Forestry and wildfire

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

Timeline

The timeline counts 1,840 dated events in Fire perimeters, Ignitions, Fuel treatments and 1 more per quarter, from Q3 2017 to Q3 2026 12345. 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) 910. 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.

Findings

• Evidenced: Hot spots on Ignitions: Hot spot analysis: 1 layer saved: Hot spot analysis: Ignitions (344 features) 910.
• 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) 1213. 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.

Findings

• Evidenced: Hot spots on Fire perimeters: Hot spot analysis: 1 layer saved: Hot spot analysis: Fire perimeters (10 features) 1213.
• 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) 1516. 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.

Findings

• Evidenced: Hot spots on Fuel treatments: Hot spot analysis: 1 layer saved: Hot spot analysis: Fuel treatments (12 features) 1516.
• 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) 1019. 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.

Findings

• Evidenced: Space time hot spots on Ignitions: Emerging hot spots: 1 layer saved: Emerging hot spots: Ignitions (135 features) 1019.
• 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.

Findings

• Evidenced: Near repeat on Ignitions: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Ignitions (900 features) 1022.
• 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 610.

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) 101327. It reported result, count 10; result, elapsed ms 3; billing, cost 0.02822; billing, list price 0.02822; billing, upstream cost 0 27.

Findings

• Evidenced: Summarize within on Ignitions, Fire perimeters: Summarize within: 1 layer saved: Summarize within: Fire perimeters (10 features) 101327.
• 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.

Findings

• Evidenced: Summarize within on Fire lookouts, Fire perimeters: Summarize within: 1 layer saved: Summarize within: Fire perimeters (10 features) 132829.
• 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) 133031. It reported result, count 10; result, elapsed ms 2; billing, cost 0.01034; billing, list price 0.01034; billing, upstream cost 0 30.

Findings

• Evidenced: Summarize within on Weather stations, Fire perimeters: Summarize within: 1 layer saved: Summarize within: Fire perimeters (10 features) 133031.
• 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) 1039. 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.

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) 1039.
• 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 1042. 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.

Findings

• Evidenced: Nearest neighbor index on Ignitions: Average nearest neighbor: done; no map output 1042.
• 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.

Findings

• Evidenced: Nearest neighbor index on Weather stations: Average nearest neighbor: done; no map output 3144.
• 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.

Findings

• Evidenced: Calculate density on Ignitions: Calculate density: 1 layer saved: Calculate density: Ignitions (2136 features) 1047.
• 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) 1350. 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.

Findings

• Evidenced: Outliers on Fire perimeters: Find outliers: 1 layer saved: Find outliers: Fire perimeters (10 features) 1350.
• 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) 1652. 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.

Findings

• Evidenced: Outliers on Fuel treatments: Find outliers: 1 layer saved: Find outliers: Fuel treatments (12 features) 1652.
• 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.

Findings

• Evidenced: Outliers on Fuel units: Find outliers: 1 layer saved: Find outliers: Fuel units (36 features) 5455.
• 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) 5657585960616263646566676869707172 and 25 chart(s) 73747576777879808182838485868788899091929394959697.

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.

Changes since the last run

This is the first report of this project.

Sources

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