Water resources

Sample: Water resources, Chiang Mai: report

Project: Sample: Water resources, Chiang Mai
Goal: Describe the project Sample: Water resources, Chiang Mai: run the analyses that tell the most about its data, draw every result, and write a cited report.
Run: d6604a47-a47a-419f-9baf-8c790e459434 (running, 11 agent run(s))
Generated: 2026-10-18T05:15:55Z, version 1
Model cost: USD 0.0000 over 0 model call(s)

Data used

Timeline

The timeline counts 4,003 dated events in Mapped flood extents, Citizen flood reports, Gauge readings by day and 1 more per month, from January 2024 to December 2025 12345. By dataset: Gauge readings by day 2,740; Citizen flood reports 900; Rainfall by month 360; Mapped flood extents 3 1. The trend is +7.75 events per month; the second half of the months has 2,426 against 1,577 in the first (+53.8%) 1. Change point detection found no shift in the counts 6. The project's documents name 2 dates inside this span, drawn as dotted lines; the most named is 2025-10-06 (Flood bulletin, page 1, 1 mention) 7.

Busiest months

• October 2025: 438 events 1
• August 2025: 287 events 1
• September 2024: 255 events 1

Quietest months

• January 2024: 15 events 1
• February 2024: 15 events 1
• March 2024: 15 events 1

Space time hot spots

What this analysis is. Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time 8. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 8.

Why it was run on this data. Citizen flood reports carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per month 9.

What it found. The analysis generated 54 features identifying emerging hot spots from the Citizen flood reports 10. This result utilized 900 features and required 0 time neighbors 10. The process achieved a resolution of 8 and completed in 15 milliseconds 10. The associated costs for this operation were 0.145, reflecting that the upstream cost was 0 10.

Findings

• Evidenced: Space time hot spots on Citizen flood reports: Emerging hot spots: 1 layer saved: Emerging hot spots: Citizen flood reports (54 features) 1011.
• Evidenced: It reported result, count 54; result, elapsed ms 15; result, resolution 8; result, features used 900; result, time neighbors 0; billing, cost 0.145 10.

Tool gp_space_time_hot_spots; parameters {"project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "Citizen flood reports (e21d361b)"}, "interval": "month", "bin_shape": "h3", "resolution": 8, "time_field": "reported_at"}

Near repeat

What this analysis is. Repeat and near repeat classification: Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table 12. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator 12.

Why it was run on this data. Near repeat analysis tests whether Citizen flood reports close in space also follow each other within 30 days 13.

What it found. Near repeat on Citizen flood reports: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Citizen flood reports (900 features) 1114. It reported result, days 30; result, count 900; result, chains 136; result, distance m 200; result, elapsed ms 25; result, pairs linked 474 14.

Findings

• Evidenced: Near repeat on Citizen flood reports: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Citizen flood reports (900 features) 1114.
• Evidenced: It reported result, days 30; result, count 900; result, chains 136; result, distance m 200; result, elapsed ms 25; result, pairs linked 474 14.

Tool gp_near_repeat; parameters {"days": 30, "project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "Citizen flood reports (e21d361b)"}, "distance_m": 200.0, "time_field": "reported_at"}

Change point detection

What this analysis is. Change point detection: When did the level, count, trend or variability change 15? For the whole study area or for every bin 15. In ArcGIS it is Change Point Detection 15.

Why it was run on this data. Change point detection finds when the monthly count of Citizen flood reports shifted 16.

What it found. Change point detection was successfully completed, though no map output was generated for the analysis 6.

Findings

• Evidenced: Change point detection on Citizen flood reports: Change point detection: done; no map output 611.
• Evidenced: It reported result, n changes 0; result, elapsed ms 4; result, events used 900; billing, cost 0.077; billing, list price 0.077; billing, upstream cost 0 6.

Tool gp_change_point_detection; parameters {"method": "count", "project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "Citizen flood reports (e21d361b)"}, "interval": "month", "time_field": "reported_at"}

Hot spots

What this analysis is. Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins 17. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 17.

Why it was run on this data. 900 points: Getis-Ord Gi* tests whether the concentrations in Citizen flood reports are statistically significant 18.

What it found. Hot spot analysis was performed using the Citizen flood reports 11, resulting in the identification of 192 features 19. This analysis, which covered 900 incidents, completed with an elapsed time of 17 milliseconds 19.

Findings

• Evidenced: Hot spots on Citizen flood reports: Hot spot analysis: 1 layer saved: Hot spot analysis: Citizen flood reports (192 features) 1119.
• Evidenced: It reported result, bins 192; result, count 192; result, elapsed ms 17; result, cell size m 500; result, incidents used 900; result, distance band m 500 19.

Tool gp_hot_spots; parameters {"project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "Citizen flood reports (e21d361b)"}, "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 17. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 17.

Why it was run on this data. Gi* hot spots of 'mean_rain_mm' across the areas 20.

What it found. Hot spots on Sub-catchments: Hot spot analysis: 1 layer saved: Hot spot analysis: Sub-catchments (10 features) 2122. It reported result, count 10; result, elapsed ms 1; result, excluded null 0; result, distance band m 3,807; billing, cost 0.0502; billing, list price 0.0502 21.

Findings

• Evidenced: Hot spots on Sub-catchments: Hot spot analysis: 1 layer saved: Hot spot analysis: Sub-catchments (10 features) 2122.
• Evidenced: It reported result, count 10; result, elapsed ms 1; result, excluded null 0; result, distance band m 3,807; billing, cost 0.0502; billing, list price 0.0502 21.

Tool gp_hot_spots; parameters {"field": "mean_rain_mm", "project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "Sub-catchments (93ab58bf)"}}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 23. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 23.

Why it was run on this data. 900 points in Citizen flood reports: a density surface shows where they concentrate 24.

What it found. Using citizen flood reports as the input, density was calculated with the gp_calculate_density tool [11, 25]. This process analyzed 1,058 features 25 and determined a maximum density of 73.99 per km2 25. The total cost associated with the density calculation was 0.068 25.

Findings

• Evidenced: Calculate density on Citizen flood reports: Calculate density: 1 layer saved: Calculate density: Citizen flood reports (1058 features) 1125.
• Evidenced: It reported result, count 1,058; result, elapsed ms 245; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 73.99 25.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "Citizen flood reports (e21d361b)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 26. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 26.

Why it was run on this data. Count Citizen flood reports inside each feature of Sub-catchments by type; the counts map as a choropleth (natural breaks, sequential colors) 27.

What it found. Summarize within on Citizen flood reports, Sub-catchments: Summarize within: 1 layer saved: Summarize within: Sub-catchments (10 features) 112228. It reported result, count 10; result, elapsed ms 2; billing, cost 0.02822; billing, list price 0.02822; billing, upstream cost 0 28.

Findings

• Evidenced: Summarize within on Citizen flood reports, Sub-catchments: Summarize within: 1 layer saved: Summarize within: Sub-catchments (10 features) 112228.
• Evidenced: It reported result, count 10; result, elapsed ms 2; billing, cost 0.02822; billing, list price 0.02822; billing, upstream cost 0 28.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "water_depth_cm"}], "project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "Citizen flood reports (e21d361b)"}, "group_by": "type", "polygons": {"layer": "Sub-catchments (93ab58bf)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 26. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 26.

Why it was run on this data. Count Citizen flood reports inside each feature of Sub-catchments by type; the counts map as a choropleth (natural breaks, sequential colors) 27.

What it found. Summarize within on River gauge stations, Sub-catchments: Summarize within: 1 layer saved: Summarize within: Sub-catchments (10 features) 222930. It reported result, count 10; result, elapsed ms 0; billing, cost 0.01042; billing, list price 0.01042; billing, upstream cost 0 29.

Findings

• Evidenced: Summarize within on River gauge stations, Sub-catchments: Summarize within: 1 layer saved: Summarize within: Sub-catchments (10 features) 222930.
• Evidenced: It reported result, count 10; result, elapsed ms 0; billing, cost 0.01042; billing, list price 0.01042; billing, upstream cost 0 29.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "bank_full_m"}], "project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "River gauge stations (78d4554f)"}, "group_by": "river", "polygons": {"layer": "Sub-catchments (93ab58bf)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 26. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 26.

Why it was run on this data. Count Citizen flood reports inside each feature of Sub-catchments by type; the counts map as a choropleth (natural breaks, sequential colors) 27.

What it found. Summarize within on Rain gauges, Sub-catchments: Summarize within: 1 layer saved: Summarize within: Sub-catchments (10 features) 223132. It reported result, count 10; result, elapsed ms 0; billing, cost 0.01052; billing, list price 0.01052; billing, upstream cost 0 31.

Findings

• Evidenced: Summarize within on Rain gauges, Sub-catchments: Summarize within: 1 layer saved: Summarize within: Sub-catchments (10 features) 223132.
• Evidenced: It reported result, count 10; result, elapsed ms 0; billing, cost 0.01052; billing, list price 0.01052; billing, upstream cost 0 31.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "elevation_m"}], "project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "Rain gauges (3db367dc)"}, "polygons": {"layer": "Sub-catchments (93ab58bf)"}}

Zonal statistics

What this analysis is. Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON 33. In ArcGIS it is Zonal Statistics as Table (polygons) 33.

Why it was run on this data. Statistics of Catchment terrain model (DEM) inside each feature of Sub-catchments 34.

What it found. The analysis utilized the Zonal statistics tool, specifically rs_zonal_statistics 35. The resulting output contained 10 row(s) 35. This statistical breakdown successfully processed 10 zones 35.

Findings

• Evidenced: Zonal statistics: Zonal statistics: 10 row(s) 35.
• Evidenced: zones 10 35.

Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Sub-catchments (93ab58bf)", "raster_id": "Catchment terrain model (DEM) (8a54f320)"}

Zonal statistics

What this analysis is. Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON 33. In ArcGIS it is Zonal Statistics as Table (polygons) 33.

Why it was run on this data. Statistics of Catchment terrain model (DEM) inside each feature of Sub-catchments 34.

What it found. The analysis utilized the tool rs_zonal_statistics for zonal statistics 36. Based on the tool summary, the output contained 3 row(s) 36. Additionally, the process processed 3 zones 36.

Findings

• Evidenced: Zonal statistics: Zonal statistics: 3 row(s) 36.
• Evidenced: zones 3 36.

Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Mapped flood extents (32d0b09d)", "raster_id": "Catchment terrain model (DEM) (8a54f320)"}

Slope

What this analysis is. Slope: Horn slope in degrees or percent, with the share of ground in six grade classes 37. In ArcGIS it is Slope (Spatial Analyst); in QGIS, Slope (GDAL DEM) 37.

Why it was run on this data. Slope of the Catchment terrain model (DEM) surface 38.

What it found. Slope on Catchment terrain model (DEM): Slope over 119.88 km2: mean 8.48 degrees, max 20.58 degrees; 68% of the ground is steeper than 15% 3940. DEM: Catchment terrain model (DEM) 3940. Verified: 2 checks passed 3940. It reported dem, cols 400; dem, rows 333; dem, cell m 30; dem, relief m 1,812; dem, elev max m 2,062; dem, elev min m 250 39.

Findings

• Evidenced: Slope on Catchment terrain model (DEM): Slope over 119.88 km2: mean 8.48 degrees, max 20.58 degrees; 68% of the ground is steeper than 15% 3940.
• Evidenced: DEM: Catchment terrain model (DEM) 3940.
• Evidenced: Verified: 2 checks passed 3940.
• Evidenced: It reported dem, cols 400; dem, rows 333; dem, cell m 30; dem, relief m 1,812; dem, elev max m 2,062; dem, elev min m 250 39.

Tool tr_slope; parameters {"dem": {"layer": "Catchment terrain model (DEM) (8a54f320)"}, "project": "61868fa2-4b0c-4a49-9b21-03b342758885"}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 41. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 41.

Why it was run on this data. DBSCAN finds dense groups of Citizen flood reports and separates noise 42.

What it found. Find point clusters on Citizen flood reports: Find point clusters: 1 layer saved: Find point clusters: Citizen flood reports (900 features) 1143. It reported result, count 900; result, noise 900; result, distance m 200; result, elapsed ms 13; result, min points 20; billing, cost 0.068 43.

Findings

• Evidenced: Find point clusters on Citizen flood reports: Find point clusters: 1 layer saved: Find point clusters: Citizen flood reports (900 features) 1143.
• Evidenced: It reported result, count 900; result, noise 900; result, distance m 200; result, elapsed ms 13; result, min points 20; billing, cost 0.068 43.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "Citizen flood reports (e21d361b)"}, "distance_m": 200.0, "min_points": 20}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 41. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 41.

Why it was run on this data. DBSCAN finds dense groups of River gauge stations and separates noise 44.

What it found. Find point clusters on River gauge stations: Find point clusters: 2 layers saved: Find point clusters: River gauge stations (10 features); Find point clusters: cluster hulls (2 features) 3045. It reported result, count 10; result, noise 3; result, distance m 2,000; result, elapsed ms 85; result, min points 3; billing, cost 0.0502 45.

Findings

• Evidenced: Find point clusters on River gauge stations: Find point clusters: 2 layers saved: Find point clusters: River gauge stations (10 features); Find point clusters: cluster hulls (2 features) 3045.
• Evidenced: It reported result, count 10; result, noise 3; result, distance m 2,000; result, elapsed ms 85; result, min points 3; billing, cost 0.0502 45.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "River gauge stations (78d4554f)"}, "distance_m": 2000.0, "min_points": 3}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 41. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 41.

Why it was run on this data. DBSCAN finds dense groups of Rain gauges and separates noise 46.

What it found. Find point clusters on Rain gauges: Find point clusters: 2 layers saved: Find point clusters: Rain gauges (15 features); Find point clusters: cluster hulls (1 feature) 3247. It reported result, count 15; result, noise 0; result, distance m 5,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0503 47.

Findings

• Evidenced: Find point clusters on Rain gauges: Find point clusters: 2 layers saved: Find point clusters: Rain gauges (15 features); Find point clusters: cluster hulls (1 feature) 3247.
• Evidenced: It reported result, count 15; result, noise 0; result, distance m 5,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0503 47.

A model draft was discarded because a sentence did not cite a source of this section.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "61868fa2-4b0c-4a49-9b21-03b342758885", "features": {"layer": "Rain gauges (3db367dc)"}, "distance_m": 5000.0, "min_points": 3}

Maps and charts

The run also made 16 map image(s) 48495051525354555657585960616263 and 18 chart(s) 646566676869707172737475767778798081.

What the map shows. The map, titled "Emerging hot spots: Citizen flood reports," displays an analysis of potential flood reporting clusters across a gridded area. According to the legend, the pattern result for this analysis is "No pattern detected." Visually, the entire area is covered by a uniform hexagonal grid structure, indicating the scope of the report, but no specific hot spots or distinct groupings are highlighted. The overall reading suggests that citizen flood reports are not concentrated in any specific, statistically significant area within the mapped region. Therefore, the image indicates that 48

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

Changes since the last run

This is the first report of this project.

Sources

1. 1Rendering: Chart: Timeline: events per month by dataset. 96 rows id ab73206e-a519-4180-9535-698e58b41091
2. 2Dataset: Mapped flood extents. vector, 3 rows id db7dcc69-6952-403c-92c2-c3bc2246001a
3. 3Dataset: Citizen flood reports. vector, 900 rows id 1efc03c7-deb5-479a-8a37-e719f58c2b20
4. 4Dataset: Gauge readings by day. table, 2740 rows id 2f1b0f9a-faac-4f0f-a937-14d42ba33293
5. 5Dataset: Rainfall by month. table, 360 rows id b74ec6fb-9f9c-443c-9bf1-60b415ac2d65
6. 6Tool result: gp_change_point_detection, step 2. Change point detection: done; no map output. id 2d6ff918-4a3d-4d6b-90bb-880ab63700de:2
7. 7Document page: Flood bulletin, page 1. 6 October 2025 id 85d1f153-b632-4f92-9c75-e27e602d03cd:1
8. 8Catalog: 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
9. 9Plan: Space time hot spots of Citizen flood reports (the Start plan's candidate c_8817aba79f40). Citizen flood reports carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per month. id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_8817aba79f40
10. 10Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Citizen flood reports (54 features). id 2d6ff918-4a3d-4d6b-90bb-880ab63700de:0
11. 11Layer: Citizen flood reports. 900 features id e21d361b-1aea-4c6c-983a-db6793334e54
12. 12Catalog: 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
13. 13Plan: Near repeat of Citizen flood reports (the Start plan's candidate c_dc6752c63c8e). Near repeat analysis tests whether Citizen flood reports close in space also follow each other within 30 days. id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_dc6752c63c8e
14. 14Tool result: gp_near_repeat, step 1. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Citizen flood reports (900 features). id 2d6ff918-4a3d-4d6b-90bb-880ab63700de:1
15. 15Catalog: 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
16. 16Plan: Change points of Citizen flood reports (the Start plan's candidate c_026cfc884747). Change point detection finds when the monthly count of Citizen flood reports shifted. id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_026cfc884747
17. 17Catalog: 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
18. 18Plan: Hot spots of Citizen flood reports (the Start plan's candidate c_55817c6df3fb). 900 points: Getis-Ord Gi* tests whether the concentrations in Citizen flood reports are statistically significant. id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_55817c6df3fb
19. 19Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Citizen flood reports (192 features). id d01cf43d-7790-4072-93f9-676ff4a27366:0
20. 20Plan: Hot spots of Sub-catchments (the Start plan's candidate c_65d5fe8b1094). Gi* hot spots of 'mean_rain_mm' across the areas. id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_65d5fe8b1094
21. 21Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Sub-catchments (10 features). id d01cf43d-7790-4072-93f9-676ff4a27366:1
22. 22Layer: Sub-catchments. 10 features id 93ab58bf-7296-4170-ad95-b10fa72c5e22
23. 23Catalog: 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
24. 24Plan: Kernel density of Citizen flood reports (the Start plan's candidate c_ae50d36cd0d9). 900 points in Citizen flood reports: a density surface shows where they concentrate. id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_ae50d36cd0d9
25. 25Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Citizen flood reports (1058 features). id 8747d1d4-2b67-4155-bcb2-a4e53f193c9d:0
26. 26Catalog: 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
27. 27Plan: Citizen flood reports per feature of Sub-catchments (the Start plan's candidate c_3b154a6e32f0). Count Citizen flood reports inside each feature of Sub-catchments by type; the counts map as a choropleth (natural breaks, sequential colors). id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_3b154a6e32f0
28. 28Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Sub-catchments (10 features). id 8dd97c93-e555-418d-a9c0-cfdf74a7bac8:0
29. 29Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Sub-catchments (10 features). id 8dd97c93-e555-418d-a9c0-cfdf74a7bac8:1
30. 30Layer: River gauge stations. 10 features id 78d4554f-40bd-482a-8b8e-976f0f3f0fe9
31. 31Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Sub-catchments (10 features). id 8dd97c93-e555-418d-a9c0-cfdf74a7bac8:2
32. 32Layer: Rain gauges. 15 features id 3db367dc-7928-4385-9039-bfe0bba84b6d
33. 33Catalog: 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
34. 34Plan: Zonal statistics of Catchment terrain model (DEM) and Sub-catchments (the Start plan's candidate c_252b1099847d). Statistics of Catchment terrain model (DEM) inside each feature of Sub-catchments. id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_252b1099847d
35. 35Tool result: rs_zonal_statistics, step 0. Zonal statistics: 10 row(s). zones 10. id a7316d79-1e6d-45dd-9d15-38c74be5152e:0
36. 36Tool result: rs_zonal_statistics, step 1. Zonal statistics: 3 row(s). zones 3. id a7316d79-1e6d-45dd-9d15-38c74be5152e:1
37. 37Catalog: Slope (analysis catalog, tr_slope). Horn slope in degrees or percent, with the share of ground in six grade classes. ArcGIS: Slope (Spatial Analyst). QGIS: Slope (GDAL DEM). id tr_slope
38. 38Plan: Slope of Catchment terrain model (DEM) (the Start plan's candidate c_7150677d61cb). Slope of the Catchment terrain model (DEM) surface. id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_7150677d61cb
39. 39Tool result: tr_slope, step 2. Slope over 119.88 km2: mean 8.48 degrees, max 20.58 degrees; 68% of the ground is steeper than 15%. DEM: Catchment terrain model (DEM). Verified: 2 checks passed. id a7316d79-1e6d-45dd-9d15-38c74be5152e:2
40. 40Layer: Catchment terrain model (DEM). 0 features id 8a54f320-9f63-4fda-934a-2891d0746533
41. 41Catalog: 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
42. 42Plan: Cluster points of Citizen flood reports (the Start plan's candidate c_14cb11cdaf9e). DBSCAN finds dense groups of Citizen flood reports and separates noise. id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_14cb11cdaf9e
43. 43Tool result: gp_find_point_clusters, step 0. Find point clusters: 1 layer saved: Find point clusters: Citizen flood reports (900 features). id e37a3cf8-9f42-4186-9a30-b91e39fd4753:0
44. 44Plan: Cluster points of River gauge stations (the Start plan's candidate c_850adaee1a23). DBSCAN finds dense groups of River gauge stations and separates noise. id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_850adaee1a23
45. 45Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: River gauge stations (10 features); Find point clusters: cluster hulls (2 features). id e37a3cf8-9f42-4186-9a30-b91e39fd4753:1
46. 46Plan: Cluster points of Rain gauges (the Start plan's candidate c_880362120631). DBSCAN finds dense groups of Rain gauges and separates noise. id 782d5b2b-9474-44b9-a11b-9d4d784bcedb:c_880362120631
47. 47Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Rain gauges (15 features); Find point clusters: cluster hulls (1 feature). id e37a3cf8-9f42-4186-9a30-b91e39fd4753:2
48. 48Rendering: Map: Emerging hot spots: Citizen flood reports id df84019c-853e-4480-ae80-4360a5057762
49. 49Rendering: Map: Repeat and near repeat classification: Citizen flood reports id db30ad7f-93e1-49a6-8051-b2115ddb3932
50. 50Rendering: Map: Hot spot analysis: Citizen flood reports id 58d7b995-0988-4e8e-9630-4bc577176a92
51. 51Rendering: Map: Hot spot analysis: Sub-catchments id 235866fd-a134-4c95-9350-8f2aece11b24
52. 52Rendering: Map: Calculate density: Citizen flood reports id 691e1041-8462-4990-a2b4-333fec815627
53. 53Rendering: Map: Summarize within: Sub-catchments id 2a79fecc-b8b2-4472-b272-fb43b039dbd2
54. 54Rendering: Map: Summarize within: Sub-catchments id 857ca1b5-8fec-48a4-b304-266e68d31b94
55. 55Rendering: Map: Summarize within: Sub-catchments id 1e84fe9a-d7b2-4902-98c6-88f83d1753eb
56. 56Rendering: Map: Slope (degrees) id 1d6c4200-1fcb-4126-ad0a-641d77b24050
57. 57Rendering: Map: Find point clusters: Citizen flood reports id 322bd37e-7f71-4ec2-be16-3d3eb394fa1a
58. 58Rendering: Map: Find point clusters: River gauge stations id 42494457-0017-4176-b060-8c623d818af7
59. 59Rendering: Map: Find point clusters: cluster hulls id cbd218da-1936-4b2f-928b-583963a4d05b
60. 60Rendering: Map: Find point clusters: Rain gauges id de3b65e7-1df0-45b3-a9d9-3f16c3ec25e0
61. 61Rendering: Map: Find point clusters: cluster hulls id bb029bff-1f80-4876-9c36-9f206c27cb52
62. 62Rendering: Map: Sub-catchments by mean_rain_mm id 629ae5d8-0e0f-40be-8efe-59858715407c
63. 63Rendering: Map: Mapped flood extents by max_depth_m id 672a21a8-f21f-47d9-915d-f1215f2351ea
64. 64Rendering: Chart: Citizen flood reports over time (reported_at). 72 rows id 1f057250-f2f3-4444-8ee4-312f7e3f29b8
65. 65Rendering: Chart: Gauge readings by day over time (date). 18 rows id 1cefadf3-c3b0-4bdc-b14d-0912c06be92b
66. 66Rendering: Chart: Rainfall by month over time (month). 24 rows id 68e438aa-5686-4857-9a3f-cd8c3cde2386
67. 67Rendering: Chart: Emerging hot spots: Citizen flood reports. 7 rows id 2f15b311-199d-4056-abbf-7f7d18fe96f7
68. 68Rendering: Chart: Repeat and near repeat classification: Citizen flood reports. 72 rows id ad285a50-1301-486d-8690-dc043ed0946c
69. 69Rendering: Chart: Hot spot analysis: Citizen flood reports. 12 rows id 3368b83c-fef0-4ed7-a8a9-0b1b9da8619b
70. 70Rendering: Chart: Hot spot analysis: Sub-catchments. 6 rows id c9d116d9-5701-464b-b839-63cf43f651de
71. 71Rendering: Chart: Calculate density: Citizen flood reports. 12 rows id c7966614-d1c9-46e7-8925-8f2f0bac5902
72. 72Rendering: Chart: Summarize within: Sub-catchments. 5 rows id 8c848fa1-3d91-47af-9649-288a226486ed
73. 73Rendering: Chart: Summarize within: Sub-catchments. 5 rows id dfeafeea-e8b5-4e66-bec7-146625c650af
74. 74Rendering: Chart: Summarize within: Sub-catchments. 5 rows id a13f8b52-7627-46cb-b4b5-97051fbb536c
75. 75Rendering: Chart: Find point clusters: Citizen flood reports. 1 rows id 0788ef2e-51b6-4142-92a3-ee3cbdd4a246
76. 76Rendering: Chart: Find point clusters: River gauge stations. 3 rows id ec2f47a4-6cfe-4935-ae97-f19fd059d77c
77. 77Rendering: Chart: Find point clusters: cluster hulls. 2 rows id 0c8ac877-ebc0-43c8-a153-654498de370e
78. 78Rendering: Chart: Find point clusters: Rain gauges. 1 rows id 28dec6f4-3a37-41d3-901b-dde7b707ca85
79. 79Rendering: Chart: Find point clusters: cluster hulls. 1 rows id 88019df2-74d5-411f-aa91-914142d1020b
80. 80Rendering: Chart: Find point clusters: Citizen flood reports over time. 72 rows id 9aed8e2a-dca7-41a3-88ab-dcad1081cc59
81. 81Rendering: Chart: Repeat and near repeat classification: Citizen flood reports over time. 72 rows id 682239cc-117a-45c4-932d-cd4f72d53c6a
82. 82Dataset: River gauge stations. vector, 10 rows id 51e6f3e7-39f7-409d-b75a-5e3c4f2111ba
83. 83Dataset: Rivers. vector, 5 rows id c3beaebe-fab3-43f4-8bd5-fd534de0bc90
84. 84Dataset: Sub-catchments. vector, 10 rows id 3b3a6800-4b3b-485f-9a6e-9cb94a905fda
85. 85Dataset: Rain gauges. vector, 15 rows id 798fc52e-f033-441e-89de-9aba95030abe
86. 86Dataset: Levees. vector, 4 rows id 452fb0cc-8b65-48aa-80fb-aa893b871288
87. 87Dataset: Catchment terrain model (DEM). raster, ? rows id 1e838159-31fe-49cd-9295-eb56c3c76602
88. 88Dataset: Flood bulletin. document, ? rows id 0d72e77d-8ce0-4130-85e5-3594ccc48b8b
89. 89Record: Sample: Water resources, Chiang Mai id 61868fa2-4b0c-4a49-9b21-03b342758885
90. 90Layer: Emerging hot spots: Citizen flood reports. 54 features id 76aee3c4-5662-40a6-a573-f7385c27cb6f
91. 91Layer: Repeat and near repeat classification: Citizen flood reports. 900 features id 453b38fc-b031-4019-9f36-cea25aa9d8bc
92. 92Layer: Hot spot analysis: Citizen flood reports. 192 features id 194f8e1a-bb85-455a-a877-c8e63e1e9706
93. 93Layer: Hot spot analysis: Sub-catchments. 10 features id 1a25513d-2337-4e12-b048-9964386b8caf
94. 94Layer: Calculate density: Citizen flood reports. 1,058 features id d06650d9-2045-4364-b4eb-7e2d08340918
95. 95Layer: Summarize within: Sub-catchments. 10 features id fd0b3217-d60a-47c7-90ab-ea465e8c7c5d
96. 96Layer: Summarize within: Sub-catchments. 10 features id ea36acc2-c9e4-47d1-ae43-8e8a01796331
97. 97Layer: Summarize within: Sub-catchments. 10 features id e444fc8a-915a-41cf-b527-64e4d1f433ad
98. 98Layer: Slope (degrees). 0 features id a753c90d-d085-4dc3-9dbd-73f27c7b0ce4
99. 99Layer: Find point clusters: Citizen flood reports. 900 features id 74df7279-bf60-476d-a11f-318baf9e29c1
100. 100Layer: Find point clusters: River gauge stations. 10 features id 206d0831-64d5-4e32-8f3e-6a20b952bafa
101. 101Layer: Find point clusters: cluster hulls. 2 features id fdb581a7-158f-4075-9090-44eea9d091f3
102. 102Layer: Find point clusters: Rain gauges. 15 features id c05c732b-60cd-4aa0-9e1c-3c5d0446cfb9
103. 103Layer: Find point clusters: cluster hulls. 1 features id f339f855-d239-4237-9f3a-0d4890abce5e
104. 104Document: Flood bulletin id 85d1f153-b632-4f92-9c75-e27e602d03cd