# Sample: Forestry and wildfire, Bend: report

Project: Sample: Forestry and wildfire, Bend  
Goal: Describe the project Sample: Forestry and wildfire, Bend: run the analyses that tell the most about its data, draw every result, and write a cited report.  
Run: 8ded6244-4d76-45b7-a92e-f62fd8a76afe (running, 11 agent run(s))  
Generated: 2026-10-18T06:17:30Z, version 1  
Model cost: USD 0.0000 over 0 model call(s)  

## Data used

| Data | Kind | Rows | Time span | Summary | Source |
| --- | --- | --- | --- | --- | --- |
| Fuel units | vector, Polygon | 36 |  | 36 polygon (area) features; 'fuel_model' has 5 values (top: timber litter 10, timber understory 7, slash 7); 'fuel_load_t_ha' 1.5 to 37.1 (mean 15.26); 'canopy_cover_pct' 7 to 91 (mean 45.36); 'last_burned' 1996 to 2020 (mean 2011); total area 80.03 km2; extent -121.4833,43.9831 to -121.3566,44.0568. Areas (Fuel units) with fuel_load_t_ha to compare and map. | [98] |
| Fire perimeters | vector, Polygon | 10 | 2017-08-10 to 2026-07-16 | 10 polygon (area) features; dated by 'start_date' from 2017-08-10 to 2026-07-16; 'cause' has 3 values (top: equipment 6, human 3, lightning 1); 'acres' 85 to 378 (mean 219.2); 'containment_days' 2 to 20 (mean 11); named by 'fire_name' (e.g. Westbrook Fire, Station Quarter Fire, Riverside Fire); total area 8.894 km2; extent -121.4744,43.9952 to -121.3725,44.0490. Areas (Fire perimeters) with acres to compare and map. | [2] |
| Ignitions | vector, Point | 900 | 2018-01-07 to 2026-09-30 | 900 point features; dated by 'detected_at' from 2018-01-07 to 2026-09-30; 'cause' has 7 values (top: lightning 243, campfire 186, vehicle 112); 'final_acres' 0 to 45.9 (mean 1.727); 'elevation_m' 1158 to 1737 (mean 1449); extent -121.4824,43.9841 to -121.3576,44.0560. Dated point events by cause: where and when Ignitions happen. | [3] |
| Forest roads | vector, LineString | 15 |  | 15 line features; 'surface' has 3 values (top: native 8, gravel 6, paved 1); 'gated' has 2 values (top: False 10, True 5); named by 'name' (e.g. Highway corridor, Forest Road 314, Forest Road 321); total length 74.47 km; extent -121.4826,43.9869 to -121.3584,44.0558. Lines (Forest roads) such as routes or networks. | [99] |
| Weather stations | vector, Point | 6 |  | 6 point features; 'elevation_m' 1253 to 1663 (mean 1503); named by 'name' (e.g. RAWS weather station 1, RAWS weather station 2, RAWS weather station 3); extent -121.4578,44.0051 to -121.3674,44.0455. Point locations of Weather stations. | [100] |
| Fire lookouts | vector, Point | 3 |  | 3 point features; 'staffed' has 2 values (top: True 2, False 1); 'tower_height_m' 9 to 12 (mean 11); named by 'name' (e.g. Pine Butte lookout, Cinder Butte lookout, Eagle Butte lookout); extent -121.4430,44.0109 to -121.3763,44.0430. Point locations of Fire lookouts by staffed. | [101] |
| Fuel treatments | vector, Polygon | 12 | 2019-06-01 to 2025-01-01 | 12 polygon (area) features; dated by 'completed' from 2019-06-01 to 2025-01-01; 'method' has 3 values (top: prescribed burn 7, mastication 3, thinning 2); 'acres' 62 to 188 (mean 119.8); total area 5.842 km2; extent -121.4681,44.0078 to -121.3572,44.0561. Areas (Fuel treatments) with acres to compare and map. | [4] |
| Forest terrain model (DEM) | raster |  |  | 1-band float32 raster (dem), 333 x 267 cells; resolution 30; band 1 1152 to 1745; extent -121.4833,43.9830 to -121.3568,44.0568. An elevation surface for terrain analysis. | [102] |
| Station weather by day | table | 918 | 2026-05-01 to 2026-09-30 | Table of 918 rows and 6 columns without geometry; dated by 'date' from 2026-05-01 to 2026-09-30; 'temp_max_c' 14.6 to 42.8 (mean 29.96); 'rh_min_pct' 5 to 56 (mean 25.28); 'wind_kmh' 0 to 29 (mean 13.63). A table of Station weather by day without locations. | [5] |
| Treatment costs | table | 12 |  | Table of 12 rows and 4 columns without geometry; 'method' has 3 values (top: prescribed burn 7, mastication 3, thinning 2); 'cost_usd_per_acre' 472 to 1,391 (mean 910); 'crew_days' 5 to 40 (mean 25.67). A table of Treatment costs without locations. | [103] |
| Fuels treatment plan | document |  |  | 1-page pdf document 'Fuels treatment plan'; language en; mentions 5 place(s): Highway corridor (2), Thornbury Vale Fire (1), Queensway Fire (1), Pine Butte lookout (1), Cinder Butte lookout (1). A document about Highway corridor, Thornbury Vale Fire, Queensway Fire. | [104] |
| Sample: Forestry and wildfire, Bend | record |  |  |  | [105] |
| Hot spot analysis: Ignitions | layer, Polygon | 344 |  |  | [106] |
| Hot spot analysis: Fire perimeters | layer, Polygon | 10 |  |  | [107] |
| Hot spot analysis: Fuel treatments | layer, Polygon | 12 |  |  | [108] |
| Emerging hot spots: Ignitions | layer, Polygon | 135 |  |  | [109] |
| Repeat and near repeat classification: Ignitions | layer, Point | 900 |  |  | [110] |
| Summarize within: Fire perimeters | layer, Polygon | 10 |  |  | [111] |
| Summarize within: Fire perimeters | layer, Polygon | 10 |  |  | [112] |
| Summarize within: Fire perimeters | layer, Polygon | 10 |  |  | [113] |
| Find point clusters: Ignitions | layer, Point | 900 |  |  | [114] |
| Find point clusters: cluster hulls | layer, Polygon | 1 |  |  | [115] |
| Calculate density: Ignitions | layer, Polygon | 2,136 |  |  | [116] |
| Find outliers: Fire perimeters | layer, Polygon | 10 |  |  | [117] |
| Find outliers: Fuel treatments | layer, Polygon | 12 |  |  | [118] |
| Find outliers: Fuel units | layer, Polygon | 36 |  |  | [119] |

## Timeline

![Timeline: events per quarter by dataset](charts/26-timeline-events-per-quarter-by-dataset.png)

*Figure 1. Timeline: events per quarter by dataset [1]. Per quarter, 2017-07-01 to 2026-07-01. Source: Fire perimeters, Ignitions, Fuel treatments, Station weather by day (1,840 features). The data is in the appendix.*

The timeline counts 1,840 dated events in Fire perimeters, Ignitions, Fuel treatments and 1 more per quarter, from Q3 2017 to Q3 2026 [1][2][3][4][5]. By dataset: Station weather by day 918; Ignitions 900; Fuel treatments 12; Fire perimeters 10 [1]. The trend is +4.46 events per quarter; the second half of the quarters has 1,475 against 360 in the first (+309.7%) [1]. 1 of the 37 quarters had no events [1]. Change point detection found no shift in the counts [6].

**Busiest quarters**

- Q3 2026: 610 events [1]
- Q2 2026: 386 events [1]
- Q3 2025: 62 events [1]

**Quietest quarters with events**

- Q3 2017: 1 event [1]
- Q1 2018: 1 event [1]
- Q1 2021: 1 event [1]

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [7]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [7].

**Why it was run on this data.** 900 points: Getis-Ord Gi* tests whether the concentrations in Ignitions are statistically significant [8].

**What it found.** Hot spots on Ignitions: Hot spot analysis: 1 layer saved: Hot spot analysis: Ignitions (344 features) [9][10]. It reported result, bins 344; result, count 344; result, elapsed ms 21; result, cell size m 500; result, incidents used 900; result, distance band m 500 [9].

| Result | Value |
| --- | --- |
| result, bins | 344 |
| result, count | 344 |
| result, elapsed ms | 21 |
| 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 Ignitions: Hot spot analysis: 1 layer saved: Hot spot analysis: Ignitions (344 features) [9][10].
- **Evidenced:** It reported result, bins 344; result, count 344; result, elapsed ms 21; result, cell size m 500; result, incidents used 900; result, distance band m 500 [9].

*Tool gp_hot_spots; parameters {"project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Ignitions (73bb3d60)"}, "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 [7]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [7].

**Why it was run on this data.** Gi* hot spots of 'acres' across the areas [11].

**What it found.** Hot spots on Fire perimeters: Hot spot analysis: 1 layer saved: Hot spot analysis: Fire perimeters (10 features) [12][13]. It reported result, count 10; result, elapsed ms 3; result, excluded null 0; result, distance band m 2,652; billing, cost 0.0502; billing, list price 0.0502 [12].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 3 |
| result, excluded null | 0 |
| result, distance band m | 2,652 |
| billing, cost | 0.0502 |
| billing, list price | 0.0502 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Fire perimeters: Hot spot analysis: 1 layer saved: Hot spot analysis: Fire perimeters (10 features) [12][13].
- **Evidenced:** It reported result, count 10; result, elapsed ms 3; result, excluded null 0; result, distance band m 2,652; billing, cost 0.0502; billing, list price 0.0502 [12].

*Tool gp_hot_spots; parameters {"field": "acres", "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Fire perimeters (4401c868)"}}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [7]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [7].

**Why it was run on this data.** Gi* hot spots of 'acres' across the areas [14].

**What it found.** Hot spots on Fuel treatments: Hot spot analysis: 1 layer saved: Hot spot analysis: Fuel treatments (12 features) [15][16]. It reported result, count 12; result, elapsed ms 2; result, excluded null 0; result, distance band m 2,799; billing, cost 0.05024; billing, list price 0.05024 [15].

| Result | Value |
| --- | --- |
| result, count | 12 |
| result, elapsed ms | 2 |
| result, excluded null | 0 |
| result, distance band m | 2,799 |
| billing, cost | 0.05024 |
| billing, list price | 0.05024 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Fuel treatments: Hot spot analysis: 1 layer saved: Hot spot analysis: Fuel treatments (12 features) [15][16].
- **Evidenced:** It reported result, count 12; result, elapsed ms 2; result, excluded null 0; result, distance band m 2,799; billing, cost 0.05024; billing, list price 0.05024 [15].

*Tool gp_hot_spots; parameters {"field": "acres", "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Fuel treatments (d603668d)"}}*

## 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 [17]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [17].

**Why it was run on this data.** Ignitions carry dates in 'detected_at': emerging hot spot analysis finds new, intensifying and fading hot spots per month [18].

**What it found.** Space time hot spots on Ignitions: Emerging hot spots: 1 layer saved: Emerging hot spots: Ignitions (135 features) [10][19]. It reported result, count 135; result, elapsed ms 35; result, resolution 8; result, features used 900; result, time neighbors 0; billing, cost 0.145 [19].

| Result | Value |
| --- | --- |
| result, count | 135 |
| result, elapsed ms | 35 |
| 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 Ignitions: Emerging hot spots: 1 layer saved: Emerging hot spots: Ignitions (135 features) [10][19].
- **Evidenced:** It reported result, count 135; result, elapsed ms 35; result, resolution 8; result, features used 900; result, time neighbors 0; billing, cost 0.145 [19].

*Tool gp_space_time_hot_spots; parameters {"project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Ignitions (73bb3d60)"}, "interval": "month", "bin_shape": "h3", "resolution": 8, "time_field": "detected_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 [20]. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator [20].

**Why it was run on this data.** Near repeat analysis tests whether Ignitions close in space also follow each other within 30 days [21].

**What it found.** The near repeat classification utilized the `gp_near_repeat` tool [22] and processed the Ignitions dataset [10]. The tool yielded overall results showing a count of 900 [22], 63 chains [22], and a maximum distance of 200 m [22]. The execution was efficient, linking 80 pairs in an elapsed time of 18 ms [22]. Financially, the operation incurred a cost of 0.077 [22].

| Result | Value |
| --- | --- |
| result, days | 30 |
| result, count | 900 |
| result, chains | 63 |
| result, distance m | 200 |
| result, elapsed ms | 18 |
| result, pairs linked | 80 |
| billing, cost | 0.077 |
| billing, list price | 0.077 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Near repeat on Ignitions: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Ignitions (900 features) [10][22].
- **Evidenced:** It reported result, days 30; result, count 900; result, chains 63; result, distance m 200; result, elapsed ms 18; result, pairs linked 80 [22].

*Tool gp_near_repeat; parameters {"days": 30, "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Ignitions (73bb3d60)"}, "distance_m": 200.0, "time_field": "detected_at"}*

## Change point detection

**What this analysis is.** Change point detection: When did the level, count, trend or variability change [23]? For the whole study area or for every bin [23]. In ArcGIS it is Change Point Detection [23].

**Why it was run on this data.** Change point detection finds when the monthly count of Ignitions shifted [24].

**What it found.** Change point detection was executed using the tool `gp_change_point_detection` [6]. The input data for the analysis was Ignitions [10]. The tool summary reported that change point detection was successfully done, although no map output was generated [6].

**Findings**

- **Evidenced:** Change point detection on Ignitions: Change point detection: done; no map output [6][10].

*Tool gp_change_point_detection; parameters {"method": "count", "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Ignitions (73bb3d60)"}, "interval": "month", "time_field": "detected_at"}*

## Summarize within

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

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

**What it found.** Summarize within on Ignitions, Fire perimeters: Summarize within: 1 layer saved: Summarize within: Fire perimeters (10 features) [10][13][27]. It reported result, count 10; result, elapsed ms 3; billing, cost 0.02822; billing, list price 0.02822; billing, upstream cost 0 [27].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 3 |
| billing, cost | 0.02822 |
| billing, list price | 0.02822 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Ignitions, Fire perimeters: Summarize within: 1 layer saved: Summarize within: Fire perimeters (10 features) [10][13][27].
- **Evidenced:** It reported result, count 10; result, elapsed ms 3; billing, cost 0.02822; billing, list price 0.02822; billing, upstream cost 0 [27].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "final_acres"}], "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Ignitions (73bb3d60)"}, "group_by": "cause", "polygons": {"layer": "Fire perimeters (4401c868)"}}*

## Summarize within

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

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

**What it found.** The analysis used Fire perimeters [13] and Fire lookouts [29] as inputs for the `gp_summarize_within` function [28].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 2 |
| billing, cost | 0.01028 |
| billing, list price | 0.01028 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Fire lookouts, Fire perimeters: Summarize within: 1 layer saved: Summarize within: Fire perimeters (10 features) [13][28][29].
- **Evidenced:** It reported result, count 10; result, elapsed ms 2; billing, cost 0.01028; billing, list price 0.01028; billing, upstream cost 0 [28].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "tower_height_m"}], "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Fire lookouts (81fdbe6e)"}, "group_by": "staffed", "polygons": {"layer": "Fire perimeters (4401c868)"}}*

## Summarize within

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

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

**What it found.** Summarize within on Weather stations, Fire perimeters: Summarize within: 1 layer saved: Summarize within: Fire perimeters (10 features) [13][30][31]. It reported result, count 10; result, elapsed ms 2; billing, cost 0.01034; billing, list price 0.01034; billing, upstream cost 0 [30].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 2 |
| billing, cost | 0.01034 |
| billing, list price | 0.01034 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Weather stations, Fire perimeters: Summarize within: 1 layer saved: Summarize within: Fire perimeters (10 features) [13][30][31].
- **Evidenced:** It reported result, count 10; result, elapsed ms 2; billing, cost 0.01034; billing, list price 0.01034; billing, upstream cost 0 [30].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "elevation_m"}], "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Weather stations (6b183e69)"}, "polygons": {"layer": "Fire perimeters (4401c868)"}}*

## Zonal statistics

**What this analysis is.** Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON [32]. In ArcGIS it is Zonal Statistics as Table (polygons) [32].

**Why it was run on this data.** Statistics of Forest terrain model (DEM) inside each feature of Fire perimeters [33].

**What it found.** The analysis utilized the zonal statistics tool, specifically tool rs_zonal_statistics [34]. This tool summary indicates that the zonal statistics were run, yielding 10 row(s) for 10 zones [34]. Available source information for this process is provided by rs_zonal_statistics at step 0 [34].

**Findings**

- **Evidenced:** Zonal statistics: Zonal statistics: 10 row(s) [34].
- **Evidenced:** zones 10 [34].

*Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Fire perimeters (4401c868)", "raster_id": "Forest terrain model (DEM) (71497b8b)"}*

## Zonal statistics

**What this analysis is.** Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON [32]. In ArcGIS it is Zonal Statistics as Table (polygons) [32].

**Why it was run on this data.** Statistics of Forest terrain model (DEM) inside each feature of Fire perimeters [33].

**What it found.** The tool rs_zonal_statistics processes 12 rows and 12 zones [35]. It is listed as the function rs_zonal_statistics during step 1 [35].

**Findings**

- **Evidenced:** Zonal statistics: Zonal statistics: 12 row(s) [35].
- **Evidenced:** zones 12 [35].

*Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Fuel treatments (d603668d)", "raster_id": "Forest terrain model (DEM) (71497b8b)"}*

## Zonal statistics

**What this analysis is.** Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON [32]. In ArcGIS it is Zonal Statistics as Table (polygons) [32].

**Why it was run on this data.** Statistics of Forest terrain model (DEM) inside each feature of Fire perimeters [33].

**What it found.** The Zonal statistics analysis utilized the rs_zonal_statistics tool [36]. The tool summary indicates that Zonal statistics generated 36 row(s) [36].

**Findings**

- **Evidenced:** Zonal statistics: Zonal statistics: 36 row(s) [36].
- **Evidenced:** zones 36 [36].

*Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Fuel units (42f78cee)", "raster_id": "Forest terrain model (DEM) (71497b8b)"}*

## Find point clusters

**What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [37]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [37].

**Why it was run on this data.** DBSCAN finds dense groups of Ignitions and separates noise [38].

**What it found.** Find point clusters on Ignitions: Find point clusters: 2 layers saved: Find point clusters: Ignitions (900 features); Find point clusters: cluster hulls (1 feature) [10][39]. It reported result, count 900; result, noise 376; result, distance m 500; result, elapsed ms 15; result, min points 20; billing, cost 0.068 [39].

| Result | Value |
| --- | --- |
| result, count | 900 |
| result, noise | 376 |
| result, distance m | 500 |
| 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 Ignitions: Find point clusters: 2 layers saved: Find point clusters: Ignitions (900 features); Find point clusters: cluster hulls (1 feature) [10][39].
- **Evidenced:** It reported result, count 900; result, noise 376; result, distance m 500; result, elapsed ms 15; result, min points 20; billing, cost 0.068 [39].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Ignitions (73bb3d60)"}, "distance_m": 500.0, "min_points": 20}*

## Nearest neighbor index

**What this analysis is.** Average nearest neighbor: Clark-Evans ratio: clustered, dispersed or random [40]. In ArcGIS it is Average Nearest Neighbor; in QGIS, native:nearestneighbouranalysis [40].

**Why it was run on this data.** The average nearest neighbor ratio says whether Ignitions are clustered, random or dispersed [41].

**What it found.** Nearest neighbor index on Ignitions: Average nearest neighbor: done; no map output [10][42]. It reported result, n 900; result, p value 0; result, z score -9.013; result, elapsed ms 4; result, study area m2 76,190,873; result, nearest neighbor ratio 0.843 [42].

| Result | Value |
| --- | --- |
| result, n | 900 |
| result, p value | 0 |
| result, z score | -9.013 |
| result, elapsed ms | 4 |
| result, study area m2 | 76,190,873 |
| result, nearest neighbor ratio | 0.843 |
| result, expected mean distance m | 145.5 |
| result, observed mean distance m | 122.6 |
| billing, cost | 0.02 |
| billing, list price | 0.02 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Nearest neighbor index on Ignitions: Average nearest neighbor: done; no map output [10][42].
- **Evidenced:** It reported result, n 900; result, p value 0; result, z score -9.013; result, elapsed ms 4; result, study area m2 76,190,873; result, nearest neighbor ratio 0.843 [42].

*Tool gp_nearest_neighbor_index; parameters {"project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Ignitions (73bb3d60)"}}*

## Nearest neighbor index

**What this analysis is.** Average nearest neighbor: Clark-Evans ratio: clustered, dispersed or random [40]. In ArcGIS it is Average Nearest Neighbor; in QGIS, native:nearestneighbouranalysis [40].

**Why it was run on this data.** The average nearest neighbor ratio says whether Weather stations are clustered, random or dispersed [43].

**What it found.** The nearest neighbor index tool was applied using Weather stations [31] and calculated a nearest neighbor ratio of 2.891 [44].

| Result | Value |
| --- | --- |
| result, n | 6 |
| result, p value | 0 |
| result, z score | 8.863 |
| result, elapsed ms | 0 |
| result, study area m2 | 26,350,582 |
| result, nearest neighbor ratio | 2.891 |
| result, expected mean distance m | 1,048 |
| result, observed mean distance m | 3,030 |
| billing, cost | 0.02 |
| billing, list price | 0.02 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Nearest neighbor index on Weather stations: Average nearest neighbor: done; no map output [31][44].
- **Evidenced:** It reported result, n 6; result, p value 0; result, z score 8.863; result, elapsed ms 0; result, study area m2 26,350,582; result, nearest neighbor ratio 2.891 [44].

*Tool gp_nearest_neighbor_index; parameters {"project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Weather stations (6b183e69)"}}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [45]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [45].

**Why it was run on this data.** 900 points in Ignitions: a density surface shows where they concentrate [46].

**What it found.** The density calculation utilized the Ignitions data [10], resulting in 2,136 features [47]. The process was completed in 61 elapsed milliseconds [47], using a 500 meter bandwidth and a 200 meter cell size [47].

| Result | Value |
| --- | --- |
| result, count | 2,136 |
| result, elapsed ms | 61 |
| result, bandwidth m | 500 |
| result, cell size m | 200 |
| result, excluded null | 0 |
| result, max density per km2 | 102.6 |
| billing, cost | 0.068 |
| billing, list price | 0.068 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Ignitions: Calculate density: 1 layer saved: Calculate density: Ignitions (2136 features) [10][47].
- **Evidenced:** It reported result, count 2,136; result, elapsed ms 61; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 102.6 [47].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Ignitions (73bb3d60)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0}*

## Outliers

**What this analysis is.** Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers [48]. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) [48].

**Why it was run on this data.** Local Moran's I maps clusters and outliers of 'acres' [49].

**What it found.** Outliers on Fire perimeters: Find outliers: 1 layer saved: Find outliers: Fire perimeters (10 features) [13][50]. It reported result, count 10; result, elapsed ms 8; result, permutations 199; result, excluded null 0; result, distance band m 2,652; billing, cost 0.0503 [50].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 8 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 2,652 |
| billing, cost | 0.0503 |
| billing, list price | 0.0503 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Fire perimeters: Find outliers: 1 layer saved: Find outliers: Fire perimeters (10 features) [13][50].
- **Evidenced:** It reported result, count 10; result, elapsed ms 8; result, permutations 199; result, excluded null 0; result, distance band m 2,652; billing, cost 0.0503 [50].

*Tool gp_outliers; parameters {"field": "acres", "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Fire perimeters (4401c868)"}, "aggregation": "hexagon"}*

## Outliers

**What this analysis is.** Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers [48]. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) [48].

**Why it was run on this data.** Local Moran's I maps clusters and outliers of 'acres' [51].

**What it found.** Outliers on Fuel treatments: Find outliers: 1 layer saved: Find outliers: Fuel treatments (12 features) [16][52]. It reported result, count 12; result, elapsed ms 3; result, permutations 199; result, excluded null 0; result, distance band m 2,799; billing, cost 0.05036 [52].

| Result | Value |
| --- | --- |
| result, count | 12 |
| result, elapsed ms | 3 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 2,799 |
| billing, cost | 0.05036 |
| billing, list price | 0.05036 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Fuel treatments: Find outliers: 1 layer saved: Find outliers: Fuel treatments (12 features) [16][52].
- **Evidenced:** It reported result, count 12; result, elapsed ms 3; result, permutations 199; result, excluded null 0; result, distance band m 2,799; billing, cost 0.05036 [52].

*Tool gp_outliers; parameters {"field": "acres", "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Fuel treatments (d603668d)"}, "aggregation": "hexagon"}*

## Outliers

**What this analysis is.** Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers [48]. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) [48].

**Why it was run on this data.** Local Moran's I maps clusters and outliers of 'fuel_load_t_ha' [53].

**What it found.** The outlier analysis was performed on the Fuel units layer using the gp_outliers tool [54], which successfully saved 1 layer and analyzed 36 features [54]. Key results indicate a result count of 36, and the process required an elapsed time of 2 milliseconds [54].

| Result | Value |
| --- | --- |
| result, count | 36 |
| result, elapsed ms | 2 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 1,580 |
| billing, cost | 0.05108 |
| billing, list price | 0.05108 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Fuel units: Find outliers: 1 layer saved: Find outliers: Fuel units (36 features) [54][55].
- **Evidenced:** It reported result, count 36; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 1,580; billing, cost 0.05108 [54].

*Tool gp_outliers; parameters {"field": "fuel_load_t_ha", "project": "721d878b-84af-42eb-9c9c-858bf9a6a75e", "features": {"layer": "Fuel units (42f78cee)"}, "aggregation": "hexagon"}*

## Maps and charts

The run also made 17 map image(s) [56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72] and 25 chart(s) [73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97].

![Hot spot analysis: Ignitions](maps/01-hot-spot-analysis-ignitions.png)

*Figure 2. Hot spot analysis: Ignitions [56]*

![Hot spot analysis: Fire perimeters](maps/02-hot-spot-analysis-fire-perimeters.png)

*Figure 3. Hot spot analysis: Fire perimeters [57]*

![Hot spot analysis: Fuel treatments](maps/03-hot-spot-analysis-fuel-treatments.png)

*Figure 4. Hot spot analysis: Fuel treatments [58]*

![Emerging hot spots: Ignitions](maps/04-emerging-hot-spots-ignitions.png)

*Figure 5. Emerging hot spots: Ignitions [59]*

![Repeat and near repeat classification: Ignitions](maps/05-repeat-and-near-repeat-classification-ignitions.png)

*Figure 6. Repeat and near repeat classification: Ignitions [60]*

![Summarize within: Fire perimeters](maps/06-summarize-within-fire-perimeters.png)

*Figure 7. Summarize within: Fire perimeters [61]*

![Summarize within: Fire perimeters](maps/07-summarize-within-fire-perimeters.png)

*Figure 8. Summarize within: Fire perimeters [62]*

![Summarize within: Fire perimeters](maps/08-summarize-within-fire-perimeters.png)

*Figure 9. Summarize within: Fire perimeters [63]*

![Find point clusters: Ignitions](maps/09-find-point-clusters-ignitions.png)

*Figure 10. Find point clusters: Ignitions [64]*

![Find point clusters: cluster hulls](maps/10-find-point-clusters-cluster-hulls.png)

*Figure 11. Find point clusters: cluster hulls [65]*

![Calculate density: Ignitions](maps/11-calculate-density-ignitions.png)

*Figure 12. Calculate density: Ignitions [66]*

![Fire perimeters by acres](maps/12-fire-perimeters-by-acres.png)

*Figure 13. Fire perimeters by acres [67]*

![Fuel treatments by acres](maps/13-fuel-treatments-by-acres.png)

*Figure 14. Fuel treatments by acres [68]*

![Fuel units by fuel_load_t_ha](maps/14-fuel-units-by-fuel-load-t-ha.png)

*Figure 15. Fuel units by fuel_load_t_ha [69]*

![Find outliers: Fire perimeters](maps/15-find-outliers-fire-perimeters.png)

*Figure 16. Find outliers: Fire perimeters [70]*

![Find outliers: Fuel treatments](maps/16-find-outliers-fuel-treatments.png)

*Figure 17. Find outliers: Fuel treatments [71]*

![Find outliers: Fuel units](maps/17-find-outliers-fuel-units.png)

*Figure 18. Find outliers: Fuel units [72]*

**What the map shows.** The map displays a clear, concentrated hot spot pattern for ignitions, showing a pronounced band of elevated activity [56]

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

![Fire perimeters over time (start_date)](charts/01-fire-perimeters-over-time-start-date.png)

*Figure 19. Fire perimeters over time (start_date) [73]. Per quarter, 2017-07-01 to 2026-07-01. Source: Fire perimeters (10 features). The data is in the appendix.*

![Station weather by day over time (date)](charts/02-station-weather-by-day-over-time-date.png)

*Figure 20. Station weather by day over time (date) [74]. Per week, 2026-04-27 to 2026-09-28. Source: Station weather by day (918 features). The data is in the appendix.*

![Hot spot analysis: Ignitions](charts/03-hot-spot-analysis-ignitions.png)

*Figure 21. Hot spot analysis: Ignitions [75]. Source: Hot spot analysis: Ignitions (344 features). The data is in the appendix.*

![Hot spot analysis: Fire perimeters](charts/04-hot-spot-analysis-fire-perimeters.png)

*Figure 22. Hot spot analysis: Fire perimeters [76]. Period: 2017-08-10 to 2026-07-16 (start_date). Source: Hot spot analysis: Fire perimeters (10 features). The data is in the appendix.*

![Hot spot analysis: Fuel treatments](charts/05-hot-spot-analysis-fuel-treatments.png)

*Figure 23. Hot spot analysis: Fuel treatments [77]. Period: 2019-06-01 to 2025-01-01 (completed). Source: Hot spot analysis: Fuel treatments (12 features). The data is in the appendix.*

![Emerging hot spots: Ignitions](charts/06-emerging-hot-spots-ignitions.png)

*Figure 24. Emerging hot spots: Ignitions [78]. Source: Emerging hot spots: Ignitions (135 features). The data is in the appendix.*

![Repeat and near repeat classification: Ignitions](charts/07-repeat-and-near-repeat-classification-ignitions.png)

*Figure 25. Repeat and near repeat classification: Ignitions [79]. Per quarter, 2018-01-01 to 2026-07-01. Source: Repeat and near repeat classification: Ignitions (900 features). The data is in the appendix.*

![Summarize within: Fire perimeters](charts/08-summarize-within-fire-perimeters.png)

*Figure 26. Summarize within: Fire perimeters [80]. Per quarter, 2017-07-01 to 2026-07-01. Source: Summarize within: Fire perimeters (10 features). The data is in the appendix.*

![Summarize within: Fire perimeters](charts/09-summarize-within-fire-perimeters.png)

*Figure 27. Summarize within: Fire perimeters [81]. Per quarter, 2017-07-01 to 2026-07-01. Source: Summarize within: Fire perimeters (10 features). The data is in the appendix.*

![Summarize within: Fire perimeters](charts/10-summarize-within-fire-perimeters.png)

*Figure 28. Summarize within: Fire perimeters [82]. Per quarter, 2017-07-01 to 2026-07-01. Source: Summarize within: Fire perimeters (10 features). The data is in the appendix.*

![Find point clusters: Ignitions](charts/11-find-point-clusters-ignitions.png)

*Figure 29. Find point clusters: Ignitions [83]. Period: 2018-01-07 to 2026-09-30 (detected_at). Source: Find point clusters: Ignitions (900 features). The data is in the appendix.*

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

*Figure 30. Find point clusters: cluster hulls [84]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.*

![Calculate density: Ignitions](charts/13-calculate-density-ignitions.png)

*Figure 31. Calculate density: Ignitions [85]. Source: Calculate density: Ignitions (2,136 features). The data is in the appendix.*

![Find outliers: Fire perimeters](charts/14-find-outliers-fire-perimeters.png)

*Figure 32. Find outliers: Fire perimeters [86]. Period: 2017-08-10 to 2026-07-16 (start_date). Source: Find outliers: Fire perimeters (10 features). The data is in the appendix.*

![Find outliers: Fuel treatments](charts/15-find-outliers-fuel-treatments.png)

*Figure 33. Find outliers: Fuel treatments [87]. Period: 2019-06-01 to 2025-01-01 (completed). Source: Find outliers: Fuel treatments (12 features). The data is in the appendix.*

![Find outliers: Fuel units](charts/16-find-outliers-fuel-units.png)

*Figure 34. Find outliers: Fuel units [88]. Source: Find outliers: Fuel units (36 features). The data is in the appendix.*

![Find outliers: Fuel treatments over time](charts/17-find-outliers-fuel-treatments-over-time.png)

*Figure 35. Find outliers: Fuel treatments over time [89]. Per quarter, 2019-04-01 to 2025-01-01. Source: Find outliers: Fuel treatments (12 features). The data is in the appendix.*

![Find outliers: Fire perimeters over time](charts/18-find-outliers-fire-perimeters-over-time.png)

*Figure 36. Find outliers: Fire perimeters over time [90]. Per quarter, 2017-07-01 to 2026-07-01. Source: Find outliers: Fire perimeters (10 features). The data is in the appendix.*

![Find point clusters: Ignitions over time](charts/19-find-point-clusters-ignitions-over-time.png)

*Figure 37. Find point clusters: Ignitions over time [91]. Per quarter, 2018-01-01 to 2026-07-01. Source: Find point clusters: Ignitions (900 features). The data is in the appendix.*

![Summarize within: Fire perimeters over time](charts/20-summarize-within-fire-perimeters-over-time.png)

*Figure 38. Summarize within: Fire perimeters over time [92]. Per quarter, 2017-07-01 to 2026-07-01. Source: Summarize within: Fire perimeters (10 features). The data is in the appendix.*

![Summarize within: Fire perimeters over time](charts/21-summarize-within-fire-perimeters-over-time.png)

*Figure 39. Summarize within: Fire perimeters over time [93]. Per quarter, 2017-07-01 to 2026-07-01. Source: Summarize within: Fire perimeters (10 features). The data is in the appendix.*

![Summarize within: Fire perimeters over time](charts/22-summarize-within-fire-perimeters-over-time.png)

*Figure 40. Summarize within: Fire perimeters over time [94]. Per quarter, 2017-07-01 to 2026-07-01. Source: Summarize within: Fire perimeters (10 features). The data is in the appendix.*

![Repeat and near repeat classification: Ignitions over time](charts/23-repeat-and-near-repeat-classification-ignitions-over-time.png)

*Figure 41. Repeat and near repeat classification: Ignitions over time [95]. Per quarter, 2018-01-01 to 2026-07-01. Source: Repeat and near repeat classification: Ignitions (900 features). The data is in the appendix.*

![Hot spot analysis: Fuel treatments over time](charts/24-hot-spot-analysis-fuel-treatments-over-time.png)

*Figure 42. Hot spot analysis: Fuel treatments over time [96]. Per quarter, 2019-04-01 to 2025-01-01. Source: Hot spot analysis: Fuel treatments (12 features). The data is in the appendix.*

![Hot spot analysis: Fire perimeters over time](charts/25-hot-spot-analysis-fire-perimeters-over-time.png)

*Figure 43. Hot spot analysis: Fire perimeters over time [97]. Per quarter, 2017-07-01 to 2026-07-01. Source: Hot spot analysis: Fire perimeters (10 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. 148 rows (id 797c6488-16c8-42a7-904d-8adb7f974668)  
[2] Dataset: Fire perimeters. vector, 10 rows (id 0b1e7d40-89ae-4b37-84e1-6536d5653088)  
[3] Dataset: Ignitions. vector, 900 rows (id 02464168-4470-4a98-855b-8ef6d9cdbfa9)  
[4] Dataset: Fuel treatments. vector, 12 rows (id cfd664dc-8716-42bf-982f-5f1be0e31a76)  
[5] Dataset: Station weather by day. table, 918 rows (id 6ea34ef2-dc8d-4e46-8fa7-1ac3431365c9)  
[6] Tool result: gp_change_point_detection, step 2. Change point detection: done; no map output. (id 47a00a32-eb67-4c26-bfcb-905c59a0b600:2)  
[7] 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)  
[8] Plan: Hot spots of Ignitions (the Start plan's candidate c_0e0a46dce832). 900 points: Getis-Ord Gi* tests whether the concentrations in Ignitions are statistically significant. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_0e0a46dce832)  
[9] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Ignitions (344 features). (id 8f951621-3c96-471a-b3ef-37aee18172da:0)  
[10] Layer: Ignitions. 900 features (id 73bb3d60-f194-4b32-b9a6-832891cc6297)  
[11] Plan: Hot spots of Fire perimeters (the Start plan's candidate c_9b033ea5babc). Gi* hot spots of 'acres' across the areas. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_9b033ea5babc)  
[12] Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Fire perimeters (10 features). (id 8f951621-3c96-471a-b3ef-37aee18172da:1)  
[13] Layer: Fire perimeters. 10 features (id 4401c868-dc08-4f13-9bc9-d6e9bb6c8164)  
[14] Plan: Hot spots of Fuel treatments (the Start plan's candidate c_a70567ae8653). Gi* hot spots of 'acres' across the areas. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_a70567ae8653)  
[15] Tool result: gp_hot_spots, step 2. Hot spot analysis: 1 layer saved: Hot spot analysis: Fuel treatments (12 features). (id 8f951621-3c96-471a-b3ef-37aee18172da:2)  
[16] Layer: Fuel treatments. 12 features (id d603668d-1752-4332-9003-5646b7e22e12)  
[17] 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)  
[18] Plan: Space time hot spots of Ignitions (the Start plan's candidate c_60f3f9ce8f26). Ignitions carry dates in 'detected_at': emerging hot spot analysis finds new, intensifying and fading hot spots per month. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_60f3f9ce8f26)  
[19] Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Ignitions (135 features). (id 47a00a32-eb67-4c26-bfcb-905c59a0b600:0)  
[20] 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)  
[21] Plan: Near repeat of Ignitions (the Start plan's candidate c_10f86c3efd81). Near repeat analysis tests whether Ignitions close in space also follow each other within 30 days. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_10f86c3efd81)  
[22] Tool result: gp_near_repeat, step 1. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Ignitions (900 features). (id 47a00a32-eb67-4c26-bfcb-905c59a0b600:1)  
[23] Catalog: Change point detection (operation catalog, gp_change_point_detection). When did the level, count, trend or variability change? For the whole study area or for every bin. ArcGIS: Change Point Detection. (id gp_change_point_detection)  
[24] Plan: Change points of Ignitions (the Start plan's candidate c_03da66dfe7e8). Change point detection finds when the monthly count of Ignitions shifted. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_03da66dfe7e8)  
[25] Catalog: Summarize within (operation catalog, gp_summarize_within). Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons. ArcGIS: Summarize Within, Aggregate Points. QGIS: native:countpointsinpolygon, native:sumlinelengths. (id gp_summarize_within)  
[26] Plan: Ignitions per feature of Fire perimeters (the Start plan's candidate c_2c750e088dcf). Count Ignitions inside each feature of Fire perimeters by cause; the counts map as a choropleth (natural breaks, sequential colors). (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_2c750e088dcf)  
[27] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Fire perimeters (10 features). (id d0d6a5af-89c4-4ed0-a643-2d3531685abc:0)  
[28] Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Fire perimeters (10 features). (id d0d6a5af-89c4-4ed0-a643-2d3531685abc:1)  
[29] Layer: Fire lookouts. 3 features (id 81fdbe6e-83c1-4f4d-8dc9-e59c43c15d96)  
[30] Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Fire perimeters (10 features). (id d0d6a5af-89c4-4ed0-a643-2d3531685abc:2)  
[31] Layer: Weather stations. 6 features (id 6b183e69-c85a-4bf5-8192-07431db200f4)  
[32] Catalog: Zonal statistics (analysis catalog, rs_zonal_statistics). count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON. ArcGIS: Zonal Statistics as Table (polygons). (id rs_zonal_statistics)  
[33] Plan: Zonal statistics of Forest terrain model (DEM) and Fire perimeters (the Start plan's candidate c_f8c34509825d). Statistics of Forest terrain model (DEM) inside each feature of Fire perimeters. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_f8c34509825d)  
[34] Tool result: rs_zonal_statistics, step 0. Zonal statistics: 10 row(s). zones 10. (id 585cf8a3-03c6-4f2b-ae1f-4e561bc33bf0:0)  
[35] Tool result: rs_zonal_statistics, step 1. Zonal statistics: 12 row(s). zones 12. (id 585cf8a3-03c6-4f2b-ae1f-4e561bc33bf0:1)  
[36] Tool result: rs_zonal_statistics, step 2. Zonal statistics: 36 row(s). zones 36. (id 585cf8a3-03c6-4f2b-ae1f-4e561bc33bf0:2)  
[37] 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)  
[38] Plan: Cluster points of Ignitions (the Start plan's candidate c_03b5374e7f2f). DBSCAN finds dense groups of Ignitions and separates noise. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_03b5374e7f2f)  
[39] Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Ignitions (900 features); Find point clusters: cluster hulls (1 feature). (id d750a3d5-f804-48d7-a5ab-cea0c64036e0:0)  
[40] Catalog: Average nearest neighbor (operation catalog, gp_nearest_neighbor_index). Clark-Evans ratio: clustered, dispersed or random. ArcGIS: Average Nearest Neighbor. QGIS: native:nearestneighbouranalysis. (id gp_nearest_neighbor_index)  
[41] Plan: Nearest neighbor index of Ignitions (the Start plan's candidate c_394da09ac152). The average nearest neighbor ratio says whether Ignitions are clustered, random or dispersed. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_394da09ac152)  
[42] Tool result: gp_nearest_neighbor_index, step 1. Average nearest neighbor: done; no map output. (id d750a3d5-f804-48d7-a5ab-cea0c64036e0:1)  
[43] Plan: Nearest neighbor index of Weather stations (the Start plan's candidate c_30762cad9c11). The average nearest neighbor ratio says whether Weather stations are clustered, random or dispersed. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_30762cad9c11)  
[44] Tool result: gp_nearest_neighbor_index, step 2. Average nearest neighbor: done; no map output. (id d750a3d5-f804-48d7-a5ab-cea0c64036e0:2)  
[45] 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)  
[46] Plan: Kernel density of Ignitions (the Start plan's candidate c_8031c96859c6). 900 points in Ignitions: a density surface shows where they concentrate. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_8031c96859c6)  
[47] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Ignitions (2136 features). (id a9724881-6b90-401e-86d3-b3e78a11a086:0)  
[48] 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)  
[49] Plan: Local morans i of Fire perimeters (the Start plan's candidate c_97cb8e0f2668). Local Moran's I maps clusters and outliers of 'acres'. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_97cb8e0f2668)  
[50] Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: Fire perimeters (10 features). (id d2855091-5bee-4e72-92bc-f4d96c2ad80a:0)  
[51] Plan: Local morans i of Fuel treatments (the Start plan's candidate c_f3914935665b). Local Moran's I maps clusters and outliers of 'acres'. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_f3914935665b)  
[52] Tool result: gp_outliers, step 1. Find outliers: 1 layer saved: Find outliers: Fuel treatments (12 features). (id d2855091-5bee-4e72-92bc-f4d96c2ad80a:1)  
[53] Plan: Local morans i of Fuel units (the Start plan's candidate c_d1cbc9b93230). Local Moran's I maps clusters and outliers of 'fuel_load_t_ha'. (id 186bc213-2630-4d8c-bbef-bc1854404d12:c_d1cbc9b93230)  
[54] Tool result: gp_outliers, step 2. Find outliers: 1 layer saved: Find outliers: Fuel units (36 features). (id d2855091-5bee-4e72-92bc-f4d96c2ad80a:2)  
[55] Layer: Fuel units. 36 features (id 42f78cee-8de2-430c-8f54-a3b77306f0e7)  
[56] Rendering: Map: Hot spot analysis: Ignitions (id bb63cb93-55aa-4f5b-9b90-3bf45d6dc57f)  
[57] Rendering: Map: Hot spot analysis: Fire perimeters (id c5abc0f0-1d90-42a7-890d-973a23eae10f)  
[58] Rendering: Map: Hot spot analysis: Fuel treatments (id b4e60dce-bab6-4223-a5db-f854367edcc9)  
[59] Rendering: Map: Emerging hot spots: Ignitions (id 86125b2e-c24e-46d9-bf32-2bc320dd67bf)  
[60] Rendering: Map: Repeat and near repeat classification: Ignitions (id d2b0ab7d-8519-4953-a574-41a63e22f158)  
[61] Rendering: Map: Summarize within: Fire perimeters (id 1b0b59ae-d26e-448d-99f4-13f97a2e6e22)  
[62] Rendering: Map: Summarize within: Fire perimeters (id 79f494a0-8339-4630-9f2b-1eb51920b75e)  
[63] Rendering: Map: Summarize within: Fire perimeters (id 157a695e-39d9-4eda-a959-2757ef39dd1f)  
[64] Rendering: Map: Find point clusters: Ignitions (id 769cd190-b6cd-4baf-bb26-89b68980f41b)  
[65] Rendering: Map: Find point clusters: cluster hulls (id 39ce8fbd-9d74-4a4f-9be5-a6510ea09ce6)  
[66] Rendering: Map: Calculate density: Ignitions (id dc62fd3d-336d-4e9a-b0cb-53b02e35914a)  
[67] Rendering: Map: Fire perimeters by acres (id b5a6d9fb-5e10-4e3d-8f27-49001ad00173)  
[68] Rendering: Map: Fuel treatments by acres (id ad8c35df-63f5-4cf4-bc47-ecf9d02f74f8)  
[69] Rendering: Map: Fuel units by fuel_load_t_ha (id 102ee2da-8af4-42d9-9311-7ee514591e21)  
[70] Rendering: Map: Find outliers: Fire perimeters (id 74f7137e-d925-4cf8-8637-4cf18c53ed90)  
[71] Rendering: Map: Find outliers: Fuel treatments (id f7d92071-015a-4f7a-853e-6834d900f625)  
[72] Rendering: Map: Find outliers: Fuel units (id 2b4bb626-f282-4cd0-9678-a86f686584ce)  
[73] Rendering: Chart: Fire perimeters over time (start_date). 111 rows (id 3cb8b4ab-f4bc-4698-939c-3d9a7ff84f07)  
[74] Rendering: Chart: Station weather by day over time (date). 23 rows (id ddf56570-bae6-49fc-b4c3-4800008acb48)  
[75] Rendering: Chart: Hot spot analysis: Ignitions. 13 rows (id ac627744-6825-4c3d-9670-c93a9bfb2e42)  
[76] Rendering: Chart: Hot spot analysis: Fire perimeters. 7 rows (id 3c9aad31-e9ac-49eb-982d-37bc396afad8)  
[77] Rendering: Chart: Hot spot analysis: Fuel treatments. 4 rows (id 638ff2d9-34f9-403b-a35d-d9a308e3ec25)  
[78] Rendering: Chart: Emerging hot spots: Ignitions. 9 rows (id 0a4caa2f-337b-4a6b-b4d4-b142afafe031)  
[79] Rendering: Chart: Repeat and near repeat classification: Ignitions. 210 rows (id 632a779a-47b0-4035-b53d-354cabd5d360)  
[80] Rendering: Chart: Summarize within: Fire perimeters. 111 rows (id 22ee21d5-58dc-4e1e-a070-6a67a901c965)  
[81] Rendering: Chart: Summarize within: Fire perimeters. 111 rows (id db2f619e-43fa-4d26-9640-fb162068f74e)  
[82] Rendering: Chart: Summarize within: Fire perimeters. 111 rows (id b7f0dd9d-22ad-4e93-9b93-cf9a83fadf84)  
[83] Rendering: Chart: Find point clusters: Ignitions. 2 rows (id 1e027e4b-22a2-45d5-9086-2a36c8138f69)  
[84] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id 0789a6b7-999c-4f6b-9d42-1cd13d066287)  
[85] Rendering: Chart: Calculate density: Ignitions. 13 rows (id 0be10378-cba5-4926-a390-c5d82995b1a3)  
[86] Rendering: Chart: Find outliers: Fire perimeters. 10 rows (id 84e826fe-c1a8-45f7-9c15-b536ccc175c6)  
[87] Rendering: Chart: Find outliers: Fuel treatments. 12 rows (id 8efc3778-a287-41ed-b887-f7929e06afc2)  
[88] Rendering: Chart: Find outliers: Fuel units. 36 rows (id 05890114-2897-4a1f-b199-efec992907fe)  
[89] Rendering: Chart: Find outliers: Fuel treatments over time. 72 rows (id af3a74cb-f5e6-45e1-aee7-be2f209870ab)  
[90] Rendering: Chart: Find outliers: Fire perimeters over time. 111 rows (id ffd1204d-4f08-4461-8cd1-44fcce92e2c2)  
[91] Rendering: Chart: Find point clusters: Ignitions over time. 210 rows (id 2d0515c1-e371-4a5e-aded-37ced56bb910)  
[92] Rendering: Chart: Summarize within: Fire perimeters over time. 111 rows (id 3f4630a7-8117-4a45-ad96-0b64158a7db3)  
[93] Rendering: Chart: Summarize within: Fire perimeters over time. 111 rows (id 01777e29-c207-4718-a2ee-d69065a84d99)  
[94] Rendering: Chart: Summarize within: Fire perimeters over time. 111 rows (id ff1e5279-5ea0-474a-9e56-4e42867284ca)  
[95] Rendering: Chart: Repeat and near repeat classification: Ignitions over time. 210 rows (id cb6bd5ac-4983-40a6-adb2-f8cbdca32ee7)  
[96] Rendering: Chart: Hot spot analysis: Fuel treatments over time. 48 rows (id 94c4e65c-ee4e-4a2b-9d8c-c1fa5cd5282a)  
[97] Rendering: Chart: Hot spot analysis: Fire perimeters over time. 111 rows (id d8ab7b19-e10f-4683-bd70-1708bd93b637)  
[98] Dataset: Fuel units. vector, 36 rows (id fc5028cb-a84d-444c-a982-49a4f156c28e)  
[99] Dataset: Forest roads. vector, 15 rows (id c0892d0e-cdf3-4371-9e82-477a931a88d7)  
[100] Dataset: Weather stations. vector, 6 rows (id 720b6843-88fb-4d45-b04c-efa7b051e0a4)  
[101] Dataset: Fire lookouts. vector, 3 rows (id 57d287f4-2d44-451b-9f1c-7e45316002b6)  
[102] Dataset: Forest terrain model (DEM). raster, ? rows (id 81d246c7-6131-4938-9f61-f7279a97cc5e)  
[103] Dataset: Treatment costs. table, 12 rows (id 8c3b32c2-3535-489c-94ca-7cf557f9e65d)  
[104] Dataset: Fuels treatment plan. document, ? rows (id 275ae7d9-0d0b-407c-aa5b-b35298e6ba4e)  
[105] Record: Sample: Forestry and wildfire, Bend (id 721d878b-84af-42eb-9c9c-858bf9a6a75e)  
[106] Layer: Hot spot analysis: Ignitions. 344 features (id 0fd14c27-6dc5-40ab-9367-9526fed96488)  
[107] Layer: Hot spot analysis: Fire perimeters. 10 features (id 712f8542-d935-4ffb-9017-4996ae740395)  
[108] Layer: Hot spot analysis: Fuel treatments. 12 features (id 17998548-cdf7-4623-86fa-b34232ee3f92)  
[109] Layer: Emerging hot spots: Ignitions. 135 features (id 6cd30a8d-a6bb-41fa-aa35-bde687bf9907)  
[110] Layer: Repeat and near repeat classification: Ignitions. 900 features (id e22031fb-e461-400b-8e9e-72ffaa953304)  
[111] Layer: Summarize within: Fire perimeters. 10 features (id b8d71f1c-d5f1-49e2-a2bd-f7b2532714da)  
[112] Layer: Summarize within: Fire perimeters. 10 features (id f9a795e9-aa11-4cf7-919f-3fd6479f41aa)  
[113] Layer: Summarize within: Fire perimeters. 10 features (id 072dac32-c222-4fd9-a81b-67d2bbd389f9)  
[114] Layer: Find point clusters: Ignitions. 900 features (id f0f43900-3843-44c7-a966-3fad40f516a7)  
[115] Layer: Find point clusters: cluster hulls. 1 features (id 9eb7e362-3675-466c-b2bd-13b526913ffe)  
[116] Layer: Calculate density: Ignitions. 2,136 features (id 4c8afb06-e51c-4b81-abd6-8abc0805636a)  
[117] Layer: Find outliers: Fire perimeters. 10 features (id 32df7e83-bd50-4624-8151-7a1c53594cda)  
[118] Layer: Find outliers: Fuel treatments. 12 features (id d3cbfd02-4bf8-4255-8459-f80c861e5a3c)  
[119] Layer: Find outliers: Fuel units. 36 features (id 4f521496-6165-4997-b713-4947d4e59c52)  

## Appendix: the data behind the charts

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

| period | series | value |
| --- | --- | --- |
| 2017-07-01 | Station weather by day | 0 |
| 2017-07-01 | Ignitions | 0 |
| 2017-07-01 | Fuel treatments | 0 |
| 2017-07-01 | Fire perimeters | 1 |
| 2017-10-01 | Station weather by day | 0 |
| 2017-10-01 | Ignitions | 0 |
| 2017-10-01 | Fuel treatments | 0 |
| 2017-10-01 | Fire perimeters | 0 |
| 2018-01-01 | Station weather by day | 0 |
| 2018-01-01 | Ignitions | 1 |
| 2018-01-01 | Fuel treatments | 0 |
| 2018-01-01 | Fire perimeters | 0 |
| 2018-04-01 | Station weather by day | 0 |
| 2018-04-01 | Ignitions | 18 |
| 2018-04-01 | Fuel treatments | 0 |
| 2018-04-01 | Fire perimeters | 0 |
| 2018-07-01 | Station weather by day | 0 |
| 2018-07-01 | Ignitions | 33 |
| 2018-07-01 | Fuel treatments | 0 |
| 2018-07-01 | Fire perimeters | 1 |
| 2018-10-01 | Station weather by day | 0 |
| 2018-10-01 | Ignitions | 22 |
| 2018-10-01 | Fuel treatments | 0 |
| 2018-10-01 | Fire perimeters | 0 |
| 2019-01-01 | Station weather by day | 0 |
| 2019-01-01 | Ignitions | 5 |
| 2019-01-01 | Fuel treatments | 0 |
| 2019-01-01 | Fire perimeters | 0 |
| 2019-04-01 | Station weather by day | 0 |
| 2019-04-01 | Ignitions | 26 |
| 2019-04-01 | Fuel treatments | 1 |
| 2019-04-01 | Fire perimeters | 0 |
| 2019-07-01 | Station weather by day | 0 |
| 2019-07-01 | Ignitions | 39 |
| 2019-07-01 | Fuel treatments | 0 |
| 2019-07-01 | Fire perimeters | 1 |
| 2019-10-01 | Station weather by day | 0 |
| 2019-10-01 | Ignitions | 16 |
| 2019-10-01 | Fuel treatments | 0 |
| 2019-10-01 | Fire perimeters | 0 |
| 2020-01-01 | Station weather by day | 0 |
| 2020-01-01 | Ignitions | 2 |
| 2020-01-01 | Fuel treatments | 1 |
| 2020-01-01 | Fire perimeters | 0 |
| 2020-04-01 | Station weather by day | 0 |
| 2020-04-01 | Ignitions | 21 |
| 2020-04-01 | Fuel treatments | 1 |
| 2020-04-01 | Fire perimeters | 0 |
| 2020-07-01 | Station weather by day | 0 |
| 2020-07-01 | Ignitions | 38 |
| 2020-07-01 | Fuel treatments | 0 |
| 2020-07-01 | Fire perimeters | 1 |
| 2020-10-01 | Station weather by day | 0 |
| 2020-10-01 | Ignitions | 22 |
| 2020-10-01 | Fuel treatments | 0 |
| 2020-10-01 | Fire perimeters | 0 |
| 2021-01-01 | Station weather by day | 0 |
| 2021-01-01 | Ignitions | 1 |
| 2021-01-01 | Fuel treatments | 0 |
| 2021-01-01 | Fire perimeters | 0 |
| 2021-04-01 | Station weather by day | 0 |
| 2021-04-01 | Ignitions | 27 |
| 2021-04-01 | Fuel treatments | 0 |
| 2021-04-01 | Fire perimeters | 0 |
| 2021-07-01 | Station weather by day | 0 |
| 2021-07-01 | Ignitions | 47 |
| 2021-07-01 | Fuel treatments | 2 |
| 2021-07-01 | Fire perimeters | 1 |
| 2021-10-01 | Station weather by day | 0 |
| 2021-10-01 | Ignitions | 32 |
| 2021-10-01 | Fuel treatments | 0 |
| 2021-10-01 | Fire perimeters | 0 |
| 2022-01-01 | Station weather by day | 0 |
| 2022-01-01 | Ignitions | 5 |
| 2022-01-01 | Fuel treatments | 0 |
| 2022-01-01 | Fire perimeters | 0 |
| 2022-04-01 | Station weather by day | 0 |
| 2022-04-01 | Ignitions | 21 |
| 2022-04-01 | Fuel treatments | 1 |
| 2022-04-01 | Fire perimeters | 0 |
| 2022-07-01 | Station weather by day | 0 |
| 2022-07-01 | Ignitions | 52 |
| 2022-07-01 | Fuel treatments | 0 |
| 2022-07-01 | Fire perimeters | 1 |
| 2022-10-01 | Station weather by day | 0 |
| 2022-10-01 | Ignitions | 28 |
| 2022-10-01 | Fuel treatments | 1 |
| 2022-10-01 | Fire perimeters | 0 |
| 2023-01-01 | Station weather by day | 0 |
| 2023-01-01 | Ignitions | 5 |
| 2023-01-01 | Fuel treatments | 1 |
| 2023-01-01 | Fire perimeters | 0 |
| 2023-04-01 | Station weather by day | 0 |
| 2023-04-01 | Ignitions | 31 |
| 2023-04-01 | Fuel treatments | 1 |
| 2023-04-01 | Fire perimeters | 0 |
| 2023-07-01 | Station weather by day | 0 |
| 2023-07-01 | Ignitions | 48 |
| 2023-07-01 | Fuel treatments | 0 |
| 2023-07-01 | Fire perimeters | 1 |
| 2023-10-01 | Station weather by day | 0 |
| 2023-10-01 | Ignitions | 34 |
| 2023-10-01 | Fuel treatments | 0 |
| 2023-10-01 | Fire perimeters | 0 |
| 2024-01-01 | Station weather by day | 0 |
| 2024-01-01 | Ignitions | 4 |
| 2024-01-01 | Fuel treatments | 0 |
| 2024-01-01 | Fire perimeters | 0 |
| 2024-04-01 | Station weather by day | 0 |
| 2024-04-01 | Ignitions | 36 |
| 2024-04-01 | Fuel treatments | 1 |
| 2024-04-01 | Fire perimeters | 0 |
| 2024-07-01 | Station weather by day | 0 |
| 2024-07-01 | Ignitions | 48 |
| 2024-07-01 | Fuel treatments | 0 |
| 2024-07-01 | Fire perimeters | 1 |
| 2024-10-01 | Station weather by day | 0 |
| 2024-10-01 | Ignitions | 32 |
| 2024-10-01 | Fuel treatments | 1 |
| 2024-10-01 | Fire perimeters | 0 |
| 2025-01-01 | Station weather by day | 0 |
| 2025-01-01 | Ignitions | 7 |
| 2025-01-01 | Fuel treatments | 1 |
| 2025-01-01 | Fire perimeters | 0 |
| 2025-04-01 | Station weather by day | 0 |
| 2025-04-01 | Ignitions | 35 |
| 2025-04-01 | Fuel treatments | 0 |
| 2025-04-01 | Fire perimeters | 0 |
| 2025-07-01 | Station weather by day | 0 |
| 2025-07-01 | Ignitions | 61 |
| 2025-07-01 | Fuel treatments | 0 |
| 2025-07-01 | Fire perimeters | 1 |
| 2025-10-01 | Station weather by day | 0 |
| 2025-10-01 | Ignitions | 24 |
| 2025-10-01 | Fuel treatments | 0 |
| 2025-10-01 | Fire perimeters | 0 |
| 2026-01-01 | Station weather by day | 0 |
| 2026-01-01 | Ignitions | 2 |
| 2026-01-01 | Fuel treatments | 0 |
| 2026-01-01 | Fire perimeters | 0 |
| 2026-04-01 | Station weather by day | 366 |
| 2026-04-01 | Ignitions | 20 |
| 2026-04-01 | Fuel treatments | 0 |
| 2026-04-01 | Fire perimeters | 0 |
| 2026-07-01 | Station weather by day | 552 |
| 2026-07-01 | Ignitions | 57 |
| 2026-07-01 | Fuel treatments | 0 |
| 2026-07-01 | Fire perimeters | 1 |

### Figure 19. Fire perimeters over time (start_date) [73]

| period | series | value |
| --- | --- | --- |
| 2017-07-01 | equipment | 1 |
| 2017-07-01 | human | 0 |
| 2017-07-01 | lightning | 0 |
| 2017-10-01 | equipment | 0 |
| 2017-10-01 | human | 0 |
| 2017-10-01 | lightning | 0 |
| 2018-01-01 | equipment | 0 |
| 2018-01-01 | human | 0 |
| 2018-01-01 | lightning | 0 |
| 2018-04-01 | equipment | 0 |
| 2018-04-01 | human | 0 |
| 2018-04-01 | lightning | 0 |
| 2018-07-01 | equipment | 0 |
| 2018-07-01 | human | 1 |
| 2018-07-01 | lightning | 0 |
| 2018-10-01 | equipment | 0 |
| 2018-10-01 | human | 0 |
| 2018-10-01 | lightning | 0 |
| 2019-01-01 | equipment | 0 |
| 2019-01-01 | human | 0 |
| 2019-01-01 | lightning | 0 |
| 2019-04-01 | equipment | 0 |
| 2019-04-01 | human | 0 |
| 2019-04-01 | lightning | 0 |
| 2019-07-01 | equipment | 0 |
| 2019-07-01 | human | 0 |
| 2019-07-01 | lightning | 1 |
| 2019-10-01 | equipment | 0 |
| 2019-10-01 | human | 0 |
| 2019-10-01 | lightning | 0 |
| 2020-01-01 | equipment | 0 |
| 2020-01-01 | human | 0 |
| 2020-01-01 | lightning | 0 |
| 2020-04-01 | equipment | 0 |
| 2020-04-01 | human | 0 |
| 2020-04-01 | lightning | 0 |
| 2020-07-01 | equipment | 1 |
| 2020-07-01 | human | 0 |
| 2020-07-01 | lightning | 0 |
| 2020-10-01 | equipment | 0 |
| 2020-10-01 | human | 0 |
| 2020-10-01 | lightning | 0 |
| 2021-01-01 | equipment | 0 |
| 2021-01-01 | human | 0 |
| 2021-01-01 | lightning | 0 |
| 2021-04-01 | equipment | 0 |
| 2021-04-01 | human | 0 |
| 2021-04-01 | lightning | 0 |
| 2021-07-01 | equipment | 1 |
| 2021-07-01 | human | 0 |
| 2021-07-01 | lightning | 0 |
| 2021-10-01 | equipment | 0 |
| 2021-10-01 | human | 0 |
| 2021-10-01 | lightning | 0 |
| 2022-01-01 | equipment | 0 |
| 2022-01-01 | human | 0 |
| 2022-01-01 | lightning | 0 |
| 2022-04-01 | equipment | 0 |
| 2022-04-01 | human | 0 |
| 2022-04-01 | lightning | 0 |
| 2022-07-01 | equipment | 0 |
| 2022-07-01 | human | 1 |
| 2022-07-01 | lightning | 0 |
| 2022-10-01 | equipment | 0 |
| 2022-10-01 | human | 0 |
| 2022-10-01 | lightning | 0 |
| 2023-01-01 | equipment | 0 |
| 2023-01-01 | human | 0 |
| 2023-01-01 | lightning | 0 |
| 2023-04-01 | equipment | 0 |
| 2023-04-01 | human | 0 |
| 2023-04-01 | lightning | 0 |
| 2023-07-01 | equipment | 0 |
| 2023-07-01 | human | 1 |
| 2023-07-01 | lightning | 0 |
| 2023-10-01 | equipment | 0 |
| 2023-10-01 | human | 0 |
| 2023-10-01 | lightning | 0 |
| 2024-01-01 | equipment | 0 |
| 2024-01-01 | human | 0 |
| 2024-01-01 | lightning | 0 |
| 2024-04-01 | equipment | 0 |
| 2024-04-01 | human | 0 |
| 2024-04-01 | lightning | 0 |
| 2024-07-01 | equipment | 1 |
| 2024-07-01 | human | 0 |
| 2024-07-01 | lightning | 0 |
| 2024-10-01 | equipment | 0 |
| 2024-10-01 | human | 0 |
| 2024-10-01 | lightning | 0 |
| 2025-01-01 | equipment | 0 |
| 2025-01-01 | human | 0 |
| 2025-01-01 | lightning | 0 |
| 2025-04-01 | equipment | 0 |
| 2025-04-01 | human | 0 |
| 2025-04-01 | lightning | 0 |
| 2025-07-01 | equipment | 1 |
| 2025-07-01 | human | 0 |
| 2025-07-01 | lightning | 0 |
| 2025-10-01 | equipment | 0 |
| 2025-10-01 | human | 0 |
| 2025-10-01 | lightning | 0 |
| 2026-01-01 | equipment | 0 |
| 2026-01-01 | human | 0 |
| 2026-01-01 | lightning | 0 |
| 2026-04-01 | equipment | 0 |
| 2026-04-01 | human | 0 |
| 2026-04-01 | lightning | 0 |
| 2026-07-01 | equipment | 1 |
| 2026-07-01 | human | 0 |
| 2026-07-01 | lightning | 0 |

### Figure 20. Station weather by day over time (date) [74]

| period | value |
| --- | --- |
| 2026-04-27 | 18 |
| 2026-05-04 | 42 |
| 2026-05-11 | 42 |
| 2026-05-18 | 42 |
| 2026-05-25 | 42 |
| 2026-06-01 | 42 |
| 2026-06-08 | 42 |
| 2026-06-15 | 42 |
| 2026-06-22 | 42 |
| 2026-06-29 | 42 |
| 2026-07-06 | 42 |
| 2026-07-13 | 42 |
| 2026-07-20 | 42 |
| 2026-07-27 | 42 |
| 2026-08-03 | 42 |
| 2026-08-10 | 42 |
| 2026-08-17 | 42 |
| 2026-08-24 | 42 |
| 2026-08-31 | 42 |
| 2026-09-07 | 42 |
| 2026-09-14 | 42 |
| 2026-09-21 | 42 |
| 2026-09-28 | 18 |

### Figure 21. Hot spot analysis: Ignitions [75]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 104 |
| -0.5 | 0 | not_significant | Not significant | 152 |
| 0 | 0.5 | not_significant | Not significant | 44 |
| 0.5 | 1 | not_significant | Not significant | 16 |
| 1 | 1.5 | not_significant | Not significant | 5 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 4 |
| 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 | 8 |
| 4 | 4.5 | hot_99 | Hot spot, 99% confidence | 2 |
| 4.5 | 5 | hot_99 | Hot spot, 99% confidence | 2 |
| 5 | 5.5 | hot_99 | Hot spot, 99% confidence | 3 |

### Figure 22. Hot spot analysis: Fire perimeters [76]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1.5 | -1 | not_significant | Not significant | 2 |
| -1 | -0.5 | not_significant | Not significant | 1 |
| -0.5 | 0 | not_significant | Not significant | 1 |
| 0 | 0.5 | not_significant | Not significant | 2 |
| 0.5 | 1 | not_significant | Not significant | 2 |
| 1 | 1.5 | not_significant | Not significant | 1 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 1 |

### Figure 23. Hot spot analysis: Fuel treatments [77]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -2 | -1.5 | cold_90 | Cold spot, 90% confidence | 1 |
| -0.5 | 0 | not_significant | Not significant | 6 |
| 0 | 0.5 | not_significant | Not significant | 3 |
| 0.5 | 1 | not_significant | Not significant | 2 |

### Figure 24. Emerging hot spots: Ignitions [78]

| bin_start | bin_end | count |
| --- | --- | --- |
| -0.534 | 0.3166 | 84 |
| 0.3166 | 1.167 | 23 |
| 1.167 | 2.018 | 3 |
| 2.018 | 2.868 | 11 |
| 2.868 | 3.719 | 0 |
| 3.719 | 4.569 | 7 |
| 4.569 | 5.42 | 0 |
| 5.42 | 6.27 | 6 |
| 6.27 | 7.121 | 1 |

### Figure 25. Repeat and near repeat classification: Ignitions [79]

| period | series | value |
| --- | --- | --- |
| 2018-01-01 | lightning | 0 |
| 2018-01-01 | campfire | 1 |
| 2018-01-01 | vehicle | 0 |
| 2018-01-01 | debris burning | 0 |
| 2018-01-01 | arson | 0 |
| 2018-01-01 | Other | 0 |
| 2018-04-01 | lightning | 4 |
| 2018-04-01 | campfire | 7 |
| 2018-04-01 | vehicle | 1 |
| 2018-04-01 | debris burning | 3 |
| 2018-04-01 | arson | 1 |
| 2018-04-01 | Other | 2 |
| 2018-07-01 | lightning | 8 |
| 2018-07-01 | campfire | 7 |
| 2018-07-01 | vehicle | 4 |
| 2018-07-01 | debris burning | 5 |
| 2018-07-01 | arson | 7 |
| 2018-07-01 | Other | 2 |
| 2018-10-01 | lightning | 4 |
| 2018-10-01 | campfire | 4 |
| 2018-10-01 | vehicle | 2 |
| 2018-10-01 | debris burning | 1 |
| 2018-10-01 | arson | 3 |
| 2018-10-01 | Other | 8 |
| 2019-01-01 | lightning | 0 |
| 2019-01-01 | campfire | 1 |
| 2019-01-01 | vehicle | 0 |
| 2019-01-01 | debris burning | 2 |
| 2019-01-01 | arson | 1 |
| 2019-01-01 | Other | 1 |
| 2019-04-01 | lightning | 8 |
| 2019-04-01 | campfire | 7 |
| 2019-04-01 | vehicle | 3 |
| 2019-04-01 | debris burning | 3 |
| 2019-04-01 | arson | 1 |
| 2019-04-01 | Other | 4 |
| 2019-07-01 | lightning | 15 |
| 2019-07-01 | campfire | 1 |
| 2019-07-01 | vehicle | 7 |
| 2019-07-01 | debris burning | 5 |
| 2019-07-01 | arson | 7 |
| 2019-07-01 | Other | 4 |
| 2019-10-01 | lightning | 4 |
| 2019-10-01 | campfire | 4 |
| 2019-10-01 | vehicle | 1 |
| 2019-10-01 | debris burning | 4 |
| 2019-10-01 | arson | 1 |
| 2019-10-01 | Other | 2 |
| 2020-01-01 | lightning | 0 |
| 2020-01-01 | campfire | 1 |
| 2020-01-01 | vehicle | 0 |
| 2020-01-01 | debris burning | 0 |
| 2020-01-01 | arson | 1 |
| 2020-01-01 | Other | 0 |
| 2020-04-01 | lightning | 6 |
| 2020-04-01 | campfire | 1 |
| 2020-04-01 | vehicle | 3 |
| 2020-04-01 | debris burning | 4 |
| 2020-04-01 | arson | 3 |
| 2020-04-01 | Other | 4 |
| 2020-07-01 | lightning | 13 |
| 2020-07-01 | campfire | 9 |
| 2020-07-01 | vehicle | 4 |
| 2020-07-01 | debris burning | 1 |
| 2020-07-01 | arson | 6 |
| 2020-07-01 | Other | 5 |
| 2020-10-01 | lightning | 5 |
| 2020-10-01 | campfire | 3 |
| 2020-10-01 | vehicle | 5 |
| 2020-10-01 | debris burning | 4 |
| 2020-10-01 | arson | 2 |
| 2020-10-01 | Other | 3 |
| 2021-01-01 | lightning | 1 |
| 2021-01-01 | campfire | 0 |
| 2021-01-01 | vehicle | 0 |
| 2021-01-01 | debris burning | 0 |
| 2021-01-01 | arson | 0 |
| 2021-01-01 | Other | 0 |
| 2021-04-01 | lightning | 6 |
| 2021-04-01 | campfire | 8 |
| 2021-04-01 | vehicle | 0 |
| 2021-04-01 | debris burning | 5 |
| 2021-04-01 | arson | 3 |
| 2021-04-01 | Other | 5 |
| 2021-07-01 | lightning | 21 |
| 2021-07-01 | campfire | 8 |
| 2021-07-01 | vehicle | 4 |
| 2021-07-01 | debris burning | 8 |
| 2021-07-01 | arson | 3 |
| 2021-07-01 | Other | 3 |
| 2021-10-01 | lightning | 3 |
| 2021-10-01 | campfire | 11 |
| 2021-10-01 | vehicle | 3 |
| 2021-10-01 | debris burning | 5 |
| 2021-10-01 | arson | 1 |
| 2021-10-01 | Other | 9 |
| 2022-01-01 | lightning | 0 |
| 2022-01-01 | campfire | 1 |
| 2022-01-01 | vehicle | 0 |
| 2022-01-01 | debris burning | 3 |
| 2022-01-01 | arson | 0 |
| 2022-01-01 | Other | 1 |
| 2022-04-01 | lightning | 9 |
| 2022-04-01 | campfire | 7 |
| 2022-04-01 | vehicle | 2 |
| 2022-04-01 | debris burning | 2 |
| 2022-04-01 | arson | 0 |
| 2022-04-01 | Other | 1 |
| 2022-07-01 | lightning | 23 |
| 2022-07-01 | campfire | 7 |
| 2022-07-01 | vehicle | 8 |
| 2022-07-01 | debris burning | 5 |
| 2022-07-01 | arson | 6 |
| 2022-07-01 | Other | 3 |
| 2022-10-01 | lightning | 1 |
| 2022-10-01 | campfire | 7 |
| 2022-10-01 | vehicle | 3 |
| 2022-10-01 | debris burning | 5 |
| 2022-10-01 | arson | 3 |
| 2022-10-01 | Other | 9 |
| 2023-01-01 | lightning | 0 |
| 2023-01-01 | campfire | 1 |
| 2023-01-01 | vehicle | 0 |
| 2023-01-01 | debris burning | 2 |
| 2023-01-01 | arson | 2 |
| 2023-01-01 | Other | 0 |
| 2023-04-01 | lightning | 10 |
| 2023-04-01 | campfire | 4 |
| 2023-04-01 | vehicle | 4 |
| 2023-04-01 | debris burning | 2 |
| 2023-04-01 | arson | 4 |
| 2023-04-01 | Other | 7 |
| 2023-07-01 | lightning | 16 |
| 2023-07-01 | campfire | 9 |
| 2023-07-01 | vehicle | 6 |
| 2023-07-01 | debris burning | 4 |
| 2023-07-01 | arson | 10 |
| 2023-07-01 | Other | 3 |
| 2023-10-01 | lightning | 1 |
| 2023-10-01 | campfire | 9 |
| 2023-10-01 | vehicle | 6 |
| 2023-10-01 | debris burning | 2 |
| 2023-10-01 | arson | 5 |
| 2023-10-01 | Other | 11 |
| 2024-01-01 | lightning | 1 |
| 2024-01-01 | campfire | 0 |
| 2024-01-01 | vehicle | 1 |
| 2024-01-01 | debris burning | 0 |
| 2024-01-01 | arson | 0 |
| 2024-01-01 | Other | 2 |
| 2024-04-01 | lightning | 6 |
| 2024-04-01 | campfire | 6 |
| 2024-04-01 | vehicle | 6 |
| 2024-04-01 | debris burning | 3 |
| 2024-04-01 | arson | 3 |
| 2024-04-01 | Other | 12 |
| 2024-07-01 | lightning | 14 |
| 2024-07-01 | campfire | 17 |
| 2024-07-01 | vehicle | 4 |
| 2024-07-01 | debris burning | 5 |
| 2024-07-01 | arson | 5 |
| 2024-07-01 | Other | 3 |
| 2024-10-01 | lightning | 4 |
| 2024-10-01 | campfire | 6 |
| 2024-10-01 | vehicle | 1 |
| 2024-10-01 | debris burning | 6 |
| 2024-10-01 | arson | 2 |
| 2024-10-01 | Other | 13 |
| 2025-01-01 | lightning | 1 |
| 2025-01-01 | campfire | 1 |
| 2025-01-01 | vehicle | 0 |
| 2025-01-01 | debris burning | 1 |
| 2025-01-01 | arson | 2 |
| 2025-01-01 | Other | 2 |
| 2025-04-01 | lightning | 10 |
| 2025-04-01 | campfire | 7 |
| 2025-04-01 | vehicle | 6 |
| 2025-04-01 | debris burning | 3 |
| 2025-04-01 | arson | 3 |
| 2025-04-01 | Other | 6 |
| 2025-07-01 | lightning | 27 |
| 2025-07-01 | campfire | 10 |
| 2025-07-01 | vehicle | 7 |
| 2025-07-01 | debris burning | 5 |
| 2025-07-01 | arson | 6 |
| 2025-07-01 | Other | 6 |
| 2025-10-01 | lightning | 4 |
| 2025-10-01 | campfire | 4 |
| 2025-10-01 | vehicle | 5 |
| 2025-10-01 | debris burning | 1 |
| 2025-10-01 | arson | 2 |
| 2025-10-01 | Other | 8 |
| 2026-01-01 | lightning | 0 |
| 2026-01-01 | campfire | 0 |
| 2026-01-01 | vehicle | 2 |
| 2026-01-01 | debris burning | 0 |
| 2026-01-01 | arson | 0 |
| 2026-01-01 | Other | 0 |
| 2026-04-01 | lightning | 2 |
| 2026-04-01 | campfire | 6 |
| 2026-04-01 | vehicle | 3 |
| 2026-04-01 | debris burning | 5 |
| 2026-04-01 | arson | 0 |
| 2026-04-01 | Other | 4 |
| 2026-07-01 | lightning | 16 |
| 2026-07-01 | campfire | 11 |
| 2026-07-01 | vehicle | 11 |
| 2026-07-01 | debris burning | 6 |
| 2026-07-01 | arson | 6 |
| 2026-07-01 | Other | 7 |

### Figure 26. Summarize within: Fire perimeters [80]

| period | series | value |
| --- | --- | --- |
| 2017-07-01 | equipment | 1 |
| 2017-07-01 | human | 0 |
| 2017-07-01 | lightning | 0 |
| 2017-10-01 | equipment | 0 |
| 2017-10-01 | human | 0 |
| 2017-10-01 | lightning | 0 |
| 2018-01-01 | equipment | 0 |
| 2018-01-01 | human | 0 |
| 2018-01-01 | lightning | 0 |
| 2018-04-01 | equipment | 0 |
| 2018-04-01 | human | 0 |
| 2018-04-01 | lightning | 0 |
| 2018-07-01 | equipment | 0 |
| 2018-07-01 | human | 1 |
| 2018-07-01 | lightning | 0 |
| 2018-10-01 | equipment | 0 |
| 2018-10-01 | human | 0 |
| 2018-10-01 | lightning | 0 |
| 2019-01-01 | equipment | 0 |
| 2019-01-01 | human | 0 |
| 2019-01-01 | lightning | 0 |
| 2019-04-01 | equipment | 0 |
| 2019-04-01 | human | 0 |
| 2019-04-01 | lightning | 0 |
| 2019-07-01 | equipment | 0 |
| 2019-07-01 | human | 0 |
| 2019-07-01 | lightning | 1 |
| 2019-10-01 | equipment | 0 |
| 2019-10-01 | human | 0 |
| 2019-10-01 | lightning | 0 |
| 2020-01-01 | equipment | 0 |
| 2020-01-01 | human | 0 |
| 2020-01-01 | lightning | 0 |
| 2020-04-01 | equipment | 0 |
| 2020-04-01 | human | 0 |
| 2020-04-01 | lightning | 0 |
| 2020-07-01 | equipment | 1 |
| 2020-07-01 | human | 0 |
| 2020-07-01 | lightning | 0 |
| 2020-10-01 | equipment | 0 |
| 2020-10-01 | human | 0 |
| 2020-10-01 | lightning | 0 |
| 2021-01-01 | equipment | 0 |
| 2021-01-01 | human | 0 |
| 2021-01-01 | lightning | 0 |
| 2021-04-01 | equipment | 0 |
| 2021-04-01 | human | 0 |
| 2021-04-01 | lightning | 0 |
| 2021-07-01 | equipment | 1 |
| 2021-07-01 | human | 0 |
| 2021-07-01 | lightning | 0 |
| 2021-10-01 | equipment | 0 |
| 2021-10-01 | human | 0 |
| 2021-10-01 | lightning | 0 |
| 2022-01-01 | equipment | 0 |
| 2022-01-01 | human | 0 |
| 2022-01-01 | lightning | 0 |
| 2022-04-01 | equipment | 0 |
| 2022-04-01 | human | 0 |
| 2022-04-01 | lightning | 0 |
| 2022-07-01 | equipment | 0 |
| 2022-07-01 | human | 1 |
| 2022-07-01 | lightning | 0 |
| 2022-10-01 | equipment | 0 |
| 2022-10-01 | human | 0 |
| 2022-10-01 | lightning | 0 |
| 2023-01-01 | equipment | 0 |
| 2023-01-01 | human | 0 |
| 2023-01-01 | lightning | 0 |
| 2023-04-01 | equipment | 0 |
| 2023-04-01 | human | 0 |
| 2023-04-01 | lightning | 0 |
| 2023-07-01 | equipment | 0 |
| 2023-07-01 | human | 1 |
| 2023-07-01 | lightning | 0 |
| 2023-10-01 | equipment | 0 |
| 2023-10-01 | human | 0 |
| 2023-10-01 | lightning | 0 |
| 2024-01-01 | equipment | 0 |
| 2024-01-01 | human | 0 |
| 2024-01-01 | lightning | 0 |
| 2024-04-01 | equipment | 0 |
| 2024-04-01 | human | 0 |
| 2024-04-01 | lightning | 0 |
| 2024-07-01 | equipment | 1 |
| 2024-07-01 | human | 0 |
| 2024-07-01 | lightning | 0 |
| 2024-10-01 | equipment | 0 |
| 2024-10-01 | human | 0 |
| 2024-10-01 | lightning | 0 |
| 2025-01-01 | equipment | 0 |
| 2025-01-01 | human | 0 |
| 2025-01-01 | lightning | 0 |
| 2025-04-01 | equipment | 0 |
| 2025-04-01 | human | 0 |
| 2025-04-01 | lightning | 0 |
| 2025-07-01 | equipment | 1 |
| 2025-07-01 | human | 0 |
| 2025-07-01 | lightning | 0 |
| 2025-10-01 | equipment | 0 |
| 2025-10-01 | human | 0 |
| 2025-10-01 | lightning | 0 |
| 2026-01-01 | equipment | 0 |
| 2026-01-01 | human | 0 |
| 2026-01-01 | lightning | 0 |
| 2026-04-01 | equipment | 0 |
| 2026-04-01 | human | 0 |
| 2026-04-01 | lightning | 0 |
| 2026-07-01 | equipment | 1 |
| 2026-07-01 | human | 0 |
| 2026-07-01 | lightning | 0 |

### Figure 27. Summarize within: Fire perimeters [81]

| period | series | value |
| --- | --- | --- |
| 2017-07-01 | equipment | 1 |
| 2017-07-01 | human | 0 |
| 2017-07-01 | lightning | 0 |
| 2017-10-01 | equipment | 0 |
| 2017-10-01 | human | 0 |
| 2017-10-01 | lightning | 0 |
| 2018-01-01 | equipment | 0 |
| 2018-01-01 | human | 0 |
| 2018-01-01 | lightning | 0 |
| 2018-04-01 | equipment | 0 |
| 2018-04-01 | human | 0 |
| 2018-04-01 | lightning | 0 |
| 2018-07-01 | equipment | 0 |
| 2018-07-01 | human | 1 |
| 2018-07-01 | lightning | 0 |
| 2018-10-01 | equipment | 0 |
| 2018-10-01 | human | 0 |
| 2018-10-01 | lightning | 0 |
| 2019-01-01 | equipment | 0 |
| 2019-01-01 | human | 0 |
| 2019-01-01 | lightning | 0 |
| 2019-04-01 | equipment | 0 |
| 2019-04-01 | human | 0 |
| 2019-04-01 | lightning | 0 |
| 2019-07-01 | equipment | 0 |
| 2019-07-01 | human | 0 |
| 2019-07-01 | lightning | 1 |
| 2019-10-01 | equipment | 0 |
| 2019-10-01 | human | 0 |
| 2019-10-01 | lightning | 0 |
| 2020-01-01 | equipment | 0 |
| 2020-01-01 | human | 0 |
| 2020-01-01 | lightning | 0 |
| 2020-04-01 | equipment | 0 |
| 2020-04-01 | human | 0 |
| 2020-04-01 | lightning | 0 |
| 2020-07-01 | equipment | 1 |
| 2020-07-01 | human | 0 |
| 2020-07-01 | lightning | 0 |
| 2020-10-01 | equipment | 0 |
| 2020-10-01 | human | 0 |
| 2020-10-01 | lightning | 0 |
| 2021-01-01 | equipment | 0 |
| 2021-01-01 | human | 0 |
| 2021-01-01 | lightning | 0 |
| 2021-04-01 | equipment | 0 |
| 2021-04-01 | human | 0 |
| 2021-04-01 | lightning | 0 |
| 2021-07-01 | equipment | 1 |
| 2021-07-01 | human | 0 |
| 2021-07-01 | lightning | 0 |
| 2021-10-01 | equipment | 0 |
| 2021-10-01 | human | 0 |
| 2021-10-01 | lightning | 0 |
| 2022-01-01 | equipment | 0 |
| 2022-01-01 | human | 0 |
| 2022-01-01 | lightning | 0 |
| 2022-04-01 | equipment | 0 |
| 2022-04-01 | human | 0 |
| 2022-04-01 | lightning | 0 |
| 2022-07-01 | equipment | 0 |
| 2022-07-01 | human | 1 |
| 2022-07-01 | lightning | 0 |
| 2022-10-01 | equipment | 0 |
| 2022-10-01 | human | 0 |
| 2022-10-01 | lightning | 0 |
| 2023-01-01 | equipment | 0 |
| 2023-01-01 | human | 0 |
| 2023-01-01 | lightning | 0 |
| 2023-04-01 | equipment | 0 |
| 2023-04-01 | human | 0 |
| 2023-04-01 | lightning | 0 |
| 2023-07-01 | equipment | 0 |
| 2023-07-01 | human | 1 |
| 2023-07-01 | lightning | 0 |
| 2023-10-01 | equipment | 0 |
| 2023-10-01 | human | 0 |
| 2023-10-01 | lightning | 0 |
| 2024-01-01 | equipment | 0 |
| 2024-01-01 | human | 0 |
| 2024-01-01 | lightning | 0 |
| 2024-04-01 | equipment | 0 |
| 2024-04-01 | human | 0 |
| 2024-04-01 | lightning | 0 |
| 2024-07-01 | equipment | 1 |
| 2024-07-01 | human | 0 |
| 2024-07-01 | lightning | 0 |
| 2024-10-01 | equipment | 0 |
| 2024-10-01 | human | 0 |
| 2024-10-01 | lightning | 0 |
| 2025-01-01 | equipment | 0 |
| 2025-01-01 | human | 0 |
| 2025-01-01 | lightning | 0 |
| 2025-04-01 | equipment | 0 |
| 2025-04-01 | human | 0 |
| 2025-04-01 | lightning | 0 |
| 2025-07-01 | equipment | 1 |
| 2025-07-01 | human | 0 |
| 2025-07-01 | lightning | 0 |
| 2025-10-01 | equipment | 0 |
| 2025-10-01 | human | 0 |
| 2025-10-01 | lightning | 0 |
| 2026-01-01 | equipment | 0 |
| 2026-01-01 | human | 0 |
| 2026-01-01 | lightning | 0 |
| 2026-04-01 | equipment | 0 |
| 2026-04-01 | human | 0 |
| 2026-04-01 | lightning | 0 |
| 2026-07-01 | equipment | 1 |
| 2026-07-01 | human | 0 |
| 2026-07-01 | lightning | 0 |

### Figure 28. Summarize within: Fire perimeters [82]

| period | series | value |
| --- | --- | --- |
| 2017-07-01 | equipment | 1 |
| 2017-07-01 | human | 0 |
| 2017-07-01 | lightning | 0 |
| 2017-10-01 | equipment | 0 |
| 2017-10-01 | human | 0 |
| 2017-10-01 | lightning | 0 |
| 2018-01-01 | equipment | 0 |
| 2018-01-01 | human | 0 |
| 2018-01-01 | lightning | 0 |
| 2018-04-01 | equipment | 0 |
| 2018-04-01 | human | 0 |
| 2018-04-01 | lightning | 0 |
| 2018-07-01 | equipment | 0 |
| 2018-07-01 | human | 1 |
| 2018-07-01 | lightning | 0 |
| 2018-10-01 | equipment | 0 |
| 2018-10-01 | human | 0 |
| 2018-10-01 | lightning | 0 |
| 2019-01-01 | equipment | 0 |
| 2019-01-01 | human | 0 |
| 2019-01-01 | lightning | 0 |
| 2019-04-01 | equipment | 0 |
| 2019-04-01 | human | 0 |
| 2019-04-01 | lightning | 0 |
| 2019-07-01 | equipment | 0 |
| 2019-07-01 | human | 0 |
| 2019-07-01 | lightning | 1 |
| 2019-10-01 | equipment | 0 |
| 2019-10-01 | human | 0 |
| 2019-10-01 | lightning | 0 |
| 2020-01-01 | equipment | 0 |
| 2020-01-01 | human | 0 |
| 2020-01-01 | lightning | 0 |
| 2020-04-01 | equipment | 0 |
| 2020-04-01 | human | 0 |
| 2020-04-01 | lightning | 0 |
| 2020-07-01 | equipment | 1 |
| 2020-07-01 | human | 0 |
| 2020-07-01 | lightning | 0 |
| 2020-10-01 | equipment | 0 |
| 2020-10-01 | human | 0 |
| 2020-10-01 | lightning | 0 |
| 2021-01-01 | equipment | 0 |
| 2021-01-01 | human | 0 |
| 2021-01-01 | lightning | 0 |
| 2021-04-01 | equipment | 0 |
| 2021-04-01 | human | 0 |
| 2021-04-01 | lightning | 0 |
| 2021-07-01 | equipment | 1 |
| 2021-07-01 | human | 0 |
| 2021-07-01 | lightning | 0 |
| 2021-10-01 | equipment | 0 |
| 2021-10-01 | human | 0 |
| 2021-10-01 | lightning | 0 |
| 2022-01-01 | equipment | 0 |
| 2022-01-01 | human | 0 |
| 2022-01-01 | lightning | 0 |
| 2022-04-01 | equipment | 0 |
| 2022-04-01 | human | 0 |
| 2022-04-01 | lightning | 0 |
| 2022-07-01 | equipment | 0 |
| 2022-07-01 | human | 1 |
| 2022-07-01 | lightning | 0 |
| 2022-10-01 | equipment | 0 |
| 2022-10-01 | human | 0 |
| 2022-10-01 | lightning | 0 |
| 2023-01-01 | equipment | 0 |
| 2023-01-01 | human | 0 |
| 2023-01-01 | lightning | 0 |
| 2023-04-01 | equipment | 0 |
| 2023-04-01 | human | 0 |
| 2023-04-01 | lightning | 0 |
| 2023-07-01 | equipment | 0 |
| 2023-07-01 | human | 1 |
| 2023-07-01 | lightning | 0 |
| 2023-10-01 | equipment | 0 |
| 2023-10-01 | human | 0 |
| 2023-10-01 | lightning | 0 |
| 2024-01-01 | equipment | 0 |
| 2024-01-01 | human | 0 |
| 2024-01-01 | lightning | 0 |
| 2024-04-01 | equipment | 0 |
| 2024-04-01 | human | 0 |
| 2024-04-01 | lightning | 0 |
| 2024-07-01 | equipment | 1 |
| 2024-07-01 | human | 0 |
| 2024-07-01 | lightning | 0 |
| 2024-10-01 | equipment | 0 |
| 2024-10-01 | human | 0 |
| 2024-10-01 | lightning | 0 |
| 2025-01-01 | equipment | 0 |
| 2025-01-01 | human | 0 |
| 2025-01-01 | lightning | 0 |
| 2025-04-01 | equipment | 0 |
| 2025-04-01 | human | 0 |
| 2025-04-01 | lightning | 0 |
| 2025-07-01 | equipment | 1 |
| 2025-07-01 | human | 0 |
| 2025-07-01 | lightning | 0 |
| 2025-10-01 | equipment | 0 |
| 2025-10-01 | human | 0 |
| 2025-10-01 | lightning | 0 |
| 2026-01-01 | equipment | 0 |
| 2026-01-01 | human | 0 |
| 2026-01-01 | lightning | 0 |
| 2026-04-01 | equipment | 0 |
| 2026-04-01 | human | 0 |
| 2026-04-01 | lightning | 0 |
| 2026-07-01 | equipment | 1 |
| 2026-07-01 | human | 0 |
| 2026-07-01 | lightning | 0 |

### Figure 29. Find point clusters: Ignitions [83]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 524 | 0.5822 | 58.2% |
| Noise | 376 | 0.4178 | 41.8% |

### Figure 30. Find point clusters: cluster hulls [84]

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

### Figure 31. Calculate density: Ignitions [85]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 4.154 | 145 |
| 4.154 | 8.308 | 161 |
| 8.308 | 12.46 | 160 |
| 12.46 | 16.62 | 164 |
| 16.62 | 20.77 | 166 |
| 20.77 | 24.92 | 165 |
| 24.92 | 29.08 | 209 |
| 29.08 | 33.23 | 167 |
| 33.23 | 37.38 | 163 |
| 37.38 | 41.54 | 138 |
| 41.54 | 45.69 | 160 |
| 45.69 | 49.85 | 166 |
| 49.85 | 54 | 172 |

### Figure 32. Find outliers: Fire perimeters [86]

| value | lag | type |
| --- | --- | --- |
| 1.294 | 0.0567 | NS |
| -0.956 | 0.1016 | NS |
| 1.647 | -0.9042 | NS |
| -0.9664 | 0.6304 | NS |
| -1.391 | 0.3297 | NS |
| -0.9042 | 0.9363 | NS |
| -0.1265 | -0.9301 | NS |
| 0.1431 | 1.294 | NS |
| 0.9311 | -0.0617 | NS |
| 0.3297 | -0.5957 | NS |

### Figure 33. Find outliers: Fuel treatments [87]

| value | lag | type |
| --- | --- | --- |
| -0.4185 | 0.1036 | NS |
| 1.695 | -0.6008 | NS |
| -0.1947 | -0.0373 | NS |
| -0.5179 | 0.0704 | NS |
| -0.8411 | 0.8245 | NS |
| -1.015 | -1.438 | NS |
| 1.198 | -0.203 | NS |
| -1.438 | 0.6174 | NS |
| 1.67 | -0.1202 | NS |
| -0.5428 | 0.3771 | NS |
| -0.145 | -0.1761 | NS |
| 0.5511 | -0.2859 | NS |

### Figure 34. Find outliers: Fuel units [88]

| value | lag | type |
| --- | --- | --- |
| 0.2369 | 0.0143 | NS |
| 1.289 | -0.0868 | NS |
| 0.4292 | 0.1965 | NS |
| 0.3179 | 0.554 | NS |
| -0.3803 | 0.2369 | NS |
| 0.3179 | -1.336 | NS |
| -0.5219 | -0.7344 | NS |
| 0.8035 | -0.1551 | NS |
| -1.392 | 0.9781 | NS |
| 0.2774 | -0.8153 | NS |
| 0.0953 | 0.4089 | NS |
| 1.239 | 1.279 | NS |
| -0.7749 | 0.8895 | NS |
| 2.21 | -0.6484 | NS |
| -1.352 | -0.043 | NS |
| 1.218 | 0.1965 | NS |
| -0.876 | -1.331 | NS |
| -0.3904 | 1.132 | NS |
| 0.9249 | -0.9013 | NS |
| -1.331 | -0.6459 | NS |
| -0.532 | 0.4292 | NS |
| -0.8963 | 1.218 | NS |
| -0.4106 | -1.352 | NS |
| 2.2 | 0.3179 | NS |
| -1.281 | -0.5067 | NS |
| -0.0868 | -0.188 | NS |
| -1.352 | -0.1206 | NS |
| 0.1459 | 0.075 | NS |
| 1.461 | -0.1206 | NS |
| -0.876 | -0.5573 | NS |
| 1.279 | 1.239 | NS |
| -0.8153 | 0.2774 | NS |
| -0.3094 | -0.2892 | NS |
| 0.2369 | -0.3128 | NS |
| -1.382 | -0.0093 | NS |
| 0.2774 | -0.5573 | NS |

### Figure 35. Find outliers: Fuel treatments over time [89]

| period | series | value |
| --- | --- | --- |
| 2019-04-01 | prescribed burn | 0 |
| 2019-04-01 | mastication | 1 |
| 2019-04-01 | thinning | 0 |
| 2019-07-01 | prescribed burn | 0 |
| 2019-07-01 | mastication | 0 |
| 2019-07-01 | thinning | 0 |
| 2019-10-01 | prescribed burn | 0 |
| 2019-10-01 | mastication | 0 |
| 2019-10-01 | thinning | 0 |
| 2020-01-01 | prescribed burn | 1 |
| 2020-01-01 | mastication | 0 |
| 2020-01-01 | thinning | 0 |
| 2020-04-01 | prescribed burn | 0 |
| 2020-04-01 | mastication | 0 |
| 2020-04-01 | thinning | 1 |
| 2020-07-01 | prescribed burn | 0 |
| 2020-07-01 | mastication | 0 |
| 2020-07-01 | thinning | 0 |
| 2020-10-01 | prescribed burn | 0 |
| 2020-10-01 | mastication | 0 |
| 2020-10-01 | thinning | 0 |
| 2021-01-01 | prescribed burn | 0 |
| 2021-01-01 | mastication | 0 |
| 2021-01-01 | thinning | 0 |
| 2021-04-01 | prescribed burn | 0 |
| 2021-04-01 | mastication | 0 |
| 2021-04-01 | thinning | 0 |
| 2021-07-01 | prescribed burn | 1 |
| 2021-07-01 | mastication | 1 |
| 2021-07-01 | thinning | 0 |
| 2021-10-01 | prescribed burn | 0 |
| 2021-10-01 | mastication | 0 |
| 2021-10-01 | thinning | 0 |
| 2022-01-01 | prescribed burn | 0 |
| 2022-01-01 | mastication | 0 |
| 2022-01-01 | thinning | 0 |
| 2022-04-01 | prescribed burn | 1 |
| 2022-04-01 | mastication | 0 |
| 2022-04-01 | thinning | 0 |
| 2022-07-01 | prescribed burn | 0 |
| 2022-07-01 | mastication | 0 |
| 2022-07-01 | thinning | 0 |
| 2022-10-01 | prescribed burn | 0 |
| 2022-10-01 | mastication | 1 |
| 2022-10-01 | thinning | 0 |
| 2023-01-01 | prescribed burn | 0 |
| 2023-01-01 | mastication | 0 |
| 2023-01-01 | thinning | 1 |
| 2023-04-01 | prescribed burn | 1 |
| 2023-04-01 | mastication | 0 |
| 2023-04-01 | thinning | 0 |
| 2023-07-01 | prescribed burn | 0 |
| 2023-07-01 | mastication | 0 |
| 2023-07-01 | thinning | 0 |
| 2023-10-01 | prescribed burn | 0 |
| 2023-10-01 | mastication | 0 |
| 2023-10-01 | thinning | 0 |
| 2024-01-01 | prescribed burn | 0 |
| 2024-01-01 | mastication | 0 |
| 2024-01-01 | thinning | 0 |
| 2024-04-01 | prescribed burn | 1 |
| 2024-04-01 | mastication | 0 |
| 2024-04-01 | thinning | 0 |
| 2024-07-01 | prescribed burn | 0 |
| 2024-07-01 | mastication | 0 |
| 2024-07-01 | thinning | 0 |
| 2024-10-01 | prescribed burn | 1 |
| 2024-10-01 | mastication | 0 |
| 2024-10-01 | thinning | 0 |
| 2025-01-01 | prescribed burn | 1 |
| 2025-01-01 | mastication | 0 |
| 2025-01-01 | thinning | 0 |

### Figure 36. Find outliers: Fire perimeters over time [90]

| period | series | value |
| --- | --- | --- |
| 2017-07-01 | equipment | 1 |
| 2017-07-01 | human | 0 |
| 2017-07-01 | lightning | 0 |
| 2017-10-01 | equipment | 0 |
| 2017-10-01 | human | 0 |
| 2017-10-01 | lightning | 0 |
| 2018-01-01 | equipment | 0 |
| 2018-01-01 | human | 0 |
| 2018-01-01 | lightning | 0 |
| 2018-04-01 | equipment | 0 |
| 2018-04-01 | human | 0 |
| 2018-04-01 | lightning | 0 |
| 2018-07-01 | equipment | 0 |
| 2018-07-01 | human | 1 |
| 2018-07-01 | lightning | 0 |
| 2018-10-01 | equipment | 0 |
| 2018-10-01 | human | 0 |
| 2018-10-01 | lightning | 0 |
| 2019-01-01 | equipment | 0 |
| 2019-01-01 | human | 0 |
| 2019-01-01 | lightning | 0 |
| 2019-04-01 | equipment | 0 |
| 2019-04-01 | human | 0 |
| 2019-04-01 | lightning | 0 |
| 2019-07-01 | equipment | 0 |
| 2019-07-01 | human | 0 |
| 2019-07-01 | lightning | 1 |
| 2019-10-01 | equipment | 0 |
| 2019-10-01 | human | 0 |
| 2019-10-01 | lightning | 0 |
| 2020-01-01 | equipment | 0 |
| 2020-01-01 | human | 0 |
| 2020-01-01 | lightning | 0 |
| 2020-04-01 | equipment | 0 |
| 2020-04-01 | human | 0 |
| 2020-04-01 | lightning | 0 |
| 2020-07-01 | equipment | 1 |
| 2020-07-01 | human | 0 |
| 2020-07-01 | lightning | 0 |
| 2020-10-01 | equipment | 0 |
| 2020-10-01 | human | 0 |
| 2020-10-01 | lightning | 0 |
| 2021-01-01 | equipment | 0 |
| 2021-01-01 | human | 0 |
| 2021-01-01 | lightning | 0 |
| 2021-04-01 | equipment | 0 |
| 2021-04-01 | human | 0 |
| 2021-04-01 | lightning | 0 |
| 2021-07-01 | equipment | 1 |
| 2021-07-01 | human | 0 |
| 2021-07-01 | lightning | 0 |
| 2021-10-01 | equipment | 0 |
| 2021-10-01 | human | 0 |
| 2021-10-01 | lightning | 0 |
| 2022-01-01 | equipment | 0 |
| 2022-01-01 | human | 0 |
| 2022-01-01 | lightning | 0 |
| 2022-04-01 | equipment | 0 |
| 2022-04-01 | human | 0 |
| 2022-04-01 | lightning | 0 |
| 2022-07-01 | equipment | 0 |
| 2022-07-01 | human | 1 |
| 2022-07-01 | lightning | 0 |
| 2022-10-01 | equipment | 0 |
| 2022-10-01 | human | 0 |
| 2022-10-01 | lightning | 0 |
| 2023-01-01 | equipment | 0 |
| 2023-01-01 | human | 0 |
| 2023-01-01 | lightning | 0 |
| 2023-04-01 | equipment | 0 |
| 2023-04-01 | human | 0 |
| 2023-04-01 | lightning | 0 |
| 2023-07-01 | equipment | 0 |
| 2023-07-01 | human | 1 |
| 2023-07-01 | lightning | 0 |
| 2023-10-01 | equipment | 0 |
| 2023-10-01 | human | 0 |
| 2023-10-01 | lightning | 0 |
| 2024-01-01 | equipment | 0 |
| 2024-01-01 | human | 0 |
| 2024-01-01 | lightning | 0 |
| 2024-04-01 | equipment | 0 |
| 2024-04-01 | human | 0 |
| 2024-04-01 | lightning | 0 |
| 2024-07-01 | equipment | 1 |
| 2024-07-01 | human | 0 |
| 2024-07-01 | lightning | 0 |
| 2024-10-01 | equipment | 0 |
| 2024-10-01 | human | 0 |
| 2024-10-01 | lightning | 0 |
| 2025-01-01 | equipment | 0 |
| 2025-01-01 | human | 0 |
| 2025-01-01 | lightning | 0 |
| 2025-04-01 | equipment | 0 |
| 2025-04-01 | human | 0 |
| 2025-04-01 | lightning | 0 |
| 2025-07-01 | equipment | 1 |
| 2025-07-01 | human | 0 |
| 2025-07-01 | lightning | 0 |
| 2025-10-01 | equipment | 0 |
| 2025-10-01 | human | 0 |
| 2025-10-01 | lightning | 0 |
| 2026-01-01 | equipment | 0 |
| 2026-01-01 | human | 0 |
| 2026-01-01 | lightning | 0 |
| 2026-04-01 | equipment | 0 |
| 2026-04-01 | human | 0 |
| 2026-04-01 | lightning | 0 |
| 2026-07-01 | equipment | 1 |
| 2026-07-01 | human | 0 |
| 2026-07-01 | lightning | 0 |

### Figure 37. Find point clusters: Ignitions over time [91]

| period | series | value |
| --- | --- | --- |
| 2018-01-01 | lightning | 0 |
| 2018-01-01 | campfire | 1 |
| 2018-01-01 | vehicle | 0 |
| 2018-01-01 | debris burning | 0 |
| 2018-01-01 | arson | 0 |
| 2018-01-01 | Other | 0 |
| 2018-04-01 | lightning | 4 |
| 2018-04-01 | campfire | 7 |
| 2018-04-01 | vehicle | 1 |
| 2018-04-01 | debris burning | 3 |
| 2018-04-01 | arson | 1 |
| 2018-04-01 | Other | 2 |
| 2018-07-01 | lightning | 8 |
| 2018-07-01 | campfire | 7 |
| 2018-07-01 | vehicle | 4 |
| 2018-07-01 | debris burning | 5 |
| 2018-07-01 | arson | 7 |
| 2018-07-01 | Other | 2 |
| 2018-10-01 | lightning | 4 |
| 2018-10-01 | campfire | 4 |
| 2018-10-01 | vehicle | 2 |
| 2018-10-01 | debris burning | 1 |
| 2018-10-01 | arson | 3 |
| 2018-10-01 | Other | 8 |
| 2019-01-01 | lightning | 0 |
| 2019-01-01 | campfire | 1 |
| 2019-01-01 | vehicle | 0 |
| 2019-01-01 | debris burning | 2 |
| 2019-01-01 | arson | 1 |
| 2019-01-01 | Other | 1 |
| 2019-04-01 | lightning | 8 |
| 2019-04-01 | campfire | 7 |
| 2019-04-01 | vehicle | 3 |
| 2019-04-01 | debris burning | 3 |
| 2019-04-01 | arson | 1 |
| 2019-04-01 | Other | 4 |
| 2019-07-01 | lightning | 15 |
| 2019-07-01 | campfire | 1 |
| 2019-07-01 | vehicle | 7 |
| 2019-07-01 | debris burning | 5 |
| 2019-07-01 | arson | 7 |
| 2019-07-01 | Other | 4 |
| 2019-10-01 | lightning | 4 |
| 2019-10-01 | campfire | 4 |
| 2019-10-01 | vehicle | 1 |
| 2019-10-01 | debris burning | 4 |
| 2019-10-01 | arson | 1 |
| 2019-10-01 | Other | 2 |
| 2020-01-01 | lightning | 0 |
| 2020-01-01 | campfire | 1 |
| 2020-01-01 | vehicle | 0 |
| 2020-01-01 | debris burning | 0 |
| 2020-01-01 | arson | 1 |
| 2020-01-01 | Other | 0 |
| 2020-04-01 | lightning | 6 |
| 2020-04-01 | campfire | 1 |
| 2020-04-01 | vehicle | 3 |
| 2020-04-01 | debris burning | 4 |
| 2020-04-01 | arson | 3 |
| 2020-04-01 | Other | 4 |
| 2020-07-01 | lightning | 13 |
| 2020-07-01 | campfire | 9 |
| 2020-07-01 | vehicle | 4 |
| 2020-07-01 | debris burning | 1 |
| 2020-07-01 | arson | 6 |
| 2020-07-01 | Other | 5 |
| 2020-10-01 | lightning | 5 |
| 2020-10-01 | campfire | 3 |
| 2020-10-01 | vehicle | 5 |
| 2020-10-01 | debris burning | 4 |
| 2020-10-01 | arson | 2 |
| 2020-10-01 | Other | 3 |
| 2021-01-01 | lightning | 1 |
| 2021-01-01 | campfire | 0 |
| 2021-01-01 | vehicle | 0 |
| 2021-01-01 | debris burning | 0 |
| 2021-01-01 | arson | 0 |
| 2021-01-01 | Other | 0 |
| 2021-04-01 | lightning | 6 |
| 2021-04-01 | campfire | 8 |
| 2021-04-01 | vehicle | 0 |
| 2021-04-01 | debris burning | 5 |
| 2021-04-01 | arson | 3 |
| 2021-04-01 | Other | 5 |
| 2021-07-01 | lightning | 21 |
| 2021-07-01 | campfire | 8 |
| 2021-07-01 | vehicle | 4 |
| 2021-07-01 | debris burning | 8 |
| 2021-07-01 | arson | 3 |
| 2021-07-01 | Other | 3 |
| 2021-10-01 | lightning | 3 |
| 2021-10-01 | campfire | 11 |
| 2021-10-01 | vehicle | 3 |
| 2021-10-01 | debris burning | 5 |
| 2021-10-01 | arson | 1 |
| 2021-10-01 | Other | 9 |
| 2022-01-01 | lightning | 0 |
| 2022-01-01 | campfire | 1 |
| 2022-01-01 | vehicle | 0 |
| 2022-01-01 | debris burning | 3 |
| 2022-01-01 | arson | 0 |
| 2022-01-01 | Other | 1 |
| 2022-04-01 | lightning | 9 |
| 2022-04-01 | campfire | 7 |
| 2022-04-01 | vehicle | 2 |
| 2022-04-01 | debris burning | 2 |
| 2022-04-01 | arson | 0 |
| 2022-04-01 | Other | 1 |
| 2022-07-01 | lightning | 23 |
| 2022-07-01 | campfire | 7 |
| 2022-07-01 | vehicle | 8 |
| 2022-07-01 | debris burning | 5 |
| 2022-07-01 | arson | 6 |
| 2022-07-01 | Other | 3 |
| 2022-10-01 | lightning | 1 |
| 2022-10-01 | campfire | 7 |
| 2022-10-01 | vehicle | 3 |
| 2022-10-01 | debris burning | 5 |
| 2022-10-01 | arson | 3 |
| 2022-10-01 | Other | 9 |
| 2023-01-01 | lightning | 0 |
| 2023-01-01 | campfire | 1 |
| 2023-01-01 | vehicle | 0 |
| 2023-01-01 | debris burning | 2 |
| 2023-01-01 | arson | 2 |
| 2023-01-01 | Other | 0 |
| 2023-04-01 | lightning | 10 |
| 2023-04-01 | campfire | 4 |
| 2023-04-01 | vehicle | 4 |
| 2023-04-01 | debris burning | 2 |
| 2023-04-01 | arson | 4 |
| 2023-04-01 | Other | 7 |
| 2023-07-01 | lightning | 16 |
| 2023-07-01 | campfire | 9 |
| 2023-07-01 | vehicle | 6 |
| 2023-07-01 | debris burning | 4 |
| 2023-07-01 | arson | 10 |
| 2023-07-01 | Other | 3 |
| 2023-10-01 | lightning | 1 |
| 2023-10-01 | campfire | 9 |
| 2023-10-01 | vehicle | 6 |
| 2023-10-01 | debris burning | 2 |
| 2023-10-01 | arson | 5 |
| 2023-10-01 | Other | 11 |
| 2024-01-01 | lightning | 1 |
| 2024-01-01 | campfire | 0 |
| 2024-01-01 | vehicle | 1 |
| 2024-01-01 | debris burning | 0 |
| 2024-01-01 | arson | 0 |
| 2024-01-01 | Other | 2 |
| 2024-04-01 | lightning | 6 |
| 2024-04-01 | campfire | 6 |
| 2024-04-01 | vehicle | 6 |
| 2024-04-01 | debris burning | 3 |
| 2024-04-01 | arson | 3 |
| 2024-04-01 | Other | 12 |
| 2024-07-01 | lightning | 14 |
| 2024-07-01 | campfire | 17 |
| 2024-07-01 | vehicle | 4 |
| 2024-07-01 | debris burning | 5 |
| 2024-07-01 | arson | 5 |
| 2024-07-01 | Other | 3 |
| 2024-10-01 | lightning | 4 |
| 2024-10-01 | campfire | 6 |
| 2024-10-01 | vehicle | 1 |
| 2024-10-01 | debris burning | 6 |
| 2024-10-01 | arson | 2 |
| 2024-10-01 | Other | 13 |
| 2025-01-01 | lightning | 1 |
| 2025-01-01 | campfire | 1 |
| 2025-01-01 | vehicle | 0 |
| 2025-01-01 | debris burning | 1 |
| 2025-01-01 | arson | 2 |
| 2025-01-01 | Other | 2 |
| 2025-04-01 | lightning | 10 |
| 2025-04-01 | campfire | 7 |
| 2025-04-01 | vehicle | 6 |
| 2025-04-01 | debris burning | 3 |
| 2025-04-01 | arson | 3 |
| 2025-04-01 | Other | 6 |
| 2025-07-01 | lightning | 27 |
| 2025-07-01 | campfire | 10 |
| 2025-07-01 | vehicle | 7 |
| 2025-07-01 | debris burning | 5 |
| 2025-07-01 | arson | 6 |
| 2025-07-01 | Other | 6 |
| 2025-10-01 | lightning | 4 |
| 2025-10-01 | campfire | 4 |
| 2025-10-01 | vehicle | 5 |
| 2025-10-01 | debris burning | 1 |
| 2025-10-01 | arson | 2 |
| 2025-10-01 | Other | 8 |
| 2026-01-01 | lightning | 0 |
| 2026-01-01 | campfire | 0 |
| 2026-01-01 | vehicle | 2 |
| 2026-01-01 | debris burning | 0 |
| 2026-01-01 | arson | 0 |
| 2026-01-01 | Other | 0 |
| 2026-04-01 | lightning | 2 |
| 2026-04-01 | campfire | 6 |
| 2026-04-01 | vehicle | 3 |
| 2026-04-01 | debris burning | 5 |
| 2026-04-01 | arson | 0 |
| 2026-04-01 | Other | 4 |
| 2026-07-01 | lightning | 16 |
| 2026-07-01 | campfire | 11 |
| 2026-07-01 | vehicle | 11 |
| 2026-07-01 | debris burning | 6 |
| 2026-07-01 | arson | 6 |
| 2026-07-01 | Other | 7 |

### Figure 38. Summarize within: Fire perimeters over time [92]

| period | series | value |
| --- | --- | --- |
| 2017-07-01 | equipment | 1 |
| 2017-07-01 | human | 0 |
| 2017-07-01 | lightning | 0 |
| 2017-10-01 | equipment | 0 |
| 2017-10-01 | human | 0 |
| 2017-10-01 | lightning | 0 |
| 2018-01-01 | equipment | 0 |
| 2018-01-01 | human | 0 |
| 2018-01-01 | lightning | 0 |
| 2018-04-01 | equipment | 0 |
| 2018-04-01 | human | 0 |
| 2018-04-01 | lightning | 0 |
| 2018-07-01 | equipment | 0 |
| 2018-07-01 | human | 1 |
| 2018-07-01 | lightning | 0 |
| 2018-10-01 | equipment | 0 |
| 2018-10-01 | human | 0 |
| 2018-10-01 | lightning | 0 |
| 2019-01-01 | equipment | 0 |
| 2019-01-01 | human | 0 |
| 2019-01-01 | lightning | 0 |
| 2019-04-01 | equipment | 0 |
| 2019-04-01 | human | 0 |
| 2019-04-01 | lightning | 0 |
| 2019-07-01 | equipment | 0 |
| 2019-07-01 | human | 0 |
| 2019-07-01 | lightning | 1 |
| 2019-10-01 | equipment | 0 |
| 2019-10-01 | human | 0 |
| 2019-10-01 | lightning | 0 |
| 2020-01-01 | equipment | 0 |
| 2020-01-01 | human | 0 |
| 2020-01-01 | lightning | 0 |
| 2020-04-01 | equipment | 0 |
| 2020-04-01 | human | 0 |
| 2020-04-01 | lightning | 0 |
| 2020-07-01 | equipment | 1 |
| 2020-07-01 | human | 0 |
| 2020-07-01 | lightning | 0 |
| 2020-10-01 | equipment | 0 |
| 2020-10-01 | human | 0 |
| 2020-10-01 | lightning | 0 |
| 2021-01-01 | equipment | 0 |
| 2021-01-01 | human | 0 |
| 2021-01-01 | lightning | 0 |
| 2021-04-01 | equipment | 0 |
| 2021-04-01 | human | 0 |
| 2021-04-01 | lightning | 0 |
| 2021-07-01 | equipment | 1 |
| 2021-07-01 | human | 0 |
| 2021-07-01 | lightning | 0 |
| 2021-10-01 | equipment | 0 |
| 2021-10-01 | human | 0 |
| 2021-10-01 | lightning | 0 |
| 2022-01-01 | equipment | 0 |
| 2022-01-01 | human | 0 |
| 2022-01-01 | lightning | 0 |
| 2022-04-01 | equipment | 0 |
| 2022-04-01 | human | 0 |
| 2022-04-01 | lightning | 0 |
| 2022-07-01 | equipment | 0 |
| 2022-07-01 | human | 1 |
| 2022-07-01 | lightning | 0 |
| 2022-10-01 | equipment | 0 |
| 2022-10-01 | human | 0 |
| 2022-10-01 | lightning | 0 |
| 2023-01-01 | equipment | 0 |
| 2023-01-01 | human | 0 |
| 2023-01-01 | lightning | 0 |
| 2023-04-01 | equipment | 0 |
| 2023-04-01 | human | 0 |
| 2023-04-01 | lightning | 0 |
| 2023-07-01 | equipment | 0 |
| 2023-07-01 | human | 1 |
| 2023-07-01 | lightning | 0 |
| 2023-10-01 | equipment | 0 |
| 2023-10-01 | human | 0 |
| 2023-10-01 | lightning | 0 |
| 2024-01-01 | equipment | 0 |
| 2024-01-01 | human | 0 |
| 2024-01-01 | lightning | 0 |
| 2024-04-01 | equipment | 0 |
| 2024-04-01 | human | 0 |
| 2024-04-01 | lightning | 0 |
| 2024-07-01 | equipment | 1 |
| 2024-07-01 | human | 0 |
| 2024-07-01 | lightning | 0 |
| 2024-10-01 | equipment | 0 |
| 2024-10-01 | human | 0 |
| 2024-10-01 | lightning | 0 |
| 2025-01-01 | equipment | 0 |
| 2025-01-01 | human | 0 |
| 2025-01-01 | lightning | 0 |
| 2025-04-01 | equipment | 0 |
| 2025-04-01 | human | 0 |
| 2025-04-01 | lightning | 0 |
| 2025-07-01 | equipment | 1 |
| 2025-07-01 | human | 0 |
| 2025-07-01 | lightning | 0 |
| 2025-10-01 | equipment | 0 |
| 2025-10-01 | human | 0 |
| 2025-10-01 | lightning | 0 |
| 2026-01-01 | equipment | 0 |
| 2026-01-01 | human | 0 |
| 2026-01-01 | lightning | 0 |
| 2026-04-01 | equipment | 0 |
| 2026-04-01 | human | 0 |
| 2026-04-01 | lightning | 0 |
| 2026-07-01 | equipment | 1 |
| 2026-07-01 | human | 0 |
| 2026-07-01 | lightning | 0 |

### Figure 39. Summarize within: Fire perimeters over time [93]

| period | series | value |
| --- | --- | --- |
| 2017-07-01 | equipment | 1 |
| 2017-07-01 | human | 0 |
| 2017-07-01 | lightning | 0 |
| 2017-10-01 | equipment | 0 |
| 2017-10-01 | human | 0 |
| 2017-10-01 | lightning | 0 |
| 2018-01-01 | equipment | 0 |
| 2018-01-01 | human | 0 |
| 2018-01-01 | lightning | 0 |
| 2018-04-01 | equipment | 0 |
| 2018-04-01 | human | 0 |
| 2018-04-01 | lightning | 0 |
| 2018-07-01 | equipment | 0 |
| 2018-07-01 | human | 1 |
| 2018-07-01 | lightning | 0 |
| 2018-10-01 | equipment | 0 |
| 2018-10-01 | human | 0 |
| 2018-10-01 | lightning | 0 |
| 2019-01-01 | equipment | 0 |
| 2019-01-01 | human | 0 |
| 2019-01-01 | lightning | 0 |
| 2019-04-01 | equipment | 0 |
| 2019-04-01 | human | 0 |
| 2019-04-01 | lightning | 0 |
| 2019-07-01 | equipment | 0 |
| 2019-07-01 | human | 0 |
| 2019-07-01 | lightning | 1 |
| 2019-10-01 | equipment | 0 |
| 2019-10-01 | human | 0 |
| 2019-10-01 | lightning | 0 |
| 2020-01-01 | equipment | 0 |
| 2020-01-01 | human | 0 |
| 2020-01-01 | lightning | 0 |
| 2020-04-01 | equipment | 0 |
| 2020-04-01 | human | 0 |
| 2020-04-01 | lightning | 0 |
| 2020-07-01 | equipment | 1 |
| 2020-07-01 | human | 0 |
| 2020-07-01 | lightning | 0 |
| 2020-10-01 | equipment | 0 |
| 2020-10-01 | human | 0 |
| 2020-10-01 | lightning | 0 |
| 2021-01-01 | equipment | 0 |
| 2021-01-01 | human | 0 |
| 2021-01-01 | lightning | 0 |
| 2021-04-01 | equipment | 0 |
| 2021-04-01 | human | 0 |
| 2021-04-01 | lightning | 0 |
| 2021-07-01 | equipment | 1 |
| 2021-07-01 | human | 0 |
| 2021-07-01 | lightning | 0 |
| 2021-10-01 | equipment | 0 |
| 2021-10-01 | human | 0 |
| 2021-10-01 | lightning | 0 |
| 2022-01-01 | equipment | 0 |
| 2022-01-01 | human | 0 |
| 2022-01-01 | lightning | 0 |
| 2022-04-01 | equipment | 0 |
| 2022-04-01 | human | 0 |
| 2022-04-01 | lightning | 0 |
| 2022-07-01 | equipment | 0 |
| 2022-07-01 | human | 1 |
| 2022-07-01 | lightning | 0 |
| 2022-10-01 | equipment | 0 |
| 2022-10-01 | human | 0 |
| 2022-10-01 | lightning | 0 |
| 2023-01-01 | equipment | 0 |
| 2023-01-01 | human | 0 |
| 2023-01-01 | lightning | 0 |
| 2023-04-01 | equipment | 0 |
| 2023-04-01 | human | 0 |
| 2023-04-01 | lightning | 0 |
| 2023-07-01 | equipment | 0 |
| 2023-07-01 | human | 1 |
| 2023-07-01 | lightning | 0 |
| 2023-10-01 | equipment | 0 |
| 2023-10-01 | human | 0 |
| 2023-10-01 | lightning | 0 |
| 2024-01-01 | equipment | 0 |
| 2024-01-01 | human | 0 |
| 2024-01-01 | lightning | 0 |
| 2024-04-01 | equipment | 0 |
| 2024-04-01 | human | 0 |
| 2024-04-01 | lightning | 0 |
| 2024-07-01 | equipment | 1 |
| 2024-07-01 | human | 0 |
| 2024-07-01 | lightning | 0 |
| 2024-10-01 | equipment | 0 |
| 2024-10-01 | human | 0 |
| 2024-10-01 | lightning | 0 |
| 2025-01-01 | equipment | 0 |
| 2025-01-01 | human | 0 |
| 2025-01-01 | lightning | 0 |
| 2025-04-01 | equipment | 0 |
| 2025-04-01 | human | 0 |
| 2025-04-01 | lightning | 0 |
| 2025-07-01 | equipment | 1 |
| 2025-07-01 | human | 0 |
| 2025-07-01 | lightning | 0 |
| 2025-10-01 | equipment | 0 |
| 2025-10-01 | human | 0 |
| 2025-10-01 | lightning | 0 |
| 2026-01-01 | equipment | 0 |
| 2026-01-01 | human | 0 |
| 2026-01-01 | lightning | 0 |
| 2026-04-01 | equipment | 0 |
| 2026-04-01 | human | 0 |
| 2026-04-01 | lightning | 0 |
| 2026-07-01 | equipment | 1 |
| 2026-07-01 | human | 0 |
| 2026-07-01 | lightning | 0 |

### Figure 40. Summarize within: Fire perimeters over time [94]

| period | series | value |
| --- | --- | --- |
| 2017-07-01 | equipment | 1 |
| 2017-07-01 | human | 0 |
| 2017-07-01 | lightning | 0 |
| 2017-10-01 | equipment | 0 |
| 2017-10-01 | human | 0 |
| 2017-10-01 | lightning | 0 |
| 2018-01-01 | equipment | 0 |
| 2018-01-01 | human | 0 |
| 2018-01-01 | lightning | 0 |
| 2018-04-01 | equipment | 0 |
| 2018-04-01 | human | 0 |
| 2018-04-01 | lightning | 0 |
| 2018-07-01 | equipment | 0 |
| 2018-07-01 | human | 1 |
| 2018-07-01 | lightning | 0 |
| 2018-10-01 | equipment | 0 |
| 2018-10-01 | human | 0 |
| 2018-10-01 | lightning | 0 |
| 2019-01-01 | equipment | 0 |
| 2019-01-01 | human | 0 |
| 2019-01-01 | lightning | 0 |
| 2019-04-01 | equipment | 0 |
| 2019-04-01 | human | 0 |
| 2019-04-01 | lightning | 0 |
| 2019-07-01 | equipment | 0 |
| 2019-07-01 | human | 0 |
| 2019-07-01 | lightning | 1 |
| 2019-10-01 | equipment | 0 |
| 2019-10-01 | human | 0 |
| 2019-10-01 | lightning | 0 |
| 2020-01-01 | equipment | 0 |
| 2020-01-01 | human | 0 |
| 2020-01-01 | lightning | 0 |
| 2020-04-01 | equipment | 0 |
| 2020-04-01 | human | 0 |
| 2020-04-01 | lightning | 0 |
| 2020-07-01 | equipment | 1 |
| 2020-07-01 | human | 0 |
| 2020-07-01 | lightning | 0 |
| 2020-10-01 | equipment | 0 |
| 2020-10-01 | human | 0 |
| 2020-10-01 | lightning | 0 |
| 2021-01-01 | equipment | 0 |
| 2021-01-01 | human | 0 |
| 2021-01-01 | lightning | 0 |
| 2021-04-01 | equipment | 0 |
| 2021-04-01 | human | 0 |
| 2021-04-01 | lightning | 0 |
| 2021-07-01 | equipment | 1 |
| 2021-07-01 | human | 0 |
| 2021-07-01 | lightning | 0 |
| 2021-10-01 | equipment | 0 |
| 2021-10-01 | human | 0 |
| 2021-10-01 | lightning | 0 |
| 2022-01-01 | equipment | 0 |
| 2022-01-01 | human | 0 |
| 2022-01-01 | lightning | 0 |
| 2022-04-01 | equipment | 0 |
| 2022-04-01 | human | 0 |
| 2022-04-01 | lightning | 0 |
| 2022-07-01 | equipment | 0 |
| 2022-07-01 | human | 1 |
| 2022-07-01 | lightning | 0 |
| 2022-10-01 | equipment | 0 |
| 2022-10-01 | human | 0 |
| 2022-10-01 | lightning | 0 |
| 2023-01-01 | equipment | 0 |
| 2023-01-01 | human | 0 |
| 2023-01-01 | lightning | 0 |
| 2023-04-01 | equipment | 0 |
| 2023-04-01 | human | 0 |
| 2023-04-01 | lightning | 0 |
| 2023-07-01 | equipment | 0 |
| 2023-07-01 | human | 1 |
| 2023-07-01 | lightning | 0 |
| 2023-10-01 | equipment | 0 |
| 2023-10-01 | human | 0 |
| 2023-10-01 | lightning | 0 |
| 2024-01-01 | equipment | 0 |
| 2024-01-01 | human | 0 |
| 2024-01-01 | lightning | 0 |
| 2024-04-01 | equipment | 0 |
| 2024-04-01 | human | 0 |
| 2024-04-01 | lightning | 0 |
| 2024-07-01 | equipment | 1 |
| 2024-07-01 | human | 0 |
| 2024-07-01 | lightning | 0 |
| 2024-10-01 | equipment | 0 |
| 2024-10-01 | human | 0 |
| 2024-10-01 | lightning | 0 |
| 2025-01-01 | equipment | 0 |
| 2025-01-01 | human | 0 |
| 2025-01-01 | lightning | 0 |
| 2025-04-01 | equipment | 0 |
| 2025-04-01 | human | 0 |
| 2025-04-01 | lightning | 0 |
| 2025-07-01 | equipment | 1 |
| 2025-07-01 | human | 0 |
| 2025-07-01 | lightning | 0 |
| 2025-10-01 | equipment | 0 |
| 2025-10-01 | human | 0 |
| 2025-10-01 | lightning | 0 |
| 2026-01-01 | equipment | 0 |
| 2026-01-01 | human | 0 |
| 2026-01-01 | lightning | 0 |
| 2026-04-01 | equipment | 0 |
| 2026-04-01 | human | 0 |
| 2026-04-01 | lightning | 0 |
| 2026-07-01 | equipment | 1 |
| 2026-07-01 | human | 0 |
| 2026-07-01 | lightning | 0 |

### Figure 41. Repeat and near repeat classification: Ignitions over time [95]

| period | series | value |
| --- | --- | --- |
| 2018-01-01 | lightning | 0 |
| 2018-01-01 | campfire | 1 |
| 2018-01-01 | vehicle | 0 |
| 2018-01-01 | debris burning | 0 |
| 2018-01-01 | arson | 0 |
| 2018-01-01 | Other | 0 |
| 2018-04-01 | lightning | 4 |
| 2018-04-01 | campfire | 7 |
| 2018-04-01 | vehicle | 1 |
| 2018-04-01 | debris burning | 3 |
| 2018-04-01 | arson | 1 |
| 2018-04-01 | Other | 2 |
| 2018-07-01 | lightning | 8 |
| 2018-07-01 | campfire | 7 |
| 2018-07-01 | vehicle | 4 |
| 2018-07-01 | debris burning | 5 |
| 2018-07-01 | arson | 7 |
| 2018-07-01 | Other | 2 |
| 2018-10-01 | lightning | 4 |
| 2018-10-01 | campfire | 4 |
| 2018-10-01 | vehicle | 2 |
| 2018-10-01 | debris burning | 1 |
| 2018-10-01 | arson | 3 |
| 2018-10-01 | Other | 8 |
| 2019-01-01 | lightning | 0 |
| 2019-01-01 | campfire | 1 |
| 2019-01-01 | vehicle | 0 |
| 2019-01-01 | debris burning | 2 |
| 2019-01-01 | arson | 1 |
| 2019-01-01 | Other | 1 |
| 2019-04-01 | lightning | 8 |
| 2019-04-01 | campfire | 7 |
| 2019-04-01 | vehicle | 3 |
| 2019-04-01 | debris burning | 3 |
| 2019-04-01 | arson | 1 |
| 2019-04-01 | Other | 4 |
| 2019-07-01 | lightning | 15 |
| 2019-07-01 | campfire | 1 |
| 2019-07-01 | vehicle | 7 |
| 2019-07-01 | debris burning | 5 |
| 2019-07-01 | arson | 7 |
| 2019-07-01 | Other | 4 |
| 2019-10-01 | lightning | 4 |
| 2019-10-01 | campfire | 4 |
| 2019-10-01 | vehicle | 1 |
| 2019-10-01 | debris burning | 4 |
| 2019-10-01 | arson | 1 |
| 2019-10-01 | Other | 2 |
| 2020-01-01 | lightning | 0 |
| 2020-01-01 | campfire | 1 |
| 2020-01-01 | vehicle | 0 |
| 2020-01-01 | debris burning | 0 |
| 2020-01-01 | arson | 1 |
| 2020-01-01 | Other | 0 |
| 2020-04-01 | lightning | 6 |
| 2020-04-01 | campfire | 1 |
| 2020-04-01 | vehicle | 3 |
| 2020-04-01 | debris burning | 4 |
| 2020-04-01 | arson | 3 |
| 2020-04-01 | Other | 4 |
| 2020-07-01 | lightning | 13 |
| 2020-07-01 | campfire | 9 |
| 2020-07-01 | vehicle | 4 |
| 2020-07-01 | debris burning | 1 |
| 2020-07-01 | arson | 6 |
| 2020-07-01 | Other | 5 |
| 2020-10-01 | lightning | 5 |
| 2020-10-01 | campfire | 3 |
| 2020-10-01 | vehicle | 5 |
| 2020-10-01 | debris burning | 4 |
| 2020-10-01 | arson | 2 |
| 2020-10-01 | Other | 3 |
| 2021-01-01 | lightning | 1 |
| 2021-01-01 | campfire | 0 |
| 2021-01-01 | vehicle | 0 |
| 2021-01-01 | debris burning | 0 |
| 2021-01-01 | arson | 0 |
| 2021-01-01 | Other | 0 |
| 2021-04-01 | lightning | 6 |
| 2021-04-01 | campfire | 8 |
| 2021-04-01 | vehicle | 0 |
| 2021-04-01 | debris burning | 5 |
| 2021-04-01 | arson | 3 |
| 2021-04-01 | Other | 5 |
| 2021-07-01 | lightning | 21 |
| 2021-07-01 | campfire | 8 |
| 2021-07-01 | vehicle | 4 |
| 2021-07-01 | debris burning | 8 |
| 2021-07-01 | arson | 3 |
| 2021-07-01 | Other | 3 |
| 2021-10-01 | lightning | 3 |
| 2021-10-01 | campfire | 11 |
| 2021-10-01 | vehicle | 3 |
| 2021-10-01 | debris burning | 5 |
| 2021-10-01 | arson | 1 |
| 2021-10-01 | Other | 9 |
| 2022-01-01 | lightning | 0 |
| 2022-01-01 | campfire | 1 |
| 2022-01-01 | vehicle | 0 |
| 2022-01-01 | debris burning | 3 |
| 2022-01-01 | arson | 0 |
| 2022-01-01 | Other | 1 |
| 2022-04-01 | lightning | 9 |
| 2022-04-01 | campfire | 7 |
| 2022-04-01 | vehicle | 2 |
| 2022-04-01 | debris burning | 2 |
| 2022-04-01 | arson | 0 |
| 2022-04-01 | Other | 1 |
| 2022-07-01 | lightning | 23 |
| 2022-07-01 | campfire | 7 |
| 2022-07-01 | vehicle | 8 |
| 2022-07-01 | debris burning | 5 |
| 2022-07-01 | arson | 6 |
| 2022-07-01 | Other | 3 |
| 2022-10-01 | lightning | 1 |
| 2022-10-01 | campfire | 7 |
| 2022-10-01 | vehicle | 3 |
| 2022-10-01 | debris burning | 5 |
| 2022-10-01 | arson | 3 |
| 2022-10-01 | Other | 9 |
| 2023-01-01 | lightning | 0 |
| 2023-01-01 | campfire | 1 |
| 2023-01-01 | vehicle | 0 |
| 2023-01-01 | debris burning | 2 |
| 2023-01-01 | arson | 2 |
| 2023-01-01 | Other | 0 |
| 2023-04-01 | lightning | 10 |
| 2023-04-01 | campfire | 4 |
| 2023-04-01 | vehicle | 4 |
| 2023-04-01 | debris burning | 2 |
| 2023-04-01 | arson | 4 |
| 2023-04-01 | Other | 7 |
| 2023-07-01 | lightning | 16 |
| 2023-07-01 | campfire | 9 |
| 2023-07-01 | vehicle | 6 |
| 2023-07-01 | debris burning | 4 |
| 2023-07-01 | arson | 10 |
| 2023-07-01 | Other | 3 |
| 2023-10-01 | lightning | 1 |
| 2023-10-01 | campfire | 9 |
| 2023-10-01 | vehicle | 6 |
| 2023-10-01 | debris burning | 2 |
| 2023-10-01 | arson | 5 |
| 2023-10-01 | Other | 11 |
| 2024-01-01 | lightning | 1 |
| 2024-01-01 | campfire | 0 |
| 2024-01-01 | vehicle | 1 |
| 2024-01-01 | debris burning | 0 |
| 2024-01-01 | arson | 0 |
| 2024-01-01 | Other | 2 |
| 2024-04-01 | lightning | 6 |
| 2024-04-01 | campfire | 6 |
| 2024-04-01 | vehicle | 6 |
| 2024-04-01 | debris burning | 3 |
| 2024-04-01 | arson | 3 |
| 2024-04-01 | Other | 12 |
| 2024-07-01 | lightning | 14 |
| 2024-07-01 | campfire | 17 |
| 2024-07-01 | vehicle | 4 |
| 2024-07-01 | debris burning | 5 |
| 2024-07-01 | arson | 5 |
| 2024-07-01 | Other | 3 |
| 2024-10-01 | lightning | 4 |
| 2024-10-01 | campfire | 6 |
| 2024-10-01 | vehicle | 1 |
| 2024-10-01 | debris burning | 6 |
| 2024-10-01 | arson | 2 |
| 2024-10-01 | Other | 13 |
| 2025-01-01 | lightning | 1 |
| 2025-01-01 | campfire | 1 |
| 2025-01-01 | vehicle | 0 |
| 2025-01-01 | debris burning | 1 |
| 2025-01-01 | arson | 2 |
| 2025-01-01 | Other | 2 |
| 2025-04-01 | lightning | 10 |
| 2025-04-01 | campfire | 7 |
| 2025-04-01 | vehicle | 6 |
| 2025-04-01 | debris burning | 3 |
| 2025-04-01 | arson | 3 |
| 2025-04-01 | Other | 6 |
| 2025-07-01 | lightning | 27 |
| 2025-07-01 | campfire | 10 |
| 2025-07-01 | vehicle | 7 |
| 2025-07-01 | debris burning | 5 |
| 2025-07-01 | arson | 6 |
| 2025-07-01 | Other | 6 |
| 2025-10-01 | lightning | 4 |
| 2025-10-01 | campfire | 4 |
| 2025-10-01 | vehicle | 5 |
| 2025-10-01 | debris burning | 1 |
| 2025-10-01 | arson | 2 |
| 2025-10-01 | Other | 8 |
| 2026-01-01 | lightning | 0 |
| 2026-01-01 | campfire | 0 |
| 2026-01-01 | vehicle | 2 |
| 2026-01-01 | debris burning | 0 |
| 2026-01-01 | arson | 0 |
| 2026-01-01 | Other | 0 |
| 2026-04-01 | lightning | 2 |
| 2026-04-01 | campfire | 6 |
| 2026-04-01 | vehicle | 3 |
| 2026-04-01 | debris burning | 5 |
| 2026-04-01 | arson | 0 |
| 2026-04-01 | Other | 4 |
| 2026-07-01 | lightning | 16 |
| 2026-07-01 | campfire | 11 |
| 2026-07-01 | vehicle | 11 |
| 2026-07-01 | debris burning | 6 |
| 2026-07-01 | arson | 6 |
| 2026-07-01 | Other | 7 |

### Figure 42. Hot spot analysis: Fuel treatments over time [96]

| period | series | value |
| --- | --- | --- |
| 2019-04-01 | not_significant | 1 |
| 2019-04-01 | cold_90 | 0 |
| 2019-07-01 | not_significant | 0 |
| 2019-07-01 | cold_90 | 0 |
| 2019-10-01 | not_significant | 0 |
| 2019-10-01 | cold_90 | 0 |
| 2020-01-01 | not_significant | 1 |
| 2020-01-01 | cold_90 | 0 |
| 2020-04-01 | not_significant | 1 |
| 2020-04-01 | cold_90 | 0 |
| 2020-07-01 | not_significant | 0 |
| 2020-07-01 | cold_90 | 0 |
| 2020-10-01 | not_significant | 0 |
| 2020-10-01 | cold_90 | 0 |
| 2021-01-01 | not_significant | 0 |
| 2021-01-01 | cold_90 | 0 |
| 2021-04-01 | not_significant | 0 |
| 2021-04-01 | cold_90 | 0 |
| 2021-07-01 | not_significant | 2 |
| 2021-07-01 | cold_90 | 0 |
| 2021-10-01 | not_significant | 0 |
| 2021-10-01 | cold_90 | 0 |
| 2022-01-01 | not_significant | 0 |
| 2022-01-01 | cold_90 | 0 |
| 2022-04-01 | not_significant | 1 |
| 2022-04-01 | cold_90 | 0 |
| 2022-07-01 | not_significant | 0 |
| 2022-07-01 | cold_90 | 0 |
| 2022-10-01 | not_significant | 1 |
| 2022-10-01 | cold_90 | 0 |
| 2023-01-01 | not_significant | 1 |
| 2023-01-01 | cold_90 | 0 |
| 2023-04-01 | not_significant | 1 |
| 2023-04-01 | cold_90 | 0 |
| 2023-07-01 | not_significant | 0 |
| 2023-07-01 | cold_90 | 0 |
| 2023-10-01 | not_significant | 0 |
| 2023-10-01 | cold_90 | 0 |
| 2024-01-01 | not_significant | 0 |
| 2024-01-01 | cold_90 | 0 |
| 2024-04-01 | not_significant | 1 |
| 2024-04-01 | cold_90 | 0 |
| 2024-07-01 | not_significant | 0 |
| 2024-07-01 | cold_90 | 0 |
| 2024-10-01 | not_significant | 1 |
| 2024-10-01 | cold_90 | 0 |
| 2025-01-01 | not_significant | 0 |
| 2025-01-01 | cold_90 | 1 |

### Figure 43. Hot spot analysis: Fire perimeters over time [97]

| period | series | value |
| --- | --- | --- |
| 2017-07-01 | equipment | 1 |
| 2017-07-01 | human | 0 |
| 2017-07-01 | lightning | 0 |
| 2017-10-01 | equipment | 0 |
| 2017-10-01 | human | 0 |
| 2017-10-01 | lightning | 0 |
| 2018-01-01 | equipment | 0 |
| 2018-01-01 | human | 0 |
| 2018-01-01 | lightning | 0 |
| 2018-04-01 | equipment | 0 |
| 2018-04-01 | human | 0 |
| 2018-04-01 | lightning | 0 |
| 2018-07-01 | equipment | 0 |
| 2018-07-01 | human | 1 |
| 2018-07-01 | lightning | 0 |
| 2018-10-01 | equipment | 0 |
| 2018-10-01 | human | 0 |
| 2018-10-01 | lightning | 0 |
| 2019-01-01 | equipment | 0 |
| 2019-01-01 | human | 0 |
| 2019-01-01 | lightning | 0 |
| 2019-04-01 | equipment | 0 |
| 2019-04-01 | human | 0 |
| 2019-04-01 | lightning | 0 |
| 2019-07-01 | equipment | 0 |
| 2019-07-01 | human | 0 |
| 2019-07-01 | lightning | 1 |
| 2019-10-01 | equipment | 0 |
| 2019-10-01 | human | 0 |
| 2019-10-01 | lightning | 0 |
| 2020-01-01 | equipment | 0 |
| 2020-01-01 | human | 0 |
| 2020-01-01 | lightning | 0 |
| 2020-04-01 | equipment | 0 |
| 2020-04-01 | human | 0 |
| 2020-04-01 | lightning | 0 |
| 2020-07-01 | equipment | 1 |
| 2020-07-01 | human | 0 |
| 2020-07-01 | lightning | 0 |
| 2020-10-01 | equipment | 0 |
| 2020-10-01 | human | 0 |
| 2020-10-01 | lightning | 0 |
| 2021-01-01 | equipment | 0 |
| 2021-01-01 | human | 0 |
| 2021-01-01 | lightning | 0 |
| 2021-04-01 | equipment | 0 |
| 2021-04-01 | human | 0 |
| 2021-04-01 | lightning | 0 |
| 2021-07-01 | equipment | 1 |
| 2021-07-01 | human | 0 |
| 2021-07-01 | lightning | 0 |
| 2021-10-01 | equipment | 0 |
| 2021-10-01 | human | 0 |
| 2021-10-01 | lightning | 0 |
| 2022-01-01 | equipment | 0 |
| 2022-01-01 | human | 0 |
| 2022-01-01 | lightning | 0 |
| 2022-04-01 | equipment | 0 |
| 2022-04-01 | human | 0 |
| 2022-04-01 | lightning | 0 |
| 2022-07-01 | equipment | 0 |
| 2022-07-01 | human | 1 |
| 2022-07-01 | lightning | 0 |
| 2022-10-01 | equipment | 0 |
| 2022-10-01 | human | 0 |
| 2022-10-01 | lightning | 0 |
| 2023-01-01 | equipment | 0 |
| 2023-01-01 | human | 0 |
| 2023-01-01 | lightning | 0 |
| 2023-04-01 | equipment | 0 |
| 2023-04-01 | human | 0 |
| 2023-04-01 | lightning | 0 |
| 2023-07-01 | equipment | 0 |
| 2023-07-01 | human | 1 |
| 2023-07-01 | lightning | 0 |
| 2023-10-01 | equipment | 0 |
| 2023-10-01 | human | 0 |
| 2023-10-01 | lightning | 0 |
| 2024-01-01 | equipment | 0 |
| 2024-01-01 | human | 0 |
| 2024-01-01 | lightning | 0 |
| 2024-04-01 | equipment | 0 |
| 2024-04-01 | human | 0 |
| 2024-04-01 | lightning | 0 |
| 2024-07-01 | equipment | 1 |
| 2024-07-01 | human | 0 |
| 2024-07-01 | lightning | 0 |
| 2024-10-01 | equipment | 0 |
| 2024-10-01 | human | 0 |
| 2024-10-01 | lightning | 0 |
| 2025-01-01 | equipment | 0 |
| 2025-01-01 | human | 0 |
| 2025-01-01 | lightning | 0 |
| 2025-04-01 | equipment | 0 |
| 2025-04-01 | human | 0 |
| 2025-04-01 | lightning | 0 |
| 2025-07-01 | equipment | 1 |
| 2025-07-01 | human | 0 |
| 2025-07-01 | lightning | 0 |
| 2025-10-01 | equipment | 0 |
| 2025-10-01 | human | 0 |
| 2025-10-01 | lightning | 0 |
| 2026-01-01 | equipment | 0 |
| 2026-01-01 | human | 0 |
| 2026-01-01 | lightning | 0 |
| 2026-04-01 | equipment | 0 |
| 2026-04-01 | human | 0 |
| 2026-04-01 | lightning | 0 |
| 2026-07-01 | equipment | 1 |
| 2026-07-01 | human | 0 |
| 2026-07-01 | lightning | 0 |


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