105] |
| Find point clusters: cluster hulls | layer, Polygon | 1 |  |  | [106] |
| Find point clusters: Competitor stores | layer, Point | 70 |  |  | [107] |
| Find point clusters: cluster hulls | layer, Polygon | 2 |  |  | [108] |
| Find point clusters: Footfall counts by week | layer, Point | 780 |  |  | [109] |
| Find point clusters: cluster hulls | layer, Polygon | 30 |  |  | [110] |
| Hot spot analysis: Loyalty transactions | layer, Polygon | 1,071 |  |  | [111] |
| Repeat and near repeat classification: Footfall counts by week | layer, Point | 780 |  |  | [112] |

## Timeline

![Timeline: events per week by dataset](charts/23-timeline-events-per-week-by-dataset.png)

*Figure 1. Timeline: events per week by dataset [1]. Per week, 2026-03-30 to 2026-09-28. Source: Loyalty transactions, Footfall counts by week, Weekly store sales (3,344 features). The data is in the appendix.*

The timeline counts 3,344 dated events in Loyalty transactions, Footfall counts by week, Weekly store sales per week, from week of 2026-03-30 to week of 2026-09-28 [1][2][3][4]. By dataset: Loyalty transactions 2,200; Footfall counts by week 780; Weekly store sales 364 [1]. The trend is +1.27 events per week; the second half of the weeks has 1,741 against 1,484 in the first (+17.3%) [1].

**Busiest weeks**

- week of 2026-09-14: 158 events [1]
- week of 2026-08-31: 157 events [1]
- week of 2026-09-21: 151 events [1]

**Quietest weeks**

- week of 2026-03-30: 54 events [1]
- week of 2026-09-28: 91 events [1]
- week of 2026-06-22: 98 events [1]

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [5]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [5].

**Why it was run on this data.** 70 points: Getis-Ord Gi* tests whether the concentrations in Competitor stores are statistically significant [6].

**What it found.** Hot spot analysis was conducted using the Competitor stores layer [8]. The resulting dataset contained 43 features and 43 counts [7].

| Result | Value |
| --- | --- |
| result, bins | 43 |
| result, count | 43 |
| result, elapsed ms | 5 |
| result, cell size m | 1,000 |
| result, incidents used | 70 |
| result, distance band m | 2,000 |
| billing, cost | 0.0514 |
| billing, list price | 0.0514 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Competitor stores: Hot spot analysis: 1 layer saved: Hot spot analysis: Competitor stores (43 features) [7][8].
- **Evidenced:** It reported result, bins 43; result, count 43; result, elapsed ms 5; result, cell size m 1,000; result, incidents used 70; result, distance band m 2,000 [7].

*Tool gp_hot_spots; parameters {"project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Competitor stores (4c436f86)"}, "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 [5]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [5].

**Why it was run on this data.** 780 points: Getis-Ord Gi* tests whether the concentrations in Footfall counts by week are statistically significant [9].

**What it found.** Hot spots on Footfall counts by week: Hot spot analysis: 1 layer saved: Hot spot analysis: Footfall counts by week (130 features) [10][11]. It reported result, bins 130; result, count 130; result, elapsed ms 14; result, cell size m 500; result, incidents used 780; result, distance band m 500 [10].

| Result | Value |
| --- | --- |
| result, bins | 130 |
| result, count | 130 |
| result, elapsed ms | 14 |
| result, cell size m | 500 |
| result, incidents used | 780 |
| result, distance band m | 500 |
| billing, cost | 0.0656 |
| billing, list price | 0.0656 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Footfall counts by week: Hot spot analysis: 1 layer saved: Hot spot analysis: Footfall counts by week (130 features) [10][11].
- **Evidenced:** It reported result, bins 130; result, count 130; result, elapsed ms 14; result, cell size m 500; result, incidents used 780; result, distance band m 500 [10].

*Tool gp_hot_spots; parameters {"project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Footfall counts by week (f2397865)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 500.0}*

## 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 [12]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [12].

**Why it was run on this data.** Footfall counts by week carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per week [13].

**What it found.** Space time hot spots on Footfall counts by week: Emerging hot spots: 1 layer saved: Emerging hot spots: Footfall counts by week (25 features) [11][14]. It reported result, count 25; result, elapsed ms 11; result, resolution 8; result, features used 780; result, time neighbors 0; billing, cost 0.139 [14].

| Result | Value |
| --- | --- |
| result, count | 25 |
| result, elapsed ms | 11 |
| result, resolution | 8 |
| result, features used | 780 |
| result, time neighbors | 0 |
| billing, cost | 0.139 |
| billing, list price | 0.139 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Footfall counts by week: Emerging hot spots: 1 layer saved: Emerging hot spots: Footfall counts by week (25 features) [11][14].
- **Evidenced:** It reported result, count 25; result, elapsed ms 11; result, resolution 8; result, features used 780; result, time neighbors 0; billing, cost 0.139 [14].

*Tool gp_space_time_hot_spots; parameters {"project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Footfall counts by week (f2397865)"}, "interval": "week", "bin_shape": "h3", "resolution": 8, "time_field": "date"}*

## Space time hot spots

**What this analysis is.** Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time [12]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [12].

**Why it was run on this data.** Loyalty transactions carry dates in 'purchased_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week [15].

**What it found.** The analysis utilized the Loyalty transactions data [17] to identify emerging hot spots [16].

| Result | Value |
| --- | --- |
| result, count | 395 |
| result, elapsed ms | 66 |
| result, resolution | 9 |
| result, features used | 2,200 |
| result, time neighbors | 0 |
| billing, cost | 0.21 |
| billing, list price | 0.21 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Loyalty transactions: Emerging hot spots: 1 layer saved: Emerging hot spots: Loyalty transactions (395 features) [16][17].
- **Evidenced:** It reported result, count 395; result, elapsed ms 66; result, resolution 9; result, features used 2,200; result, time neighbors 0; billing, cost 0.21 [16].

*Tool gp_space_time_hot_spots; parameters {"project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Loyalty transactions (aa524a0b)"}, "interval": "week", "bin_shape": "h3", "resolution": 9, "time_field": "purchased_at"}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [18]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [18].

**Why it was run on this data.** 70 points in Competitor stores: a density surface shows where they concentrate [19].

**What it found.** Calculate density on Competitor stores: Calculate density: 1 layer saved: Calculate density: Competitor stores (322 features) [8][20]. It reported result, count 322; result, elapsed ms 15; result, bandwidth m 2,000; result, cell size m 500; result, excluded null 0; result, max density per km2 2.995 [20].

| Result | Value |
| --- | --- |
| result, count | 322 |
| result, elapsed ms | 15 |
| result, bandwidth m | 2,000 |
| result, cell size m | 500 |
| result, excluded null | 0 |
| result, max density per km2 | 2.995 |
| billing, cost | 0.0514 |
| billing, list price | 0.0514 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Competitor stores: Calculate density: 1 layer saved: Calculate density: Competitor stores (322 features) [8][20].
- **Evidenced:** It reported result, count 322; result, elapsed ms 15; result, bandwidth m 2,000; result, cell size m 500; result, excluded null 0; result, max density per km2 2.995 [20].

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

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Competitor stores (4c436f86)"}, "bandwidth_m": 2000.0, "cell_size_m": 500.0}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [18]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [18].

**Why it was run on this data.** 780 points in Footfall counts by week: a density surface shows where they concentrate [21].

**What it found.** Calculate density on Footfall counts by week: Calculate density: 1 layer saved: Calculate density: Footfall counts by week (428 features) [11][22]. It reported result, count 428; result, elapsed ms 19; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 195.5 [22].

| Result | Value |
| --- | --- |
| result, count | 428 |
| result, elapsed ms | 19 |
| result, bandwidth m | 500 |
| result, cell size m | 200 |
| result, excluded null | 0 |
| result, max density per km2 | 195.5 |
| billing, cost | 0.0656 |
| billing, list price | 0.0656 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Footfall counts by week: Calculate density: 1 layer saved: Calculate density: Footfall counts by week (428 features) [11][22].
- **Evidenced:** It reported result, count 428; result, elapsed ms 19; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 195.5 [22].

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

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Footfall counts by week (f2397865)"}, "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 [18]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [18].

**Why it was run on this data.** 2200 points in Loyalty transactions: a density surface shows where they concentrate [23].

**What it found.** Calculate density on Loyalty transactions: Calculate density: 1 layer saved: Calculate density: Loyalty transactions (3864 features) [17][24]. It reported result, count 3,864; result, elapsed ms 134; result, bandwidth m 200; result, cell size m 100; result, excluded null 0; result, max density per km2 386.8 [24].

| Result | Value |
| --- | --- |
| result, count | 3,864 |
| result, elapsed ms | 134 |
| result, bandwidth m | 200 |
| result, cell size m | 100 |
| result, excluded null | 0 |
| result, max density per km2 | 386.8 |
| billing, cost | 0.094 |
| billing, list price | 0.094 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Loyalty transactions: Calculate density: 1 layer saved: Calculate density: Loyalty transactions (3864 features) [17][24].
- **Evidenced:** It reported result, count 3,864; result, elapsed ms 134; result, bandwidth m 200; result, cell size m 100; result, excluded null 0; result, max density per km2 386.8 [24].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Loyalty transactions (aa524a0b)"}, "bandwidth_m": 200.0, "cell_size_m": 100.0}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [25]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [25].

**Why it was run on this data.** Count Stores inside each feature of Trade areas by format; the counts map as a choropleth (natural breaks, sequential colors) [26].

**What it found.** Summarize within on Stores, Trade areas: Summarize within: 1 layer saved: Summarize within: Trade areas (14 features) [27][28][29]. It reported result, count 14; result, elapsed ms 1; billing, cost 0.01058; billing, list price 0.01058; billing, upstream cost 0 [27].

| Result | Value |
| --- | --- |
| result, count | 14 |
| result, elapsed ms | 1 |
| billing, cost | 0.01058 |
| billing, list price | 0.01058 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Stores, Trade areas: Summarize within: 1 layer saved: Summarize within: Trade areas (14 features) [27][28][29].
- **Evidenced:** It reported result, count 14; result, elapsed ms 1; billing, cost 0.01058; billing, list price 0.01058; billing, upstream cost 0 [27].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "sales_gbp_week"}], "project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Stores (18363c3a)"}, "group_by": "format", "polygons": {"layer": "Trade areas (e712834d)"}}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [25]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [25].

**Why it was run on this data.** Count Stores inside each feature of Trade areas by format; the counts map as a choropleth (natural breaks, sequential colors) [26].

**What it found.** Summarize within on Competitor stores, Trade areas: Summarize within: 1 layer saved: Summarize within: Trade areas (14 features) [8][29][30]. It reported result, count 14; result, elapsed ms 0; billing, cost 0.0117; billing, list price 0.0117; billing, upstream cost 0 [30].

| Result | Value |
| --- | --- |
| result, count | 14 |
| result, elapsed ms | 0 |
| billing, cost | 0.0117 |
| billing, list price | 0.0117 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Competitor stores, Trade areas: Summarize within: 1 layer saved: Summarize within: Trade areas (14 features) [8][29][30].
- **Evidenced:** It reported result, count 14; result, elapsed ms 0; billing, cost 0.0117; billing, list price 0.0117; billing, upstream cost 0 [30].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "est_sales_gbp_week"}], "project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Competitor stores (4c436f86)"}, "group_by": "brand", "polygons": {"layer": "Trade areas (e712834d)"}}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [25]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [25].

**Why it was run on this data.** Count Stores inside each feature of Trade areas by format; the counts map as a choropleth (natural breaks, sequential colors) [26].

**What it found.** Summarize within on Footfall counts by week, Trade areas: Summarize within: 1 layer saved: Summarize within: Trade areas (14 features) [11][29][31]. It reported result, count 14; result, elapsed ms 2; billing, cost 0.0259; billing, list price 0.0259; billing, upstream cost 0 [31].

| Result | Value |
| --- | --- |
| result, count | 14 |
| result, elapsed ms | 2 |
| billing, cost | 0.0259 |
| billing, list price | 0.0259 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Footfall counts by week, Trade areas: Summarize within: 1 layer saved: Summarize within: Trade areas (14 features) [11][29][31].
- **Evidenced:** It reported result, count 14; result, elapsed ms 2; billing, cost 0.0259; billing, list price 0.0259; billing, upstream cost 0 [31].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "visitors"}], "project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Footfall counts by week (f2397865)"}, "polygons": {"layer": "Trade areas (e712834d)"}}*

## Find point clusters

**What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [32]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [32].

**Why it was run on this data.** DBSCAN finds dense groups of Stores and separates noise [33].

**What it found.** Find point clusters on Stores: Find point clusters: 2 layers saved: Find point clusters: Stores (14 features); Find point clusters: cluster hulls (1 feature) [28][34]. It reported result, count 14; result, noise 0; result, distance m 2,000; result, elapsed ms 5; result, min points 3; billing, cost 0.05028 [34].

| Result | Value |
| --- | --- |
| result, count | 14 |
| result, noise | 0 |
| result, distance m | 2,000 |
| result, elapsed ms | 5 |
| result, min points | 3 |
| billing, cost | 0.05028 |
| billing, list price | 0.05028 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Stores: Find point clusters: 2 layers saved: Find point clusters: Stores (14 features); Find point clusters: cluster hulls (1 feature) [28][34].
- **Evidenced:** It reported result, count 14; result, noise 0; result, distance m 2,000; result, elapsed ms 5; result, min points 3; billing, cost 0.05028 [34].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Stores (18363c3a)"}, "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 [32]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [32].

**Why it was run on this data.** DBSCAN finds dense groups of Competitor stores and separates noise [35].

**What it found.** The `gp_find_point_clusters` tool was executed using the Competitor stores dataset [8]. This analysis saved two layers, including Competitor stores with 70 features and cluster hulls with 2 features [36]. The tool returned 70 results and 2 noise points [36]. The entire process completed with an elapsed time of 3 milliseconds [36], and the associated billing cost was 0.0514 [36].

| Result | Value |
| --- | --- |
| result, count | 70 |
| result, noise | 2 |
| result, distance m | 1,000 |
| result, elapsed ms | 3 |
| result, min points | 3 |
| billing, cost | 0.0514 |
| billing, list price | 0.0514 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Competitor stores: Find point clusters: 2 layers saved: Find point clusters: Competitor stores (70 features); Find point clusters: cluster hulls (2 features) [8][36].
- **Evidenced:** It reported result, count 70; result, noise 2; result, distance m 1,000; result, elapsed ms 3; result, min points 3; billing, cost 0.0514 [36].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Competitor stores (4c436f86)"}, "distance_m": 1000.0, "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 [32]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [32].

**Why it was run on this data.** DBSCAN finds dense groups of Footfall counts by week and separates noise [37].

**What it found.** Find point clusters on Footfall counts by week: Find point clusters: 2 layers saved: Find point clusters: Footfall counts by week (780 features); Find point clusters: cluster hulls (30 features) [11][38]. It reported result, count 780; result, noise 0; result, distance m 200; result, elapsed ms 29; result, min points 20; billing, cost 0.0656 [38].

| Result | Value |
| --- | --- |
| result, count | 780 |
| result, noise | 0 |
| result, distance m | 200 |
| result, elapsed ms | 29 |
| result, min points | 20 |
| billing, cost | 0.0656 |
| billing, list price | 0.0656 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Footfall counts by week: Find point clusters: 2 layers saved: Find point clusters: Footfall counts by week (780 features); Find point clusters: cluster hulls (30 features) [11][38].
- **Evidenced:** It reported result, count 780; result, noise 0; result, distance m 200; result, elapsed ms 29; result, min points 20; billing, cost 0.0656 [38].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Footfall counts by week (f2397865)"}, "distance_m": 200.0, "min_points": 20}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [5]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [5].

**Why it was run on this data.** 2200 points: Getis-Ord Gi* tests whether the concentrations in Loyalty transactions are statistically significant [39].

**What it found.** Hot spots on Loyalty transactions: Hot spot analysis: 1 layer saved: Hot spot analysis: Loyalty transactions (1071 features) [17][40]. It reported result, bins 1,071; result, count 1,071; result, elapsed ms 59; result, cell size m 200; result, incidents used 2,200; result, distance band m 200 [40].

| Result | Value |
| --- | --- |
| result, bins | 1,071 |
| result, count | 1,071 |
| result, elapsed ms | 59 |
| result, cell size m | 200 |
| result, incidents used | 2,200 |
| result, distance band m | 200 |
| billing, cost | 0.094 |
| billing, list price | 0.094 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Loyalty transactions: Hot spot analysis: 1 layer saved: Hot spot analysis: Loyalty transactions (1071 features) [17][40].
- **Evidenced:** It reported result, bins 1,071; result, count 1,071; result, elapsed ms 59; result, cell size m 200; result, incidents used 2,200; result, distance band m 200 [40].

*Tool gp_hot_spots; parameters {"project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Loyalty transactions (aa524a0b)"}, "aggregation": "hexagon", "cell_size_m": 200.0, "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 [41]. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator [41].

**Why it was run on this data.** Near repeat analysis tests whether Footfall counts by week close in space also follow each other within 18 days [42].

**What it found.** Near repeat on Footfall counts by week: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Footfall counts by week (780 features) [11][43]. It reported result, days 18; result, count 780; result, chains 30; result, distance m 200; result, elapsed ms 40; result, pairs linked 1,470 [43].

| Result | Value |
| --- | --- |
| result, days | 18 |
| result, count | 780 |
| result, chains | 30 |
| result, distance m | 200 |
| result, elapsed ms | 40 |
| result, pairs linked | 1,470 |
| billing, cost | 0.0734 |
| billing, list price | 0.0734 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Near repeat on Footfall counts by week: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Footfall counts by week (780 features) [11][43].
- **Evidenced:** It reported result, days 18; result, count 780; result, chains 30; result, distance m 200; result, elapsed ms 40; result, pairs linked 1,470 [43].

*Tool gp_near_repeat; parameters {"days": 18, "project": "e415669e-4f91-46e1-8afb-4604f1d21ffb", "features": {"layer": "Footfall counts by week (f2397865)"}, "distance_m": 200.0, "time_field": "date"}*

## Maps and charts

The run also made 20 map image(s) [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63] and 22 chart(s) [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85].

![Hot spot analysis: Competitor stores](maps/01-hot-spot-analysis-competitor-stores.png)

*Figure 2. Hot spot analysis: Competitor stores [44]*

![Hot spot analysis: Footfall counts by week](maps/02-hot-spot-analysis-footfall-counts-by-week.png)

*Figure 3. Hot spot analysis: Footfall counts by week [45]*

![Emerging hot spots: Footfall counts by week](maps/03-emerging-hot-spots-footfall-counts-by-week.png)

*Figure 4. Emerging hot spots: Footfall counts by week [46]*

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

*Figure 5. Emerging hot spots: Loyalty transactions [47]*

![Calculate density: Competitor stores](maps/05-calculate-density-competitor-stores.png)

*Figure 6. Calculate density: Competitor stores [48]*

![Calculate density: Footfall counts by week](maps/06-calculate-density-footfall-counts-by-week.png)

*Figure 7. Calculate density: Footfall counts by week [49]*

![Calculate density: Loyalty transactions](maps/07-calculate-density-loyalty-transactions.png)

*Figure 8. Calculate density: Loyalty transactions [50]*

![Summarize within: Trade areas](maps/08-summarize-within-trade-areas.png)

*Figure 9. Summarize within: Trade areas [51]*

![Summarize within: Trade areas](maps/09-summarize-within-trade-areas.png)

*Figure 10. Summarize within: Trade areas [52]*

![Summarize within: Trade areas](maps/10-summarize-within-trade-areas.png)

*Figure 11. Summarize within: Trade areas [53]*

![Find point clusters: Stores](maps/11-find-point-clusters-stores.png)

*Figure 12. Find point clusters: Stores [54]*

![Find point clusters: cluster hulls](maps/12-find-point-clusters-cluster-hulls.png)

*Figure 13. Find point clusters: cluster hulls [55]*

![Find point clusters: Competitor stores](maps/13-find-point-clusters-competitor-stores.png)

*Figure 14. Find point clusters: Competitor stores [56]*

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

*Figure 15. Find point clusters: cluster hulls [57]*

![Find point clusters: Footfall counts by week](maps/15-find-point-clusters-footfall-counts-by-week.png)

*Figure 16. Find point clusters: Footfall counts by week [58]*

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

*Figure 17. Find point clusters: cluster hulls [59]*

![Trade areas by households](maps/17-trade-areas-by-households.png)

*Figure 18. Trade areas by households [60]*

![Neighborhoods by population](maps/18-neighborhoods-by-population.png)

*Figure 19. Neighborhoods by population [61]*

![Hot spot analysis: Loyalty transactions](maps/19-hot-spot-analysis-loyalty-transactions.png)

*Figure 20. Hot spot analysis: Loyalty transactions [62]*

![Repeat and near repeat classification: Footfall counts by week](maps/20-repeat-and-near-repeat-classification-footfall-counts-by-wee.png)

*Figure 21. Repeat and near repeat classification: Footfall counts by week [63]*

**What the map shows.** The "Hot spot analysis: Competitor stores" shows a noticeable cluster of high activity located in the [44]

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

![Stores over time (opened_year)](charts/01-stores-over-time-opened-year.png)

*Figure 22. Stores over time (opened_year) [64]. Source: Stores (14 features). The data is in the appendix.*

![Footfall counts by week over time (date)](charts/02-footfall-counts-by-week-over-time-date.png)

*Figure 23. Footfall counts by week over time (date) [65]. Per week, 2026-04-06 to 2026-09-28. Source: Footfall counts by week (780 features). The data is in the appendix.*

![Hot spot analysis: Competitor stores](charts/03-hot-spot-analysis-competitor-stores.png)

*Figure 24. Hot spot analysis: Competitor stores [66]. Source: Hot spot analysis: Competitor stores (43 features). The data is in the appendix.*

![Hot spot analysis: Footfall counts by week](charts/04-hot-spot-analysis-footfall-counts-by-week.png)

*Figure 25. Hot spot analysis: Footfall counts by week [67]. Source: Hot spot analysis: Footfall counts by week (130 features). The data is in the appendix.*

![Emerging hot spots: Footfall counts by week](charts/05-emerging-hot-spots-footfall-counts-by-week.png)

*Figure 26. Emerging hot spots: Footfall counts by week [68]. Source: Emerging hot spots: Footfall counts by week (25 features). The data is in the appendix.*

![Emerging hot spots: Loyalty transactions](charts/06-emerging-hot-spots-loyalty-transactions.png)

*Figure 27. Emerging hot spots: Loyalty transactions [69]. Source: Emerging hot spots: Loyalty transactions (395 features). The data is in the appendix.*

![Calculate density: Competitor stores](charts/07-calculate-density-competitor-stores.png)

*Figure 28. Calculate density: Competitor stores [70]. Source: Calculate density: Competitor stores (322 features). The data is in the appendix.*

![Calculate density: Footfall counts by week](charts/08-calculate-density-footfall-counts-by-week.png)

*Figure 29. Calculate density: Footfall counts by week [71]. Source: Calculate density: Footfall counts by week (428 features). The data is in the appendix.*

![Calculate density: Loyalty transactions](charts/09-calculate-density-loyalty-transactions.png)

*Figure 30. Calculate density: Loyalty transactions [72]. Source: Calculate density: Loyalty transactions (3,864 features). The data is in the appendix.*

![Summarize within: Trade areas](charts/10-summarize-within-trade-areas.png)

*Figure 31. Summarize within: Trade areas [73]. Source: Summarize within: Trade areas (14 features). The data is in the appendix.*

![Summarize within: Trade areas](charts/11-summarize-within-trade-areas.png)

*Figure 32. Summarize within: Trade areas [74]. Source: Summarize within: Trade areas (14 features). The data is in the appendix.*

![Summarize within: Trade areas](charts/12-summarize-within-trade-areas.png)

*Figure 33. Summarize within: Trade areas [75]. Source: Summarize within: Trade areas (14 features). The data is in the appendix.*

![Find point clusters: Stores](charts/13-find-point-clusters-stores.png)

*Figure 34. Find point clusters: Stores [76]. Source: Find point clusters: Stores (14 features). The data is in the appendix.*

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

*Figure 35. Find point clusters: cluster hulls [77]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.*

![Find point clusters: Competitor stores](charts/15-find-point-clusters-competitor-stores.png)

*Figure 36. Find point clusters: Competitor stores [78]. Source: Find point clusters: Competitor stores (70 features). The data is in the appendix.*

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

*Figure 37. Find point clusters: cluster hulls [79]. Source: Find point clusters: cluster hulls (2 features). The data is in the appendix.*

![Find point clusters: Footfall counts by week](charts/17-find-point-clusters-footfall-counts-by-week.png)

*Figure 38. Find point clusters: Footfall counts by week [80]. Period: 2026-04-06 to 2026-09-28 (date). Source: Find point clusters: Footfall counts by week (780 features). The data is in the appendix.*

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

*Figure 39. Find point clusters: cluster hulls [81]. Source: Find point clusters: cluster hulls (30 features). The data is in the appendix.*

![Hot spot analysis: Loyalty transactions](charts/19-hot-spot-analysis-loyalty-transactions.png)

*Figure 40. Hot spot analysis: Loyalty transactions [82]. Source: Hot spot analysis: Loyalty transactions (1,071 features). The data is in the appendix.*

![Repeat and near repeat classification: Footfall counts by week](charts/20-repeat-and-near-repeat-classification-footfall-counts-by-wee.png)

*Figure 41. Repeat and near repeat classification: Footfall counts by week [83]. Per week, 2026-04-06 to 2026-09-28. Source: Repeat and near repeat classification: Footfall counts by week (780 features). The data is in the appendix.*

![Repeat and near repeat classification: Footfall counts by week over time](charts/21-repeat-and-near-repeat-classification-footfall-counts-by-wee.png)

*Figure 42. Repeat and near repeat classification: Footfall counts by week over time [84]. Per week, 2026-04-06 to 2026-09-28. Source: Repeat and near repeat classification: Footfall counts by week (780 features). The data is in the appendix.*

![Find point clusters: Footfall counts by week over time](charts/22-find-point-clusters-footfall-counts-by-week-over-time.png)

*Figure 43. Find point clusters: Footfall counts by week over time [85]. Per week, 2026-04-06 to 2026-09-28. Source: Find point clusters: Footfall counts by week (780 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. 81 rows (id b6ec7ced-8908-403a-82e8-b707067abf11)  
[2] Dataset: Loyalty transactions. vector, 2200 rows (id c92f0326-813e-48e4-b969-2af33dd6f105)  
[3] Dataset: Footfall counts by week. vector, 780 rows (id b8b278cf-9521-499e-b4b7-e6f243cb9269)  
[4] Dataset: Weekly store sales. table, 364 rows (id 0fd06209-c790-4dc2-9c7b-6ef4117b8bf2)  
[5] 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)  
[6] Plan: Hot spots of Competitor stores (the Start plan's candidate c_768477a30749). 70 points: Getis-Ord Gi* tests whether the concentrations in Competitor stores are statistically significant. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_768477a30749)  
[7] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Competitor stores (43 features). (id b507865e-e41b-4810-90c3-5f92db83053c:0)  
[8] Layer: Competitor stores. 70 features (id 4c436f86-a921-4bc7-ab04-ec1160edd973)  
[9] Plan: Hot spots of Footfall counts by week (the Start plan's candidate c_e591398dec9c). 780 points: Getis-Ord Gi* tests whether the concentrations in Footfall counts by week are statistically significant. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_e591398dec9c)  
[10] Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Footfall counts by week (130 features). (id b507865e-e41b-4810-90c3-5f92db83053c:1)  
[11] Layer: Footfall counts by week. 780 features (id f2397865-1eae-4be9-8276-1cefdc53f340)  
[12] 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)  
[13] Plan: Space time hot spots of Footfall counts by week (the Start plan's candidate c_1fdb05aba998). Footfall counts by week carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per week. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_1fdb05aba998)  
[14] Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Footfall counts by week (25 features). (id 21998efd-79ff-497c-8ea5-61d8a01f2b62:0)  
[15] Plan: Space time hot spots of Loyalty transactions (the Start plan's candidate c_a7d38c7e2909). Loyalty transactions carry dates in 'purchased_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_a7d38c7e2909)  
[16] Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: Loyalty transactions (395 features). (id 21998efd-79ff-497c-8ea5-61d8a01f2b62:1)  
[17] Layer: Loyalty transactions. 2,200 features (id aa524a0b-a973-4a26-9744-54d5e4cfc549)  
[18] 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)  
[19] Plan: Kernel density of Competitor stores (the Start plan's candidate c_395aeab7e765). 70 points in Competitor stores: a density surface shows where they concentrate. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_395aeab7e765)  
[20] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Competitor stores (322 features). (id 430ebe02-9fb6-4905-a3f4-d3f833474337:0)  
[21] Plan: Kernel density of Footfall counts by week (the Start plan's candidate c_af8a7417d64e). 780 points in Footfall counts by week: a density surface shows where they concentrate. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_af8a7417d64e)  
[22] Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Footfall counts by week (428 features). (id 430ebe02-9fb6-4905-a3f4-d3f833474337:1)  
[23] Plan: Kernel density of Loyalty transactions (the Start plan's candidate c_17cb4893e203). 2200 points in Loyalty transactions: a density surface shows where they concentrate. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_17cb4893e203)  
[24] Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: Loyalty transactions (3864 features). (id 430ebe02-9fb6-4905-a3f4-d3f833474337:2)  
[25] 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)  
[26] Plan: Stores per feature of Trade areas (the Start plan's candidate c_2c5b6056dd2b). Count Stores inside each feature of Trade areas by format; the counts map as a choropleth (natural breaks, sequential colors). (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_2c5b6056dd2b)  
[27] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Trade areas (14 features). (id 19ff26ef-a2c9-471b-a599-9000ca42c12a:0)  
[28] Layer: Stores. 14 features (id 18363c3a-9918-4ce0-ab48-033ac000c916)  
[29] Layer: Trade areas. 14 features (id e712834d-448e-41b7-b460-06c5bedc46af)  
[30] Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Trade areas (14 features). (id 19ff26ef-a2c9-471b-a599-9000ca42c12a:1)  
[31] Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Trade areas (14 features). (id 19ff26ef-a2c9-471b-a599-9000ca42c12a:2)  
[32] 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)  
[33] Plan: Cluster points of Stores (the Start plan's candidate c_6507de56c2a3). DBSCAN finds dense groups of Stores and separates noise. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_6507de56c2a3)  
[34] Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Stores (14 features); Find point clusters: cluster hulls (1 feature). (id ca920f5a-83d3-4ecf-82d9-45a80921c7b9:0)  
[35] Plan: Cluster points of Competitor stores (the Start plan's candidate c_53b613e74a4d). DBSCAN finds dense groups of Competitor stores and separates noise. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_53b613e74a4d)  
[36] Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Competitor stores (70 features); Find point clusters: cluster hulls (2 features). (id ca920f5a-83d3-4ecf-82d9-45a80921c7b9:1)  
[37] Plan: Cluster points of Footfall counts by week (the Start plan's candidate c_d5d62e4a81d6). DBSCAN finds dense groups of Footfall counts by week and separates noise. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_d5d62e4a81d6)  
[38] Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Footfall counts by week (780 features); Find point clusters: cluster hulls (30 features). (id ca920f5a-83d3-4ecf-82d9-45a80921c7b9:2)  
[39] Plan: Hot spots of Loyalty transactions (the Start plan's candidate c_47bf1f0a2ecf). 2200 points: Getis-Ord Gi* tests whether the concentrations in Loyalty transactions are statistically significant. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_47bf1f0a2ecf)  
[40] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Loyalty transactions (1071 features). (id 5a6f1c3e-75be-4e48-a441-9e9439c69a91:0)  
[41] 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)  
[42] Plan: Near repeat of Footfall counts by week (the Start plan's candidate c_73acbbcef86d). Near repeat analysis tests whether Footfall counts by week close in space also follow each other within 18 days. (id 0b0bb769-454c-448a-a67b-f44e5a60fefc:c_73acbbcef86d)  
[43] Tool result: gp_near_repeat, step 0. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Footfall counts by week (780 features). (id 1f9ce8d2-9c80-423d-af47-a65148d65bf2:0)  
[44] Rendering: Map: Hot spot analysis: Competitor stores (id 41888450-f89e-4987-b1c8-dd5bf46def36)  
[45] Rendering: Map: Hot spot analysis: Footfall counts by week (id f93b9c24-c668-48a3-bbb7-19c453deff48)  
[46] Rendering: Map: Emerging hot spots: Footfall counts by week (id cb7f1de8-6f92-4f7b-851f-d1e165a7b7cc)  
[47] Rendering: Map: Emerging hot spots: Loyalty transactions (id 4b865cd2-b745-4c5f-8633-9afed6258161)  
[48] Rendering: Map: Calculate density: Competitor stores (id e400313e-2c8f-4e48-a9f7-25769b8d11a0)  
[49] Rendering: Map: Calculate density: Footfall counts by week (id f2d8ba2d-fb20-4c4c-a479-297dc0b8b234)  
[50] Rendering: Map: Calculate density: Loyalty transactions (id 3e01b21f-9a4f-4179-9e11-68a06635b4a7)  
[51] Rendering: Map: Summarize within: Trade areas (id d11fb076-b43e-46f5-90e6-45ae509035e4)  
[52] Rendering: Map: Summarize within: Trade areas (id 73cc3108-93e2-4779-b12c-3b92ec9c25a2)  
[53] Rendering: Map: Summarize within: Trade areas (id 924e39d5-2308-4dff-b4e4-f47f5728463f)  
[54] Rendering: Map: Find point clusters: Stores (id 78c40d3d-dee7-49cb-be7d-ef0ed8325367)  
[55] Rendering: Map: Find point clusters: cluster hulls (id e9a1e8eb-0435-4209-9bd4-f5dbf7b51be9)  
[56] Rendering: Map: Find point clusters: Competitor stores (id d3819598-0281-437f-992f-f0f54e12724e)  
[57] Rendering: Map: Find point clusters: cluster hulls (id 70d2c2da-7629-473a-9f6f-106e6d23bab1)  
[58] Rendering: Map: Find point clusters: Footfall counts by week (id 7112a329-ab18-44fb-a596-e82e1bee032d)  
[59] Rendering: Map: Find point clusters: cluster hulls (id bf8ff644-7688-4db7-8bb6-f3cc36fe7901)  
[60] Rendering: Map: Trade areas by households (id 33981aa9-656c-4956-b45f-c22bca1b6eeb)  
[61] Rendering: Map: Neighborhoods by population (id a8598722-b6b3-4fc1-93e3-d3453c8c18d7)  
[62] Rendering: Map: Hot spot analysis: Loyalty transactions (id fce9171a-60ec-4705-a035-aaccbcaba8e8)  
[63] Rendering: Map: Repeat and near repeat classification: Footfall counts by week (id e70b2db0-fea4-47cf-8ec3-7183fee9e834)  
[64] Rendering: Chart: Stores over time (opened_year). 5 rows (id 88a4e6a1-e252-4fad-bb98-135337862108)  
[65] Rendering: Chart: Footfall counts by week over time (date). 26 rows (id 6e13d856-d944-439a-8903-ae33a8ab0da3)  
[66] Rendering: Chart: Hot spot analysis: Competitor stores. 11 rows (id 9c3ad16e-b912-4594-a05a-87cded10ab78)  
[67] Rendering: Chart: Hot spot analysis: Footfall counts by week. 3 rows (id 0b831a4b-dee4-45be-b7d7-e986b73d50bc)  
[68] Rendering: Chart: Emerging hot spots: Footfall counts by week. 6 rows (id 789e8923-f0a6-4857-9ab7-b66bc1cd1c3f)  
[69] Rendering: Chart: Emerging hot spots: Loyalty transactions. 10 rows (id 813722a9-1e8c-4450-aec2-7394e0775dba)  
[70] Rendering: Chart: Calculate density: Competitor stores. 10 rows (id 7d20b087-1407-433c-8dc8-d356ce1f082e)  
[71] Rendering: Chart: Calculate density: Footfall counts by week. 10 rows (id 17d4ba42-821c-40ae-b3d1-7b35ca038652)  
[72] Rendering: Chart: Calculate density: Loyalty transactions. 13 rows (id f692141a-e0e4-42c1-ac87-80825e412def)  
[73] Rendering: Chart: Summarize within: Trade areas. 1 rows (id 8f97ef4e-3866-4c95-a751-112dfd457eef)  
[74] Rendering: Chart: Summarize within: Trade areas. 5 rows (id 35a513b9-93bb-465d-b882-5d348e39b6dc)  
[75] Rendering: Chart: Summarize within: Trade areas. 5 rows (id 8e3c214d-56fd-4e64-929e-30c9c09c29f5)  
[76] Rendering: Chart: Find point clusters: Stores. 1 rows (id b9c77e7c-1f5c-400c-bf75-b3aca56b70e7)  
[77] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id 467acba3-533e-407d-9fbd-23f5820d4419)  
[78] Rendering: Chart: Find point clusters: Competitor stores. 3 rows (id 6d7a778d-7a18-45e4-adf0-4bd41485ad7e)  
[79] Rendering: Chart: Find point clusters: cluster hulls. 2 rows (id eb67f5bb-ba8e-4094-b82d-ec0756a3a23a)  
[80] Rendering: Chart: Find point clusters: Footfall counts by week. 30 rows (id 45f91f53-c509-412f-a771-62cba1d96b4f)  
[81] Rendering: Chart: Find point clusters: cluster hulls. 30 rows (id 7541ae38-9abb-4836-9e35-aa37edae4d9e)  
[82] Rendering: Chart: Hot spot analysis: Loyalty transactions. 15 rows (id b9623d33-4a1f-466f-887c-2aecc180a640)  
[83] Rendering: Chart: Repeat and near repeat classification: Footfall counts by week. 52 rows (id 693cb1b3-8254-4cfb-8525-f6aab230d985)  
[84] Rendering: Chart: Repeat and near repeat classification: Footfall counts by week over time. 52 rows (id da2c2ad8-5c02-46ad-be96-5afb7abf90a7)  
[85] Rendering: Chart: Find point clusters: Footfall counts by week over time. 26 rows (id 43bccbb2-c646-4616-8780-2bd48f81912e)  
[86] Dataset: Neighborhoods. vector, 32 rows (id fa0b8017-5ddc-4e1f-a444-31fd07e52b97)  
[87] Dataset: Stores. vector, 14 rows (id 82660798-29d0-4c66-a9c0-444823fa0479)  
[88] Dataset: Competitor stores. vector, 70 rows (id 9c4d2224-f1d8-4fc0-8667-d94268e2460a)  
[89] Dataset: Trade areas. vector, 14 rows (id 5172e6d7-515f-4a32-89d2-408d49039a96)  
[90] Dataset: Parking lots. vector, 25 rows (id ef40dd81-eb91-41ff-a8ce-d61b98dfa2bc)  
[91] Dataset: Main roads. vector, 18 rows (id 1d4e054d-0664-4e98-9a81-0645bcbb69a7)  
[92] Dataset: Neighborhood demographics. table, 32 rows (id e76e880f-2f9b-4745-8cb1-c1977effb3ab)  
[93] Dataset: Store network expansion memo. document, ? rows (id 8f7f2a26-905f-48fc-8056-46e9aaa7cd21)  
[94] Record: Sample: Retail, Manchester (id e415669e-4f91-46e1-8afb-4604f1d21ffb)  
[95] Layer: Hot spot analysis: Competitor stores. 43 features (id b80aa42a-d8ea-4e32-8d49-6e5773e54e39)  
[96] Layer: Hot spot analysis: Footfall counts by week. 130 features (id f0b07952-61d1-4329-8f15-30737eeffd7b)  
[97] Layer: Emerging hot spots: Footfall counts by week. 25 features (id 0e4e31f2-68f4-4616-9467-013b9d001713)  
[98] Layer: Emerging hot spots: Loyalty transactions. 395 features (id a138b441-1405-4566-b2be-a105c298c8bc)  
[99] Layer: Calculate density: Competitor stores. 322 features (id 66cc8107-cef3-4817-88bc-3d844fef8c5d)  
[100] Layer: Calculate density: Footfall counts by week. 428 features (id 127e81c4-1813-4a14-bf8a-e02c15922197)  
[101] Layer: Calculate density: Loyalty transactions. 3,864 features (id c60805b0-dabf-417a-9c74-56991faf551d)  
[102] Layer: Summarize within: Trade areas. 14 features (id 7c9a641f-d6ec-4516-94be-bf77837866f9)  
[103] Layer: Summarize within: Trade areas. 14 features (id 18fcae7b-5cd3-43b0-8df8-db89ccdf1ce1)  
[104] Layer: Summarize within: Trade areas. 14 features (id aa777707-952c-4358-950b-2b0b19102664)  
[105] Layer: Find point clusters: Stores. 14 features (id 99f04d61-5c35-4915-81c3-07e1c20e77a9)  
[106] Layer: Find point clusters: cluster hulls. 1 features (id 307f2beb-8d40-4d28-80e7-f41546cea1f1)  
[107] Layer: Find point clusters: Competitor stores. 70 features (id b588c0ef-eaf1-43aa-9e46-eb574ee308cd)  
[108] Layer: Find point clusters: cluster hulls. 2 features (id 937090ee-0478-421a-b9ea-436ec735517e)  
[109] Layer: Find point clusters: Footfall counts by week. 780 features (id 9185731c-b554-4959-be37-e8b614083c36)  
[110] Layer: Find point clusters: cluster hulls. 30 features (id 589e2574-90d1-40c4-9a13-7a72b83fb52f)  
[111] Layer: Hot spot analysis: Loyalty transactions. 1,071 features (id e5715bae-7cde-4109-98c9-68e36ec45e14)  
[112] Layer: Repeat and near repeat classification: Footfall counts by week. 780 features (id e233d1b4-20ea-4a50-8578-dd34d19532fc)  

## Appendix: the data behind the charts

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

| period | series | value |
| --- | --- | --- |
| 2026-03-30 | Loyalty transactions | 54 |
| 2026-03-30 | Footfall counts by week | 0 |
| 2026-03-30 | Weekly store sales | 0 |
| 2026-04-06 | Loyalty transactions | 77 |
| 2026-04-06 | Footfall counts by week | 30 |
| 2026-04-06 | Weekly store sales | 14 |
| 2026-04-13 | Loyalty transactions | 90 |
| 2026-04-13 | Footfall counts by week | 30 |
| 2026-04-13 | Weekly store sales | 14 |
| 2026-04-20 | Loyalty transactions | 70 |
| 2026-04-20 | Footfall counts by week | 30 |
| 2026-04-20 | Weekly store sales | 14 |
| 2026-04-27 | Loyalty transactions | 80 |
| 2026-04-27 | Footfall counts by week | 30 |
| 2026-04-27 | Weekly store sales | 14 |
| 2026-05-04 | Loyalty transactions | 84 |
| 2026-05-04 | Footfall counts by week | 30 |
| 2026-05-04 | Weekly store sales | 14 |
| 2026-05-11 | Loyalty transactions | 83 |
| 2026-05-11 | Footfall counts by week | 30 |
| 2026-05-11 | Weekly store sales | 14 |
| 2026-05-18 | Loyalty transactions | 83 |
| 2026-05-18 | Footfall counts by week | 30 |
| 2026-05-18 | Weekly store sales | 14 |
| 2026-05-25 | Loyalty transactions | 63 |
| 2026-05-25 | Footfall counts by week | 30 |
| 2026-05-25 | Weekly store sales | 14 |
| 2026-06-01 | Loyalty transactions | 64 |
| 2026-06-01 | Footfall counts by week | 30 |
| 2026-06-01 | Weekly store sales | 14 |
| 2026-06-08 | Loyalty transactions | 73 |
| 2026-06-08 | Footfall counts by week | 30 |
| 2026-06-08 | Weekly store sales | 14 |
| 2026-06-15 | Loyalty transactions | 81 |
| 2026-06-15 | Footfall counts by week | 30 |
| 2026-06-15 | Weekly store sales | 14 |
| 2026-06-22 | Loyalty transactions | 54 |
| 2026-06-22 | Footfall counts by week | 30 |
| 2026-06-22 | Weekly store sales | 14 |
| 2026-06-29 | Loyalty transactions | 75 |
| 2026-06-29 | Footfall counts by week | 30 |
| 2026-06-29 | Weekly store sales | 14 |
| 2026-07-06 | Loyalty transactions | 79 |
| 2026-07-06 | Footfall counts by week | 30 |
| 2026-07-06 | Weekly store sales | 14 |
| 2026-07-13 | Loyalty transactions | 78 |
| 2026-07-13 | Footfall counts by week | 30 |
| 2026-07-13 | Weekly store sales | 14 |
| 2026-07-20 | Loyalty transactions | 76 |
| 2026-07-20 | Footfall counts by week | 30 |
| 2026-07-20 | Weekly store sales | 14 |
| 2026-07-27 | Loyalty transactions | 101 |
| 2026-07-27 | Footfall counts by week | 30 |
| 2026-07-27 | Weekly store sales | 14 |
| 2026-08-03 | Loyalty transactions | 105 |
| 2026-08-03 | Footfall counts by week | 30 |
| 2026-08-03 | Weekly store sales | 14 |
| 2026-08-10 | Loyalty transactions | 79 |
| 2026-08-10 | Footfall counts by week | 30 |
| 2026-08-10 | Weekly store sales | 14 |
| 2026-08-17 | Loyalty transactions | 85 |
| 2026-08-17 | Footfall counts by week | 30 |
| 2026-08-17 | Weekly store sales | 14 |
| 2026-08-24 | Loyalty transactions | 88 |
| 2026-08-24 | Footfall counts by week | 30 |
| 2026-08-24 | Weekly store sales | 14 |
| 2026-08-31 | Loyalty transactions | 113 |
| 2026-08-31 | Footfall counts by week | 30 |
| 2026-08-31 | Weekly store sales | 14 |
| 2026-09-07 | Loyalty transactions | 97 |
| 2026-09-07 | Footfall counts by week | 30 |
| 2026-09-07 | Weekly store sales | 14 |
| 2026-09-14 | Loyalty transactions | 114 |
| 2026-09-14 | Footfall counts by week | 30 |
| 2026-09-14 | Weekly store sales | 14 |
| 2026-09-21 | Loyalty transactions | 107 |
| 2026-09-21 | Footfall counts by week | 30 |
| 2026-09-21 | Weekly store sales | 14 |
| 2026-09-28 | Loyalty transactions | 47 |
| 2026-09-28 | Footfall counts by week | 30 |
| 2026-09-28 | Weekly store sales | 14 |

### Figure 22. Stores over time (opened_year) [64]

| bin_start | bin_end | count |
| --- | --- | --- |
| 5.178e+04 | 1.463e+05 | 8 |
| 1.463e+05 | 2.408e+05 | 3 |
| 2.408e+05 | 3.353e+05 | 0 |
| 3.353e+05 | 4.298e+05 | 1 |
| 4.298e+05 | 5.243e+05 | 2 |

### Figure 23. Footfall counts by week over time (date) [65]

| period | value |
| --- | --- |
| 2026-04-06 | 30 |
| 2026-04-13 | 30 |
| 2026-04-20 | 30 |
| 2026-04-27 | 30 |
| 2026-05-04 | 30 |
| 2026-05-11 | 30 |
| 2026-05-18 | 30 |
| 2026-05-25 | 30 |
| 2026-06-01 | 30 |
| 2026-06-08 | 30 |
| 2026-06-15 | 30 |
| 2026-06-22 | 30 |
| 2026-06-29 | 30 |
| 2026-07-06 | 30 |
| 2026-07-13 | 30 |
| 2026-07-20 | 30 |
| 2026-07-27 | 30 |
| 2026-08-03 | 30 |
| 2026-08-10 | 30 |
| 2026-08-17 | 30 |
| 2026-08-24 | 30 |
| 2026-08-31 | 30 |
| 2026-09-07 | 30 |
| 2026-09-14 | 30 |
| 2026-09-21 | 30 |
| 2026-09-28 | 30 |

### Figure 24. Hot spot analysis: Competitor stores [66]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -2 | -1.5 | not_significant | Not significant | 1 |
| -1.5 | -1 | not_significant | Not significant | 6 |
| -1 | -0.5 | not_significant | Not significant | 5 |
| -0.5 | 0 | not_significant | Not significant | 5 |
| 0 | 0.5 | not_significant | Not significant | 7 |
| 0.5 | 1 | not_significant | Not significant | 6 |
| 1 | 1.5 | not_significant | Not significant | 4 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 2 |
| 1.5 | 2 | not_significant | Not significant | 3 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 1 |
| 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 3 |

### Figure 25. Hot spot analysis: Footfall counts by week [67]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -0.5 | 0 | not_significant | Not significant | 103 |
| 1.5 | 2 | not_significant | Not significant | 24 |
| 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 3 |

### Figure 26. Emerging hot spots: Footfall counts by week [68]

| bin_start | bin_end | count |
| --- | --- | --- |
| -1.012 | -0.1687 | 4 |
| -0.1687 | 0.6747 | 6 |
| 0.6747 | 1.518 | 7 |
| 1.518 | 2.361 | 5 |
| 2.361 | 3.205 | 1 |
| 3.205 | 4.048 | 2 |

### Figure 27. Emerging hot spots: Loyalty transactions [69]

| bin_start | bin_end | count |
| --- | --- | --- |
| -0.941 | -0.2532 | 173 |
| -0.2532 | 0.4346 | 88 |
| 0.4346 | 1.122 | 64 |
| 1.122 | 1.81 | 33 |
| 1.81 | 2.498 | 0 |
| 2.498 | 3.186 | 10 |
| 3.186 | 3.874 | 11 |
| 3.874 | 4.561 | 13 |
| 4.561 | 5.249 | 2 |
| 5.249 | 5.937 | 1 |

### Figure 28. Calculate density: Competitor stores [70]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 2 | 19 |
| 2 | 4 | 32 |
| 4 | 6 | 34 |
| 6 | 8 | 33 |
| 8 | 10 | 31 |
| 10 | 12 | 32 |
| 12 | 14 | 36 |
| 14 | 16 | 36 |
| 16 | 18 | 35 |
| 18 | 20 | 34 |

### Figure 29. Calculate density: Footfall counts by week [71]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 3.6 | 28 |
| 3.6 | 7.2 | 58 |
| 7.2 | 10.8 | 57 |
| 10.8 | 14.4 | 54 |
| 14.4 | 18 | 46 |
| 18 | 21.6 | 23 |
| 21.6 | 25.2 | 43 |
| 25.2 | 28.8 | 29 |
| 28.8 | 32.4 | 49 |
| 32.4 | 36 | 41 |

### Figure 30. Calculate density: Loyalty transactions [72]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 5.538 | 278 |
| 5.538 | 11.08 | 364 |
| 11.08 | 16.62 | 303 |
| 16.62 | 22.15 | 362 |
| 22.15 | 27.69 | 299 |
| 27.69 | 33.23 | 353 |
| 33.23 | 38.77 | 288 |
| 38.77 | 44.31 | 362 |
| 44.31 | 49.85 | 269 |
| 49.85 | 55.38 | 293 |
| 55.38 | 60.92 | 238 |
| 60.92 | 66.46 | 263 |
| 66.46 | 72 | 192 |

### Figure 31. Summarize within: Trade areas [73]

| bin_start | bin_end | count |
| --- | --- | --- |
| 1 | 1 | 14 |

### Figure 32. Summarize within: Trade areas [74]

| bin_start | bin_end | count |
| --- | --- | --- |
| 3 | 4.2 | 6 |
| 4.2 | 5.4 | 5 |
| 5.4 | 6.6 | 0 |
| 6.6 | 7.8 | 1 |
| 7.8 | 9 | 2 |

### Figure 33. Summarize within: Trade areas [75]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 36.4 | 5 |
| 36.4 | 72.8 | 4 |
| 72.8 | 109.2 | 4 |
| 109.2 | 145.6 | 0 |
| 145.6 | 182 | 1 |

### Figure 34. Find point clusters: Stores [76]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 14 | 1 | 100.0% |

### Figure 35. Find point clusters: cluster hulls [77]

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

### Figure 36. Find point clusters: Competitor stores [78]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 65 | 0.9286 | 92.9% |
| 1 | 3 | 0.04286 | 4.3% |
| Noise | 2 | 0.02857 | 2.9% |

### Figure 37. Find point clusters: cluster hulls [79]

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

### Figure 38. Find point clusters: Footfall counts by week [80]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 26 | 0.03333 | 3.3% |
| 1 | 26 | 0.03333 | 3.3% |
| 10 | 26 | 0.03333 | 3.3% |
| 11 | 26 | 0.03333 | 3.3% |
| 12 | 26 | 0.03333 | 3.3% |
| 13 | 26 | 0.03333 | 3.3% |
| 14 | 26 | 0.03333 | 3.3% |
| 15 | 26 | 0.03333 | 3.3% |
| 16 | 26 | 0.03333 | 3.3% |
| 17 | 26 | 0.03333 | 3.3% |
| 18 | 26 | 0.03333 | 3.3% |
| 19 | 26 | 0.03333 | 3.3% |
| 2 | 26 | 0.03333 | 3.3% |
| 20 | 26 | 0.03333 | 3.3% |
| 21 | 26 | 0.03333 | 3.3% |
| 22 | 26 | 0.03333 | 3.3% |
| 23 | 26 | 0.03333 | 3.3% |
| 24 | 26 | 0.03333 | 3.3% |
| 25 | 26 | 0.03333 | 3.3% |
| 26 | 26 | 0.03333 | 3.3% |
| 27 | 26 | 0.03333 | 3.3% |
| 28 | 26 | 0.03333 | 3.3% |
| 29 | 26 | 0.03333 | 3.3% |
| 3 | 26 | 0.03333 | 3.3% |
| 4 | 26 | 0.03333 | 3.3% |
| 5 | 26 | 0.03333 | 3.3% |
| 6 | 26 | 0.03333 | 3.3% |
| 7 | 26 | 0.03333 | 3.3% |
| 8 | 26 | 0.03333 | 3.3% |
| 9 | 26 | 0.03333 | 3.3% |

### Figure 39. Find point clusters: cluster hulls [81]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.03333 | 3.3% |
| 1 | 1 | 0.03333 | 3.3% |
| 10 | 1 | 0.03333 | 3.3% |
| 11 | 1 | 0.03333 | 3.3% |
| 12 | 1 | 0.03333 | 3.3% |
| 13 | 1 | 0.03333 | 3.3% |
| 14 | 1 | 0.03333 | 3.3% |
| 15 | 1 | 0.03333 | 3.3% |
| 16 | 1 | 0.03333 | 3.3% |
| 17 | 1 | 0.03333 | 3.3% |
| 18 | 1 | 0.03333 | 3.3% |
| 19 | 1 | 0.03333 | 3.3% |
| 2 | 1 | 0.03333 | 3.3% |
| 20 | 1 | 0.03333 | 3.3% |
| 21 | 1 | 0.03333 | 3.3% |
| 22 | 1 | 0.03333 | 3.3% |
| 23 | 1 | 0.03333 | 3.3% |
| 24 | 1 | 0.03333 | 3.3% |
| 25 | 1 | 0.03333 | 3.3% |
| 26 | 1 | 0.03333 | 3.3% |
| 27 | 1 | 0.03333 | 3.3% |
| 28 | 1 | 0.03333 | 3.3% |
| 29 | 1 | 0.03333 | 3.3% |
| 3 | 1 | 0.03333 | 3.3% |
| 4 | 1 | 0.03333 | 3.3% |
| 5 | 1 | 0.03333 | 3.3% |
| 6 | 1 | 0.03333 | 3.3% |
| 7 | 1 | 0.03333 | 3.3% |
| 8 | 1 | 0.03333 | 3.3% |
| 9 | 1 | 0.03333 | 3.3% |

### Figure 40. Hot spot analysis: Loyalty transactions [82]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 335 |
| -0.5 | 0 | not_significant | Not significant | 423 |
| 0 | 0.5 | not_significant | Not significant | 107 |
| 0.5 | 1 | not_significant | Not significant | 65 |
| 1 | 1.5 | not_significant | Not significant | 48 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 16 |
| 1.5 | 2 | not_significant | Not significant | 24 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 15 |
| 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 10 |
| 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 12 |
| 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 10 |
| 4 | 4.5 | hot_99 | Hot spot, 99% confidence | 3 |
| 4.5 | 5 | hot_99 | Hot spot, 99% confidence | 1 |
| 5 | 5.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 6.5 | 7 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 41. Repeat and near repeat classification: Footfall counts by week [83]

| period | series | value |
| --- | --- | --- |
| 2026-04-06 | repeat | 0 |
| 2026-04-06 | originator | 30 |
| 2026-04-13 | repeat | 30 |
| 2026-04-13 | originator | 0 |
| 2026-04-20 | repeat | 30 |
| 2026-04-20 | originator | 0 |
| 2026-04-27 | repeat | 30 |
| 2026-04-27 | originator | 0 |
| 2026-05-04 | repeat | 30 |
| 2026-05-04 | originator | 0 |
| 2026-05-11 | repeat | 30 |
| 2026-05-11 | originator | 0 |
| 2026-05-18 | repeat | 30 |
| 2026-05-18 | originator | 0 |
| 2026-05-25 | repeat | 30 |
| 2026-05-25 | originator | 0 |
| 2026-06-01 | repeat | 30 |
| 2026-06-01 | originator | 0 |
| 2026-06-08 | repeat | 30 |
| 2026-06-08 | originator | 0 |
| 2026-06-15 | repeat | 30 |
| 2026-06-15 | originator | 0 |
| 2026-06-22 | repeat | 30 |
| 2026-06-22 | originator | 0 |
| 2026-06-29 | repeat | 30 |
| 2026-06-29 | originator | 0 |
| 2026-07-06 | repeat | 30 |
| 2026-07-06 | originator | 0 |
| 2026-07-13 | repeat | 30 |
| 2026-07-13 | originator | 0 |
| 2026-07-20 | repeat | 30 |
| 2026-07-20 | originator | 0 |
| 2026-07-27 | repeat | 30 |
| 2026-07-27 | originator | 0 |
| 2026-08-03 | repeat | 30 |
| 2026-08-03 | originator | 0 |
| 2026-08-10 | repeat | 30 |
| 2026-08-10 | originator | 0 |
| 2026-08-17 | repeat | 30 |
| 2026-08-17 | originator | 0 |
| 2026-08-24 | repeat | 30 |
| 2026-08-24 | originator | 0 |
| 2026-08-31 | repeat | 30 |
| 2026-08-31 | originator | 0 |
| 2026-09-07 | repeat | 30 |
| 2026-09-07 | originator | 0 |
| 2026-09-14 | repeat | 30 |
| 2026-09-14 | originator | 0 |
| 2026-09-21 | repeat | 30 |
| 2026-09-21 | originator | 0 |
| 2026-09-28 | repeat | 30 |
| 2026-09-28 | originator | 0 |

### Figure 42. Repeat and near repeat classification: Footfall counts by week over time [84]

| period | series | value |
| --- | --- | --- |
| 2026-04-06 | repeat | 0 |
| 2026-04-06 | originator | 30 |
| 2026-04-13 | repeat | 30 |
| 2026-04-13 | originator | 0 |
| 2026-04-20 | repeat | 30 |
| 2026-04-20 | originator | 0 |
| 2026-04-27 | repeat | 30 |
| 2026-04-27 | originator | 0 |
| 2026-05-04 | repeat | 30 |
| 2026-05-04 | originator | 0 |
| 2026-05-11 | repeat | 30 |
| 2026-05-11 | originator | 0 |
| 2026-05-18 | repeat | 30 |
| 2026-05-18 | originator | 0 |
| 2026-05-25 | repeat | 30 |
| 2026-05-25 | originator | 0 |
| 2026-06-01 | repeat | 30 |
| 2026-06-01 | originator | 0 |
| 2026-06-08 | repeat | 30 |
| 2026-06-08 | originator | 0 |
| 2026-06-15 | repeat | 30 |
| 2026-06-15 | originator | 0 |
| 2026-06-22 | repeat | 30 |
| 2026-06-22 | originator | 0 |
| 2026-06-29 | repeat | 30 |
| 2026-06-29 | originator | 0 |
| 2026-07-06 | repeat | 30 |
| 2026-07-06 | originator | 0 |
| 2026-07-13 | repeat | 30 |
| 2026-07-13 | originator | 0 |
| 2026-07-20 | repeat | 30 |
| 2026-07-20 | originator | 0 |
| 2026-07-27 | repeat | 30 |
| 2026-07-27 | originator | 0 |
| 2026-08-03 | repeat | 30 |
| 2026-08-03 | originator | 0 |
| 2026-08-10 | repeat | 30 |
| 2026-08-10 | originator | 0 |
| 2026-08-17 | repeat | 30 |
| 2026-08-17 | originator | 0 |
| 2026-08-24 | repeat | 30 |
| 2026-08-24 | originator | 0 |
| 2026-08-31 | repeat | 30 |
| 2026-08-31 | originator | 0 |
| 2026-09-07 | repeat | 30 |
| 2026-09-07 | originator | 0 |
| 2026-09-14 | repeat | 30 |
| 2026-09-14 | originator | 0 |
| 2026-09-21 | repeat | 30 |
| 2026-09-21 | originator | 0 |
| 2026-09-28 | repeat | 30 |
| 2026-09-28 | originator | 0 |

### Figure 43. Find point clusters: Footfall counts by week over time [85]

| period | value |
| --- | --- |
| 2026-04-06 | 30 |
| 2026-04-13 | 30 |
| 2026-04-20 | 30 |
| 2026-04-27 | 30 |
| 2026-05-04 | 30 |
| 2026-05-11 | 30 |
| 2026-05-18 | 30 |
| 2026-05-25 | 30 |
| 2026-06-01 | 30 |
| 2026-06-08 | 30 |
| 2026-06-15 | 30 |
| 2026-06-22 | 30 |
| 2026-06-29 | 30 |
| 2026-07-06 | 30 |
| 2026-07-13 | 30 |
| 2026-07-20 | 30 |
| 2026-07-27 | 30 |
| 2026-08-03 | 30 |
| 2026-08-10 | 30 |
| 2026-08-17 | 30 |
| 2026-08-24 | 30 |
| 2026-08-31 | 30 |
| 2026-09-07 | 30 |
| 2026-09-14 | 30 |
| 2026-09-21 | 30 |
| 2026-09-28 | 30 |


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