# 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

| Data | Kind | Rows | Time span | Summary | Source |
| --- | --- | --- | --- | --- | --- |
| Flood zones | vector, MultiPolygon | 3 |  | 3 polygon (area) features; 'zone' has 3 values (top: 0.2 percent AEP 1, 1 percent AEP 1, 5 percent AEP 1); 'typical_depth_m' 0.4 to 2.1 (mean 1.233); 'aep_pct' 0.2 to 5 (mean 2.067); 'return_period_years' 20 to 500 (mean 206.7); total area 17.53 km2; extent 152.9695,-27.4991 to 153.0505,-27.4631. Areas (Flood zones) with typical_depth_m to compare and map. | [89] |
| River | vector, LineString | 1 |  | 1 line features; 'width_m' 70 to 70 (mean 70); named by 'name' (e.g. Main river channel); total length 10.52 km; extent 152.9695,-27.4910 to 153.0505,-27.4712. Lines (River) such as routes or networks. | [90] |
| Suburbs | vector, Polygon | 14 |  | 14 polygon (area) features; 'households' 2,478 to 11,774 (mean 6905); 'median_value_aud' 514,269 to 1,216,669 (mean 8.224e+05); named by 'name' (e.g. Parkside, Riverside, Brickfield); total area 48.04 km2; extent 152.9695,-27.5071 to 153.0505,-27.4529. Areas (Suburbs) with households to compare and map. | [91] |
| Insured properties | vector, Point | 1,400 |  | 1,400 point features; 'occupancy' has 3 values (top: residential 996, commercial 255, industrial 149); 'construction' has 4 values (top: timber 641, brick veneer 406, double brick 207); 'sum_insured_aud' 231,235 to 10,738,456 (mean 1,371,380); 'year_built' 1,925 to 2,024 (mean 1976); 'floors' 1 to 3 (mean 1.804); extent 152.9705,-27.5064 to 153.0494,-27.4535. Point locations of Insured properties by occupancy. | [92] |
| Storm and hail footprints | vector, Polygon | 8 | 2023-01-09 to 2026-03-14 | 8 polygon (area) features; dated by 'event_date' from 2023-01-09 to 2026-03-14; 'peril' has 3 values (top: storm 4, hail 3, flood rain 1); 'max_hail_mm' 0 to 52 (mean 13.38); 'max_gust_kmh' 74 to 124 (mean 93); 'rain_24h_mm' 91 to 247 (mean 153.6); total area 25.39 km2; extent 152.9695,-27.5071 to 153.0407,-27.4529. Areas (Storm and hail footprints) with max_hail_mm to compare and map. | [2] |
| Claims | vector, Point | 900 | 2022-01-09 to 2026-09-08 | 900 point features; dated by 'reported_at' from 2022-01-09 to 2026-09-08; 'peril' has 6 values (top: hail 237, flood 230, storm 176); 'status' has 2 values (top: closed 895, open 5); 'paid_aud' 1,025 to 363,329 (mean 3.385e+04); extent 152.9705,-27.5063 to 153.0494,-27.4538. Dated point events by peril: where and when Claims happen. | [3] |
| River gauges | vector, Point | 8 |  | 8 point features; 'minor_flood_m' 1.82 to 2.16 (mean 2.005); 'major_flood_m' 3.44 to 4.38 (mean 3.775); named by 'name' (e.g. River gauge station 1, River gauge station 2, River gauge station 3); extent 152.9743,-27.4905 to 153.0442,-27.4711. Point locations of River gauges. | [93] |
| Levees | vector, LineString | 6 |  | 6 line features; 'condition' has 3 values (top: poor 3, fair 2, good 1); 'crest_height_m' 2.86 to 3.97 (mean 3.44); 'built' 1,986 to 2,010 (mean 1995); total length 4.202 km; extent 152.9916,-27.4906 to 153.0325,-27.4737. Lines (Levees) such as routes or networks. | [94] |
| Policy register | table | 1,400 |  | Table of 1,400 rows and 5 columns without geometry; 'renewal_month' has 4 values (top: Apr 368, Jul 362, Jan 353); 'channel' has 3 values (top: broker 488, bank partner 458, direct 454); 'annual_premium_aud' 474 to 27,543 (mean 3951); 'deductible_aud' 500 to 5,000 (mean 2170). A table of Policy register without locations. | [95] |
| Depth-damage curves | table | 28 |  | Table of 28 rows and 3 columns without geometry; 'construction' has 4 values (top: timber 7, brick veneer 7, double brick 7); 'depth_m' 0 to 3 (mean 1.179); 'damage_ratio' 0 to 1 (mean 0.3857). A table of Depth-damage curves without locations. | [96] |
| Portfolio exposure memo | document |  |  | 1-page pdf document 'Portfolio exposure memo'; language en; mentions 9 place(s): Riverside (1), -27.47128, 153.01572 (1), Elmstead (1), Foundry Row (1), Ashby Green (1). A document about Riverside, -27.47128, 153.01572, Elmstead. | [97] |
| Underwriting guidelines for flood zones | document |  |  | 1-page md document 'Underwriting guidelines for flood zones'; language en; mentions 2 place(s): Elmstead (2), Foundry Row (1). A document about Elmstead, Foundry Row. | [98] |
| Sample: Insurance, Brisbane | record |  |  |  | [99] |
| Summarize within: Storm and hail footprints | layer, Polygon | 8 |  |  | [100] |
| Summarize within: Storm and hail footprints | layer, Polygon | 8 |  |  | [101] |
| Hot spot analysis: Claims | layer, Polygon | 207 |  |  | [102] |
| Hot spot analysis: Insured properties | layer, Polygon | 208 |  |  | [103] |
| Hot spot analysis: Storm and hail footprints | layer, Polygon | 8 |  |  | [104] |
| Emerging hot spots: Claims | layer, Polygon | 68 |  |  | [105] |
| Repeat and near repeat classification: Claims | layer, Point | 900 |  |  | [106] |
| Calculate density: Claims | layer, Polygon | 1,334 |  |  | [107] |
| Calculate density: Insured properties | layer, Polygon | 1,421 |  |  | [108] |
| Find point clusters: Claims | layer, Point | 900 |  |  | [109] |
| Find point clusters: cluster hulls | layer, Polygon | 2 |  |  | [110] |
| Find point clusters: Insured properties | layer, Point | 1,400 |  |  | [111] |
| Find point clusters: cluster hulls | layer, Polygon | 1 |  |  | [112] |
| Find outliers: Storm and hail footprints | layer, Polygon | 8 |  |  | [113] |
| Find outliers: Suburbs | layer, Polygon | 14 |  |  | [114] |
| Summarize within: Storm and hail footprints | layer, Polygon | 8 |  |  | [115] |

## Timeline

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

*Figure 1. Timeline: events per quarter by dataset [1]. Per quarter, 2022-01-01 to 2026-07-01. Source: Storm and hail footprints, Claims (908 features). The data is in the appendix.*

The timeline counts 908 dated events in Storm and hail footprints, Claims per quarter, from Q1 2022 to Q3 2026 [1][2][3]. 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].

| Result | Value |
| --- | --- |
| result, count | 8 |
| result, elapsed ms | 2 |
| billing, cost | 0.02818 |
| billing, list price | 0.02818 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Claims, Storm and hail footprints: Summarize within: 1 layer saved: Summarize within: Storm and hail footprints (8 features) [6][7][8].
- **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) [8][9][10]. It reported result, count 8; result, elapsed ms 0; billing, cost 0.01034; billing, list price 0.01034; billing, upstream cost 0 [9].

| Result | Value |
| --- | --- |
| result, count | 8 |
| result, elapsed ms | 0 |
| billing, cost | 0.01034 |
| billing, list price | 0.01034 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on River gauges, Storm and hail footprints: Summarize within: 1 layer saved: Summarize within: Storm and hail footprints (8 features) [8][9][10].
- **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].

| Result | Value |
| --- | --- |
| result, bins | 207 |
| result, count | 207 |
| result, elapsed ms | 13 |
| result, cell size m | 500 |
| result, incidents used | 900 |
| result, distance band m | 500 |
| billing, cost | 0.068 |
| billing, list price | 0.068 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Claims: Hot spot analysis: 1 layer saved: Hot spot analysis: Claims (207 features) [7][13].
- **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) [15][16]. 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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.078 |
| billing, list price | 0.078 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Insured properties: Hot spot analysis: 1 layer saved: Hot spot analysis: Insured properties (208 features) [15][16].
- **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].

| Result | Value |
| --- | --- |
| 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 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Storm and hail footprints: Hot spot analysis: 1 layer saved: Hot spot analysis: Storm and hail footprints (8 features) [8][18].
- **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].

| Result | Value |
| --- | --- |
| result, count | 68 |
| result, elapsed ms | 15 |
| result, resolution | 8 |
| result, features used | 900 |
| result, time neighbors | 0 |
| billing, cost | 0.145 |
| billing, list price | 0.145 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Claims: Emerging hot spots: 1 layer saved: Emerging hot spots: Claims (68 features) [7][21].
- **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) [7][24]. 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].

| Result | Value |
| --- | --- |
| result, days | 30 |
| result, count | 900 |
| result, chains | 83 |
| result, distance m | 200 |
| result, elapsed ms | 22 |
| result, pairs linked | 1,571 |
| billing, cost | 0.077 |
| billing, list price | 0.077 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Near repeat on Claims: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Claims (900 features) [7][24].
- **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) [7][27]. 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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.068 |
| billing, list price | 0.068 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Claims: Calculate density: 1 layer saved: Calculate density: Claims (1334 features) [7][27].
- **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) [16][29]. 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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.078 |
| billing, list price | 0.078 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Insured properties: Calculate density: 1 layer saved: Calculate density: Insured properties (1421 features) [16][29].
- **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) [7][32]. 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].

| Result | Value |
| --- | --- |
| result, count | 900 |
| result, noise | 659 |
| result, distance m | 200 |
| result, elapsed ms | 15 |
| result, min points | 20 |
| billing, cost | 0.068 |
| billing, list price | 0.068 |
| billing, upstream cost | 0 |

**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) [7][32].
- **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) [16][34]. 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].

| Result | Value |
| --- | --- |
| result, count | 1,400 |
| result, noise | 1,134 |
| result, distance m | 200 |
| result, elapsed ms | 17 |
| result, min points | 20 |
| billing, cost | 0.078 |
| billing, list price | 0.078 |
| billing, upstream cost | 0 |

**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) [16][34].
- **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) [8][37]. 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].

| Result | Value |
| --- | --- |
| result, count | 8 |
| result, elapsed ms | 4 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 2,528 |
| billing, cost | 0.05024 |
| billing, list price | 0.05024 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Storm and hail footprints: Find outliers: 1 layer saved: Find outliers: Storm and hail footprints (8 features) [8][37].
- **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) [39][40]. 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].

| Result | Value |
| --- | --- |
| result, count | 14 |
| result, elapsed ms | 1 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 1,936 |
| billing, cost | 0.05042 |
| billing, list price | 0.05042 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Suburbs: Find outliers: 1 layer saved: Find outliers: Suburbs (14 features) [39][40].
- **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) [8][16][42]. It reported result, count 8; result, elapsed ms 2; billing, cost 0.03818; billing, list price 0.03818; billing, upstream cost 0 [42].

| Result | Value |
| --- | --- |
| result, count | 8 |
| result, elapsed ms | 2 |
| billing, cost | 0.03818 |
| billing, list price | 0.03818 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Insured properties, Storm and hail footprints: Summarize within: 1 layer saved: Summarize within: Storm and hail footprints (8 features) [8][16][42].
- **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) [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61] and 27 chart(s) [62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88].

![Summarize within: Storm and hail footprints](maps/01-summarize-within-storm-and-hail-footprints.png)

*Figure 2. Summarize within: Storm and hail footprints [43]*

![Summarize within: Storm and hail footprints](maps/02-summarize-within-storm-and-hail-footprints.png)

*Figure 3. Summarize within: Storm and hail footprints [44]*

![Hot spot analysis: Claims](maps/03-hot-spot-analysis-claims.png)

*Figure 4. Hot spot analysis: Claims [45]*

![Hot spot analysis: Insured properties](maps/04-hot-spot-analysis-insured-properties.png)

*Figure 5. Hot spot analysis: Insured properties [46]*

![Hot spot analysis: Storm and hail footprints](maps/05-hot-spot-analysis-storm-and-hail-footprints.png)

*Figure 6. Hot spot analysis: Storm and hail footprints [47]*

![Emerging hot spots: Claims](maps/06-emerging-hot-spots-claims.png)

*Figure 7. Emerging hot spots: Claims [48]*

![Repeat and near repeat classification: Claims](maps/07-repeat-and-near-repeat-classification-claims.png)

*Figure 8. Repeat and near repeat classification: Claims [49]*

![Calculate density: Claims](maps/08-calculate-density-claims.png)

*Figure 9. Calculate density: Claims [50]*

![Calculate density: Insured properties](maps/09-calculate-density-insured-properties.png)

*Figure 10. Calculate density: Insured properties [51]*

![Storm and hail footprints by max_hail_mm](maps/10-storm-and-hail-footprints-by-max-hail-mm.png)

*Figure 11. Storm and hail footprints by max_hail_mm [52]*

![Flood zones by typical_depth_m](maps/11-flood-zones-by-typical-depth-m.png)

*Figure 12. Flood zones by typical_depth_m [53]*

![Suburbs by households](maps/12-suburbs-by-households.png)

*Figure 13. Suburbs by households [54]*

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

*Figure 14. Find point clusters: Claims [55]*

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

*Figure 15. Find point clusters: cluster hulls [56]*

![Find point clusters: Insured properties](maps/15-find-point-clusters-insured-properties.png)

*Figure 16. Find point clusters: Insured properties [57]*

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

*Figure 17. Find point clusters: cluster hulls [58]*

![Find outliers: Storm and hail footprints](maps/17-find-outliers-storm-and-hail-footprints.png)

*Figure 18. Find outliers: Storm and hail footprints [59]*

![Find outliers: Suburbs](maps/18-find-outliers-suburbs.png)

*Figure 19. Find outliers: Suburbs [60]*

![Summarize within: Storm and hail footprints](maps/19-summarize-within-storm-and-hail-footprints.png)

*Figure 20. Summarize within: Storm and hail footprints [61]*

![Insured properties over time (year_built)](charts/01-insured-properties-over-time-year-built.png)

*Figure 21. Insured properties over time (year_built) [62]. Source: Insured properties (1,400 features). The data is in the appendix.*

![Storm and hail footprints over time (event_date)](charts/02-storm-and-hail-footprints-over-time-event-date.png)

*Figure 22. Storm and hail footprints over time (event_date) [63]. Per month, 2023-01-01 to 2026-03-01. Source: Storm and hail footprints (8 features). The data is in the appendix.*

![Claims over time (reported_at)](charts/03-claims-over-time-reported-at.png)

*Figure 23. Claims over time (reported_at) [64]. Per quarter, 2022-01-01 to 2026-07-01. Source: Claims (900 features). The data is in the appendix.*

![Policy register over time (renewal_month)](charts/04-policy-register-over-time-renewal-month.png)

*Figure 24. Policy register over time (renewal_month) [65]. Source: Policy register (1,400 features). The data is in the appendix.*

![Summarize within: Storm and hail footprints](charts/05-summarize-within-storm-and-hail-footprints.png)

*Figure 25. Summarize within: Storm and hail footprints [66]. Per month, 2023-01-01 to 2026-03-01. Source: Summarize within: Storm and hail footprints (8 features). The data is in the appendix.*

![Summarize within: Storm and hail footprints](charts/06-summarize-within-storm-and-hail-footprints.png)

*Figure 26. Summarize within: Storm and hail footprints [67]. Per month, 2023-01-01 to 2026-03-01. Source: Summarize within: Storm and hail footprints (8 features). The data is in the appendix.*

![Hot spot analysis: Claims](charts/07-hot-spot-analysis-claims.png)

*Figure 27. Hot spot analysis: Claims [68]. Source: Hot spot analysis: Claims (207 features). The data is in the appendix.*

![Hot spot analysis: Insured properties](charts/08-hot-spot-analysis-insured-properties.png)

*Figure 28. Hot spot analysis: Insured properties [69]. Source: Hot spot analysis: Insured properties (208 features). The data is in the appendix.*

![Hot spot analysis: Storm and hail footprints](charts/09-hot-spot-analysis-storm-and-hail-footprints.png)

*Figure 29. Hot spot analysis: Storm and hail footprints [70]. Period: 2023-01-09 to 2026-03-14 (event_date). Source: Hot spot analysis: Storm and hail footprints (8 features). The data is in the appendix.*

![Emerging hot spots: Claims](charts/10-emerging-hot-spots-claims.png)

*Figure 30. Emerging hot spots: Claims [71]. Source: Emerging hot spots: Claims (68 features). The data is in the appendix.*

![Repeat and near repeat classification: Claims](charts/11-repeat-and-near-repeat-classification-claims.png)

*Figure 31. Repeat and near repeat classification: Claims [72]. Per quarter, 2022-01-01 to 2026-07-01. Source: Repeat and near repeat classification: Claims (900 features). The data is in the appendix.*

![Calculate density: Claims](charts/12-calculate-density-claims.png)

*Figure 32. Calculate density: Claims [73]. Source: Calculate density: Claims (1,334 features). The data is in the appendix.*

![Calculate density: Insured properties](charts/13-calculate-density-insured-properties.png)

*Figure 33. Calculate density: Insured properties [74]. Source: Calculate density: Insured properties (1,421 features). The data is in the appendix.*

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

*Figure 34. Find point clusters: Claims [75]. Period: 2022-01-09 to 2026-09-08 (reported_at). Source: Find point clusters: Claims (900 features). The data is in the appendix.*

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

*Figure 35. Find point clusters: cluster hulls [76]. Source: Find point clusters: cluster hulls (2 features). The data is in the appendix.*

![Find point clusters: Insured properties](charts/16-find-point-clusters-insured-properties.png)

*Figure 36. Find point clusters: Insured properties [77]. Source: Find point clusters: Insured properties (1,400 features). The data is in the appendix.*

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

*Figure 37. Find point clusters: cluster hulls [78]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.*

![Find outliers: Storm and hail footprints](charts/18-find-outliers-storm-and-hail-footprints.png)

*Figure 38. Find outliers: Storm and hail footprints [79]. Period: 2023-01-09 to 2026-03-14 (event_date). Source: Find outliers: Storm and hail footprints (8 features). The data is in the appendix.*

![Find outliers: Suburbs](charts/19-find-outliers-suburbs.png)

*Figure 39. Find outliers: Suburbs [80]. Source: Find outliers: Suburbs (14 features). The data is in the appendix.*

![Summarize within: Storm and hail footprints](charts/20-summarize-within-storm-and-hail-footprints.png)

*Figure 40. Summarize within: Storm and hail footprints [81]. Per month, 2023-01-01 to 2026-03-01. Source: Summarize within: Storm and hail footprints (8 features). The data is in the appendix.*

![Summarize within: Storm and hail footprints over time](charts/21-summarize-within-storm-and-hail-footprints-over-time.png)

*Figure 41. Summarize within: Storm and hail footprints over time [82]. Per month, 2023-01-01 to 2026-03-01. Source: Summarize within: Storm and hail footprints (8 features). The data is in the appendix.*

![Find outliers: Storm and hail footprints over time](charts/22-find-outliers-storm-and-hail-footprints-over-time.png)

*Figure 42. Find outliers: Storm and hail footprints over time [83]. Per month, 2023-01-01 to 2026-03-01. Source: Find outliers: Storm and hail footprints (8 features). The data is in the appendix.*

![Find point clusters: Claims over time](charts/23-find-point-clusters-claims-over-time.png)

*Figure 43. Find point clusters: Claims over time [84]. Per quarter, 2022-01-01 to 2026-07-01. Source: Find point clusters: Claims (900 features). The data is in the appendix.*

![Repeat and near repeat classification: Claims over time](charts/24-repeat-and-near-repeat-classification-claims-over-time.png)

*Figure 44. Repeat and near repeat classification: Claims over time [85]. Per quarter, 2022-01-01 to 2026-07-01. Source: Repeat and near repeat classification: Claims (900 features). The data is in the appendix.*

![Hot spot analysis: Storm and hail footprints over time](charts/25-hot-spot-analysis-storm-and-hail-footprints-over-time.png)

*Figure 45. Hot spot analysis: Storm and hail footprints over time [86]. Per month, 2023-01-01 to 2026-03-01. Source: Hot spot analysis: Storm and hail footprints (8 features). The data is in the appendix.*

![Summarize within: Storm and hail footprints over time](charts/26-summarize-within-storm-and-hail-footprints-over-time.png)

*Figure 46. Summarize within: Storm and hail footprints over time [87]. Per month, 2023-01-01 to 2026-03-01. Source: Summarize within: Storm and hail footprints (8 features). The data is in the appendix.*

![Summarize within: Storm and hail footprints over time](charts/27-summarize-within-storm-and-hail-footprints-over-time.png)

*Figure 47. Summarize within: Storm and hail footprints over time [88]. Per month, 2023-01-01 to 2026-03-01. Source: Summarize within: Storm and hail footprints (8 features). The data is in the appendix.*

## Changes since the last run

This is the first report of this project.

## Sources

[1] Rendering: Chart: Timeline: events per quarter by dataset. 38 rows (id 5eff943c-4f40-4974-826f-340c196a25f8)  
[2] Dataset: Storm and hail footprints. vector, 8 rows (id 677fcb18-5109-46eb-a1ee-fa3ad15dd34f)  
[3] Dataset: Claims. vector, 900 rows (id 6588dd91-5576-4a92-8d10-39bd636340c7)  
[4] Catalog: Summarize within (operation catalog, gp_summarize_within). Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons. ArcGIS: Summarize Within, Aggregate Points. QGIS: native:countpointsinpolygon, native:sumlinelengths. (id gp_summarize_within)  
[5] Plan: 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] Tool 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] Layer: Claims. 900 features (id 1572e189-e7b2-4e6f-a830-ed552da56575)  
[8] Layer: Storm and hail footprints. 8 features (id 2681856b-be9b-4f63-8284-615f870c0be2)  
[9] Tool 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] Layer: River gauges. 8 features (id af5a84eb-6d2b-423b-bb0a-445cd0c09e95)  
[11] Catalog: Hot spot analysis (operation catalog, gp_hot_spots). Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins. ArcGIS: Find Hot Spots, Hot Spot Analysis (Getis-Ord Gi*). QGIS: qgis:hotspot (plugin). (id gp_hot_spots)  
[12] Plan: 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] Tool 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] Plan: 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] Tool 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] Layer: Insured properties. 1,400 features (id 7fe53ce9-7e3f-4c5c-9f52-a8f6ec7fe611)  
[17] Plan: 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] Tool 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] Catalog: Emerging hot spots (operation catalog, gp_space_time_hot_spots). Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time. ArcGIS: Emerging Hot Spot Analysis, Create Space Time Cube. (id gp_space_time_hot_spots)  
[20] Plan: 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] Tool 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] Catalog: Repeat and near repeat classification (operation catalog, gp_near_repeat). Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table. ArcGIS: Repeat and Near Repeat Classification, Near Repeat Calculator. (id gp_near_repeat)  
[23] Plan: 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] Tool 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] Catalog: Calculate density (operation catalog, gp_calculate_density). Kernel density per square kilometer on a grid. ArcGIS: Calculate Density, Kernel Density. QGIS: native:heatmapkerneldensityestimation. (id gp_calculate_density)  
[26] Plan: 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] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Claims (1334 features). (id 9884c7e7-d853-4005-bf7a-999f5bc9cc52:0)  
[28] Plan: 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] Tool 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] Catalog: Find point clusters (operation catalog, gp_find_point_clusters). Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise. ArcGIS: Find Point Clusters, Density-based Clustering. QGIS: native:dbscanclustering, native:stdbscanclustering. (id gp_find_point_clusters)  
[31] Plan: 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] Tool 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] Plan: 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] Tool 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] Catalog: Find outliers (operation catalog, gp_outliers). Anselin local Moran’s I: HH, LL clusters and HL, LH outliers. ArcGIS: Find Outliers, Cluster and Outlier Analysis (Anselin Local Moran’s I). (id gp_outliers)  
[36] Plan: 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] Tool 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] Plan: 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] Tool result: gp_outliers, step 1. Find outliers: 1 layer saved: Find outliers: Suburbs (14 features). (id 98ffd9d7-e22c-464f-acd6-d17bdd616b2d:1)  
[40] Layer: Suburbs. 14 features (id fb82f048-f105-416a-bf03-e2acaab297e7)  
[41] Plan: 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] Tool 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] Rendering: Map: Summarize within: Storm and hail footprints (id 2a1aaf16-223e-4075-bceb-302fef50736e)  
[44] Rendering: Map: Summarize within: Storm and hail footprints (id a7e144f5-0b71-48f6-ac87-e92dd74fcb2a)  
[45] Rendering: Map: Hot spot analysis: Claims (id 7982e9a2-c0e5-45be-a5c5-31b9b945fc19)  
[46] Rendering: Map: Hot spot analysis: Insured properties (id e409fec4-375a-4521-bc6c-9e9ba19cdee1)  
[47] Rendering: Map: Hot spot analysis: Storm and hail footprints (id 4157c216-416d-4e7b-9591-e76aa734122a)  
[48] Rendering: Map: Emerging hot spots: Claims (id a2572cd7-31cf-49d7-8590-9c9188958738)  
[49] Rendering: Map: Repeat and near repeat classification: Claims (id ad4d31e6-edd2-4091-ae9e-986c5980635d)  
[50] Rendering: Map: Calculate density: Claims (id e90e2e84-8ae2-4ddf-89c1-8e9cb99846f5)  
[51] Rendering: Map: Calculate density: Insured properties (id aca6ffec-56b4-437a-9237-8042f2451842)  
[52] Rendering: Map: Storm and hail footprints by max_hail_mm (id 542c2970-4f8f-4518-9047-759e443972dc)  
[53] Rendering: Map: Flood zones by typical_depth_m (id c2b90df1-67a4-4858-8e9d-46309defb94d)  
[54] Rendering: Map: Suburbs by households (id 47988ada-460b-44ad-8fbf-b6caf5890b21)  
[55] Rendering: Map: Find point clusters: Claims (id 65781e45-6d77-4466-abc1-ccf65b978fd5)  
[56] Rendering: Map: Find point clusters: cluster hulls (id 13ddfb40-5c9a-46c0-9af5-ae2bbe72be38)  
[57] Rendering: Map: Find point clusters: Insured properties (id a28a92fa-f6da-4952-95bd-bcd717ae80e5)  
[58] Rendering: Map: Find point clusters: cluster hulls (id 93578793-0a4a-409b-917d-f245b87c7c0a)  
[59] Rendering: Map: Find outliers: Storm and hail footprints (id 21b81e4b-9076-4527-acfc-0ac44c04a562)  
[60] Rendering: Map: Find outliers: Suburbs (id 83af531c-9820-486f-b04f-30b70b9056df)  
[61] Rendering: Map: Summarize within: Storm and hail footprints (id 1d135757-0516-446f-a7c6-b6db7e98747b)  
[62] Rendering: Chart: Insured properties over time (year_built). 12 rows (id 8d4e2d79-aac5-4c6e-abac-e21331c131b5)  
[63] Rendering: Chart: Storm and hail footprints over time (event_date). 117 rows (id 64775539-9daf-497b-9274-e7024b9b7580)  
[64] Rendering: Chart: Claims over time (reported_at). 114 rows (id 3e7bf944-1b18-4cfd-92e7-02084d4ad6af)  
[65] Rendering: Chart: Policy register over time (renewal_month). 12 rows (id bb394e48-05d5-45c8-8e10-e9c6cd1c1e22)  
[66] Rendering: Chart: Summarize within: Storm and hail footprints. 117 rows (id 8d611e4f-5e30-41e5-a17b-8fd774fbb333)  
[67] Rendering: Chart: Summarize within: Storm and hail footprints. 117 rows (id 2921949c-cc8b-49e8-8f36-372708ec0ec9)  
[68] Rendering: Chart: Hot spot analysis: Claims. 13 rows (id 1c547987-63a6-4225-9fc5-f8a1f4f40bc9)  
[69] Rendering: Chart: Hot spot analysis: Insured properties. 10 rows (id c688d50d-41b5-4273-bee3-14304883a86e)  
[70] Rendering: Chart: Hot spot analysis: Storm and hail footprints. 5 rows (id 7a11d17b-e742-4289-80a4-56c828ed814a)  
[71] Rendering: Chart: Emerging hot spots: Claims. 8 rows (id ac19af8e-92fa-4354-8343-6fe31255a078)  
[72] Rendering: Chart: Repeat and near repeat classification: Claims. 114 rows (id bb30ce9a-b6c1-4c0a-8492-1403ee006f23)  
[73] Rendering: Chart: Calculate density: Claims. 12 rows (id b980344c-c078-4dbe-a95f-0ae8eebe3ff6)  
[74] Rendering: Chart: Calculate density: Insured properties. 12 rows (id 025d939a-b4ae-4c9a-aa35-a12ef8a45586)  
[75] Rendering: Chart: Find point clusters: Claims. 3 rows (id 8728ce25-5e56-459f-92e6-de81f519a61c)  
[76] Rendering: Chart: Find point clusters: cluster hulls. 2 rows (id d2463227-185a-4d40-b5b3-e7812cc679e9)  
[77] Rendering: Chart: Find point clusters: Insured properties. 2 rows (id 5404c19d-8b94-4d9a-b40f-9912eb859cbb)  
[78] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id 1c25a4c8-6c19-45d4-aba5-9a3129c05f70)  
[79] Rendering: Chart: Find outliers: Storm and hail footprints. 8 rows (id e6de1baa-beb2-418f-8153-8793e70fc2ad)  
[80] Rendering: Chart: Find outliers: Suburbs. 14 rows (id 1f04f579-5f9b-414c-8ce1-17a7102c48dd)  
[81] Rendering: Chart: Summarize within: Storm and hail footprints. 117 rows (id 39560fea-7fad-479f-b618-a0c0204e7345)  
[82] Rendering: Chart: Summarize within: Storm and hail footprints over time. 117 rows (id 6920192b-c42f-45e7-b970-d477640fcdd8)  
[83] Rendering: Chart: Find outliers: Storm and hail footprints over time. 117 rows (id 942d51e3-f098-4b37-9152-999b3cbea4c1)  
[84] Rendering: Chart: Find point clusters: Claims over time. 114 rows (id 9e4c70ff-e34e-4809-b15a-f0dc8cc60f38)  
[85] Rendering: Chart: Repeat and near repeat classification: Claims over time. 114 rows (id d8be7ef1-a24d-4ad4-8527-372f6fa7abd4)  
[86] Rendering: Chart: Hot spot analysis: Storm and hail footprints over time. 117 rows (id 7d410c0b-09b1-4ad6-bdac-9d8ac60bd780)  
[87] Rendering: Chart: Summarize within: Storm and hail footprints over time. 117 rows (id 90935ae0-56d1-432d-a4c4-0c237d27fc7e)  
[88] Rendering: Chart: Summarize within: Storm and hail footprints over time. 117 rows (id d5a96672-ab36-47b6-9af9-6ecb9aa53ed8)  
[89] Dataset: Flood zones. vector, 3 rows (id 9ff7cd07-9707-4139-9501-699d367ac589)  
[90] Dataset: River. vector, 1 rows (id 2ae3f47a-cb7b-4a6f-8f11-4fad2d865c48)  
[91] Dataset: Suburbs. vector, 14 rows (id f84efb12-ec44-4ffb-9a4b-731fc92b0ff4)  
[92] Dataset: Insured properties. vector, 1400 rows (id db8fb622-dcb0-450a-b376-1f30939b1f04)  
[93] Dataset: River gauges. vector, 8 rows (id bc462c44-e561-4f0b-85e5-e1ed939da508)  
[94] Dataset: Levees. vector, 6 rows (id d0e2941b-d2d3-4587-bca1-8ad0d6046a7a)  
[95] Dataset: Policy register. table, 1400 rows (id 270b5574-288d-4ef6-b58a-64621756f91c)  
[96] Dataset: Depth-damage curves. table, 28 rows (id a51443ce-2e0b-48b7-b869-93eb5bf70044)  
[97] Dataset: Portfolio exposure memo. document, ? rows (id 705c2955-c1f1-464c-b094-d1aea428e6cb)  
[98] Dataset: Underwriting guidelines for flood zones. document, ? rows (id d7e25805-fa99-4630-adf1-bd51214bd5d6)  
[99] Record: Sample: Insurance, Brisbane (id 45adc477-2b11-425a-ada1-ef48522eb87e)  
[100] Layer: Summarize within: Storm and hail footprints. 8 features (id f96865cb-7df1-42b5-9e46-55d014c12fe3)  
[101] Layer: Summarize within: Storm and hail footprints. 8 features (id 96c5a7ae-3002-43b2-822a-d548a052a0a8)  
[102] Layer: Hot spot analysis: Claims. 207 features (id 4e47f295-19b9-4c46-9b06-9f9dbfb63d94)  
[103] Layer: Hot spot analysis: Insured properties. 208 features (id b322b158-2dd7-4092-9c87-b4df7b23c40a)  
[104] Layer: Hot spot analysis: Storm and hail footprints. 8 features (id 8c364057-1ad8-4de9-9bee-e32bb6309d1b)  
[105] Layer: Emerging hot spots: Claims. 68 features (id b605aa48-02fd-4f66-a10c-4cd4828bb0a3)  
[106] Layer: Repeat and near repeat classification: Claims. 900 features (id 91fe9915-8c82-43f0-8a58-c4662a7ed370)  
[107] Layer: Calculate density: Claims. 1,334 features (id d9529f47-12c6-44d1-a1ff-7650822864bb)  
[108] Layer: Calculate density: Insured properties. 1,421 features (id 22892878-2807-40a9-8985-4a9271df26df)  
[109] Layer: Find point clusters: Claims. 900 features (id c9572018-f633-4939-be7b-87082a048abb)  
[110] Layer: Find point clusters: cluster hulls. 2 features (id 38d533f5-098f-4de2-b2fa-2ed589169af1)  
[111] Layer: Find point clusters: Insured properties. 1,400 features (id dfd881e3-2b48-4be4-96e0-5ec3d592c5e9)  
[112] Layer: Find point clusters: cluster hulls. 1 features (id 8d98e96a-9a2f-4616-8164-1bac0df14ad3)  
[113] Layer: Find outliers: Storm and hail footprints. 8 features (id d4fe68e2-fd83-4c6f-8e98-04be0d55904b)  
[114] Layer: Find outliers: Suburbs. 14 features (id f8ed8015-1aa6-4628-b2cf-2994274f3295)  
[115] Layer: Summarize within: Storm and hail footprints. 8 features (id 5cc6baeb-9e4e-4539-8c3d-f2e3bb4355db)  

## Appendix: the data behind the charts

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

| period | series | value |
| --- | --- | --- |
| 2022-01-01 | Claims | 37 |
| 2022-01-01 | Storm and hail footprints | 0 |
| 2022-04-01 | Claims | 22 |
| 2022-04-01 | Storm and hail footprints | 0 |
| 2022-07-01 | Claims | 15 |
| 2022-07-01 | Storm and hail footprints | 0 |
| 2022-10-01 | Claims | 26 |
| 2022-10-01 | Storm and hail footprints | 0 |
| 2023-01-01 | Claims | 45 |
| 2023-01-01 | Storm and hail footprints | 1 |
| 2023-04-01 | Claims | 22 |
| 2023-04-01 | Storm and hail footprints | 0 |
| 2023-07-01 | Claims | 19 |
| 2023-07-01 | Storm and hail footprints | 0 |
| 2023-10-01 | Claims | 46 |
| 2023-10-01 | Storm and hail footprints | 1 |
| 2024-01-01 | Claims | 37 |
| 2024-01-01 | Storm and hail footprints | 1 |
| 2024-04-01 | Claims | 20 |
| 2024-04-01 | Storm and hail footprints | 0 |
| 2024-07-01 | Claims | 17 |
| 2024-07-01 | Storm and hail footprints | 0 |
| 2024-10-01 | Claims | 181 |
| 2024-10-01 | Storm and hail footprints | 1 |
| 2025-01-01 | Claims | 266 |
| 2025-01-01 | Storm and hail footprints | 1 |
| 2025-04-01 | Claims | 18 |
| 2025-04-01 | Storm and hail footprints | 0 |
| 2025-07-01 | Claims | 23 |
| 2025-07-01 | Storm and hail footprints | 0 |
| 2025-10-01 | Claims | 40 |
| 2025-10-01 | Storm and hail footprints | 1 |
| 2026-01-01 | Claims | 36 |
| 2026-01-01 | Storm and hail footprints | 2 |
| 2026-04-01 | Claims | 21 |
| 2026-04-01 | Storm and hail footprints | 0 |
| 2026-07-01 | Claims | 9 |
| 2026-07-01 | Storm and hail footprints | 0 |

### Figure 21. Insured properties over time (year_built) [62]

| bin_start | bin_end | count |
| --- | --- | --- |
| 2.312e+05 | 1,106,837 | 943 |
| 1,106,837 | 1,982,438 | 140 |
| 1,982,438 | 2,858,040 | 126 |
| 2,858,040 | 3,733,642 | 93 |
| 3,733,642 | 4,609,244 | 48 |
| 4,609,244 | 5,484,846 | 25 |
| 5,484,846 | 6,360,447 | 13 |
| 6,360,447 | 7,236,049 | 4 |
| 7,236,049 | 8,111,651 | 5 |
| 8,111,651 | 8,987,252 | 2 |
| 8,987,252 | 9,862,854 | 0 |
| 9,862,854 | 10,738,456 | 1 |

### Figure 22. Storm and hail footprints over time (event_date) [63]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | storm | 171 |
| 2023-01-01 | hail | 0 |
| 2023-01-01 | flood rain | 0 |
| 2023-02-01 | storm | 0 |
| 2023-02-01 | hail | 0 |
| 2023-02-01 | flood rain | 0 |
| 2023-03-01 | storm | 0 |
| 2023-03-01 | hail | 0 |
| 2023-03-01 | flood rain | 0 |
| 2023-04-01 | storm | 0 |
| 2023-04-01 | hail | 0 |
| 2023-04-01 | flood rain | 0 |
| 2023-05-01 | storm | 0 |
| 2023-05-01 | hail | 0 |
| 2023-05-01 | flood rain | 0 |
| 2023-06-01 | storm | 0 |
| 2023-06-01 | hail | 0 |
| 2023-06-01 | flood rain | 0 |
| 2023-07-01 | storm | 0 |
| 2023-07-01 | hail | 0 |
| 2023-07-01 | flood rain | 0 |
| 2023-08-01 | storm | 0 |
| 2023-08-01 | hail | 0 |
| 2023-08-01 | flood rain | 0 |
| 2023-09-01 | storm | 0 |
| 2023-09-01 | hail | 0 |
| 2023-09-01 | flood rain | 0 |
| 2023-10-01 | storm | 0 |
| 2023-10-01 | hail | 247 |
| 2023-10-01 | flood rain | 0 |
| 2023-11-01 | storm | 0 |
| 2023-11-01 | hail | 0 |
| 2023-11-01 | flood rain | 0 |
| 2023-12-01 | storm | 0 |
| 2023-12-01 | hail | 0 |
| 2023-12-01 | flood rain | 0 |
| 2024-01-01 | storm | 0 |
| 2024-01-01 | hail | 0 |
| 2024-01-01 | flood rain | 0 |
| 2024-02-01 | storm | 0 |
| 2024-02-01 | hail | 0 |
| 2024-02-01 | flood rain | 0 |
| 2024-03-01 | storm | 192 |
| 2024-03-01 | hail | 0 |
| 2024-03-01 | flood rain | 0 |
| 2024-04-01 | storm | 0 |
| 2024-04-01 | hail | 0 |
| 2024-04-01 | flood rain | 0 |
| 2024-05-01 | storm | 0 |
| 2024-05-01 | hail | 0 |
| 2024-05-01 | flood rain | 0 |
| 2024-06-01 | storm | 0 |
| 2024-06-01 | hail | 0 |
| 2024-06-01 | flood rain | 0 |
| 2024-07-01 | storm | 0 |
| 2024-07-01 | hail | 0 |
| 2024-07-01 | flood rain | 0 |
| 2024-08-01 | storm | 0 |
| 2024-08-01 | hail | 0 |
| 2024-08-01 | flood rain | 0 |
| 2024-09-01 | storm | 0 |
| 2024-09-01 | hail | 0 |
| 2024-09-01 | flood rain | 0 |
| 2024-10-01 | storm | 0 |
| 2024-10-01 | hail | 0 |
| 2024-10-01 | flood rain | 0 |
| 2024-11-01 | storm | 0 |
| 2024-11-01 | hail | 136 |
| 2024-11-01 | flood rain | 0 |
| 2024-12-01 | storm | 0 |
| 2024-12-01 | hail | 0 |
| 2024-12-01 | flood rain | 0 |
| 2025-01-01 | storm | 0 |
| 2025-01-01 | hail | 0 |
| 2025-01-01 | flood rain | 0 |
| 2025-02-01 | storm | 0 |
| 2025-02-01 | hail | 0 |
| 2025-02-01 | flood rain | 144 |
| 2025-03-01 | storm | 0 |
| 2025-03-01 | hail | 0 |
| 2025-03-01 | flood rain | 0 |
| 2025-04-01 | storm | 0 |
| 2025-04-01 | hail | 0 |
| 2025-04-01 | flood rain | 0 |
| 2025-05-01 | storm | 0 |
| 2025-05-01 | hail | 0 |
| 2025-05-01 | flood rain | 0 |
| 2025-06-01 | storm | 0 |
| 2025-06-01 | hail | 0 |
| 2025-06-01 | flood rain | 0 |
| 2025-07-01 | storm | 0 |
| 2025-07-01 | hail | 0 |
| 2025-07-01 | flood rain | 0 |
| 2025-08-01 | storm | 0 |
| 2025-08-01 | hail | 0 |
| 2025-08-01 | flood rain | 0 |
| 2025-09-01 | storm | 0 |
| 2025-09-01 | hail | 0 |
| 2025-09-01 | flood rain | 0 |
| 2025-10-01 | storm | 0 |
| 2025-10-01 | hail | 0 |
| 2025-10-01 | flood rain | 0 |
| 2025-11-01 | storm | 0 |
| 2025-11-01 | hail | 0 |
| 2025-11-01 | flood rain | 0 |
| 2025-12-01 | storm | 91 |
| 2025-12-01 | hail | 0 |
| 2025-12-01 | flood rain | 0 |
| 2026-01-01 | storm | 0 |
| 2026-01-01 | hail | 93 |
| 2026-01-01 | flood rain | 0 |
| 2026-02-01 | storm | 0 |
| 2026-02-01 | hail | 0 |
| 2026-02-01 | flood rain | 0 |
| 2026-03-01 | storm | 155 |
| 2026-03-01 | hail | 0 |
| 2026-03-01 | flood rain | 0 |

### Figure 23. Claims over time (reported_at) [64]

| period | series | value |
| --- | --- | --- |
| 2022-01-01 | hail | 7 |
| 2022-01-01 | flood | 0 |
| 2022-01-01 | storm | 12 |
| 2022-01-01 | water damage | 12 |
| 2022-01-01 | theft | 4 |
| 2022-01-01 | Other | 2 |
| 2022-04-01 | hail | 4 |
| 2022-04-01 | flood | 0 |
| 2022-04-01 | storm | 6 |
| 2022-04-01 | water damage | 10 |
| 2022-04-01 | theft | 0 |
| 2022-04-01 | Other | 2 |
| 2022-07-01 | hail | 2 |
| 2022-07-01 | flood | 0 |
| 2022-07-01 | storm | 4 |
| 2022-07-01 | water damage | 5 |
| 2022-07-01 | theft | 3 |
| 2022-07-01 | Other | 1 |
| 2022-10-01 | hail | 3 |
| 2022-10-01 | flood | 0 |
| 2022-10-01 | storm | 7 |
| 2022-10-01 | water damage | 11 |
| 2022-10-01 | theft | 2 |
| 2022-10-01 | Other | 3 |
| 2023-01-01 | hail | 11 |
| 2023-01-01 | flood | 0 |
| 2023-01-01 | storm | 16 |
| 2023-01-01 | water damage | 9 |
| 2023-01-01 | theft | 7 |
| 2023-01-01 | Other | 2 |
| 2023-04-01 | hail | 1 |
| 2023-04-01 | flood | 0 |
| 2023-04-01 | storm | 5 |
| 2023-04-01 | water damage | 11 |
| 2023-04-01 | theft | 4 |
| 2023-04-01 | Other | 1 |
| 2023-07-01 | hail | 2 |
| 2023-07-01 | flood | 0 |
| 2023-07-01 | storm | 8 |
| 2023-07-01 | water damage | 7 |
| 2023-07-01 | theft | 2 |
| 2023-07-01 | Other | 0 |
| 2023-10-01 | hail | 11 |
| 2023-10-01 | flood | 0 |
| 2023-10-01 | storm | 22 |
| 2023-10-01 | water damage | 9 |
| 2023-10-01 | theft | 2 |
| 2023-10-01 | Other | 2 |
| 2024-01-01 | hail | 6 |
| 2024-01-01 | flood | 0 |
| 2024-01-01 | storm | 11 |
| 2024-01-01 | water damage | 14 |
| 2024-01-01 | theft | 4 |
| 2024-01-01 | Other | 2 |
| 2024-04-01 | hail | 3 |
| 2024-04-01 | flood | 0 |
| 2024-04-01 | storm | 7 |
| 2024-04-01 | water damage | 6 |
| 2024-04-01 | theft | 4 |
| 2024-04-01 | Other | 0 |
| 2024-07-01 | hail | 2 |
| 2024-07-01 | flood | 0 |
| 2024-07-01 | storm | 5 |
| 2024-07-01 | water damage | 4 |
| 2024-07-01 | theft | 6 |
| 2024-07-01 | Other | 0 |
| 2024-10-01 | hail | 155 |
| 2024-10-01 | flood | 0 |
| 2024-10-01 | storm | 11 |
| 2024-10-01 | water damage | 9 |
| 2024-10-01 | theft | 4 |
| 2024-10-01 | Other | 2 |
| 2025-01-01 | hail | 8 |
| 2025-01-01 | flood | 230 |
| 2025-01-01 | storm | 12 |
| 2025-01-01 | water damage | 8 |
| 2025-01-01 | theft | 4 |
| 2025-01-01 | Other | 4 |
| 2025-04-01 | hail | 6 |
| 2025-04-01 | flood | 0 |
| 2025-04-01 | storm | 7 |
| 2025-04-01 | water damage | 4 |
| 2025-04-01 | theft | 1 |
| 2025-04-01 | Other | 0 |
| 2025-07-01 | hail | 3 |
| 2025-07-01 | flood | 0 |
| 2025-07-01 | storm | 7 |
| 2025-07-01 | water damage | 5 |
| 2025-07-01 | theft | 6 |
| 2025-07-01 | Other | 2 |
| 2025-10-01 | hail | 6 |
| 2025-10-01 | flood | 0 |
| 2025-10-01 | storm | 9 |
| 2025-10-01 | water damage | 10 |
| 2025-10-01 | theft | 11 |
| 2025-10-01 | Other | 4 |
| 2026-01-01 | hail | 5 |
| 2026-01-01 | flood | 0 |
| 2026-01-01 | storm | 13 |
| 2026-01-01 | water damage | 14 |
| 2026-01-01 | theft | 3 |
| 2026-01-01 | Other | 1 |
| 2026-04-01 | hail | 1 |
| 2026-04-01 | flood | 0 |
| 2026-04-01 | storm | 9 |
| 2026-04-01 | water damage | 8 |
| 2026-04-01 | theft | 3 |
| 2026-04-01 | Other | 0 |
| 2026-07-01 | hail | 1 |
| 2026-07-01 | flood | 0 |
| 2026-07-01 | storm | 5 |
| 2026-07-01 | water damage | 3 |
| 2026-07-01 | theft | 0 |
| 2026-07-01 | Other | 0 |

### Figure 24. Policy register over time (renewal_month) [65]

| bin_start | bin_end | count |
| --- | --- | --- |
| 474 | 2,730 | 829 |
| 2,730 | 4,986 | 204 |
| 4,986 | 7,241 | 124 |
| 7,241 | 9,497 | 115 |
| 9,497 | 1.175e+04 | 60 |
| 1.175e+04 | 1.401e+04 | 41 |
| 1.401e+04 | 1.626e+04 | 13 |
| 1.626e+04 | 1.852e+04 | 7 |
| 1.852e+04 | 2.078e+04 | 3 |
| 2.078e+04 | 2.303e+04 | 0 |
| 2.303e+04 | 2.529e+04 | 2 |
| 2.529e+04 | 2.754e+04 | 2 |

### Figure 25. Summarize within: Storm and hail footprints [66]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | storm | 1 |
| 2023-01-01 | hail | 0 |
| 2023-01-01 | flood rain | 0 |
| 2023-02-01 | storm | 0 |
| 2023-02-01 | hail | 0 |
| 2023-02-01 | flood rain | 0 |
| 2023-03-01 | storm | 0 |
| 2023-03-01 | hail | 0 |
| 2023-03-01 | flood rain | 0 |
| 2023-04-01 | storm | 0 |
| 2023-04-01 | hail | 0 |
| 2023-04-01 | flood rain | 0 |
| 2023-05-01 | storm | 0 |
| 2023-05-01 | hail | 0 |
| 2023-05-01 | flood rain | 0 |
| 2023-06-01 | storm | 0 |
| 2023-06-01 | hail | 0 |
| 2023-06-01 | flood rain | 0 |
| 2023-07-01 | storm | 0 |
| 2023-07-01 | hail | 0 |
| 2023-07-01 | flood rain | 0 |
| 2023-08-01 | storm | 0 |
| 2023-08-01 | hail | 0 |
| 2023-08-01 | flood rain | 0 |
| 2023-09-01 | storm | 0 |
| 2023-09-01 | hail | 0 |
| 2023-09-01 | flood rain | 0 |
| 2023-10-01 | storm | 0 |
| 2023-10-01 | hail | 1 |
| 2023-10-01 | flood rain | 0 |
| 2023-11-01 | storm | 0 |
| 2023-11-01 | hail | 0 |
| 2023-11-01 | flood rain | 0 |
| 2023-12-01 | storm | 0 |
| 2023-12-01 | hail | 0 |
| 2023-12-01 | flood rain | 0 |
| 2024-01-01 | storm | 0 |
| 2024-01-01 | hail | 0 |
| 2024-01-01 | flood rain | 0 |
| 2024-02-01 | storm | 0 |
| 2024-02-01 | hail | 0 |
| 2024-02-01 | flood rain | 0 |
| 2024-03-01 | storm | 1 |
| 2024-03-01 | hail | 0 |
| 2024-03-01 | flood rain | 0 |
| 2024-04-01 | storm | 0 |
| 2024-04-01 | hail | 0 |
| 2024-04-01 | flood rain | 0 |
| 2024-05-01 | storm | 0 |
| 2024-05-01 | hail | 0 |
| 2024-05-01 | flood rain | 0 |
| 2024-06-01 | storm | 0 |
| 2024-06-01 | hail | 0 |
| 2024-06-01 | flood rain | 0 |
| 2024-07-01 | storm | 0 |
| 2024-07-01 | hail | 0 |
| 2024-07-01 | flood rain | 0 |
| 2024-08-01 | storm | 0 |
| 2024-08-01 | hail | 0 |
| 2024-08-01 | flood rain | 0 |
| 2024-09-01 | storm | 0 |
| 2024-09-01 | hail | 0 |
| 2024-09-01 | flood rain | 0 |
| 2024-10-01 | storm | 0 |
| 2024-10-01 | hail | 0 |
| 2024-10-01 | flood rain | 0 |
| 2024-11-01 | storm | 0 |
| 2024-11-01 | hail | 1 |
| 2024-11-01 | flood rain | 0 |
| 2024-12-01 | storm | 0 |
| 2024-12-01 | hail | 0 |
| 2024-12-01 | flood rain | 0 |
| 2025-01-01 | storm | 0 |
| 2025-01-01 | hail | 0 |
| 2025-01-01 | flood rain | 0 |
| 2025-02-01 | storm | 0 |
| 2025-02-01 | hail | 0 |
| 2025-02-01 | flood rain | 1 |
| 2025-03-01 | storm | 0 |
| 2025-03-01 | hail | 0 |
| 2025-03-01 | flood rain | 0 |
| 2025-04-01 | storm | 0 |
| 2025-04-01 | hail | 0 |
| 2025-04-01 | flood rain | 0 |
| 2025-05-01 | storm | 0 |
| 2025-05-01 | hail | 0 |
| 2025-05-01 | flood rain | 0 |
| 2025-06-01 | storm | 0 |
| 2025-06-01 | hail | 0 |
| 2025-06-01 | flood rain | 0 |
| 2025-07-01 | storm | 0 |
| 2025-07-01 | hail | 0 |
| 2025-07-01 | flood rain | 0 |
| 2025-08-01 | storm | 0 |
| 2025-08-01 | hail | 0 |
| 2025-08-01 | flood rain | 0 |
| 2025-09-01 | storm | 0 |
| 2025-09-01 | hail | 0 |
| 2025-09-01 | flood rain | 0 |
| 2025-10-01 | storm | 0 |
| 2025-10-01 | hail | 0 |
| 2025-10-01 | flood rain | 0 |
| 2025-11-01 | storm | 0 |
| 2025-11-01 | hail | 0 |
| 2025-11-01 | flood rain | 0 |
| 2025-12-01 | storm | 1 |
| 2025-12-01 | hail | 0 |
| 2025-12-01 | flood rain | 0 |
| 2026-01-01 | storm | 0 |
| 2026-01-01 | hail | 1 |
| 2026-01-01 | flood rain | 0 |
| 2026-02-01 | storm | 0 |
| 2026-02-01 | hail | 0 |
| 2026-02-01 | flood rain | 0 |
| 2026-03-01 | storm | 1 |
| 2026-03-01 | hail | 0 |
| 2026-03-01 | flood rain | 0 |

### Figure 26. Summarize within: Storm and hail footprints [67]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | storm | 1 |
| 2023-01-01 | hail | 0 |
| 2023-01-01 | flood rain | 0 |
| 2023-02-01 | storm | 0 |
| 2023-02-01 | hail | 0 |
| 2023-02-01 | flood rain | 0 |
| 2023-03-01 | storm | 0 |
| 2023-03-01 | hail | 0 |
| 2023-03-01 | flood rain | 0 |
| 2023-04-01 | storm | 0 |
| 2023-04-01 | hail | 0 |
| 2023-04-01 | flood rain | 0 |
| 2023-05-01 | storm | 0 |
| 2023-05-01 | hail | 0 |
| 2023-05-01 | flood rain | 0 |
| 2023-06-01 | storm | 0 |
| 2023-06-01 | hail | 0 |
| 2023-06-01 | flood rain | 0 |
| 2023-07-01 | storm | 0 |
| 2023-07-01 | hail | 0 |
| 2023-07-01 | flood rain | 0 |
| 2023-08-01 | storm | 0 |
| 2023-08-01 | hail | 0 |
| 2023-08-01 | flood rain | 0 |
| 2023-09-01 | storm | 0 |
| 2023-09-01 | hail | 0 |
| 2023-09-01 | flood rain | 0 |
| 2023-10-01 | storm | 0 |
| 2023-10-01 | hail | 1 |
| 2023-10-01 | flood rain | 0 |
| 2023-11-01 | storm | 0 |
| 2023-11-01 | hail | 0 |
| 2023-11-01 | flood rain | 0 |
| 2023-12-01 | storm | 0 |
| 2023-12-01 | hail | 0 |
| 2023-12-01 | flood rain | 0 |
| 2024-01-01 | storm | 0 |
| 2024-01-01 | hail | 0 |
| 2024-01-01 | flood rain | 0 |
| 2024-02-01 | storm | 0 |
| 2024-02-01 | hail | 0 |
| 2024-02-01 | flood rain | 0 |
| 2024-03-01 | storm | 1 |
| 2024-03-01 | hail | 0 |
| 2024-03-01 | flood rain | 0 |
| 2024-04-01 | storm | 0 |
| 2024-04-01 | hail | 0 |
| 2024-04-01 | flood rain | 0 |
| 2024-05-01 | storm | 0 |
| 2024-05-01 | hail | 0 |
| 2024-05-01 | flood rain | 0 |
| 2024-06-01 | storm | 0 |
| 2024-06-01 | hail | 0 |
| 2024-06-01 | flood rain | 0 |
| 2024-07-01 | storm | 0 |
| 2024-07-01 | hail | 0 |
| 2024-07-01 | flood rain | 0 |
| 2024-08-01 | storm | 0 |
| 2024-08-01 | hail | 0 |
| 2024-08-01 | flood rain | 0 |
| 2024-09-01 | storm | 0 |
| 2024-09-01 | hail | 0 |
| 2024-09-01 | flood rain | 0 |
| 2024-10-01 | storm | 0 |
| 2024-10-01 | hail | 0 |
| 2024-10-01 | flood rain | 0 |
| 2024-11-01 | storm | 0 |
| 2024-11-01 | hail | 1 |
| 2024-11-01 | flood rain | 0 |
| 2024-12-01 | storm | 0 |
| 2024-12-01 | hail | 0 |
| 2024-12-01 | flood rain | 0 |
| 2025-01-01 | storm | 0 |
| 2025-01-01 | hail | 0 |
| 2025-01-01 | flood rain | 0 |
| 2025-02-01 | storm | 0 |
| 2025-02-01 | hail | 0 |
| 2025-02-01 | flood rain | 1 |
| 2025-03-01 | storm | 0 |
| 2025-03-01 | hail | 0 |
| 2025-03-01 | flood rain | 0 |
| 2025-04-01 | storm | 0 |
| 2025-04-01 | hail | 0 |
| 2025-04-01 | flood rain | 0 |
| 2025-05-01 | storm | 0 |
| 2025-05-01 | hail | 0 |
| 2025-05-01 | flood rain | 0 |
| 2025-06-01 | storm | 0 |
| 2025-06-01 | hail | 0 |
| 2025-06-01 | flood rain | 0 |
| 2025-07-01 | storm | 0 |
| 2025-07-01 | hail | 0 |
| 2025-07-01 | flood rain | 0 |
| 2025-08-01 | storm | 0 |
| 2025-08-01 | hail | 0 |
| 2025-08-01 | flood rain | 0 |
| 2025-09-01 | storm | 0 |
| 2025-09-01 | hail | 0 |
| 2025-09-01 | flood rain | 0 |
| 2025-10-01 | storm | 0 |
| 2025-10-01 | hail | 0 |
| 2025-10-01 | flood rain | 0 |
| 2025-11-01 | storm | 0 |
| 2025-11-01 | hail | 0 |
| 2025-11-01 | flood rain | 0 |
| 2025-12-01 | storm | 1 |
| 2025-12-01 | hail | 0 |
| 2025-12-01 | flood rain | 0 |
| 2026-01-01 | storm | 0 |
| 2026-01-01 | hail | 1 |
| 2026-01-01 | flood rain | 0 |
| 2026-02-01 | storm | 0 |
| 2026-02-01 | hail | 0 |
| 2026-02-01 | flood rain | 0 |
| 2026-03-01 | storm | 1 |
| 2026-03-01 | hail | 0 |
| 2026-03-01 | flood rain | 0 |

### Figure 27. Hot spot analysis: Claims [68]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 37 |
| -0.5 | 0 | not_significant | Not significant | 115 |
| 0 | 0.5 | not_significant | Not significant | 27 |
| 0.5 | 1 | not_significant | Not significant | 14 |
| 1 | 1.5 | not_significant | Not significant | 3 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 3 |
| 1.5 | 2 | not_significant | Not significant | 1 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 2 |
| 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 1 |
| 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 5 | 5.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 6 | 6.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 7.5 | 8 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 28. Hot spot analysis: Insured properties [69]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 34 |
| -0.5 | 0 | not_significant | Not significant | 94 |
| 0 | 0.5 | not_significant | Not significant | 63 |
| 0.5 | 1 | not_significant | Not significant | 10 |
| 1 | 1.5 | not_significant | Not significant | 1 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 2 |
| 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 1 |
| 4.5 | 5 | hot_99 | Hot spot, 99% confidence | 1 |
| 5 | 5.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 10.5 | 11 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 29. Hot spot analysis: Storm and hail footprints [70]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -2 | -1.5 | cold_90 | Cold spot, 90% confidence | 1 |
| -1.5 | -1 | not_significant | Not significant | 1 |
| -1 | -0.5 | not_significant | Not significant | 2 |
| -0.5 | 0 | not_significant | Not significant | 2 |
| 0 | 0.5 | not_significant | Not significant | 2 |

### Figure 30. Emerging hot spots: Claims [71]

| bin_start | bin_end | count |
| --- | --- | --- |
| -0.294 | -0.2607 | 60 |
| -0.2607 | -0.2275 | 1 |
| -0.2275 | -0.1943 | 0 |
| -0.1943 | -0.161 | 0 |
| -0.161 | -0.1278 | 0 |
| -0.1278 | -0.0945 | 0 |
| -0.0945 | -0.06125 | 0 |
| -0.06125 | -0.028 | 7 |

### Figure 31. Repeat and near repeat classification: Claims [72]

| period | series | value |
| --- | --- | --- |
| 2022-01-01 | hail | 7 |
| 2022-01-01 | flood | 0 |
| 2022-01-01 | storm | 12 |
| 2022-01-01 | water damage | 12 |
| 2022-01-01 | theft | 4 |
| 2022-01-01 | Other | 2 |
| 2022-04-01 | hail | 4 |
| 2022-04-01 | flood | 0 |
| 2022-04-01 | storm | 6 |
| 2022-04-01 | water damage | 10 |
| 2022-04-01 | theft | 0 |
| 2022-04-01 | Other | 2 |
| 2022-07-01 | hail | 2 |
| 2022-07-01 | flood | 0 |
| 2022-07-01 | storm | 4 |
| 2022-07-01 | water damage | 5 |
| 2022-07-01 | theft | 3 |
| 2022-07-01 | Other | 1 |
| 2022-10-01 | hail | 3 |
| 2022-10-01 | flood | 0 |
| 2022-10-01 | storm | 7 |
| 2022-10-01 | water damage | 11 |
| 2022-10-01 | theft | 2 |
| 2022-10-01 | Other | 3 |
| 2023-01-01 | hail | 11 |
| 2023-01-01 | flood | 0 |
| 2023-01-01 | storm | 16 |
| 2023-01-01 | water damage | 9 |
| 2023-01-01 | theft | 7 |
| 2023-01-01 | Other | 2 |
| 2023-04-01 | hail | 1 |
| 2023-04-01 | flood | 0 |
| 2023-04-01 | storm | 5 |
| 2023-04-01 | water damage | 11 |
| 2023-04-01 | theft | 4 |
| 2023-04-01 | Other | 1 |
| 2023-07-01 | hail | 2 |
| 2023-07-01 | flood | 0 |
| 2023-07-01 | storm | 8 |
| 2023-07-01 | water damage | 7 |
| 2023-07-01 | theft | 2 |
| 2023-07-01 | Other | 0 |
| 2023-10-01 | hail | 11 |
| 2023-10-01 | flood | 0 |
| 2023-10-01 | storm | 22 |
| 2023-10-01 | water damage | 9 |
| 2023-10-01 | theft | 2 |
| 2023-10-01 | Other | 2 |
| 2024-01-01 | hail | 6 |
| 2024-01-01 | flood | 0 |
| 2024-01-01 | storm | 11 |
| 2024-01-01 | water damage | 14 |
| 2024-01-01 | theft | 4 |
| 2024-01-01 | Other | 2 |
| 2024-04-01 | hail | 3 |
| 2024-04-01 | flood | 0 |
| 2024-04-01 | storm | 7 |
| 2024-04-01 | water damage | 6 |
| 2024-04-01 | theft | 4 |
| 2024-04-01 | Other | 0 |
| 2024-07-01 | hail | 2 |
| 2024-07-01 | flood | 0 |
| 2024-07-01 | storm | 5 |
| 2024-07-01 | water damage | 4 |
| 2024-07-01 | theft | 6 |
| 2024-07-01 | Other | 0 |
| 2024-10-01 | hail | 155 |
| 2024-10-01 | flood | 0 |
| 2024-10-01 | storm | 11 |
| 2024-10-01 | water damage | 9 |
| 2024-10-01 | theft | 4 |
| 2024-10-01 | Other | 2 |
| 2025-01-01 | hail | 8 |
| 2025-01-01 | flood | 230 |
| 2025-01-01 | storm | 12 |
| 2025-01-01 | water damage | 8 |
| 2025-01-01 | theft | 4 |
| 2025-01-01 | Other | 4 |
| 2025-04-01 | hail | 6 |
| 2025-04-01 | flood | 0 |
| 2025-04-01 | storm | 7 |
| 2025-04-01 | water damage | 4 |
| 2025-04-01 | theft | 1 |
| 2025-04-01 | Other | 0 |
| 2025-07-01 | hail | 3 |
| 2025-07-01 | flood | 0 |
| 2025-07-01 | storm | 7 |
| 2025-07-01 | water damage | 5 |
| 2025-07-01 | theft | 6 |
| 2025-07-01 | Other | 2 |
| 2025-10-01 | hail | 6 |
| 2025-10-01 | flood | 0 |
| 2025-10-01 | storm | 9 |
| 2025-10-01 | water damage | 10 |
| 2025-10-01 | theft | 11 |
| 2025-10-01 | Other | 4 |
| 2026-01-01 | hail | 5 |
| 2026-01-01 | flood | 0 |
| 2026-01-01 | storm | 13 |
| 2026-01-01 | water damage | 14 |
| 2026-01-01 | theft | 3 |
| 2026-01-01 | Other | 1 |
| 2026-04-01 | hail | 1 |
| 2026-04-01 | flood | 0 |
| 2026-04-01 | storm | 9 |
| 2026-04-01 | water damage | 8 |
| 2026-04-01 | theft | 3 |
| 2026-04-01 | Other | 0 |
| 2026-07-01 | hail | 1 |
| 2026-07-01 | flood | 0 |
| 2026-07-01 | storm | 5 |
| 2026-07-01 | water damage | 3 |
| 2026-07-01 | theft | 0 |
| 2026-07-01 | Other | 0 |

### Figure 32. Calculate density: Claims [73]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 3.583 | 100 |
| 3.583 | 7.167 | 126 |
| 7.167 | 10.75 | 98 |
| 10.75 | 14.33 | 129 |
| 14.33 | 17.92 | 91 |
| 17.92 | 21.5 | 130 |
| 21.5 | 25.08 | 124 |
| 25.08 | 28.67 | 93 |
| 28.67 | 32.25 | 127 |
| 32.25 | 35.83 | 93 |
| 35.83 | 39.42 | 129 |
| 39.42 | 43 | 94 |

### Figure 33. Calculate density: Insured properties [74]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 3.583 | 112 |
| 3.583 | 7.167 | 134 |
| 7.167 | 10.75 | 101 |
| 10.75 | 14.33 | 133 |
| 14.33 | 17.92 | 100 |
| 17.92 | 21.5 | 136 |
| 21.5 | 25.08 | 134 |
| 25.08 | 28.67 | 99 |
| 28.67 | 32.25 | 132 |
| 32.25 | 35.83 | 97 |
| 35.83 | 39.42 | 131 |
| 39.42 | 43 | 112 |

### Figure 34. Find point clusters: Claims [75]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 659 | 0.7322 | 73.2% |
| 0 | 220 | 0.2444 | 24.4% |
| 1 | 21 | 0.02333 | 2.3% |

### Figure 35. Find point clusters: cluster hulls [76]

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

### Figure 36. Find point clusters: Insured properties [77]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 1,134 | 0.81 | 81.0% |
| 0 | 266 | 0.19 | 19.0% |

### Figure 37. Find point clusters: cluster hulls [78]

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

### Figure 38. Find outliers: Storm and hail footprints [79]

| value | lag | type |
| --- | --- | --- |
| -0.7152 | 0.2651 | NS |
| 2.065 | -0.7152 | NS |
| -0.7152 | -0.7152 | NS |
| 0.6216 | -0.7152 | NS |
| -0.7152 | -0.029 | NS |
| -0.7152 | -0.0201 | NS |
| 0.889 | -0.7152 | NS |
| -0.7152 | 0.1083 | NS |

### Figure 39. Find outliers: Suburbs [80]

| value | lag | type |
| --- | --- | --- |
| -0.0327 | -0.5072 | NS |
| -0.9251 | 0.2501 | NS |
| 0.0182 | -0.35 | NS |
| 0.24 | -0.0622 | NS |
| -0.35 | 0.0182 | NS |
| -0.3156 | 0.6228 | NS |
| -0.4337 | -0.603 | NS |
| 0.866 | 0.1731 | NS |
| 2.068 | -0.1812 | NS |
| -1.88 | -0.4789 | NS |
| 0.5628 | 0.461 | NS |
| -1.446 | -0.4337 | NS |
| 1.25 | -0.3156 | NS |
| 0.3788 | 0.2752 | NS |

### Figure 40. Summarize within: Storm and hail footprints [81]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | storm | 1 |
| 2023-01-01 | hail | 0 |
| 2023-01-01 | flood rain | 0 |
| 2023-02-01 | storm | 0 |
| 2023-02-01 | hail | 0 |
| 2023-02-01 | flood rain | 0 |
| 2023-03-01 | storm | 0 |
| 2023-03-01 | hail | 0 |
| 2023-03-01 | flood rain | 0 |
| 2023-04-01 | storm | 0 |
| 2023-04-01 | hail | 0 |
| 2023-04-01 | flood rain | 0 |
| 2023-05-01 | storm | 0 |
| 2023-05-01 | hail | 0 |
| 2023-05-01 | flood rain | 0 |
| 2023-06-01 | storm | 0 |
| 2023-06-01 | hail | 0 |
| 2023-06-01 | flood rain | 0 |
| 2023-07-01 | storm | 0 |
| 2023-07-01 | hail | 0 |
| 2023-07-01 | flood rain | 0 |
| 2023-08-01 | storm | 0 |
| 2023-08-01 | hail | 0 |
| 2023-08-01 | flood rain | 0 |
| 2023-09-01 | storm | 0 |
| 2023-09-01 | hail | 0 |
| 2023-09-01 | flood rain | 0 |
| 2023-10-01 | storm | 0 |
| 2023-10-01 | hail | 1 |
| 2023-10-01 | flood rain | 0 |
| 2023-11-01 | storm | 0 |
| 2023-11-01 | hail | 0 |
| 2023-11-01 | flood rain | 0 |
| 2023-12-01 | storm | 0 |
| 2023-12-01 | hail | 0 |
| 2023-12-01 | flood rain | 0 |
| 2024-01-01 | storm | 0 |
| 2024-01-01 | hail | 0 |
| 2024-01-01 | flood rain | 0 |
| 2024-02-01 | storm | 0 |
| 2024-02-01 | hail | 0 |
| 2024-02-01 | flood rain | 0 |
| 2024-03-01 | storm | 1 |
| 2024-03-01 | hail | 0 |
| 2024-03-01 | flood rain | 0 |
| 2024-04-01 | storm | 0 |
| 2024-04-01 | hail | 0 |
| 2024-04-01 | flood rain | 0 |
| 2024-05-01 | storm | 0 |
| 2024-05-01 | hail | 0 |
| 2024-05-01 | flood rain | 0 |
| 2024-06-01 | storm | 0 |
| 2024-06-01 | hail | 0 |
| 2024-06-01 | flood rain | 0 |
| 2024-07-01 | storm | 0 |
| 2024-07-01 | hail | 0 |
| 2024-07-01 | flood rain | 0 |
| 2024-08-01 | storm | 0 |
| 2024-08-01 | hail | 0 |
| 2024-08-01 | flood rain | 0 |
| 2024-09-01 | storm | 0 |
| 2024-09-01 | hail | 0 |
| 2024-09-01 | flood rain | 0 |
| 2024-10-01 | storm | 0 |
| 2024-10-01 | hail | 0 |
| 2024-10-01 | flood rain | 0 |
| 2024-11-01 | storm | 0 |
| 2024-11-01 | hail | 1 |
| 2024-11-01 | flood rain | 0 |
| 2024-12-01 | storm | 0 |
| 2024-12-01 | hail | 0 |
| 2024-12-01 | flood rain | 0 |
| 2025-01-01 | storm | 0 |
| 2025-01-01 | hail | 0 |
| 2025-01-01 | flood rain | 0 |
| 2025-02-01 | storm | 0 |
| 2025-02-01 | hail | 0 |
| 2025-02-01 | flood rain | 1 |
| 2025-03-01 | storm | 0 |
| 2025-03-01 | hail | 0 |
| 2025-03-01 | flood rain | 0 |
| 2025-04-01 | storm | 0 |
| 2025-04-01 | hail | 0 |
| 2025-04-01 | flood rain | 0 |
| 2025-05-01 | storm | 0 |
| 2025-05-01 | hail | 0 |
| 2025-05-01 | flood rain | 0 |
| 2025-06-01 | storm | 0 |
| 2025-06-01 | hail | 0 |
| 2025-06-01 | flood rain | 0 |
| 2025-07-01 | storm | 0 |
| 2025-07-01 | hail | 0 |
| 2025-07-01 | flood rain | 0 |
| 2025-08-01 | storm | 0 |
| 2025-08-01 | hail | 0 |
| 2025-08-01 | flood rain | 0 |
| 2025-09-01 | storm | 0 |
| 2025-09-01 | hail | 0 |
| 2025-09-01 | flood rain | 0 |
| 2025-10-01 | storm | 0 |
| 2025-10-01 | hail | 0 |
| 2025-10-01 | flood rain | 0 |
| 2025-11-01 | storm | 0 |
| 2025-11-01 | hail | 0 |
| 2025-11-01 | flood rain | 0 |
| 2025-12-01 | storm | 1 |
| 2025-12-01 | hail | 0 |
| 2025-12-01 | flood rain | 0 |
| 2026-01-01 | storm | 0 |
| 2026-01-01 | hail | 1 |
| 2026-01-01 | flood rain | 0 |
| 2026-02-01 | storm | 0 |
| 2026-02-01 | hail | 0 |
| 2026-02-01 | flood rain | 0 |
| 2026-03-01 | storm | 1 |
| 2026-03-01 | hail | 0 |
| 2026-03-01 | flood rain | 0 |

### Figure 41. Summarize within: Storm and hail footprints over time [82]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | storm | 1 |
| 2023-01-01 | hail | 0 |
| 2023-01-01 | flood rain | 0 |
| 2023-02-01 | storm | 0 |
| 2023-02-01 | hail | 0 |
| 2023-02-01 | flood rain | 0 |
| 2023-03-01 | storm | 0 |
| 2023-03-01 | hail | 0 |
| 2023-03-01 | flood rain | 0 |
| 2023-04-01 | storm | 0 |
| 2023-04-01 | hail | 0 |
| 2023-04-01 | flood rain | 0 |
| 2023-05-01 | storm | 0 |
| 2023-05-01 | hail | 0 |
| 2023-05-01 | flood rain | 0 |
| 2023-06-01 | storm | 0 |
| 2023-06-01 | hail | 0 |
| 2023-06-01 | flood rain | 0 |
| 2023-07-01 | storm | 0 |
| 2023-07-01 | hail | 0 |
| 2023-07-01 | flood rain | 0 |
| 2023-08-01 | storm | 0 |
| 2023-08-01 | hail | 0 |
| 2023-08-01 | flood rain | 0 |
| 2023-09-01 | storm | 0 |
| 2023-09-01 | hail | 0 |
| 2023-09-01 | flood rain | 0 |
| 2023-10-01 | storm | 0 |
| 2023-10-01 | hail | 1 |
| 2023-10-01 | flood rain | 0 |
| 2023-11-01 | storm | 0 |
| 2023-11-01 | hail | 0 |
| 2023-11-01 | flood rain | 0 |
| 2023-12-01 | storm | 0 |
| 2023-12-01 | hail | 0 |
| 2023-12-01 | flood rain | 0 |
| 2024-01-01 | storm | 0 |
| 2024-01-01 | hail | 0 |
| 2024-01-01 | flood rain | 0 |
| 2024-02-01 | storm | 0 |
| 2024-02-01 | hail | 0 |
| 2024-02-01 | flood rain | 0 |
| 2024-03-01 | storm | 1 |
| 2024-03-01 | hail | 0 |
| 2024-03-01 | flood rain | 0 |
| 2024-04-01 | storm | 0 |
| 2024-04-01 | hail | 0 |
| 2024-04-01 | flood rain | 0 |
| 2024-05-01 | storm | 0 |
| 2024-05-01 | hail | 0 |
| 2024-05-01 | flood rain | 0 |
| 2024-06-01 | storm | 0 |
| 2024-06-01 | hail | 0 |
| 2024-06-01 | flood rain | 0 |
| 2024-07-01 | storm | 0 |
| 2024-07-01 | hail | 0 |
| 2024-07-01 | flood rain | 0 |
| 2024-08-01 | storm | 0 |
| 2024-08-01 | hail | 0 |
| 2024-08-01 | flood rain | 0 |
| 2024-09-01 | storm | 0 |
| 2024-09-01 | hail | 0 |
| 2024-09-01 | flood rain | 0 |
| 2024-10-01 | storm | 0 |
| 2024-10-01 | hail | 0 |
| 2024-10-01 | flood rain | 0 |
| 2024-11-01 | storm | 0 |
| 2024-11-01 | hail | 1 |
| 2024-11-01 | flood rain | 0 |
| 2024-12-01 | storm | 0 |
| 2024-12-01 | hail | 0 |
| 2024-12-01 | flood rain | 0 |
| 2025-01-01 | storm | 0 |
| 2025-01-01 | hail | 0 |
| 2025-01-01 | flood rain | 0 |
| 2025-02-01 | storm | 0 |
| 2025-02-01 | hail | 0 |
| 2025-02-01 | flood rain | 1 |
| 2025-03-01 | storm | 0 |
| 2025-03-01 | hail | 0 |
| 2025-03-01 | flood rain | 0 |
| 2025-04-01 | storm | 0 |
| 2025-04-01 | hail | 0 |
| 2025-04-01 | flood rain | 0 |
| 2025-05-01 | storm | 0 |
| 2025-05-01 | hail | 0 |
| 2025-05-01 | flood rain | 0 |
| 2025-06-01 | storm | 0 |
| 2025-06-01 | hail | 0 |
| 2025-06-01 | flood rain | 0 |
| 2025-07-01 | storm | 0 |
| 2025-07-01 | hail | 0 |
| 2025-07-01 | flood rain | 0 |
| 2025-08-01 | storm | 0 |
| 2025-08-01 | hail | 0 |
| 2025-08-01 | flood rain | 0 |
| 2025-09-01 | storm | 0 |
| 2025-09-01 | hail | 0 |
| 2025-09-01 | flood rain | 0 |
| 2025-10-01 | storm | 0 |
| 2025-10-01 | hail | 0 |
| 2025-10-01 | flood rain | 0 |
| 2025-11-01 | storm | 0 |
| 2025-11-01 | hail | 0 |
| 2025-11-01 | flood rain | 0 |
| 2025-12-01 | storm | 1 |
| 2025-12-01 | hail | 0 |
| 2025-12-01 | flood rain | 0 |
| 2026-01-01 | storm | 0 |
| 2026-01-01 | hail | 1 |
| 2026-01-01 | flood rain | 0 |
| 2026-02-01 | storm | 0 |
| 2026-02-01 | hail | 0 |
| 2026-02-01 | flood rain | 0 |
| 2026-03-01 | storm | 1 |
| 2026-03-01 | hail | 0 |
| 2026-03-01 | flood rain | 0 |

### Figure 42. Find outliers: Storm and hail footprints over time [83]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | storm | 1 |
| 2023-01-01 | hail | 0 |
| 2023-01-01 | flood rain | 0 |
| 2023-02-01 | storm | 0 |
| 2023-02-01 | hail | 0 |
| 2023-02-01 | flood rain | 0 |
| 2023-03-01 | storm | 0 |
| 2023-03-01 | hail | 0 |
| 2023-03-01 | flood rain | 0 |
| 2023-04-01 | storm | 0 |
| 2023-04-01 | hail | 0 |
| 2023-04-01 | flood rain | 0 |
| 2023-05-01 | storm | 0 |
| 2023-05-01 | hail | 0 |
| 2023-05-01 | flood rain | 0 |
| 2023-06-01 | storm | 0 |
| 2023-06-01 | hail | 0 |
| 2023-06-01 | flood rain | 0 |
| 2023-07-01 | storm | 0 |
| 2023-07-01 | hail | 0 |
| 2023-07-01 | flood rain | 0 |
| 2023-08-01 | storm | 0 |
| 2023-08-01 | hail | 0 |
| 2023-08-01 | flood rain | 0 |
| 2023-09-01 | storm | 0 |
| 2023-09-01 | hail | 0 |
| 2023-09-01 | flood rain | 0 |
| 2023-10-01 | storm | 0 |
| 2023-10-01 | hail | 1 |
| 2023-10-01 | flood rain | 0 |
| 2023-11-01 | storm | 0 |
| 2023-11-01 | hail | 0 |
| 2023-11-01 | flood rain | 0 |
| 2023-12-01 | storm | 0 |
| 2023-12-01 | hail | 0 |
| 2023-12-01 | flood rain | 0 |
| 2024-01-01 | storm | 0 |
| 2024-01-01 | hail | 0 |
| 2024-01-01 | flood rain | 0 |
| 2024-02-01 | storm | 0 |
| 2024-02-01 | hail | 0 |
| 2024-02-01 | flood rain | 0 |
| 2024-03-01 | storm | 1 |
| 2024-03-01 | hail | 0 |
| 2024-03-01 | flood rain | 0 |
| 2024-04-01 | storm | 0 |
| 2024-04-01 | hail | 0 |
| 2024-04-01 | flood rain | 0 |
| 2024-05-01 | storm | 0 |
| 2024-05-01 | hail | 0 |
| 2024-05-01 | flood rain | 0 |
| 2024-06-01 | storm | 0 |
| 2024-06-01 | hail | 0 |
| 2024-06-01 | flood rain | 0 |
| 2024-07-01 | storm | 0 |
| 2024-07-01 | hail | 0 |
| 2024-07-01 | flood rain | 0 |
| 2024-08-01 | storm | 0 |
| 2024-08-01 | hail | 0 |
| 2024-08-01 | flood rain | 0 |
| 2024-09-01 | storm | 0 |
| 2024-09-01 | hail | 0 |
| 2024-09-01 | flood rain | 0 |
| 2024-10-01 | storm | 0 |
| 2024-10-01 | hail | 0 |
| 2024-10-01 | flood rain | 0 |
| 2024-11-01 | storm | 0 |
| 2024-11-01 | hail | 1 |
| 2024-11-01 | flood rain | 0 |
| 2024-12-01 | storm | 0 |
| 2024-12-01 | hail | 0 |
| 2024-12-01 | flood rain | 0 |
| 2025-01-01 | storm | 0 |
| 2025-01-01 | hail | 0 |
| 2025-01-01 | flood rain | 0 |
| 2025-02-01 | storm | 0 |
| 2025-02-01 | hail | 0 |
| 2025-02-01 | flood rain | 1 |
| 2025-03-01 | storm | 0 |
| 2025-03-01 | hail | 0 |
| 2025-03-01 | flood rain | 0 |
| 2025-04-01 | storm | 0 |
| 2025-04-01 | hail | 0 |
| 2025-04-01 | flood rain | 0 |
| 2025-05-01 | storm | 0 |
| 2025-05-01 | hail | 0 |
| 2025-05-01 | flood rain | 0 |
| 2025-06-01 | storm | 0 |
| 2025-06-01 | hail | 0 |
| 2025-06-01 | flood rain | 0 |
| 2025-07-01 | storm | 0 |
| 2025-07-01 | hail | 0 |
| 2025-07-01 | flood rain | 0 |
| 2025-08-01 | storm | 0 |
| 2025-08-01 | hail | 0 |
| 2025-08-01 | flood rain | 0 |
| 2025-09-01 | storm | 0 |
| 2025-09-01 | hail | 0 |
| 2025-09-01 | flood rain | 0 |
| 2025-10-01 | storm | 0 |
| 2025-10-01 | hail | 0 |
| 2025-10-01 | flood rain | 0 |
| 2025-11-01 | storm | 0 |
| 2025-11-01 | hail | 0 |
| 2025-11-01 | flood rain | 0 |
| 2025-12-01 | storm | 1 |
| 2025-12-01 | hail | 0 |
| 2025-12-01 | flood rain | 0 |
| 2026-01-01 | storm | 0 |
| 2026-01-01 | hail | 1 |
| 2026-01-01 | flood rain | 0 |
| 2026-02-01 | storm | 0 |
| 2026-02-01 | hail | 0 |
| 2026-02-01 | flood rain | 0 |
| 2026-03-01 | storm | 1 |
| 2026-03-01 | hail | 0 |
| 2026-03-01 | flood rain | 0 |

### Figure 43. Find point clusters: Claims over time [84]

| period | series | value |
| --- | --- | --- |
| 2022-01-01 | hail | 7 |
| 2022-01-01 | flood | 0 |
| 2022-01-01 | storm | 12 |
| 2022-01-01 | water damage | 12 |
| 2022-01-01 | theft | 4 |
| 2022-01-01 | Other | 2 |
| 2022-04-01 | hail | 4 |
| 2022-04-01 | flood | 0 |
| 2022-04-01 | storm | 6 |
| 2022-04-01 | water damage | 10 |
| 2022-04-01 | theft | 0 |
| 2022-04-01 | Other | 2 |
| 2022-07-01 | hail | 2 |
| 2022-07-01 | flood | 0 |
| 2022-07-01 | storm | 4 |
| 2022-07-01 | water damage | 5 |
| 2022-07-01 | theft | 3 |
| 2022-07-01 | Other | 1 |
| 2022-10-01 | hail | 3 |
| 2022-10-01 | flood | 0 |
| 2022-10-01 | storm | 7 |
| 2022-10-01 | water damage | 11 |
| 2022-10-01 | theft | 2 |
| 2022-10-01 | Other | 3 |
| 2023-01-01 | hail | 11 |
| 2023-01-01 | flood | 0 |
| 2023-01-01 | storm | 16 |
| 2023-01-01 | water damage | 9 |
| 2023-01-01 | theft | 7 |
| 2023-01-01 | Other | 2 |
| 2023-04-01 | hail | 1 |
| 2023-04-01 | flood | 0 |
| 2023-04-01 | storm | 5 |
| 2023-04-01 | water damage | 11 |
| 2023-04-01 | theft | 4 |
| 2023-04-01 | Other | 1 |
| 2023-07-01 | hail | 2 |
| 2023-07-01 | flood | 0 |
| 2023-07-01 | storm | 8 |
| 2023-07-01 | water damage | 7 |
| 2023-07-01 | theft | 2 |
| 2023-07-01 | Other | 0 |
| 2023-10-01 | hail | 11 |
| 2023-10-01 | flood | 0 |
| 2023-10-01 | storm | 22 |
| 2023-10-01 | water damage | 9 |
| 2023-10-01 | theft | 2 |
| 2023-10-01 | Other | 2 |
| 2024-01-01 | hail | 6 |
| 2024-01-01 | flood | 0 |
| 2024-01-01 | storm | 11 |
| 2024-01-01 | water damage | 14 |
| 2024-01-01 | theft | 4 |
| 2024-01-01 | Other | 2 |
| 2024-04-01 | hail | 3 |
| 2024-04-01 | flood | 0 |
| 2024-04-01 | storm | 7 |
| 2024-04-01 | water damage | 6 |
| 2024-04-01 | theft | 4 |
| 2024-04-01 | Other | 0 |
| 2024-07-01 | hail | 2 |
| 2024-07-01 | flood | 0 |
| 2024-07-01 | storm | 5 |
| 2024-07-01 | water damage | 4 |
| 2024-07-01 | theft | 6 |
| 2024-07-01 | Other | 0 |
| 2024-10-01 | hail | 155 |
| 2024-10-01 | flood | 0 |
| 2024-10-01 | storm | 11 |
| 2024-10-01 | water damage | 9 |
| 2024-10-01 | theft | 4 |
| 2024-10-01 | Other | 2 |
| 2025-01-01 | hail | 8 |
| 2025-01-01 | flood | 230 |
| 2025-01-01 | storm | 12 |
| 2025-01-01 | water damage | 8 |
| 2025-01-01 | theft | 4 |
| 2025-01-01 | Other | 4 |
| 2025-04-01 | hail | 6 |
| 2025-04-01 | flood | 0 |
| 2025-04-01 | storm | 7 |
| 2025-04-01 | water damage | 4 |
| 2025-04-01 | theft | 1 |
| 2025-04-01 | Other | 0 |
| 2025-07-01 | hail | 3 |
| 2025-07-01 | flood | 0 |
| 2025-07-01 | storm | 7 |
| 2025-07-01 | water damage | 5 |
| 2025-07-01 | theft | 6 |
| 2025-07-01 | Other | 2 |
| 2025-10-01 | hail | 6 |
| 2025-10-01 | flood | 0 |
| 2025-10-01 | storm | 9 |
| 2025-10-01 | water damage | 10 |
| 2025-10-01 | theft | 11 |
| 2025-10-01 | Other | 4 |
| 2026-01-01 | hail | 5 |
| 2026-01-01 | flood | 0 |
| 2026-01-01 | storm | 13 |
| 2026-01-01 | water damage | 14 |
| 2026-01-01 | theft | 3 |
| 2026-01-01 | Other | 1 |
| 2026-04-01 | hail | 1 |
| 2026-04-01 | flood | 0 |
| 2026-04-01 | storm | 9 |
| 2026-04-01 | water damage | 8 |
| 2026-04-01 | theft | 3 |
| 2026-04-01 | Other | 0 |
| 2026-07-01 | hail | 1 |
| 2026-07-01 | flood | 0 |
| 2026-07-01 | storm | 5 |
| 2026-07-01 | water damage | 3 |
| 2026-07-01 | theft | 0 |
| 2026-07-01 | Other | 0 |

### Figure 44. Repeat and near repeat classification: Claims over time [85]

| period | series | value |
| --- | --- | --- |
| 2022-01-01 | hail | 7 |
| 2022-01-01 | flood | 0 |
| 2022-01-01 | storm | 12 |
| 2022-01-01 | water damage | 12 |
| 2022-01-01 | theft | 4 |
| 2022-01-01 | Other | 2 |
| 2022-04-01 | hail | 4 |
| 2022-04-01 | flood | 0 |
| 2022-04-01 | storm | 6 |
| 2022-04-01 | water damage | 10 |
| 2022-04-01 | theft | 0 |
| 2022-04-01 | Other | 2 |
| 2022-07-01 | hail | 2 |
| 2022-07-01 | flood | 0 |
| 2022-07-01 | storm | 4 |
| 2022-07-01 | water damage | 5 |
| 2022-07-01 | theft | 3 |
| 2022-07-01 | Other | 1 |
| 2022-10-01 | hail | 3 |
| 2022-10-01 | flood | 0 |
| 2022-10-01 | storm | 7 |
| 2022-10-01 | water damage | 11 |
| 2022-10-01 | theft | 2 |
| 2022-10-01 | Other | 3 |
| 2023-01-01 | hail | 11 |
| 2023-01-01 | flood | 0 |
| 2023-01-01 | storm | 16 |
| 2023-01-01 | water damage | 9 |
| 2023-01-01 | theft | 7 |
| 2023-01-01 | Other | 2 |
| 2023-04-01 | hail | 1 |
| 2023-04-01 | flood | 0 |
| 2023-04-01 | storm | 5 |
| 2023-04-01 | water damage | 11 |
| 2023-04-01 | theft | 4 |
| 2023-04-01 | Other | 1 |
| 2023-07-01 | hail | 2 |
| 2023-07-01 | flood | 0 |
| 2023-07-01 | storm | 8 |
| 2023-07-01 | water damage | 7 |
| 2023-07-01 | theft | 2 |
| 2023-07-01 | Other | 0 |
| 2023-10-01 | hail | 11 |
| 2023-10-01 | flood | 0 |
| 2023-10-01 | storm | 22 |
| 2023-10-01 | water damage | 9 |
| 2023-10-01 | theft | 2 |
| 2023-10-01 | Other | 2 |
| 2024-01-01 | hail | 6 |
| 2024-01-01 | flood | 0 |
| 2024-01-01 | storm | 11 |
| 2024-01-01 | water damage | 14 |
| 2024-01-01 | theft | 4 |
| 2024-01-01 | Other | 2 |
| 2024-04-01 | hail | 3 |
| 2024-04-01 | flood | 0 |
| 2024-04-01 | storm | 7 |
| 2024-04-01 | water damage | 6 |
| 2024-04-01 | theft | 4 |
| 2024-04-01 | Other | 0 |
| 2024-07-01 | hail | 2 |
| 2024-07-01 | flood | 0 |
| 2024-07-01 | storm | 5 |
| 2024-07-01 | water damage | 4 |
| 2024-07-01 | theft | 6 |
| 2024-07-01 | Other | 0 |
| 2024-10-01 | hail | 155 |
| 2024-10-01 | flood | 0 |
| 2024-10-01 | storm | 11 |
| 2024-10-01 | water damage | 9 |
| 2024-10-01 | theft | 4 |
| 2024-10-01 | Other | 2 |
| 2025-01-01 | hail | 8 |
| 2025-01-01 | flood | 230 |
| 2025-01-01 | storm | 12 |
| 2025-01-01 | water damage | 8 |
| 2025-01-01 | theft | 4 |
| 2025-01-01 | Other | 4 |
| 2025-04-01 | hail | 6 |
| 2025-04-01 | flood | 0 |
| 2025-04-01 | storm | 7 |
| 2025-04-01 | water damage | 4 |
| 2025-04-01 | theft | 1 |
| 2025-04-01 | Other | 0 |
| 2025-07-01 | hail | 3 |
| 2025-07-01 | flood | 0 |
| 2025-07-01 | storm | 7 |
| 2025-07-01 | water damage | 5 |
| 2025-07-01 | theft | 6 |
| 2025-07-01 | Other | 2 |
| 2025-10-01 | hail | 6 |
| 2025-10-01 | flood | 0 |
| 2025-10-01 | storm | 9 |
| 2025-10-01 | water damage | 10 |
| 2025-10-01 | theft | 11 |
| 2025-10-01 | Other | 4 |
| 2026-01-01 | hail | 5 |
| 2026-01-01 | flood | 0 |
| 2026-01-01 | storm | 13 |
| 2026-01-01 | water damage | 14 |
| 2026-01-01 | theft | 3 |
| 2026-01-01 | Other | 1 |
| 2026-04-01 | hail | 1 |
| 2026-04-01 | flood | 0 |
| 2026-04-01 | storm | 9 |
| 2026-04-01 | water damage | 8 |
| 2026-04-01 | theft | 3 |
| 2026-04-01 | Other | 0 |
| 2026-07-01 | hail | 1 |
| 2026-07-01 | flood | 0 |
| 2026-07-01 | storm | 5 |
| 2026-07-01 | water damage | 3 |
| 2026-07-01 | theft | 0 |
| 2026-07-01 | Other | 0 |

### Figure 45. Hot spot analysis: Storm and hail footprints over time [86]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | storm | 1 |
| 2023-01-01 | hail | 0 |
| 2023-01-01 | flood rain | 0 |
| 2023-02-01 | storm | 0 |
| 2023-02-01 | hail | 0 |
| 2023-02-01 | flood rain | 0 |
| 2023-03-01 | storm | 0 |
| 2023-03-01 | hail | 0 |
| 2023-03-01 | flood rain | 0 |
| 2023-04-01 | storm | 0 |
| 2023-04-01 | hail | 0 |
| 2023-04-01 | flood rain | 0 |
| 2023-05-01 | storm | 0 |
| 2023-05-01 | hail | 0 |
| 2023-05-01 | flood rain | 0 |
| 2023-06-01 | storm | 0 |
| 2023-06-01 | hail | 0 |
| 2023-06-01 | flood rain | 0 |
| 2023-07-01 | storm | 0 |
| 2023-07-01 | hail | 0 |
| 2023-07-01 | flood rain | 0 |
| 2023-08-01 | storm | 0 |
| 2023-08-01 | hail | 0 |
| 2023-08-01 | flood rain | 0 |
| 2023-09-01 | storm | 0 |
| 2023-09-01 | hail | 0 |
| 2023-09-01 | flood rain | 0 |
| 2023-10-01 | storm | 0 |
| 2023-10-01 | hail | 1 |
| 2023-10-01 | flood rain | 0 |
| 2023-11-01 | storm | 0 |
| 2023-11-01 | hail | 0 |
| 2023-11-01 | flood rain | 0 |
| 2023-12-01 | storm | 0 |
| 2023-12-01 | hail | 0 |
| 2023-12-01 | flood rain | 0 |
| 2024-01-01 | storm | 0 |
| 2024-01-01 | hail | 0 |
| 2024-01-01 | flood rain | 0 |
| 2024-02-01 | storm | 0 |
| 2024-02-01 | hail | 0 |
| 2024-02-01 | flood rain | 0 |
| 2024-03-01 | storm | 1 |
| 2024-03-01 | hail | 0 |
| 2024-03-01 | flood rain | 0 |
| 2024-04-01 | storm | 0 |
| 2024-04-01 | hail | 0 |
| 2024-04-01 | flood rain | 0 |
| 2024-05-01 | storm | 0 |
| 2024-05-01 | hail | 0 |
| 2024-05-01 | flood rain | 0 |
| 2024-06-01 | storm | 0 |
| 2024-06-01 | hail | 0 |
| 2024-06-01 | flood rain | 0 |
| 2024-07-01 | storm | 0 |
| 2024-07-01 | hail | 0 |
| 2024-07-01 | flood rain | 0 |
| 2024-08-01 | storm | 0 |
| 2024-08-01 | hail | 0 |
| 2024-08-01 | flood rain | 0 |
| 2024-09-01 | storm | 0 |
| 2024-09-01 | hail | 0 |
| 2024-09-01 | flood rain | 0 |
| 2024-10-01 | storm | 0 |
| 2024-10-01 | hail | 0 |
| 2024-10-01 | flood rain | 0 |
| 2024-11-01 | storm | 0 |
| 2024-11-01 | hail | 1 |
| 2024-11-01 | flood rain | 0 |
| 2024-12-01 | storm | 0 |
| 2024-12-01 | hail | 0 |
| 2024-12-01 | flood rain | 0 |
| 2025-01-01 | storm | 0 |
| 2025-01-01 | hail | 0 |
| 2025-01-01 | flood rain | 0 |
| 2025-02-01 | storm | 0 |
| 2025-02-01 | hail | 0 |
| 2025-02-01 | flood rain | 1 |
| 2025-03-01 | storm | 0 |
| 2025-03-01 | hail | 0 |
| 2025-03-01 | flood rain | 0 |
| 2025-04-01 | storm | 0 |
| 2025-04-01 | hail | 0 |
| 2025-04-01 | flood rain | 0 |
| 2025-05-01 | storm | 0 |
| 2025-05-01 | hail | 0 |
| 2025-05-01 | flood rain | 0 |
| 2025-06-01 | storm | 0 |
| 2025-06-01 | hail | 0 |
| 2025-06-01 | flood rain | 0 |
| 2025-07-01 | storm | 0 |
| 2025-07-01 | hail | 0 |
| 2025-07-01 | flood rain | 0 |
| 2025-08-01 | storm | 0 |
| 2025-08-01 | hail | 0 |
| 2025-08-01 | flood rain | 0 |
| 2025-09-01 | storm | 0 |
| 2025-09-01 | hail | 0 |
| 2025-09-01 | flood rain | 0 |
| 2025-10-01 | storm | 0 |
| 2025-10-01 | hail | 0 |
| 2025-10-01 | flood rain | 0 |
| 2025-11-01 | storm | 0 |
| 2025-11-01 | hail | 0 |
| 2025-11-01 | flood rain | 0 |
| 2025-12-01 | storm | 1 |
| 2025-12-01 | hail | 0 |
| 2025-12-01 | flood rain | 0 |
| 2026-01-01 | storm | 0 |
| 2026-01-01 | hail | 1 |
| 2026-01-01 | flood rain | 0 |
| 2026-02-01 | storm | 0 |
| 2026-02-01 | hail | 0 |
| 2026-02-01 | flood rain | 0 |
| 2026-03-01 | storm | 1 |
| 2026-03-01 | hail | 0 |
| 2026-03-01 | flood rain | 0 |

### Figure 46. Summarize within: Storm and hail footprints over time [87]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | storm | 1 |
| 2023-01-01 | hail | 0 |
| 2023-01-01 | flood rain | 0 |
| 2023-02-01 | storm | 0 |
| 2023-02-01 | hail | 0 |
| 2023-02-01 | flood rain | 0 |
| 2023-03-01 | storm | 0 |
| 2023-03-01 | hail | 0 |
| 2023-03-01 | flood rain | 0 |
| 2023-04-01 | storm | 0 |
| 2023-04-01 | hail | 0 |
| 2023-04-01 | flood rain | 0 |
| 2023-05-01 | storm | 0 |
| 2023-05-01 | hail | 0 |
| 2023-05-01 | flood rain | 0 |
| 2023-06-01 | storm | 0 |
| 2023-06-01 | hail | 0 |
| 2023-06-01 | flood rain | 0 |
| 2023-07-01 | storm | 0 |
| 2023-07-01 | hail | 0 |
| 2023-07-01 | flood rain | 0 |
| 2023-08-01 | storm | 0 |
| 2023-08-01 | hail | 0 |
| 2023-08-01 | flood rain | 0 |
| 2023-09-01 | storm | 0 |
| 2023-09-01 | hail | 0 |
| 2023-09-01 | flood rain | 0 |
| 2023-10-01 | storm | 0 |
| 2023-10-01 | hail | 1 |
| 2023-10-01 | flood rain | 0 |
| 2023-11-01 | storm | 0 |
| 2023-11-01 | hail | 0 |
| 2023-11-01 | flood rain | 0 |
| 2023-12-01 | storm | 0 |
| 2023-12-01 | hail | 0 |
| 2023-12-01 | flood rain | 0 |
| 2024-01-01 | storm | 0 |
| 2024-01-01 | hail | 0 |
| 2024-01-01 | flood rain | 0 |
| 2024-02-01 | storm | 0 |
| 2024-02-01 | hail | 0 |
| 2024-02-01 | flood rain | 0 |
| 2024-03-01 | storm | 1 |
| 2024-03-01 | hail | 0 |
| 2024-03-01 | flood rain | 0 |
| 2024-04-01 | storm | 0 |
| 2024-04-01 | hail | 0 |
| 2024-04-01 | flood rain | 0 |
| 2024-05-01 | storm | 0 |
| 2024-05-01 | hail | 0 |
| 2024-05-01 | flood rain | 0 |
| 2024-06-01 | storm | 0 |
| 2024-06-01 | hail | 0 |
| 2024-06-01 | flood rain | 0 |
| 2024-07-01 | storm | 0 |
| 2024-07-01 | hail | 0 |
| 2024-07-01 | flood rain | 0 |
| 2024-08-01 | storm | 0 |
| 2024-08-01 | hail | 0 |
| 2024-08-01 | flood rain | 0 |
| 2024-09-01 | storm | 0 |
| 2024-09-01 | hail | 0 |
| 2024-09-01 | flood rain | 0 |
| 2024-10-01 | storm | 0 |
| 2024-10-01 | hail | 0 |
| 2024-10-01 | flood rain | 0 |
| 2024-11-01 | storm | 0 |
| 2024-11-01 | hail | 1 |
| 2024-11-01 | flood rain | 0 |
| 2024-12-01 | storm | 0 |
| 2024-12-01 | hail | 0 |
| 2024-12-01 | flood rain | 0 |
| 2025-01-01 | storm | 0 |
| 2025-01-01 | hail | 0 |
| 2025-01-01 | flood rain | 0 |
| 2025-02-01 | storm | 0 |
| 2025-02-01 | hail | 0 |
| 2025-02-01 | flood rain | 1 |
| 2025-03-01 | storm | 0 |
| 2025-03-01 | hail | 0 |
| 2025-03-01 | flood rain | 0 |
| 2025-04-01 | storm | 0 |
| 2025-04-01 | hail | 0 |
| 2025-04-01 | flood rain | 0 |
| 2025-05-01 | storm | 0 |
| 2025-05-01 | hail | 0 |
| 2025-05-01 | flood rain | 0 |
| 2025-06-01 | storm | 0 |
| 2025-06-01 | hail | 0 |
| 2025-06-01 | flood rain | 0 |
| 2025-07-01 | storm | 0 |
| 2025-07-01 | hail | 0 |
| 2025-07-01 | flood rain | 0 |
| 2025-08-01 | storm | 0 |
| 2025-08-01 | hail | 0 |
| 2025-08-01 | flood rain | 0 |
| 2025-09-01 | storm | 0 |
| 2025-09-01 | hail | 0 |
| 2025-09-01 | flood rain | 0 |
| 2025-10-01 | storm | 0 |
| 2025-10-01 | hail | 0 |
| 2025-10-01 | flood rain | 0 |
| 2025-11-01 | storm | 0 |
| 2025-11-01 | hail | 0 |
| 2025-11-01 | flood rain | 0 |
| 2025-12-01 | storm | 1 |
| 2025-12-01 | hail | 0 |
| 2025-12-01 | flood rain | 0 |
| 2026-01-01 | storm | 0 |
| 2026-01-01 | hail | 1 |
| 2026-01-01 | flood rain | 0 |
| 2026-02-01 | storm | 0 |
| 2026-02-01 | hail | 0 |
| 2026-02-01 | flood rain | 0 |
| 2026-03-01 | storm | 1 |
| 2026-03-01 | hail | 0 |
| 2026-03-01 | flood rain | 0 |

### Figure 47. Summarize within: Storm and hail footprints over time [88]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | storm | 1 |
| 2023-01-01 | hail | 0 |
| 2023-01-01 | flood rain | 0 |
| 2023-02-01 | storm | 0 |
| 2023-02-01 | hail | 0 |
| 2023-02-01 | flood rain | 0 |
| 2023-03-01 | storm | 0 |
| 2023-03-01 | hail | 0 |
| 2023-03-01 | flood rain | 0 |
| 2023-04-01 | storm | 0 |
| 2023-04-01 | hail | 0 |
| 2023-04-01 | flood rain | 0 |
| 2023-05-01 | storm | 0 |
| 2023-05-01 | hail | 0 |
| 2023-05-01 | flood rain | 0 |
| 2023-06-01 | storm | 0 |
| 2023-06-01 | hail | 0 |
| 2023-06-01 | flood rain | 0 |
| 2023-07-01 | storm | 0 |
| 2023-07-01 | hail | 0 |
| 2023-07-01 | flood rain | 0 |
| 2023-08-01 | storm | 0 |
| 2023-08-01 | hail | 0 |
| 2023-08-01 | flood rain | 0 |
| 2023-09-01 | storm | 0 |
| 2023-09-01 | hail | 0 |
| 2023-09-01 | flood rain | 0 |
| 2023-10-01 | storm | 0 |
| 2023-10-01 | hail | 1 |
| 2023-10-01 | flood rain | 0 |
| 2023-11-01 | storm | 0 |
| 2023-11-01 | hail | 0 |
| 2023-11-01 | flood rain | 0 |
| 2023-12-01 | storm | 0 |
| 2023-12-01 | hail | 0 |
| 2023-12-01 | flood rain | 0 |
| 2024-01-01 | storm | 0 |
| 2024-01-01 | hail | 0 |
| 2024-01-01 | flood rain | 0 |
| 2024-02-01 | storm | 0 |
| 2024-02-01 | hail | 0 |
| 2024-02-01 | flood rain | 0 |
| 2024-03-01 | storm | 1 |
| 2024-03-01 | hail | 0 |
| 2024-03-01 | flood rain | 0 |
| 2024-04-01 | storm | 0 |
| 2024-04-01 | hail | 0 |
| 2024-04-01 | flood rain | 0 |
| 2024-05-01 | storm | 0 |
| 2024-05-01 | hail | 0 |
| 2024-05-01 | flood rain | 0 |
| 2024-06-01 | storm | 0 |
| 2024-06-01 | hail | 0 |
| 2024-06-01 | flood rain | 0 |
| 2024-07-01 | storm | 0 |
| 2024-07-01 | hail | 0 |
| 2024-07-01 | flood rain | 0 |
| 2024-08-01 | storm | 0 |
| 2024-08-01 | hail | 0 |
| 2024-08-01 | flood rain | 0 |
| 2024-09-01 | storm | 0 |
| 2024-09-01 | hail | 0 |
| 2024-09-01 | flood rain | 0 |
| 2024-10-01 | storm | 0 |
| 2024-10-01 | hail | 0 |
| 2024-10-01 | flood rain | 0 |
| 2024-11-01 | storm | 0 |
| 2024-11-01 | hail | 1 |
| 2024-11-01 | flood rain | 0 |
| 2024-12-01 | storm | 0 |
| 2024-12-01 | hail | 0 |
| 2024-12-01 | flood rain | 0 |
| 2025-01-01 | storm | 0 |
| 2025-01-01 | hail | 0 |
| 2025-01-01 | flood rain | 0 |
| 2025-02-01 | storm | 0 |
| 2025-02-01 | hail | 0 |
| 2025-02-01 | flood rain | 1 |
| 2025-03-01 | storm | 0 |
| 2025-03-01 | hail | 0 |
| 2025-03-01 | flood rain | 0 |
| 2025-04-01 | storm | 0 |
| 2025-04-01 | hail | 0 |
| 2025-04-01 | flood rain | 0 |
| 2025-05-01 | storm | 0 |
| 2025-05-01 | hail | 0 |
| 2025-05-01 | flood rain | 0 |
| 2025-06-01 | storm | 0 |
| 2025-06-01 | hail | 0 |
| 2025-06-01 | flood rain | 0 |
| 2025-07-01 | storm | 0 |
| 2025-07-01 | hail | 0 |
| 2025-07-01 | flood rain | 0 |
| 2025-08-01 | storm | 0 |
| 2025-08-01 | hail | 0 |
| 2025-08-01 | flood rain | 0 |
| 2025-09-01 | storm | 0 |
| 2025-09-01 | hail | 0 |
| 2025-09-01 | flood rain | 0 |
| 2025-10-01 | storm | 0 |
| 2025-10-01 | hail | 0 |
| 2025-10-01 | flood rain | 0 |
| 2025-11-01 | storm | 0 |
| 2025-11-01 | hail | 0 |
| 2025-11-01 | flood rain | 0 |
| 2025-12-01 | storm | 1 |
| 2025-12-01 | hail | 0 |
| 2025-12-01 | flood rain | 0 |
| 2026-01-01 | storm | 0 |
| 2026-01-01 | hail | 1 |
| 2026-01-01 | flood rain | 0 |
| 2026-02-01 | storm | 0 |
| 2026-02-01 | hail | 0 |
| 2026-02-01 | flood rain | 0 |
| 2026-03-01 | storm | 1 |
| 2026-03-01 | hail | 0 |
| 2026-03-01 | flood rain | 0 |


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