Insurance

Sample: Insurance, Brisbane: report

Project: Sample: Insurance, Brisbane
Goal: Describe the project Sample: Insurance, Brisbane: run the analyses that tell the most about its data, draw every result, and write a cited report.
Run: d38c36b4-096d-4cf3-8fe0-eb532bd8bcd0 (running, 11 agent run(s))
Generated: 2026-10-18T05:07:17Z, version 1
Model cost: USD 0.0000 over 0 model call(s)

Data used

Timeline

The timeline counts 908 dated events in Storm and hail footprints, Claims per quarter, from Q1 2022 to Q3 2026 123. By dataset: Claims 900; Storm and hail footprints 8 1. The trend is +1.5 events per quarter; the second half of the quarters has 616 against 272 in the first (+126.5%) 1.

Busiest quarters

• Q1 2025: 267 events 1
• Q4 2024: 182 events 1
• Q4 2023: 47 events 1

Quietest quarters

• Q3 2026: 9 events 1
• Q3 2022: 15 events 1
• Q3 2024: 17 events 1

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 4. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 4.

Why it was run on this data. Count Claims inside each feature of Storm and hail footprints by peril; the counts map as a choropleth (natural breaks, sequential colors) 5.

What it found. The analysis utilized the gp_summarize_within tool 6. This process required the inputs Claims 7 and Storm and hail footprints 8.

Findings

• Evidenced: Summarize within on Claims, Storm and hail footprints: Summarize within: 1 layer saved: Summarize within: Storm and hail footprints (8 features) 678.
• Evidenced: It reported result, count 8; result, elapsed ms 2; billing, cost 0.02818; billing, list price 0.02818; billing, upstream cost 0 6.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "paid_aud"}], "project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Claims (1572e189)"}, "group_by": "peril", "polygons": {"layer": "Storm and hail footprints (2681856b)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 4. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 4.

Why it was run on this data. Count Claims inside each feature of Storm and hail footprints by peril; the counts map as a choropleth (natural breaks, sequential colors) 5.

What it found. Summarize within on River gauges, Storm and hail footprints: Summarize within: 1 layer saved: Summarize within: Storm and hail footprints (8 features) 8910. It reported result, count 8; result, elapsed ms 0; billing, cost 0.01034; billing, list price 0.01034; billing, upstream cost 0 9.

Findings

• Evidenced: Summarize within on River gauges, Storm and hail footprints: Summarize within: 1 layer saved: Summarize within: Storm and hail footprints (8 features) 8910.
• Evidenced: It reported result, count 8; result, elapsed ms 0; billing, cost 0.01034; billing, list price 0.01034; billing, upstream cost 0 9.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "minor_flood_m"}], "project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "River gauges (af5a84eb)"}, "polygons": {"layer": "Storm and hail footprints (2681856b)"}}

Hot spots

What this analysis is. Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins 11. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 11.

Why it was run on this data. 900 points: Getis-Ord Gi* tests whether the concentrations in Claims are statistically significant 12.

What it found. The hot spot analysis was executed using Claims data as the input 7. The tool saved one layer containing 207 features as results 13. The analysis was run using a distance band of 500 m and utilized 900 incidents 13.

Findings

• Evidenced: Hot spots on Claims: Hot spot analysis: 1 layer saved: Hot spot analysis: Claims (207 features) 713.
• Evidenced: It reported result, bins 207; result, count 207; result, elapsed ms 13; result, cell size m 500; result, incidents used 900; result, distance band m 500 13.

Tool gp_hot_spots; parameters {"project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Claims (1572e189)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 500.0}

Hot spots

What this analysis is. Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins 11. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 11.

Why it was run on this data. 1400 points: Getis-Ord Gi* tests whether the concentrations in Insured properties are statistically significant 14.

What it found. Hot spots on Insured properties: Hot spot analysis: 1 layer saved: Hot spot analysis: Insured properties (208 features) 1516. It reported result, bins 208; result, count 208; result, elapsed ms 14; result, cell size m 500; result, incidents used 1,400; result, distance band m 500 15.

Findings

• Evidenced: Hot spots on Insured properties: Hot spot analysis: 1 layer saved: Hot spot analysis: Insured properties (208 features) 1516.
• Evidenced: It reported result, bins 208; result, count 208; result, elapsed ms 14; result, cell size m 500; result, incidents used 1,400; result, distance band m 500 15.

Tool gp_hot_spots; parameters {"project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Insured properties (7fe53ce9)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 500.0}

Hot spots

What this analysis is. Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins 11. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 11.

Why it was run on this data. Gi* hot spots of 'max_hail_mm' across the areas 17.

What it found. The hot spot analysis tool was applied using the Storm and hail footprints layer as input 18. The process saved one layer, titled Hot spot analysis: Storm and hail footprints 18. This analysis yielded a total of 8 features 18, completing the task in 1 elapsed ms 18.

Findings

• Evidenced: Hot spots on Storm and hail footprints: Hot spot analysis: 1 layer saved: Hot spot analysis: Storm and hail footprints (8 features) 818.
• Evidenced: It reported result, count 8; result, elapsed ms 1; result, excluded null 0; result, distance band m 2,528; billing, cost 0.05016; billing, list price 0.05016 18.

Tool gp_hot_spots; parameters {"field": "max_hail_mm", "project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Storm and hail footprints (2681856b)"}}

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 19. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 19.

Why it was run on this data. Claims carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per month 20.

What it found. The analysis used the Claims layer as its input 7. The gp_space_time_hot_spots tool successfully saved 68 features into the Emerging hot spots: Claims layer 21. This operation processed 900 features and was completed in 15 milliseconds 21. Furthermore, the resulting cost for this analysis was 0.145, which matched the list price 21.

Findings

• Evidenced: Space time hot spots on Claims: Emerging hot spots: 1 layer saved: Emerging hot spots: Claims (68 features) 721.
• Evidenced: It reported result, count 68; result, elapsed ms 15; result, resolution 8; result, features used 900; result, time neighbors 0; billing, cost 0.145 21.

Tool gp_space_time_hot_spots; parameters {"project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Claims (1572e189)"}, "interval": "month", "bin_shape": "h3", "resolution": 8, "time_field": "reported_at"}

Near repeat

What this analysis is. Repeat and near repeat classification: Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table 22. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator 22.

Why it was run on this data. Near repeat analysis tests whether Claims close in space also follow each other within 30 days 23.

What it found. Near repeat on Claims: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Claims (900 features) 724. It reported result, days 30; result, count 900; result, chains 83; result, distance m 200; result, elapsed ms 22; result, pairs linked 1,571 24.

Findings

• Evidenced: Near repeat on Claims: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Claims (900 features) 724.
• Evidenced: It reported result, days 30; result, count 900; result, chains 83; result, distance m 200; result, elapsed ms 22; result, pairs linked 1,571 24.

Tool gp_near_repeat; parameters {"days": 30, "project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Claims (1572e189)"}, "distance_m": 200.0, "time_field": "reported_at"}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 25. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 25.

Why it was run on this data. 900 points in Claims: a density surface shows where they concentrate 26.

What it found. Calculate density on Claims: Calculate density: 1 layer saved: Calculate density: Claims (1334 features) 727. It reported result, count 1,334; result, elapsed ms 36; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 303.9 27.

Findings

• Evidenced: Calculate density on Claims: Calculate density: 1 layer saved: Calculate density: Claims (1334 features) 727.
• Evidenced: It reported result, count 1,334; result, elapsed ms 36; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 303.9 27.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Claims (1572e189)"}, "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 25. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 25.

Why it was run on this data. 1400 points in Insured properties: a density surface shows where they concentrate 28.

What it found. Calculate density on Insured properties: Calculate density: 1 layer saved: Calculate density: Insured properties (1421 features) 1629. It reported result, count 1,421; result, elapsed ms 40; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 383.9 29.

Findings

• Evidenced: Calculate density on Insured properties: Calculate density: 1 layer saved: Calculate density: Insured properties (1421 features) 1629.
• Evidenced: It reported result, count 1,421; result, elapsed ms 40; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 383.9 29.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Insured properties (7fe53ce9)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 30. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 30.

Why it was run on this data. DBSCAN finds dense groups of Claims and separates noise 31.

What it found. Find point clusters on Claims: Find point clusters: 2 layers saved: Find point clusters: Claims (900 features); Find point clusters: cluster hulls (2 features) 732. It reported result, count 900; result, noise 659; result, distance m 200; result, elapsed ms 15; result, min points 20; billing, cost 0.068 32.

Findings

• Evidenced: Find point clusters on Claims: Find point clusters: 2 layers saved: Find point clusters: Claims (900 features); Find point clusters: cluster hulls (2 features) 732.
• Evidenced: It reported result, count 900; result, noise 659; result, distance m 200; result, elapsed ms 15; result, min points 20; billing, cost 0.068 32.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Claims (1572e189)"}, "distance_m": 200.0, "min_points": 20}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 30. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 30.

Why it was run on this data. DBSCAN finds dense groups of Insured properties and separates noise 33.

What it found. Find point clusters on Insured properties: Find point clusters: 2 layers saved: Find point clusters: Insured properties (1400 features); Find point clusters: cluster hulls (1 feature) 1634. It reported result, count 1,400; result, noise 1,134; result, distance m 200; result, elapsed ms 17; result, min points 20; billing, cost 0.078 34.

Findings

• Evidenced: Find point clusters on Insured properties: Find point clusters: 2 layers saved: Find point clusters: Insured properties (1400 features); Find point clusters: cluster hulls (1 feature) 1634.
• Evidenced: It reported result, count 1,400; result, noise 1,134; result, distance m 200; result, elapsed ms 17; result, min points 20; billing, cost 0.078 34.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Insured properties (7fe53ce9)"}, "distance_m": 200.0, "min_points": 20}

Outliers

What this analysis is. Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers 35. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) 35.

Why it was run on this data. Local Moran's I maps clusters and outliers of 'max_hail_mm' 36.

What it found. Outliers on Storm and hail footprints: Find outliers: 1 layer saved: Find outliers: Storm and hail footprints (8 features) 837. It reported result, count 8; result, elapsed ms 4; result, permutations 199; result, excluded null 0; result, distance band m 2,528; billing, cost 0.05024 37.

Findings

• Evidenced: Outliers on Storm and hail footprints: Find outliers: 1 layer saved: Find outliers: Storm and hail footprints (8 features) 837.
• Evidenced: It reported result, count 8; result, elapsed ms 4; result, permutations 199; result, excluded null 0; result, distance band m 2,528; billing, cost 0.05024 37.

Tool gp_outliers; parameters {"field": "max_hail_mm", "project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Storm and hail footprints (2681856b)"}, "aggregation": "hexagon"}

Outliers

What this analysis is. Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers 35. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) 35.

Why it was run on this data. Local Moran's I maps clusters and outliers of 'households' 38.

What it found. Outliers on Suburbs: Find outliers: 1 layer saved: Find outliers: Suburbs (14 features) 3940. It reported result, count 14; result, elapsed ms 1; result, permutations 199; result, excluded null 0; result, distance band m 1,936; billing, cost 0.05042 39.

Findings

• Evidenced: Outliers on Suburbs: Find outliers: 1 layer saved: Find outliers: Suburbs (14 features) 3940.
• Evidenced: It reported result, count 14; result, elapsed ms 1; result, permutations 199; result, excluded null 0; result, distance band m 1,936; billing, cost 0.05042 39.

Tool gp_outliers; parameters {"field": "households", "project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Suburbs (fb82f048)"}, "aggregation": "hexagon"}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 4. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 4.

Why it was run on this data. Count Insured properties inside each feature of Storm and hail footprints by occupancy; the counts map as a choropleth (natural breaks, sequential colors) 41.

What it found. Summarize within on Insured properties, Storm and hail footprints: Summarize within: 1 layer saved: Summarize within: Storm and hail footprints (8 features) 81642. It reported result, count 8; result, elapsed ms 2; billing, cost 0.03818; billing, list price 0.03818; billing, upstream cost 0 42.

Findings

• Evidenced: Summarize within on Insured properties, Storm and hail footprints: Summarize within: 1 layer saved: Summarize within: Storm and hail footprints (8 features) 81642.
• Evidenced: It reported result, count 8; result, elapsed ms 2; billing, cost 0.03818; billing, list price 0.03818; billing, upstream cost 0 42.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "sum_insured_aud"}], "project": "45adc477-2b11-425a-ada1-ef48522eb87e", "features": {"layer": "Insured properties (7fe53ce9)"}, "group_by": "occupancy", "polygons": {"layer": "Storm and hail footprints (2681856b)"}}

Maps and charts

The run also made 19 map image(s) 43444546474849505152535455565758596061 and 27 chart(s) 626364656667686970717273747576777879808182838485868788.

Changes since the last run

This is the first report of this project.

Sources

1. 1Rendering: Chart: Timeline: events per quarter by dataset. 38 rows id 5eff943c-4f40-4974-826f-340c196a25f8
2. 2Dataset: Storm and hail footprints. vector, 8 rows id 677fcb18-5109-46eb-a1ee-fa3ad15dd34f
3. 3Dataset: Claims. vector, 900 rows id 6588dd91-5576-4a92-8d10-39bd636340c7
4. 4Catalog: 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
5. 5Plan: Claims per feature of Storm and hail footprints (the Start plan's candidate c_3ce931de86b1). Count Claims inside each feature of Storm and hail footprints by peril; the counts map as a choropleth (natural breaks, sequential colors). id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_3ce931de86b1
6. 6Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Storm and hail footprints (8 features). id a0114b7b-51fd-42f8-9f06-c527e020edfd:0
7. 7Layer: Claims. 900 features id 1572e189-e7b2-4e6f-a830-ed552da56575
8. 8Layer: Storm and hail footprints. 8 features id 2681856b-be9b-4f63-8284-615f870c0be2
9. 9Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Storm and hail footprints (8 features). id a0114b7b-51fd-42f8-9f06-c527e020edfd:1
10. 10Layer: River gauges. 8 features id af5a84eb-6d2b-423b-bb0a-445cd0c09e95
11. 11Catalog: 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
12. 12Plan: Hot spots of Claims (the Start plan's candidate c_9ab60fc1c203). 900 points: Getis-Ord Gi* tests whether the concentrations in Claims are statistically significant. id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_9ab60fc1c203
13. 13Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Claims (207 features). id 316325ff-a684-43d3-bbf3-652ebc37b3e4:0
14. 14Plan: Hot spots of Insured properties (the Start plan's candidate c_69adcd5be75e). 1400 points: Getis-Ord Gi* tests whether the concentrations in Insured properties are statistically significant. id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_69adcd5be75e
15. 15Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Insured properties (208 features). id 316325ff-a684-43d3-bbf3-652ebc37b3e4:1
16. 16Layer: Insured properties. 1,400 features id 7fe53ce9-7e3f-4c5c-9f52-a8f6ec7fe611
17. 17Plan: Hot spots of Storm and hail footprints (the Start plan's candidate c_f4c86eb32d2b). Gi* hot spots of 'max_hail_mm' across the areas. id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_f4c86eb32d2b
18. 18Tool result: gp_hot_spots, step 2. Hot spot analysis: 1 layer saved: Hot spot analysis: Storm and hail footprints (8 features). id 316325ff-a684-43d3-bbf3-652ebc37b3e4:2
19. 19Catalog: 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
20. 20Plan: Space time hot spots of Claims (the Start plan's candidate c_40588a07718a). Claims carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per month. id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_40588a07718a
21. 21Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Claims (68 features). id d0e51480-2493-4ab8-a463-1c97e8e32710:0
22. 22Catalog: 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
23. 23Plan: Near repeat of Claims (the Start plan's candidate c_f4deb424175b). Near repeat analysis tests whether Claims close in space also follow each other within 30 days. id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_f4deb424175b
24. 24Tool result: gp_near_repeat, step 1. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Claims (900 features). id d0e51480-2493-4ab8-a463-1c97e8e32710:1
25. 25Catalog: 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
26. 26Plan: Kernel density of Claims (the Start plan's candidate c_26d4d74b1df1). 900 points in Claims: a density surface shows where they concentrate. id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_26d4d74b1df1
27. 27Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Claims (1334 features). id 9884c7e7-d853-4005-bf7a-999f5bc9cc52:0
28. 28Plan: Kernel density of Insured properties (the Start plan's candidate c_0ea6c9ca57d7). 1400 points in Insured properties: a density surface shows where they concentrate. id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_0ea6c9ca57d7
29. 29Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Insured properties (1421 features). id 9884c7e7-d853-4005-bf7a-999f5bc9cc52:1
30. 30Catalog: 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
31. 31Plan: Cluster points of Claims (the Start plan's candidate c_ce029e1407c2). DBSCAN finds dense groups of Claims and separates noise. id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_ce029e1407c2
32. 32Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Claims (900 features); Find point clusters: cluster hulls (2 features). id 7a8de26e-4331-45f4-a997-64ea2b53e411:0
33. 33Plan: Cluster points of Insured properties (the Start plan's candidate c_33558a731f2d). DBSCAN finds dense groups of Insured properties and separates noise. id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_33558a731f2d
34. 34Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Insured properties (1400 features); Find point clusters: cluster hulls (1 feature). id 7a8de26e-4331-45f4-a997-64ea2b53e411:1
35. 35Catalog: 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
36. 36Plan: Local morans i of Storm and hail footprints (the Start plan's candidate c_4980425ab5ac). Local Moran's I maps clusters and outliers of 'max_hail_mm'. id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_4980425ab5ac
37. 37Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: Storm and hail footprints (8 features). id 98ffd9d7-e22c-464f-acd6-d17bdd616b2d:0
38. 38Plan: Local morans i of Suburbs (the Start plan's candidate c_fe84d27234ac). Local Moran's I maps clusters and outliers of 'households'. id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_fe84d27234ac
39. 39Tool result: gp_outliers, step 1. Find outliers: 1 layer saved: Find outliers: Suburbs (14 features). id 98ffd9d7-e22c-464f-acd6-d17bdd616b2d:1
40. 40Layer: Suburbs. 14 features id fb82f048-f105-416a-bf03-e2acaab297e7
41. 41Plan: Insured properties per feature of Storm and hail footprints (the Start plan's candidate c_94f8ecf5d95d). Count Insured properties inside each feature of Storm and hail footprints by occupancy; the counts map as a choropleth (natural breaks, sequential colors). id 06d8dbec-fb93-4c0a-9be6-0a9d1ccc71be:c_94f8ecf5d95d
42. 42Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Storm and hail footprints (8 features). id 87fb30a5-7def-44b9-b558-2525d31918b0:0
43. 43Rendering: Map: Summarize within: Storm and hail footprints id 2a1aaf16-223e-4075-bceb-302fef50736e
44. 44Rendering: Map: Summarize within: Storm and hail footprints id a7e144f5-0b71-48f6-ac87-e92dd74fcb2a
45. 45Rendering: Map: Hot spot analysis: Claims id 7982e9a2-c0e5-45be-a5c5-31b9b945fc19
46. 46Rendering: Map: Hot spot analysis: Insured properties id e409fec4-375a-4521-bc6c-9e9ba19cdee1
47. 47Rendering: Map: Hot spot analysis: Storm and hail footprints id 4157c216-416d-4e7b-9591-e76aa734122a
48. 48Rendering: Map: Emerging hot spots: Claims id a2572cd7-31cf-49d7-8590-9c9188958738
49. 49Rendering: Map: Repeat and near repeat classification: Claims id ad4d31e6-edd2-4091-ae9e-986c5980635d
50. 50Rendering: Map: Calculate density: Claims id e90e2e84-8ae2-4ddf-89c1-8e9cb99846f5
51. 51Rendering: Map: Calculate density: Insured properties id aca6ffec-56b4-437a-9237-8042f2451842
52. 52Rendering: Map: Storm and hail footprints by max_hail_mm id 542c2970-4f8f-4518-9047-759e443972dc
53. 53Rendering: Map: Flood zones by typical_depth_m id c2b90df1-67a4-4858-8e9d-46309defb94d
54. 54Rendering: Map: Suburbs by households id 47988ada-460b-44ad-8fbf-b6caf5890b21
55. 55Rendering: Map: Find point clusters: Claims id 65781e45-6d77-4466-abc1-ccf65b978fd5
56. 56Rendering: Map: Find point clusters: cluster hulls id 13ddfb40-5c9a-46c0-9af5-ae2bbe72be38
57. 57Rendering: Map: Find point clusters: Insured properties id a28a92fa-f6da-4952-95bd-bcd717ae80e5
58. 58Rendering: Map: Find point clusters: cluster hulls id 93578793-0a4a-409b-917d-f245b87c7c0a
59. 59Rendering: Map: Find outliers: Storm and hail footprints id 21b81e4b-9076-4527-acfc-0ac44c04a562
60. 60Rendering: Map: Find outliers: Suburbs id 83af531c-9820-486f-b04f-30b70b9056df
61. 61Rendering: Map: Summarize within: Storm and hail footprints id 1d135757-0516-446f-a7c6-b6db7e98747b
62. 62Rendering: Chart: Insured properties over time (year_built). 12 rows id 8d4e2d79-aac5-4c6e-abac-e21331c131b5
63. 63Rendering: Chart: Storm and hail footprints over time (event_date). 117 rows id 64775539-9daf-497b-9274-e7024b9b7580
64. 64Rendering: Chart: Claims over time (reported_at). 114 rows id 3e7bf944-1b18-4cfd-92e7-02084d4ad6af
65. 65Rendering: Chart: Policy register over time (renewal_month). 12 rows id bb394e48-05d5-45c8-8e10-e9c6cd1c1e22
66. 66Rendering: Chart: Summarize within: Storm and hail footprints. 117 rows id 8d611e4f-5e30-41e5-a17b-8fd774fbb333
67. 67Rendering: Chart: Summarize within: Storm and hail footprints. 117 rows id 2921949c-cc8b-49e8-8f36-372708ec0ec9
68. 68Rendering: Chart: Hot spot analysis: Claims. 13 rows id 1c547987-63a6-4225-9fc5-f8a1f4f40bc9
69. 69Rendering: Chart: Hot spot analysis: Insured properties. 10 rows id c688d50d-41b5-4273-bee3-14304883a86e
70. 70Rendering: Chart: Hot spot analysis: Storm and hail footprints. 5 rows id 7a11d17b-e742-4289-80a4-56c828ed814a
71. 71Rendering: Chart: Emerging hot spots: Claims. 8 rows id ac19af8e-92fa-4354-8343-6fe31255a078
72. 72Rendering: Chart: Repeat and near repeat classification: Claims. 114 rows id bb30ce9a-b6c1-4c0a-8492-1403ee006f23
73. 73Rendering: Chart: Calculate density: Claims. 12 rows id b980344c-c078-4dbe-a95f-0ae8eebe3ff6
74. 74Rendering: Chart: Calculate density: Insured properties. 12 rows id 025d939a-b4ae-4c9a-aa35-a12ef8a45586
75. 75Rendering: Chart: Find point clusters: Claims. 3 rows id 8728ce25-5e56-459f-92e6-de81f519a61c
76. 76Rendering: Chart: Find point clusters: cluster hulls. 2 rows id d2463227-185a-4d40-b5b3-e7812cc679e9
77. 77Rendering: Chart: Find point clusters: Insured properties. 2 rows id 5404c19d-8b94-4d9a-b40f-9912eb859cbb
78. 78Rendering: Chart: Find point clusters: cluster hulls. 1 rows id 1c25a4c8-6c19-45d4-aba5-9a3129c05f70
79. 79Rendering: Chart: Find outliers: Storm and hail footprints. 8 rows id e6de1baa-beb2-418f-8153-8793e70fc2ad
80. 80Rendering: Chart: Find outliers: Suburbs. 14 rows id 1f04f579-5f9b-414c-8ce1-17a7102c48dd
81. 81Rendering: Chart: Summarize within: Storm and hail footprints. 117 rows id 39560fea-7fad-479f-b618-a0c0204e7345
82. 82Rendering: Chart: Summarize within: Storm and hail footprints over time. 117 rows id 6920192b-c42f-45e7-b970-d477640fcdd8
83. 83Rendering: Chart: Find outliers: Storm and hail footprints over time. 117 rows id 942d51e3-f098-4b37-9152-999b3cbea4c1
84. 84Rendering: Chart: Find point clusters: Claims over time. 114 rows id 9e4c70ff-e34e-4809-b15a-f0dc8cc60f38
85. 85Rendering: Chart: Repeat and near repeat classification: Claims over time. 114 rows id d8be7ef1-a24d-4ad4-8527-372f6fa7abd4
86. 86Rendering: Chart: Hot spot analysis: Storm and hail footprints over time. 117 rows id 7d410c0b-09b1-4ad6-bdac-9d8ac60bd780
87. 87Rendering: Chart: Summarize within: Storm and hail footprints over time. 117 rows id 90935ae0-56d1-432d-a4c4-0c237d27fc7e
88. 88Rendering: Chart: Summarize within: Storm and hail footprints over time. 117 rows id d5a96672-ab36-47b6-9af9-6ecb9aa53ed8
89. 89Dataset: Flood zones. vector, 3 rows id 9ff7cd07-9707-4139-9501-699d367ac589
90. 90Dataset: River. vector, 1 rows id 2ae3f47a-cb7b-4a6f-8f11-4fad2d865c48
91. 91Dataset: Suburbs. vector, 14 rows id f84efb12-ec44-4ffb-9a4b-731fc92b0ff4
92. 92Dataset: Insured properties. vector, 1400 rows id db8fb622-dcb0-450a-b376-1f30939b1f04
93. 93Dataset: River gauges. vector, 8 rows id bc462c44-e561-4f0b-85e5-e1ed939da508
94. 94Dataset: Levees. vector, 6 rows id d0e2941b-d2d3-4587-bca1-8ad0d6046a7a
95. 95Dataset: Policy register. table, 1400 rows id 270b5574-288d-4ef6-b58a-64621756f91c
96. 96Dataset: Depth-damage curves. table, 28 rows id a51443ce-2e0b-48b7-b869-93eb5bf70044
97. 97Dataset: Portfolio exposure memo. document, ? rows id 705c2955-c1f1-464c-b094-d1aea428e6cb
98. 98Dataset: Underwriting guidelines for flood zones. document, ? rows id d7e25805-fa99-4630-adf1-bd51214bd5d6
99. 99Record: Sample: Insurance, Brisbane id 45adc477-2b11-425a-ada1-ef48522eb87e
100. 100Layer: Summarize within: Storm and hail footprints. 8 features id f96865cb-7df1-42b5-9e46-55d014c12fe3
101. 101Layer: Summarize within: Storm and hail footprints. 8 features id 96c5a7ae-3002-43b2-822a-d548a052a0a8
102. 102Layer: Hot spot analysis: Claims. 207 features id 4e47f295-19b9-4c46-9b06-9f9dbfb63d94
103. 103Layer: Hot spot analysis: Insured properties. 208 features id b322b158-2dd7-4092-9c87-b4df7b23c40a
104. 104Layer: Hot spot analysis: Storm and hail footprints. 8 features id 8c364057-1ad8-4de9-9bee-e32bb6309d1b
105. 105Layer: Emerging hot spots: Claims. 68 features id b605aa48-02fd-4f66-a10c-4cd4828bb0a3
106. 106Layer: Repeat and near repeat classification: Claims. 900 features id 91fe9915-8c82-43f0-8a58-c4662a7ed370
107. 107Layer: Calculate density: Claims. 1,334 features id d9529f47-12c6-44d1-a1ff-7650822864bb
108. 108Layer: Calculate density: Insured properties. 1,421 features id 22892878-2807-40a9-8985-4a9271df26df
109. 109Layer: Find point clusters: Claims. 900 features id c9572018-f633-4939-be7b-87082a048abb
110. 110Layer: Find point clusters: cluster hulls. 2 features id 38d533f5-098f-4de2-b2fa-2ed589169af1
111. 111Layer: Find point clusters: Insured properties. 1,400 features id dfd881e3-2b48-4be4-96e0-5ec3d592c5e9
112. 112Layer: Find point clusters: cluster hulls. 1 features id 8d98e96a-9a2f-4616-8164-1bac0df14ad3
113. 113Layer: Find outliers: Storm and hail footprints. 8 features id d4fe68e2-fd83-4c6f-8e98-04be0d55904b
114. 114Layer: Find outliers: Suburbs. 14 features id f8ed8015-1aa6-4628-b2cf-2994274f3295
115. 115Layer: Summarize within: Storm and hail footprints. 8 features id 5cc6baeb-9e4e-4539-8c3d-f2e3bb4355db