GeoBitLarge Spatial Model

Retail

Store network, competitors, trade areas and demographics

Manchester, England, United Kingdomsynthetic data, real placeMaps, charts and report
The question

Where do our customers concentrate and cluster, how does demand change over time, and which neighborhoods and trade areas hold the most customers?

A retailer uploads its stores, competitor stores, trade areas, neighborhoods with a demographics table, loyalty transactions by home location, footfall counts and weekly store sales. It wants to know where customers come from, where coverage has gaps and where demand sits near competitors.

53.4800, -2.2420data covers about 7 by 6 km
11datasets uploaded
18output layers
20maps made
23charts made
35findings
432 sStart run time

Every map and chart this run made is here. 20 map images, 23 charts and the cited report (PDF, Word, Markdown), as exported on 2026-10-22.

Findings (first few)

  • Evidenced: 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 1234.
  • Evidenced: By dataset: Loyalty transactions 2,200; Footfall counts by week 780; Weekly store sales 364 1.
  • Evidenced: 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.
  • Evidenced: The busiest weeks were week of 2026-09-14 (158), week of 2026-08-31 (157), week of 2026-09-21 (151) 1.
  • Evidenced: The quietest weeks were week of 2026-03-30 (54), week of 2026-09-28 (91), week of 2026-06-22 (98) 1.

What Start ran

Describe mode, the local model only (no paid model); run status succeeded, 8 analyst agents.

gp_calculate_densitygp_find_point_clustersgp_hot_spotsgp_near_repeatgp_space_time_hot_spotsgp_summarize_within

Maps

Every map image this run exported, with legend, north arrow and scale bar. Select one to view it full size.

Hot spot analysis: Competitor stores
Hot spot analysis: Competitor storesFig. 2
Hot spot analysis: Footfall counts by week
Hot spot analysis: Footfall counts by weekFig. 3
Emerging hot spots: Footfall counts by week
Emerging hot spots: Footfall counts by weekFig. 4
Emerging hot spots: Loyalty transactions
Emerging hot spots: Loyalty transactionsFig. 5
Calculate density: Competitor stores
Calculate density: Competitor storesFig. 6
Calculate density: Footfall counts by week
Calculate density: Footfall counts by weekFig. 7
Calculate density: Loyalty transactions
Calculate density: Loyalty transactionsFig. 8
Summarize within: Trade areas
Summarize within: Trade areasFig. 9
Summarize within: Trade areas
Summarize within: Trade areasFig. 10
Summarize within: Trade areas
Summarize within: Trade areasFig. 11
Find point clusters: Stores
Find point clusters: StoresFig. 12
Find point clusters: cluster hulls
Find point clusters: cluster hullsFig. 13
Find point clusters: Competitor stores
Find point clusters: Competitor storesFig. 14
Find point clusters: cluster hulls
Find point clusters: cluster hullsFig. 15
Find point clusters: Footfall counts by week
Find point clusters: Footfall counts by weekFig. 16
Find point clusters: cluster hulls
Find point clusters: cluster hullsFig. 17
Trade areas by households
Trade areas by householdsFig. 18
Neighborhoods by population
Neighborhoods by populationFig. 19
Hot spot analysis: Loyalty transactions
Hot spot analysis: Loyalty transactionsFig. 20
Repeat and near repeat classification: Footfall counts by week
Repeat and near repeat classification: Footfall counts by weekFig. 21

Charts

Every chart image this run exported.

Stores over time (opened_year)
Stores over time (opened_year)Fig. 22
Footfall counts by week over time (date)
Footfall counts by week over time (date)Fig. 23
Hot spot analysis: Competitor stores
Hot spot analysis: Competitor storesFig. 24
Hot spot analysis: Footfall counts by week
Hot spot analysis: Footfall counts by weekFig. 25
Emerging hot spots: Footfall counts by week
Emerging hot spots: Footfall counts by weekFig. 26
Emerging hot spots: Loyalty transactions
Emerging hot spots: Loyalty transactionsFig. 27
Calculate density: Competitor stores
Calculate density: Competitor storesFig. 28
Calculate density: Footfall counts by week
Calculate density: Footfall counts by weekFig. 29
Calculate density: Loyalty transactions
Calculate density: Loyalty transactionsFig. 30
Summarize within: Trade areas
Summarize within: Trade areasFig. 31
Summarize within: Trade areas
Summarize within: Trade areasFig. 32
Summarize within: Trade areas
Summarize within: Trade areasFig. 33
Find point clusters: Stores
Find point clusters: StoresFig. 34
Find point clusters: cluster hulls
Find point clusters: cluster hullsFig. 35
Find point clusters: Competitor stores
Find point clusters: Competitor storesFig. 36
Find point clusters: cluster hulls
Find point clusters: cluster hullsFig. 37
Find point clusters: Footfall counts by week
Find point clusters: Footfall counts by weekFig. 38
Find point clusters: cluster hulls
Find point clusters: cluster hullsFig. 39
Hot spot analysis: Loyalty transactions
Hot spot analysis: Loyalty transactionsFig. 40
Repeat and near repeat classification: Footfall counts by week
Repeat and near repeat classification: Footfall counts by weekFig. 41
Repeat and near repeat classification: Footfall counts by week over time
Repeat and near repeat classification: Footfall counts by week over timeFig. 42
Find point clusters: Footfall counts by week over time
Find point clusters: Footfall counts by week over timeFig. 43
Timeline: events per week by dataset
Timeline: events per week by datasetFig. 1

The cited report

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

DataKindRowsTime spanSummarySource
Neighborhoodsvector, Polygon3232 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
Storesvector, Point1414 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 storesvector, Point7070 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 areasvector, Polygon1414 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 transactionsvector, Point2,2002026-04-01 to 2026-09-302,200 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 weekvector, Point7802026-04-06 to 2026-09-28780 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 lotsvector, Point2525 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 roadsvector, LineString1818 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 demographicstable32Table 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 salestable3642026-04-06 to 2026-09-28Table 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 memodocument1-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, Manchesterrecord94
Hot spot analysis: Competitor storeslayer, Polygon4395
Hot spot analysis: Footfall counts by weeklayer, Polygon13096
Emerging hot spots: Footfall counts by weeklayer, Polygon2597
Emerging hot spots: Loyalty transactionslayer, Polygon39598
Calculate density: Competitor storeslayer, Polygon32299
Calculate density: Footfall counts by weeklayer, Polygon428100
Calculate density: Loyalty transactionslayer, Polygon3,864101
Summarize within: Trade areaslayer, Polygon14102
Summarize within: Trade areaslayer, Polygon14103
Summarize within: Trade areaslayer, Polygon14104
Find point clusters: Storeslayer, Point14105
Find point clusters: cluster hullslayer, Polygon1106
Find point clusters: Competitor storeslayer, Point70107
Find point clusters: cluster hullslayer, Polygon2108
Find point clusters: Footfall counts by weeklayer, Point780109
Find point clusters: cluster hullslayer, Polygon30110
Hot spot analysis: Loyalty transactionslayer, Polygon1,071111
Repeat and near repeat classification: Footfall counts by weeklayer, Point780112

Timeline

Timeline: events per week by dataset
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 1234. 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.

ResultValue
result, bins43
result, count43
result, elapsed ms5
result, cell size m1,000
result, incidents used70
result, distance band m2,000
billing, cost0.0514
billing, list price0.0514
billing, upstream cost0

Findings

  • Evidenced: Hot spots on Competitor stores: Hot spot analysis: 1 layer saved: Hot spot analysis: Competitor stores (43 features) 78.
  • 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) 1011. 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.

ResultValue
result, bins130
result, count130
result, elapsed ms14
result, cell size m500
result, incidents used780
result, distance band m500
billing, cost0.0656
billing, list price0.0656
billing, upstream cost0

Findings

  • Evidenced: Hot spots on Footfall counts by week: Hot spot analysis: 1 layer saved: Hot spot analysis: Footfall counts by week (130 features) 1011.
  • 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) 1114. 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.

ResultValue
result, count25
result, elapsed ms11
result, resolution8
result, features used780
result, time neighbors0
billing, cost0.139
billing, list price0.139
billing, upstream cost0

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) 1114.
  • 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.

ResultValue
result, count395
result, elapsed ms66
result, resolution9
result, features used2,200
result, time neighbors0
billing, cost0.21
billing, list price0.21
billing, upstream cost0

Findings

  • Evidenced: Space time hot spots on Loyalty transactions: Emerging hot spots: 1 layer saved: Emerging hot spots: Loyalty transactions (395 features) 1617.
  • 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) 820. 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.

ResultValue
result, count322
result, elapsed ms15
result, bandwidth m2,000
result, cell size m500
result, excluded null0
result, max density per km22.995
billing, cost0.0514
billing, list price0.0514
billing, upstream cost0

Findings

  • Evidenced: Calculate density on Competitor stores: Calculate density: 1 layer saved: Calculate density: Competitor stores (322 features) 820.
  • 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) 1122. 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.

ResultValue
result, count428
result, elapsed ms19
result, bandwidth m500
result, cell size m200
result, excluded null0
result, max density per km2195.5
billing, cost0.0656
billing, list price0.0656
billing, upstream cost0

Findings

  • Evidenced: Calculate density on Footfall counts by week: Calculate density: 1 layer saved: Calculate density: Footfall counts by week (428 features) 1122.
  • 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) 1724. 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.

ResultValue
result, count3,864
result, elapsed ms134
result, bandwidth m200
result, cell size m100
result, excluded null0
result, max density per km2386.8
billing, cost0.094
billing, list price0.094
billing, upstream cost0

Findings

  • Evidenced: Calculate density on Loyalty transactions: Calculate density: 1 layer saved: Calculate density: Loyalty transactions (3864 features) 1724.
  • 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) 272829. It reported result, count 14; result, elapsed ms 1; billing, cost 0.01058; billing, list price 0.01058; billing, upstream cost 0 27.

ResultValue
result, count14
result, elapsed ms1
billing, cost0.01058
billing, list price0.01058
billing, upstream cost0

Findings

  • Evidenced: Summarize within on Stores, Trade areas: Summarize within: 1 layer saved: Summarize within: Trade areas (14 features) 272829.
  • 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) 82930. It reported result, count 14; result, elapsed ms 0; billing, cost 0.0117; billing, list price 0.0117; billing, upstream cost 0 30.

ResultValue
result, count14
result, elapsed ms0
billing, cost0.0117
billing, list price0.0117
billing, upstream cost0

Findings

  • Evidenced: Summarize within on Competitor stores, Trade areas: Summarize within: 1 layer saved: Summarize within: Trade areas (14 features) 82930.
  • 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) 112931. It reported result, count 14; result, elapsed ms 2; billing, cost 0.0259; billing, list price 0.0259; billing, upstream cost 0 31.

ResultValue
result, count14
result, elapsed ms2
billing, cost0.0259
billing, list price0.0259
billing, upstream cost0

Findings

  • Evidenced: Summarize within on Footfall counts by week, Trade areas: Summarize within: 1 layer saved: Summarize within: Trade areas (14 features) 112931.
  • 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) 2834. 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.

ResultValue
result, count14
result, noise0
result, distance m2,000
result, elapsed ms5
result, min points3
billing, cost0.05028
billing, list price0.05028
billing, upstream cost0

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) 2834.
  • 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.

ResultValue
result, count70
result, noise2
result, distance m1,000
result, elapsed ms3
result, min points3
billing, cost0.0514
billing, list price0.0514
billing, upstream cost0

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) 836.
  • 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) 1138. 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.

ResultValue
result, count780
result, noise0
result, distance m200
result, elapsed ms29
result, min points20
billing, cost0.0656
billing, list price0.0656
billing, upstream cost0

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) 1138.
  • 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) 1740. 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.

ResultValue
result, bins1,071
result, count1,071
result, elapsed ms59
result, cell size m200
result, incidents used2,200
result, distance band m200
billing, cost0.094
billing, list price0.094
billing, upstream cost0

Findings

  • Evidenced: Hot spots on Loyalty transactions: Hot spot analysis: 1 layer saved: Hot spot analysis: Loyalty transactions (1071 features) 1740.
  • 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) 1143. 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.

ResultValue
result, days18
result, count780
result, chains30
result, distance m200
result, elapsed ms40
result, pairs linked1,470
billing, cost0.0734
billing, list price0.0734
billing, upstream cost0

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) 1143.
  • 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) 4445464748495051525354555657585960616263 and 22 chart(s) 64656667686970717273747576777879808182838485.

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

Figure 1. Timeline: events per week by dataset 1

periodseriesvalue
2026-03-30Loyalty transactions54
2026-03-30Footfall counts by week0
2026-03-30Weekly store sales0
2026-04-06Loyalty transactions77
2026-04-06Footfall counts by week30
2026-04-06Weekly store sales14
2026-04-13Loyalty transactions90
2026-04-13Footfall counts by week30
2026-04-13Weekly store sales14
2026-04-20Loyalty transactions70
2026-04-20Footfall counts by week30
2026-04-20Weekly store sales14
2026-04-27Loyalty transactions80
2026-04-27Footfall counts by week30
2026-04-27Weekly store sales14
2026-05-04Loyalty transactions84
2026-05-04Footfall counts by week30
2026-05-04Weekly store sales14
2026-05-11Loyalty transactions83
2026-05-11Footfall counts by week30
2026-05-11Weekly store sales14
2026-05-18Loyalty transactions83
2026-05-18Footfall counts by week30
2026-05-18Weekly store sales14
2026-05-25Loyalty transactions63
2026-05-25Footfall counts by week30
2026-05-25Weekly store sales14
2026-06-01Loyalty transactions64
2026-06-01Footfall counts by week30
2026-06-01Weekly store sales14
2026-06-08Loyalty transactions73
2026-06-08Footfall counts by week30
2026-06-08Weekly store sales14
2026-06-15Loyalty transactions81
2026-06-15Footfall counts by week30
2026-06-15Weekly store sales14
2026-06-22Loyalty transactions54
2026-06-22Footfall counts by week30
2026-06-22Weekly store sales14
2026-06-29Loyalty transactions75
2026-06-29Footfall counts by week30
2026-06-29Weekly store sales14
2026-07-06Loyalty transactions79
2026-07-06Footfall counts by week30
2026-07-06Weekly store sales14
2026-07-13Loyalty transactions78
2026-07-13Footfall counts by week30
2026-07-13Weekly store sales14
2026-07-20Loyalty transactions76
2026-07-20Footfall counts by week30
2026-07-20Weekly store sales14
2026-07-27Loyalty transactions101
2026-07-27Footfall counts by week30
2026-07-27Weekly store sales14
2026-08-03Loyalty transactions105
2026-08-03Footfall counts by week30
2026-08-03Weekly store sales14
2026-08-10Loyalty transactions79
2026-08-10Footfall counts by week30
2026-08-10Weekly store sales14
2026-08-17Loyalty transactions85
2026-08-17Footfall counts by week30
2026-08-17Weekly store sales14
2026-08-24Loyalty transactions88
2026-08-24Footfall counts by week30
2026-08-24Weekly store sales14
2026-08-31Loyalty transactions113
2026-08-31Footfall counts by week30
2026-08-31Weekly store sales14
2026-09-07Loyalty transactions97
2026-09-07Footfall counts by week30
2026-09-07Weekly store sales14
2026-09-14Loyalty transactions114
2026-09-14Footfall counts by week30
2026-09-14Weekly store sales14
2026-09-21Loyalty transactions107
2026-09-21Footfall counts by week30
2026-09-21Weekly store sales14
2026-09-28Loyalty transactions47
2026-09-28Footfall counts by week30
2026-09-28Weekly store sales14

Figure 22. Stores over time (opened_year) 64

bin_startbin_endcount
5.178e+041.463e+058
1.463e+052.408e+053
2.408e+053.353e+050
3.353e+054.298e+051
4.298e+055.243e+052

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

periodvalue
2026-04-0630
2026-04-1330
2026-04-2030
2026-04-2730
2026-05-0430
2026-05-1130
2026-05-1830
2026-05-2530
2026-06-0130
2026-06-0830
2026-06-1530
2026-06-2230
2026-06-2930
2026-07-0630
2026-07-1330
2026-07-2030
2026-07-2730
2026-08-0330
2026-08-1030
2026-08-1730
2026-08-2430
2026-08-3130
2026-09-0730
2026-09-1430
2026-09-2130
2026-09-2830

Figure 24. Hot spot analysis: Competitor stores 66

bin_startbin_endbandband_labelcount
-2-1.5not_significantNot significant1
-1.5-1not_significantNot significant6
-1-0.5not_significantNot significant5
-0.50not_significantNot significant5
00.5not_significantNot significant7
0.51not_significantNot significant6
11.5not_significantNot significant4
1.52hot_90Hot spot, 90% confidence2
1.52not_significantNot significant3
22.5hot_95Hot spot, 95% confidence1
2.53hot_99Hot spot, 99% confidence3

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

bin_startbin_endbandband_labelcount
-0.50not_significantNot significant103
1.52not_significantNot significant24
3.54hot_99Hot spot, 99% confidence3

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

bin_startbin_endcount
-1.012-0.16874
-0.16870.67476
0.67471.5187
1.5182.3615
2.3613.2051
3.2054.0482

Figure 27. Emerging hot spots: Loyalty transactions 69

bin_startbin_endcount
-0.941-0.2532173
-0.25320.434688
0.43461.12264
1.1221.8133
1.812.4980
2.4983.18610
3.1863.87411
3.8744.56113
4.5615.2492
5.2495.9371

Figure 28. Calculate density: Competitor stores 70

bin_startbin_endcount
0219
2432
4634
6833
81031
101232
121436
141636
161835
182034

Figure 29. Calculate density: Footfall counts by week 71

bin_startbin_endcount
03.628
3.67.258
7.210.857
10.814.454
14.41846
1821.623
21.625.243
25.228.829
28.832.449
32.43641

Figure 30. Calculate density: Loyalty transactions 72

bin_startbin_endcount
05.538278
5.53811.08364
11.0816.62303
16.6222.15362
22.1527.69299
27.6933.23353
33.2338.77288
38.7744.31362
44.3149.85269
49.8555.38293
55.3860.92238
60.9266.46263
66.4672192

Figure 31. Summarize within: Trade areas 73

bin_startbin_endcount
1114

Figure 32. Summarize within: Trade areas 74

bin_startbin_endcount
34.26
4.25.45
5.46.60
6.67.81
7.892

Figure 33. Summarize within: Trade areas 75

bin_startbin_endcount
036.45
36.472.84
72.8109.24
109.2145.60
145.61821

Figure 34. Find point clusters: Stores 76

clusterfeaturesfractionshare
0141100.0%

Figure 35. Find point clusters: cluster hulls 77

clusterfeaturesfractionshare
011100.0%

Figure 36. Find point clusters: Competitor stores 78

clusterfeaturesfractionshare
0650.928692.9%
130.042864.3%
Noise20.028572.9%

Figure 37. Find point clusters: cluster hulls 79

clusterfeaturesfractionshare
010.550.0%
110.550.0%

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

clusterfeaturesfractionshare
0260.033333.3%
1260.033333.3%
10260.033333.3%
11260.033333.3%
12260.033333.3%
13260.033333.3%
14260.033333.3%
15260.033333.3%
16260.033333.3%
17260.033333.3%
18260.033333.3%
19260.033333.3%
2260.033333.3%
20260.033333.3%
21260.033333.3%
22260.033333.3%
23260.033333.3%
24260.033333.3%
25260.033333.3%
26260.033333.3%
27260.033333.3%
28260.033333.3%
29260.033333.3%
3260.033333.3%
4260.033333.3%
5260.033333.3%
6260.033333.3%
7260.033333.3%
8260.033333.3%
9260.033333.3%

Figure 39. Find point clusters: cluster hulls 81

clusterfeaturesfractionshare
010.033333.3%
110.033333.3%
1010.033333.3%
1110.033333.3%
1210.033333.3%
1310.033333.3%
1410.033333.3%
1510.033333.3%
1610.033333.3%
1710.033333.3%
1810.033333.3%
1910.033333.3%
210.033333.3%
2010.033333.3%
2110.033333.3%
2210.033333.3%
2310.033333.3%
2410.033333.3%
2510.033333.3%
2610.033333.3%
2710.033333.3%
2810.033333.3%
2910.033333.3%
310.033333.3%
410.033333.3%
510.033333.3%
610.033333.3%
710.033333.3%
810.033333.3%
910.033333.3%

Figure 40. Hot spot analysis: Loyalty transactions 82

bin_startbin_endbandband_labelcount
-1-0.5not_significantNot significant335
-0.50not_significantNot significant423
00.5not_significantNot significant107
0.51not_significantNot significant65
11.5not_significantNot significant48
1.52hot_90Hot spot, 90% confidence16
1.52not_significantNot significant24
22.5hot_95Hot spot, 95% confidence15
2.53hot_99Hot spot, 99% confidence10
33.5hot_99Hot spot, 99% confidence12
3.54hot_99Hot spot, 99% confidence10
44.5hot_99Hot spot, 99% confidence3
4.55hot_99Hot spot, 99% confidence1
55.5hot_99Hot spot, 99% confidence1
6.57hot_99Hot spot, 99% confidence1

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

periodseriesvalue
2026-04-06repeat0
2026-04-06originator30
2026-04-13repeat30
2026-04-13originator0
2026-04-20repeat30
2026-04-20originator0
2026-04-27repeat30
2026-04-27originator0
2026-05-04repeat30
2026-05-04originator0
2026-05-11repeat30
2026-05-11originator0
2026-05-18repeat30
2026-05-18originator0
2026-05-25repeat30
2026-05-25originator0
2026-06-01repeat30
2026-06-01originator0
2026-06-08repeat30
2026-06-08originator0
2026-06-15repeat30
2026-06-15originator0
2026-06-22repeat30
2026-06-22originator0
2026-06-29repeat30
2026-06-29originator0
2026-07-06repeat30
2026-07-06originator0
2026-07-13repeat30
2026-07-13originator0
2026-07-20repeat30
2026-07-20originator0
2026-07-27repeat30
2026-07-27originator0
2026-08-03repeat30
2026-08-03originator0
2026-08-10repeat30
2026-08-10originator0
2026-08-17repeat30
2026-08-17originator0
2026-08-24repeat30
2026-08-24originator0
2026-08-31repeat30
2026-08-31originator0
2026-09-07repeat30
2026-09-07originator0
2026-09-14repeat30
2026-09-14originator0
2026-09-21repeat30
2026-09-21originator0
2026-09-28repeat30
2026-09-28originator0

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

periodseriesvalue
2026-04-06repeat0
2026-04-06originator30
2026-04-13repeat30
2026-04-13originator0
2026-04-20repeat30
2026-04-20originator0
2026-04-27repeat30
2026-04-27originator0
2026-05-04repeat30
2026-05-04originator0
2026-05-11repeat30
2026-05-11originator0
2026-05-18repeat30
2026-05-18originator0
2026-05-25repeat30
2026-05-25originator0
2026-06-01repeat30
2026-06-01originator0
2026-06-08repeat30
2026-06-08originator0
2026-06-15repeat30
2026-06-15originator0
2026-06-22repeat30
2026-06-22originator0
2026-06-29repeat30
2026-06-29originator0
2026-07-06repeat30
2026-07-06originator0
2026-07-13repeat30
2026-07-13originator0
2026-07-20repeat30
2026-07-20originator0
2026-07-27repeat30
2026-07-27originator0
2026-08-03repeat30
2026-08-03originator0
2026-08-10repeat30
2026-08-10originator0
2026-08-17repeat30
2026-08-17originator0
2026-08-24repeat30
2026-08-24originator0
2026-08-31repeat30
2026-08-31originator0
2026-09-07repeat30
2026-09-07originator0
2026-09-14repeat30
2026-09-14originator0
2026-09-21repeat30
2026-09-21originator0
2026-09-28repeat30
2026-09-28originator0

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

periodvalue
2026-04-0630
2026-04-1330
2026-04-2030
2026-04-2730
2026-05-0430
2026-05-1130
2026-05-1830
2026-05-2530
2026-06-0130
2026-06-0830
2026-06-1530
2026-06-2230
2026-06-2930
2026-07-0630
2026-07-1330
2026-07-2030
2026-07-2730
2026-08-0330
2026-08-1030
2026-08-1730
2026-08-2430
2026-08-3130
2026-09-0730
2026-09-1430
2026-09-2130
2026-09-2830

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

The data

The files this sample uploads: synthetic data generated at a real place. Download any of them and add them to a project of your own.

DatasetKindRowsTime spanFile
Neighborhoods
Neighborhoods with population and households.		vector, Polygon32neighbourhoods.geojson
Stores
Our stores with weekly sales.		vector, Point14stores.geojson
Competitor stores
Competitor stores by brand and format.		vector, Point70competitor_stores.geojson
Trade areas
Primary trade area of each store.		vector, Polygon14trade_areas.geojson
Loyalty transactions
Loyalty card transactions placed at the customer's home postal code sector (jittered). Dated by purchased_at (minute).		vector, Point2,2002026-04-01
to 2026-09-30		loyalty_transactions.geojson
Footfall counts by week
Weekly visitor counts at street footfall counters. Dated by date (week).		vector, Point7802026-04-06
to 2026-09-28		footfall_weekly.geojson
Parking lots
Public parking lots with spaces and tariff.		vector, Point25car_parks.geojson
Main roadsvector, LineString18main_roads.geojson (not in the current sample data files)
Neighborhood demographics
Census-style demographics per neighborhood (joins to Neighborhoods on area_code).		table32demographics.csv
Weekly store sales
Weekly sales per store (joins to Stores on store_id). Dated by week_start (week).		table3642026-04-06
to 2026-09-28		weekly_store_sales.csv
Store network expansion memo
A memo naming stores, neighborhoods and a candidate site.		documentexpansion_memo.md, expansion_memo.pdf

What the data was built to show

  • Loyalty customers concentrate around the two superstores (hot spots and density).
  • The south-east near 53.46088, -2.20544 is underserved: few customers and no nearby store.
  • Footfall jumps in week 21 at every counter; express store sales rise in August.

Expected analysis families: aggregation, hot_spots, density, space_time, clusters.

How the product read each dataset

The profile written when the data was added, before Start; no model call is needed for it.

  • Neighborhoods: 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.
  • Stores: 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.
  • Competitor stores: 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.
  • Trade areas: 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.
  • Loyalty transactions: 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.
  • Footfall counts by week: 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.
  • Parking lots: 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.
  • Main roads: 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.
  • Neighborhood demographics: 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).
  • Weekly store sales: 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).
  • Store network expansion memo: 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).

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