Banking

Sample: Banking, Mumbai: report

Project: Sample: Banking, Mumbai
Goal: Describe the project Sample: Banking, Mumbai: run the analyses that tell the most about its data, draw every result, and write a cited report.
Run: cd8e89bb-fb40-4fa7-a600-6d9a7cc0c9e5 (running, 11 agent run(s))
Generated: 2026-10-18T06:25:52Z, version 1
Model cost: USD 0.0000 over 0 model call(s)

Data used

Timeline

The timeline counts 3,053 dated events in New customer accounts, ATM faults, Transactions by area and month per month, from April 2025 to September 2026 1234. By dataset: New customer accounts 1,971; ATM faults 650; Transactions by area and month 432 1. The trend is +10.8 events per month; the second half of the months has 1,951 against 1,102 in the first (+77.0%) 1.

Busiest months

• August 2026: 293 events 1
• July 2026: 270 events 1
• April 2026: 258 events 1

Quietest months

• August 2025: 102 events 1
• February 2026: 112 events 1
• May 2025: 113 events 1

Space time hot spots

What this analysis is. Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time 5. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 5.

Why it was run on this data. ATM faults carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week 6.

What it found. The process saved a layer named Emerging hot spots: ATM faults, which contained 84 features 7. Using ATM faults as input 8, the operation completed with an elapsed time of 24 milliseconds 7. The analysis utilized 650 features and generated a count of 84 results 7.

Findings

• Evidenced: Space time hot spots on ATM faults: Emerging hot spots: 1 layer saved: Emerging hot spots: ATM faults (84 features) 78.
• Evidenced: It reported result, count 84; result, elapsed ms 24; result, resolution 8; result, features used 650; result, time neighbors 0; billing, cost 0.1325 7.

Tool gp_space_time_hot_spots; parameters {"project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "ATM faults (2bdf3635)"}, "interval": "week", "bin_shape": "h3", "resolution": 8, "time_field": "reported_at"}

Space time hot spots

What this analysis is. Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time 5. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 5.

Why it was run on this data. New customer accounts carry dates in 'opened_on': emerging hot spot analysis finds new, intensifying and fading hot spots per month 9.

What it found. Space time hot spots on New customer accounts: Emerging hot spots: 1 layer saved: Emerging hot spots: New customer accounts (106 features) 1011. It reported result, count 106; result, elapsed ms 21; result, resolution 8; result, features used 1,971; result, time neighbors 0; billing, cost 0.1986 10.

Findings

• Evidenced: Space time hot spots on New customer accounts: Emerging hot spots: 1 layer saved: Emerging hot spots: New customer accounts (106 features) 1011.
• Evidenced: It reported result, count 106; result, elapsed ms 21; result, resolution 8; result, features used 1,971; result, time neighbors 0; billing, cost 0.1986 10.

Tool gp_space_time_hot_spots; parameters {"project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "New customer accounts (e03b0fae)"}, "interval": "month", "bin_shape": "h3", "resolution": 8, "time_field": "opened_on"}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 12. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 12.

Why it was run on this data. Count ATM faults inside each feature of Service areas by fault; the counts map as a choropleth (natural breaks, sequential colors) 13.

What it found. The process of summarizing ATM faults using the Service areas feature set retrieved 24 features 14. This execution involved analyzing the Service areas 15 and clocked an elapsed time of 4 ms 14. The analysis reported a billing cost and list price of 0.0235, noting an upstream cost of 0 14.

Findings

• Evidenced: Summarize within on ATM faults, Service areas: Summarize within: 1 layer saved: Summarize within: Service areas (24 features) 81415.
• Evidenced: It reported result, count 24; result, elapsed ms 4; billing, cost 0.0235; billing, list price 0.0235; billing, upstream cost 0 14.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "downtime_h"}], "project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "ATM faults (2bdf3635)"}, "group_by": "fault", "polygons": {"layer": "Service areas (6344158f)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 12. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 12.

Why it was run on this data. Count ATM faults inside each feature of Service areas by fault; the counts map as a choropleth (natural breaks, sequential colors) 13.

What it found. The gp_summarize_within tool was executed to process Service areas 15 using ATMs 17 as input 16. This process resulted in a count of 24 for the service areas 16. Costs associated with this operation included a billing cost and list price of 0.0133 16. The upstream cost for the billing was zero 16.

Findings

• Evidenced: Summarize within on ATMs, Service areas: Summarize within: 1 layer saved: Summarize within: Service areas (24 features) 151617.
• Evidenced: It reported result, count 24; result, elapsed ms 1; billing, cost 0.0133; billing, list price 0.0133; billing, upstream cost 0 16.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "uptime_pct"}], "project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "ATMs (f9f05311)"}, "group_by": "location_type", "polygons": {"layer": "Service areas (6344158f)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 12. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 12.

Why it was run on this data. Count ATM faults inside each feature of Service areas by fault; the counts map as a choropleth (natural breaks, sequential colors) 13.

What it found. Summarize within on Branches, Service areas: Summarize within: 1 layer saved: Summarize within: Service areas (24 features) 151819. It reported result, count 24; result, elapsed ms 1; billing, cost 0.0111; billing, list price 0.0111; billing, upstream cost 0 18.

Findings

• Evidenced: Summarize within on Branches, Service areas: Summarize within: 1 layer saved: Summarize within: Service areas (24 features) 151819.
• Evidenced: It reported result, count 24; result, elapsed ms 1; billing, cost 0.0111; billing, list price 0.0111; billing, upstream cost 0 18.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "staff"}], "project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "Branches (8f400be9)"}, "group_by": "type", "polygons": {"layer": "Service areas (6344158f)"}}

Hot spots

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

Why it was run on this data. 650 points: Getis-Ord Gi* tests whether the concentrations in ATM faults are statistically significant 21.

What it found. Hot spots on ATM faults: Hot spot analysis: 1 layer saved: Hot spot analysis: ATM faults (240 features) 822. It reported result, bins 240; result, count 240; result, elapsed ms 19; result, cell size m 500; result, incidents used 650; result, distance band m 1,000 22.

Findings

• Evidenced: Hot spots on ATM faults: Hot spot analysis: 1 layer saved: Hot spot analysis: ATM faults (240 features) 822.
• Evidenced: It reported result, bins 240; result, count 240; result, elapsed ms 19; result, cell size m 500; result, incidents used 650; result, distance band m 1,000 22.

Tool gp_hot_spots; parameters {"project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "ATM faults (2bdf3635)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 1000.0}

Hot spots

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

Why it was run on this data. 140 points: Getis-Ord Gi* tests whether the concentrations in ATMs are statistically significant 23.

What it found. The hot spot analysis tool processed the ATMs layer to generate one layer containing 68 features 24.

Findings

• Evidenced: Hot spots on ATMs: Hot spot analysis: 1 layer saved: Hot spot analysis: ATMs (68 features) 1724.
• Evidenced: It reported result, bins 68; result, count 68; result, elapsed ms 5; result, cell size m 1,000; result, incidents used 140; result, distance band m 2,000 24.

Tool gp_hot_spots; parameters {"project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "ATMs (f9f05311)"}, "aggregation": "hexagon", "cell_size_m": 1000.0, "distance_band_m": 2000.0}

Hot spots

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

Why it was run on this data. 30 points: Getis-Ord Gi* tests whether the concentrations in Branches are statistically significant 25.

What it found. The hot spot analysis processed the Branches layer 19, saving one layer containing 16 features 26. The analysis utilized 30 incidents and generated 16 bins 26.

Findings

• Evidenced: Hot spots on Branches: Hot spot analysis: 1 layer saved: Hot spot analysis: Branches (16 features) 1926.
• Evidenced: It reported result, bins 16; result, count 16; result, elapsed ms 2; result, cell size m 2,000; result, incidents used 30; result, distance band m 2,000 26.

Tool gp_hot_spots; parameters {"project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "Branches (8f400be9)"}, "aggregation": "hexagon", "cell_size_m": 2000.0, "distance_band_m": 2000.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 27. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 27.

Why it was run on this data. 650 points in ATM faults: a density surface shows where they concentrate 28.

What it found. Calculate density on ATM faults: Calculate density: 1 layer saved: Calculate density: ATM faults (1893 features) 829. It reported result, count 1,893; result, elapsed ms 61; result, bandwidth m 1,000; result, cell size m 200; result, excluded null 0; result, max density per km2 45.95 29.

Findings

• Evidenced: Calculate density on ATM faults: Calculate density: 1 layer saved: Calculate density: ATM faults (1893 features) 829.
• Evidenced: It reported result, count 1,893; result, elapsed ms 61; result, bandwidth m 1,000; result, cell size m 200; result, excluded null 0; result, max density per km2 45.95 29.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "ATM faults (2bdf3635)"}, "bandwidth_m": 1000.0, "cell_size_m": 200.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 27. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 27.

Why it was run on this data. 140 points in ATMs: a density surface shows where they concentrate 30.

What it found. Calculate density on ATMs: Calculate density: 1 layer saved: Calculate density: ATMs (459 features) 1731. It reported result, count 459; result, elapsed ms 15; result, bandwidth m 2,000; result, cell size m 500; result, excluded null 0; result, max density per km2 3.678 31.

Findings

• Evidenced: Calculate density on ATMs: Calculate density: 1 layer saved: Calculate density: ATMs (459 features) 1731.
• Evidenced: It reported result, count 459; result, elapsed ms 15; result, bandwidth m 2,000; result, cell size m 500; result, excluded null 0; result, max density per km2 3.678 31.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "ATMs (f9f05311)"}, "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 27. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 27.

Why it was run on this data. 30 points in Branches: a density surface shows where they concentrate 32.

What it found. The density calculation successfully processed 88 branches 33. Parameters used in the calculation included a bandwidth of 2,000 m and a cell size of 1,000 m 33. The analysis generated a maximum density of 0.9778 per km² 33. The total billing cost associated with this calculation was 0.0506 33.

Findings

• Evidenced: Calculate density on Branches: Calculate density: 1 layer saved: Calculate density: Branches (88 features) 1933.
• Evidenced: It reported result, count 88; result, elapsed ms 3; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 0.9778 33.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "Branches (8f400be9)"}, "bandwidth_m": 2000.0, "cell_size_m": 1000.0}

Find point clusters

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

Why it was run on this data. DBSCAN finds dense groups of ATM faults and separates noise 35.

What it found. Find point clusters on ATM faults: Find point clusters: 2 layers saved: Find point clusters: ATM faults (650 features); Find point clusters: cluster hulls (14 features) 836. It reported result, count 650; result, noise 232; result, distance m 500; result, elapsed ms 14; result, min points 16; billing, cost 0.063 36.

Findings

• Evidenced: Find point clusters on ATM faults: Find point clusters: 2 layers saved: Find point clusters: ATM faults (650 features); Find point clusters: cluster hulls (14 features) 836.
• Evidenced: It reported result, count 650; result, noise 232; result, distance m 500; result, elapsed ms 14; result, min points 16; billing, cost 0.063 36.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "ATM faults (2bdf3635)"}, "distance_m": 500.0, "min_points": 16}

Find point clusters

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

Why it was run on this data. DBSCAN finds dense groups of ATMs and separates noise 37.

What it found. Find point clusters on ATMs: Find point clusters: 2 layers saved: Find point clusters: ATMs (140 features); Find point clusters: cluster hulls (1 feature) 1738. It reported result, count 140; result, noise 0; result, distance m 1,000; result, elapsed ms 3; result, min points 4; billing, cost 0.0528 38.

Findings

• Evidenced: Find point clusters on ATMs: Find point clusters: 2 layers saved: Find point clusters: ATMs (140 features); Find point clusters: cluster hulls (1 feature) 1738.
• Evidenced: It reported result, count 140; result, noise 0; result, distance m 1,000; result, elapsed ms 3; result, min points 4; billing, cost 0.0528 38.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "ATMs (f9f05311)"}, "distance_m": 1000.0, "min_points": 4}

Find point clusters

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

Why it was run on this data. DBSCAN finds dense groups of Branches and separates noise 39.

What it found. Find point clusters on Branches: Find point clusters: 2 layers saved: Find point clusters: Branches (30 features); Find point clusters: cluster hulls (1 feature) 1940. It reported result, count 30; result, noise 0; result, distance m 2,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0506 40.

Findings

• Evidenced: Find point clusters on Branches: Find point clusters: 2 layers saved: Find point clusters: Branches (30 features); Find point clusters: cluster hulls (1 feature) 1940.
• Evidenced: It reported result, count 30; result, noise 0; result, distance m 2,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0506 40.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "Branches (8f400be9)"}, "distance_m": 2000.0, "min_points": 3}

Outliers

What this analysis is. Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers 41. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) 41.

Why it was run on this data. Local Moran's I maps clusters and outliers of 'population' 42.

What it found. Outliers on Service areas: Find outliers: 1 layer saved: Find outliers: Service areas (24 features) 1543. It reported result, count 24; result, elapsed ms 6; result, permutations 199; result, excluded null 0; result, distance band m 1,844; billing, cost 0.05072 43.

Findings

• Evidenced: Outliers on Service areas: Find outliers: 1 layer saved: Find outliers: Service areas (24 features) 1543.
• Evidenced: It reported result, count 24; result, elapsed ms 6; result, permutations 199; result, excluded null 0; result, distance band m 1,844; billing, cost 0.05072 43.

Tool gp_outliers; parameters {"field": "population", "project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "Service areas (6344158f)"}, "aggregation": "hexagon"}

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 44. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator 44.

Why it was run on this data. Near repeat analysis tests whether ATM faults close in space also follow each other within 18 days 45.

What it found. Near repeat on ATM faults: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: ATM faults (650 features) 846. It reported result, days 18; result, count 650; result, chains 119; result, distance m 200; result, elapsed ms 23; result, pairs linked 690 46.

Findings

• Evidenced: Near repeat on ATM faults: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: ATM faults (650 features) 846.
• Evidenced: It reported result, days 18; result, count 650; result, chains 119; result, distance m 200; result, elapsed ms 23; result, pairs linked 690 46.

Tool gp_near_repeat; parameters {"days": 18, "project": "36cc8b7b-aabc-4b71-981b-a2c53e8f31ad", "features": {"layer": "ATM faults (2bdf3635)"}, "distance_m": 200.0, "time_field": "reported_at"}

Maps and charts

The run also made 20 map image(s) 4748495051525354555657585960616263646566 and 23 chart(s) 6768697071727374757677787980818283848586878889.

What the map shows. A highly defined cluster of emerging hot spots is visible in the upper-center portion of the map, indicating concentrated areas for new customer accounts. Within this core area, the color coding reveals a 48

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

Changes since the last run

This is the first report of this project.

Sources

1. 1Rendering: Chart: Timeline: events per month by dataset. 54 rows id 5350075e-73c1-474d-950e-d360606711fc
2. 2Dataset: New customer accounts. vector, 1971 rows id ca1a5fee-d2f1-4e7a-96ff-a2ae025365b9
3. 3Dataset: ATM faults. vector, 650 rows id 1a89d4d2-5bd2-4af8-8992-58556152d450
4. 4Dataset: Transactions by area and month. table, 432 rows id d861cdce-91fa-4661-9b4c-084d3fdfa2af
5. 5Catalog: 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
6. 6Plan: Space time hot spots of ATM faults (the Start plan's candidate c_11dd443868e3). ATM faults carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. id 8966c973-8846-4a37-90ad-665c80260ba6:c_11dd443868e3
7. 7Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: ATM faults (84 features). id b6f5a2bb-6262-4265-bcee-f2cfddd03ddb:0
8. 8Layer: ATM faults. 650 features id 2bdf3635-4c99-4580-a937-de55490e43f5
9. 9Plan: Space time hot spots of New customer accounts (the Start plan's candidate c_bb1839f07414). New customer accounts carry dates in 'opened_on': emerging hot spot analysis finds new, intensifying and fading hot spots per month. id 8966c973-8846-4a37-90ad-665c80260ba6:c_bb1839f07414
10. 10Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: New customer accounts (106 features). id b6f5a2bb-6262-4265-bcee-f2cfddd03ddb:1
11. 11Layer: New customer accounts. 1,971 features id e03b0fae-60ec-4079-913e-15d82b331701
12. 12Catalog: 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
13. 13Plan: ATM faults per feature of Service areas (the Start plan's candidate c_b3b30286944f). Count ATM faults inside each feature of Service areas by fault; the counts map as a choropleth (natural breaks, sequential colors). id 8966c973-8846-4a37-90ad-665c80260ba6:c_b3b30286944f
14. 14Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Service areas (24 features). id 55a6123f-a37d-4fea-bc56-f3134e981a4d:0
15. 15Layer: Service areas. 24 features id 6344158f-5202-4018-adf5-fef914f7efc7
16. 16Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Service areas (24 features). id 55a6123f-a37d-4fea-bc56-f3134e981a4d:1
17. 17Layer: ATMs. 140 features id f9f05311-d0d9-491b-b60e-76d6074147ae
18. 18Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Service areas (24 features). id 55a6123f-a37d-4fea-bc56-f3134e981a4d:2
19. 19Layer: Branches. 30 features id 8f400be9-7e82-4433-96cb-438a2b6ed3d3
20. 20Catalog: Hot spot analysis (operation catalog, gp_hot_spots). Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins. ArcGIS: Find Hot Spots, Hot Spot Analysis (Getis-Ord Gi*). QGIS: qgis:hotspot (plugin). id gp_hot_spots
21. 21Plan: Hot spots of ATM faults (the Start plan's candidate c_33af6577a5c2). 650 points: Getis-Ord Gi* tests whether the concentrations in ATM faults are statistically significant. id 8966c973-8846-4a37-90ad-665c80260ba6:c_33af6577a5c2
22. 22Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: ATM faults (240 features). id 2f94438c-e0a6-41b8-a152-e3151a6def70:0
23. 23Plan: Hot spots of ATMs (the Start plan's candidate c_eacb2979e4db). 140 points: Getis-Ord Gi* tests whether the concentrations in ATMs are statistically significant. id 8966c973-8846-4a37-90ad-665c80260ba6:c_eacb2979e4db
24. 24Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: ATMs (68 features). id 2f94438c-e0a6-41b8-a152-e3151a6def70:1
25. 25Plan: Hot spots of Branches (the Start plan's candidate c_b3e06b1ef787). 30 points: Getis-Ord Gi* tests whether the concentrations in Branches are statistically significant. id 8966c973-8846-4a37-90ad-665c80260ba6:c_b3e06b1ef787
26. 26Tool result: gp_hot_spots, step 2. Hot spot analysis: 1 layer saved: Hot spot analysis: Branches (16 features). id 2f94438c-e0a6-41b8-a152-e3151a6def70:2
27. 27Catalog: 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
28. 28Plan: Kernel density of ATM faults (the Start plan's candidate c_984c04544f87). 650 points in ATM faults: a density surface shows where they concentrate. id 8966c973-8846-4a37-90ad-665c80260ba6:c_984c04544f87
29. 29Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: ATM faults (1893 features). id 8c87dbcb-4a64-4e28-b285-e529fd8b7cc4:0
30. 30Plan: Kernel density of ATMs (the Start plan's candidate c_0c2ac7adbfb7). 140 points in ATMs: a density surface shows where they concentrate. id 8966c973-8846-4a37-90ad-665c80260ba6:c_0c2ac7adbfb7
31. 31Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: ATMs (459 features). id 8c87dbcb-4a64-4e28-b285-e529fd8b7cc4:1
32. 32Plan: Kernel density of Branches (the Start plan's candidate c_fbae95ca186a). 30 points in Branches: a density surface shows where they concentrate. id 8966c973-8846-4a37-90ad-665c80260ba6:c_fbae95ca186a
33. 33Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: Branches (88 features). id 8c87dbcb-4a64-4e28-b285-e529fd8b7cc4:2
34. 34Catalog: Find point clusters (operation catalog, gp_find_point_clusters). Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise. ArcGIS: Find Point Clusters, Density-based Clustering. QGIS: native:dbscanclustering, native:stdbscanclustering. id gp_find_point_clusters
35. 35Plan: Cluster points of ATM faults (the Start plan's candidate c_7a7e6112a7c4). DBSCAN finds dense groups of ATM faults and separates noise. id 8966c973-8846-4a37-90ad-665c80260ba6:c_7a7e6112a7c4
36. 36Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: ATM faults (650 features); Find point clusters: cluster hulls (14 features). id ddb39481-1f8b-4e18-afe9-2b1c4db1b0ef:0
37. 37Plan: Cluster points of ATMs (the Start plan's candidate c_0807abb77372). DBSCAN finds dense groups of ATMs and separates noise. id 8966c973-8846-4a37-90ad-665c80260ba6:c_0807abb77372
38. 38Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: ATMs (140 features); Find point clusters: cluster hulls (1 feature). id ddb39481-1f8b-4e18-afe9-2b1c4db1b0ef:1
39. 39Plan: Cluster points of Branches (the Start plan's candidate c_5c9c54534fb2). DBSCAN finds dense groups of Branches and separates noise. id 8966c973-8846-4a37-90ad-665c80260ba6:c_5c9c54534fb2
40. 40Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Branches (30 features); Find point clusters: cluster hulls (1 feature). id ddb39481-1f8b-4e18-afe9-2b1c4db1b0ef:2
41. 41Catalog: Find outliers (operation catalog, gp_outliers). Anselin local Moran’s I: HH, LL clusters and HL, LH outliers. ArcGIS: Find Outliers, Cluster and Outlier Analysis (Anselin Local Moran’s I). id gp_outliers
42. 42Plan: Local morans i of Service areas (the Start plan's candidate c_82c4828c335b). Local Moran's I maps clusters and outliers of 'population'. id 8966c973-8846-4a37-90ad-665c80260ba6:c_82c4828c335b
43. 43Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: Service areas (24 features). id ebdb002d-2e5b-42ca-870b-a29352cb798d:0
44. 44Catalog: 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
45. 45Plan: Near repeat of ATM faults (the Start plan's candidate c_0525e54ffa77). Near repeat analysis tests whether ATM faults close in space also follow each other within 18 days. id 8966c973-8846-4a37-90ad-665c80260ba6:c_0525e54ffa77
46. 46Tool result: gp_near_repeat, step 0. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: ATM faults (650 features). id decafdf2-8712-4857-a4f0-3ed34c0aec6c:0
47. 47Rendering: Map: Emerging hot spots: ATM faults id 76ee6f37-74c8-4c56-85ba-bc6f47108994
48. 48Rendering: Map: Emerging hot spots: New customer accounts id 09264e18-ba78-46fb-a2ce-7ab906f160d8
49. 49Rendering: Map: Summarize within: Service areas id ef256995-4467-474b-80ab-8a608ad3fd72
50. 50Rendering: Map: Summarize within: Service areas id 35df054b-4186-4e03-a63c-67a7f2baaa23
51. 51Rendering: Map: Summarize within: Service areas id 6fbc5d26-341e-436b-b24d-678ad5b8ef4b
52. 52Rendering: Map: Service areas by population id b341088d-2db3-4a29-8635-157f59123b60
53. 53Rendering: Map: Hot spot analysis: ATM faults id b6494a6c-b71f-443e-b39b-2d54712e5f62
54. 54Rendering: Map: Hot spot analysis: ATMs id ca6b7c1c-7a7b-4dc0-96b2-9562dc7d9dda
55. 55Rendering: Map: Hot spot analysis: Branches id 71bb9dc5-68ce-41c3-b0e3-97c35130725d
56. 56Rendering: Map: Calculate density: ATM faults id 61e8469a-ca23-498c-a844-56c1eb1808e4
57. 57Rendering: Map: Calculate density: ATMs id 050ba628-bb1f-487e-a378-542769c03674
58. 58Rendering: Map: Calculate density: Branches id 2bb0e1f9-4b39-400e-8731-95ab5d3eb007
59. 59Rendering: Map: Find point clusters: ATM faults id 4a3ddce9-b286-4982-b29f-e520074e1420
60. 60Rendering: Map: Find point clusters: cluster hulls id 60fbd3c3-41cf-4188-af84-ac3eb9ec45f4
61. 61Rendering: Map: Find point clusters: ATMs id f933545e-ee47-43ac-8a7c-7f5ab4f3a26a
62. 62Rendering: Map: Find point clusters: cluster hulls id 712b47f2-0a18-4783-bd5f-42b3ab911373
63. 63Rendering: Map: Find point clusters: Branches id baa9825c-0a0a-41a0-8deb-28d4fe342b5d
64. 64Rendering: Map: Find point clusters: cluster hulls id b7e4a16c-e3ce-489f-8e8e-7709e70f30c5
65. 65Rendering: Map: Find outliers: Service areas id 097cd92c-bdca-494e-8c39-c6c9f8f013ff
66. 66Rendering: Map: Repeat and near repeat classification: ATM faults id c50405e6-0141-4a62-a6fa-302fdbda1f8e
67. 67Rendering: Chart: ATM faults over time (reported_at). 108 rows id 7e58d05f-f91f-4b43-b9a7-53ede05aab3c
68. 68Rendering: Chart: Transactions by area and month over time (month). 18 rows id 43d54876-a680-44f0-8807-97d30add6c7b
69. 69Rendering: Chart: Emerging hot spots: ATM faults. 8 rows id 05240352-4094-459b-93b9-b0b39ff66388
70. 70Rendering: Chart: Emerging hot spots: New customer accounts. 8 rows id d171a240-e329-4555-b7f7-dd2e1a4eb630
71. 71Rendering: Chart: Summarize within: Service areas. 6 rows id 34d3fffd-c931-4253-8f1c-b9a3d02f33a2
72. 72Rendering: Chart: Summarize within: Service areas. 6 rows id cfdf43ab-0f4b-483a-b49f-f59ae44a4cb6
73. 73Rendering: Chart: Summarize within: Service areas. 6 rows id 102d8194-390f-4002-a602-f35d93571a68
74. 74Rendering: Chart: Hot spot analysis: ATM faults. 16 rows id afea17ba-ef2f-4c29-a788-e9eb93790b0a
75. 75Rendering: Chart: Hot spot analysis: ATMs. 9 rows id 06f70b45-9da8-41dd-b783-99cdc39a5ed1
76. 76Rendering: Chart: Hot spot analysis: Branches. 5 rows id 8dac2f16-e156-4c0c-a3bc-1abdd53acf19
77. 77Rendering: Chart: Calculate density: ATM faults. 12 rows id 5300cc79-ddc9-4789-ba0e-8117219edc08
78. 78Rendering: Chart: Calculate density: ATMs. 10 rows id 85a5ca6f-877a-4152-ad20-9962042d8773
79. 79Rendering: Chart: Calculate density: Branches. 8 rows id a4e1be4d-2170-4fa2-8eaf-e621a6e88889
80. 80Rendering: Chart: Find point clusters: ATM faults. 15 rows id 7475dab8-8ac2-451a-896a-2cba2b21ceb0
81. 81Rendering: Chart: Find point clusters: cluster hulls. 14 rows id 050ea633-9bb7-4f6f-b729-e48e19e2f29f
82. 82Rendering: Chart: Find point clusters: ATMs. 1 rows id 0a6689e7-beeb-407c-a93a-968bb7b5fd18
83. 83Rendering: Chart: Find point clusters: cluster hulls. 1 rows id b2c962a7-8efd-4811-b98b-b95c07a42f91
84. 84Rendering: Chart: Find point clusters: Branches. 1 rows id 51e26a99-7c7f-4f46-b42b-db4446790e9b
85. 85Rendering: Chart: Find point clusters: cluster hulls. 1 rows id 74c8188c-0eb1-4132-b48c-c6f932d42986
86. 86Rendering: Chart: Find outliers: Service areas. 24 rows id 685cf60b-64b0-4c10-abc0-8d35b5d3fe39
87. 87Rendering: Chart: Repeat and near repeat classification: ATM faults. 108 rows id 5fedd8da-8a3c-43d9-b089-3ef291f6037b
88. 88Rendering: Chart: Repeat and near repeat classification: ATM faults over time. 108 rows id 39e30229-db4d-4477-9cf4-6a0e357a9214
89. 89Rendering: Chart: Find point clusters: ATM faults over time. 108 rows id 15d42b9c-5969-49e7-92a7-e2d8d7ee3bed
90. 90Dataset: Branches. vector, 30 rows id 723808dc-deb7-4f84-8a71-4d9971c7481d
91. 91Dataset: ATMs. vector, 140 rows id 67e0988a-c269-4f42-9feb-d3f8cc8e6cce
92. 92Dataset: Service areas. vector, 24 rows id edb72b32-4d2f-4324-82c3-07d95a35855a
93. 93Dataset: Business clients. vector, 259 rows id fc7b0810-e2b6-4b63-b451-6145da616183
94. 94Dataset: Competitor branches. vector, 80 rows id dc85622c-0f83-43c4-b78a-de81f9514dc0
95. 95Dataset: Branch targets. table, 90 rows id 1805a967-c33f-4bec-bae7-33ddb672f5c2
96. 96Dataset: Suburban rail lines. vector, 3 rows id be0b9524-ec6b-46e9-8d03-12477e584565
97. 97Dataset: Branch network strategy memo. document, ? rows id a09d49c1-003f-46cd-89de-81aa969e1f47
98. 98Record: Sample: Banking, Mumbai id 36cc8b7b-aabc-4b71-981b-a2c53e8f31ad
99. 99Layer: Emerging hot spots: ATM faults. 84 features id 7a9e76b7-6a9c-4142-9b1b-2ba16950826e
100. 100Layer: Emerging hot spots: New customer accounts. 106 features id d2737b97-6e34-4724-9cc1-4fc26dc27ef0
101. 101Layer: Summarize within: Service areas. 24 features id 82ed72b3-93b8-44f3-aed9-b04c683016ac
102. 102Layer: Summarize within: Service areas. 24 features id 65d1f682-3dbc-430d-9611-73e246f61c59
103. 103Layer: Summarize within: Service areas. 24 features id b0085626-4a10-4142-a439-fee139d0d654
104. 104Layer: Hot spot analysis: ATM faults. 240 features id 2f93886f-42ab-46e3-9a7f-1af360604373
105. 105Layer: Hot spot analysis: ATMs. 68 features id a1c55487-2858-4142-ad20-bd6b5d7310eb
106. 106Layer: Hot spot analysis: Branches. 16 features id 35e39240-1bef-45a6-a63c-a0d865426146
107. 107Layer: Calculate density: ATM faults. 1,893 features id 8c0ee5b1-c9d1-478d-b5b1-c98fa86bd9f8
108. 108Layer: Calculate density: ATMs. 459 features id f79c3fc8-1034-4abe-a828-0eb4004201cd
109. 109Layer: Calculate density: Branches. 88 features id 1902ddf0-4227-4a92-ac7c-939bc604a5b3
110. 110Layer: Find point clusters: ATM faults. 650 features id 05658f04-f9ea-4c40-a4c5-30b7e4e77625
111. 111Layer: Find point clusters: cluster hulls. 14 features id de772841-b4f1-4f69-8eba-cfe862a45c01
112. 112Layer: Find point clusters: ATMs. 140 features id 4db94fe3-4761-4f5a-9ec9-577c48d7d13a
113. 113Layer: Find point clusters: cluster hulls. 1 features id a0af9a60-4b16-4861-a4d6-0aac7ae08c50
114. 114Layer: Find point clusters: Branches. 30 features id 139f426e-3703-4fb2-818e-11512ded6292
115. 115Layer: Find point clusters: cluster hulls. 1 features id 8dc3f93c-e454-4aa6-a12a-36f3ccd4a02a
116. 116Layer: Find outliers: Service areas. 24 features id 02024e38-4d51-481d-a141-856333b7c347
117. 117Layer: Repeat and near repeat classification: ATM faults. 650 features id c209894e-f1ec-4882-a054-494986b09a69