Construction

Sample: Construction, Al Reem Island: report

Project: Sample: Construction, Al Reem Island
Goal: Describe the project Sample: Construction, Al Reem Island: run the analyses that tell the most about its data, draw every result, and write a cited report.
Run: 92888b09-8bee-40e6-98f7-92f663833df1 (running, 12 agent run(s))
Generated: 2026-10-18T06:18:56Z, version 1
Model cost: USD 0.0000 over 6 model call(s)

Data used

Timeline

The timeline counts 2,834 dated events in Inspections and defects, Settlement monitoring, Daily progress by zone and 1 more per week, from week of 2025-12-29 to week of 2026-09-28 12345. By dataset: Daily progress by zone 1,264; Inspections and defects 900; Deliveries log 400; Settlement monitoring 270 1. The trend is +1.31 events per week; the second half of the weeks has 1,683 against 1,151 in the first (+46.2%) 1. 6 of the 40 weeks had no events 1.

Busiest weeks

• week of 2026-03-30: 131 events 1
• week of 2026-07-27: 129 events 1
• week of 2026-06-01: 126 events 1

Quietest weeks with events

• week of 2026-09-28: 22 events 1
• week of 2026-09-07: 26 events 1
• week of 2026-09-14: 27 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 6. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 6.

Why it was run on this data. Inspections and defects carry dates in 'inspected_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week 7.

What it found. The gp_space_time_hot_spots tool was run using the Inspections and defects input layer 9 [8

Findings

• Evidenced: Space time hot spots on Inspections and defects: Emerging hot spots: 1 layer saved: Emerging hot spots: Inspections and defects (28 features) 89.
• Evidenced: It reported result, count 28; result, elapsed ms 16; result, resolution 9; result, features used 900; result, time neighbors 0; billing, cost 0.145 8.

Tool gp_space_time_hot_spots; parameters {"project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Inspections and defects (2d513b03)"}, "interval": "week", "bin_shape": "h3", "resolution": 9, "time_field": "inspected_at"}

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 6. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 6.

Why it was run on this data. Settlement monitoring carry dates in 'month': emerging hot spot analysis finds new, intensifying and fading hot spots per week 10.

What it found. Space time hot spots on Settlement monitoring: Emerging hot spots: 1 layer saved: Emerging hot spots: Settlement monitoring (10 features) 1112. It reported result, count 10; result, elapsed ms 2; result, resolution 9; result, features used 270; result, time neighbors 0; billing, cost 0.1135 11.

Findings

• Evidenced: Space time hot spots on Settlement monitoring: Emerging hot spots: 1 layer saved: Emerging hot spots: Settlement monitoring (10 features) 1112.
• Evidenced: It reported result, count 10; result, elapsed ms 2; result, resolution 9; result, features used 270; result, time neighbors 0; billing, cost 0.1135 11.

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

Tool gp_space_time_hot_spots; parameters {"project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Settlement monitoring (425841af)"}, "interval": "week", "bin_shape": "h3", "resolution": 9, "time_field": "month"}

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 13. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator 13.

Why it was run on this data. Near repeat analysis tests whether Inspections and defects close in space also follow each other within 21 days 14.

What it found. Near repeat on Inspections and defects: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Inspections and defects (900 features) 915. It reported result, days 21; result, count 900; result, chains 65; result, distance m 50; result, elapsed ms 78; result, pairs linked 3,856 15.

Findings

• Evidenced: Near repeat on Inspections and defects: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Inspections and defects (900 features) 915.
• Evidenced: It reported result, days 21; result, count 900; result, chains 65; result, distance m 50; result, elapsed ms 78; result, pairs linked 3,856 15.

Tool gp_near_repeat; parameters {"days": 21, "project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Inspections and defects (2d513b03)"}, "distance_m": 50.0, "time_field": "inspected_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 16. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 16.

Why it was run on this data. 900 points: Getis-Ord Gi* tests whether the concentrations in Inspections and defects are statistically significant 17.

What it found. The hot spot analysis was performed on the Inspections and defects layer 9, generating a result layer with 317 features 18. This process used 900 incidents 18 and covered a distance band of 200 m 18. The analysis report indicates a cell size of 100 m 18 and resulted in an elapsed time of 19 ms 18. The total billing cost for this analysis was 0.068 18.

Findings

• Evidenced: Hot spots on Inspections and defects: Hot spot analysis: 1 layer saved: Hot spot analysis: Inspections and defects (317 features) 918.
• Evidenced: It reported result, bins 317; result, count 317; result, elapsed ms 19; result, cell size m 100; result, incidents used 900; result, distance band m 200 18.

Tool gp_hot_spots; parameters {"project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Inspections and defects (2d513b03)"}, "aggregation": "hexagon", "cell_size_m": 100.0, "distance_band_m": 200.0}

Hot spots

What this analysis is. Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins 16. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 16.

Why it was run on this data. 270 points: Getis-Ord Gi* tests whether the concentrations in Settlement monitoring are statistically significant 19.

What it found. Hot spots on Settlement monitoring: Hot spot analysis: 1 layer saved: Hot spot analysis: Settlement monitoring (87 features) 1220. It reported result, bins 87; result, count 87; result, elapsed ms 5; result, cell size m 50; result, incidents used 270; result, distance band m 100 20.

Findings

• Evidenced: Hot spots on Settlement monitoring: Hot spot analysis: 1 layer saved: Hot spot analysis: Settlement monitoring (87 features) 1220.
• Evidenced: It reported result, bins 87; result, count 87; result, elapsed ms 5; result, cell size m 50; result, incidents used 270; result, distance band m 100 20.

Tool gp_hot_spots; parameters {"project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Settlement monitoring (425841af)"}, "aggregation": "hexagon", "cell_size_m": 50.0, "distance_band_m": 100.0}

Hot spots

What this analysis is. Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins 16. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 16.

Why it was run on this data. Gi* hot spots of 'pct_complete' across the areas 21.

What it found. The hot spot analysis was performed using the Work zones layer 23 and generated a saved layer titled Hot spot analysis: Work zones 22.

Findings

• Evidenced: Hot spots on Work zones: Hot spot analysis: 1 layer saved: Hot spot analysis: Work zones (8 features) 2223.
• Evidenced: It reported result, count 8; result, elapsed ms 1; result, excluded null 0; result, distance band m 872.7; billing, cost 0.05016; billing, list price 0.05016 22.

Tool gp_hot_spots; parameters {"field": "pct_complete", "project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Work zones (a87c9da1)"}}

Index change

What this analysis is. Spectral index change: Change of a spectral index (NDVI, NDWI, NDBI, NBR …) or a band expression between two images: before, after and difference rasters, decrease / increase classes beyond the threshold with polygons and areas; NBR also gives dNBR with burn severity classes 24.

Why it was run on this data. Index change between Site progress scene 2026-03-01 (2026-03-01) and Site progress scene 2026-09-01 (2026-09-01): the changed area, with the changed places as polygons 25.

What it found. Index change: Spectral index change: created NDVI change Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m) 26. plus 1 more raster layer(s) 26. and layer NDVI change polygons with 1078 feature(s) 26. mean before 0.08239; mean after 0.08535; mean difference 0.002955; decrease ha 9.00; increase ha 15.70 26. It reported stats, polygons 1,078; stats, mean after 0.08535; stats, decrease ha 9; stats, increase ha 15.7; stats, mean before 0.08239; stats, masked pixels 0 26.

Findings

• Evidenced: Index change: Spectral index change: created NDVI change Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m) 26.
• Evidenced: plus 1 more raster layer(s) 26.
• Evidenced: and layer NDVI change polygons with 1078 feature(s) 26.
• Evidenced: mean before 0.08239; mean after 0.08535; mean difference 0.002955; decrease ha 9.00; increase ha 15.70 26.
• Evidenced: It reported stats, polygons 1,078; stats, mean after 0.08535; stats, decrease ha 9; stats, increase ha 15.7; stats, mean before 0.08239; stats, masked pixels 0 26.

Tool im_index_change; parameters {"after_id": "Site progress scene 2026-09-01 (0e506696)", "polygons": true, "before_id": "Site progress scene 2026-03-01 (44bcba49)"}

Compute change raster

What this analysis is. Compute change raster: Pixel value or spectral change between two co-registered images: difference, relative difference, absolute difference, log ratio (radar, dB), Euclidean spectral distance, spectral angle (radians) or the band with most change 27.

Why it was run on this data. A change raster between Site progress scene 2026-03-01 (2026-03-01) and Site progress scene 2026-09-01 (2026-09-01): where and how much the pixels changed 28.

What it found. Compute change raster: Compute change raster: created Difference Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m) 29. mean -0.001083; std 0.01804; min -0.134; max 0.091; masked pixels 0 29. It reported stats, max 0.091; stats, min -0.134; stats, std 0.01804; stats, mean -0.001083; stats, masked pixels 0; elapsed ms 115 29.

Findings

• Evidenced: Compute change raster: Compute change raster: created Difference Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m) 29.
• Evidenced: mean -0.001083; std 0.01804; min -0.134; max 0.091; masked pixels 0 29.
• Evidenced: It reported stats, max 0.091; stats, min -0.134; stats, std 0.01804; stats, mean -0.001083; stats, masked pixels 0; elapsed ms 115 29.

Tool im_compute_change_raster; parameters {"after_id": "Site progress scene 2026-09-01 (0e506696)", "before_id": "Site progress scene 2026-03-01 (44bcba49)"}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 30. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 30.

Why it was run on this data. 900 points in Inspections and defects: a density surface shows where they concentrate 31.

What it found. The calculation of density using Inspections and defects 9 generated a result containing 976 features 32. The maximum density calculated was 7,380 per km2 32. This process was completed with an elapsed time of 36 ms 32 and incurred a cost of 0.068 32.

Findings

• Evidenced: Calculate density on Inspections and defects: Calculate density: 1 layer saved: Calculate density: Inspections and defects (976 features) 932.
• Evidenced: It reported result, count 976; result, elapsed ms 36; result, bandwidth m 200; result, cell size m 50; result, excluded null 0; result, max density per km2 7,380 32.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Inspections and defects (2d513b03)"}, "bandwidth_m": 200.0, "cell_size_m": 50.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 30. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 30.

Why it was run on this data. 270 points in Settlement monitoring: a density surface shows where they concentrate 33.

What it found. Calculate density on Settlement monitoring: Calculate density: 1 layer saved: Calculate density: Settlement monitoring (806 features) 1234. It reported result, count 806; result, elapsed ms 22; result, bandwidth m 100; result, cell size m 20; result, excluded null 0; result, max density per km2 3,721 34.

Findings

• Evidenced: Calculate density on Settlement monitoring: Calculate density: 1 layer saved: Calculate density: Settlement monitoring (806 features) 1234.
• Evidenced: It reported result, count 806; result, elapsed ms 22; result, bandwidth m 100; result, cell size m 20; result, excluded null 0; result, max density per km2 3,721 34.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Settlement monitoring (425841af)"}, "bandwidth_m": 100.0, "cell_size_m": 20.0}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 35. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 35.

Why it was run on this data. Count Inspections and defects inside each feature of Work zones by type; the counts map as a choropleth (natural breaks, sequential colors) 36.

What it found. Summarize within on Inspections and defects, Work zones: Summarize within: 1 layer saved: Summarize within: Work zones (8 features) 92337. It reported result, count 8; result, elapsed ms 1; billing, cost 0.02816; billing, list price 0.02816; billing, upstream cost 0 37.

Findings

• Evidenced: Summarize within on Inspections and defects, Work zones: Summarize within: 1 layer saved: Summarize within: Work zones (8 features) 92337.
• Evidenced: It reported result, count 8; result, elapsed ms 1; billing, cost 0.02816; billing, list price 0.02816; billing, upstream cost 0 37.

Tool gp_summarize_within; parameters {"stats": [], "project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Inspections and defects (2d513b03)"}, "group_by": "type", "polygons": {"layer": "Work zones (a87c9da1)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 35. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 35.

Why it was run on this data. Count Inspections and defects inside each feature of Work zones by type; the counts map as a choropleth (natural breaks, sequential colors) 36.

What it found. Summarize within on Settlement monitoring, Work zones: Summarize within: 1 layer saved: Summarize within: Work zones (8 features) 122338. It reported result, count 8; result, elapsed ms 0; billing, cost 0.01558; billing, list price 0.01558; billing, upstream cost 0 38.

Findings

• Evidenced: Summarize within on Settlement monitoring, Work zones: Summarize within: 1 layer saved: Summarize within: Work zones (8 features) 122338.
• Evidenced: It reported result, count 8; result, elapsed ms 0; billing, cost 0.01558; billing, list price 0.01558; billing, upstream cost 0 38.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "settlement_mm"}], "project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Settlement monitoring (425841af)"}, "polygons": {"layer": "Work zones (a87c9da1)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 35. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 35.

Why it was run on this data. Count Inspections and defects inside each feature of Work zones by type; the counts map as a choropleth (natural breaks, sequential colors) 36.

What it found. Summarize within on Tower cranes, Work zones: Summarize within: 1 layer saved: Summarize within: Work zones (8 features) 233940. It reported result, count 8; result, elapsed ms 0; billing, cost 0.01026; billing, list price 0.01026; billing, upstream cost 0 39.

Findings

• Evidenced: Summarize within on Tower cranes, Work zones: Summarize within: 1 layer saved: Summarize within: Work zones (8 features) 233940.
• Evidenced: It reported result, count 8; result, elapsed ms 0; billing, cost 0.01026; billing, list price 0.01026; billing, upstream cost 0 39.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "radius_m"}], "project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Tower cranes (a6101de1)"}, "polygons": {"layer": "Work zones (a87c9da1)"}}

Zonal statistics

What this analysis is. Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON 41. In ArcGIS it is Zonal Statistics as Table (polygons) 41.

Why it was run on this data. Statistics of Site terrain model (DEM) inside each feature of Work zones 42.

What it found. The analysis utilized the Zonal statistics tool rs_zonal_statistics 43. This tool processed 8 row(s) of zonal statistics 43. The resulting analysis covered a total of 8 zones 43.

Findings

• Evidenced: Zonal statistics: Zonal statistics: 8 row(s) 43.
• Evidenced: zones 8 43.

Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Work zones (a87c9da1)", "raster_id": "Site terrain model (DEM) (0c6d4acd)"}

Zonal statistics

What this analysis is. Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON 41. In ArcGIS it is Zonal Statistics as Table (polygons) 41.

Why it was run on this data. Statistics of Site terrain model (DEM) inside each feature of Work zones 42.

What it found. The zonal statistics generated an output containing 1 row(s) spanning 1 zone 44. The analysis confirmed that the count of zones was 1 and the count of features was 1 44. The process completed its execution in 31 elapsed ms 44.

Findings

• Evidenced: Zonal statistics: Zonal statistics: 1 row(s) 44.
• Evidenced: zones 1 44.
• Evidenced: It reported stats, zones 1; features, count 1; elapsed ms 31 44.

Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Site boundary (3902997c)", "raster_id": "Site terrain model (DEM) (0c6d4acd)"}

Slope

What this analysis is. Slope: Horn slope in degrees or percent, with the share of ground in six grade classes 45. In ArcGIS it is Slope (Spatial Analyst); in QGIS, Slope (GDAL DEM) 45.

Why it was run on this data. Slope of the Site terrain model (DEM) surface 46.

What it found. The slope analysis over 7.50 km2 revealed a mean of 0.36 degrees and a maximum slope of 18.73 degrees; specifically, 1.19 of the ground was steeper than 15% 47. This analysis utilized the Site terrain model (DEM) as its core input 48. The resulting slope grid was defined by 500 rows and 600 columns 47.

Findings

• Evidenced: Slope on Site terrain model (DEM): Slope over 7.50 km2: mean 0.36 degrees, max 18.73 degrees; 1% of the ground is steeper than 15% 4748.
• Evidenced: DEM: Site terrain model (DEM) 4748.
• Evidenced: Verified: 2 checks passed 4748.
• Evidenced: It reported dem, cols 600; dem, rows 500; dem, cell m 5; dem, relief m 20.94; dem, elev max m 10.14; dem, elev min m -10.8 47.

Tool tr_slope; parameters {"dem": {"layer": "Site terrain model (DEM) (0c6d4acd)"}, "project": "d4e5404f-06e4-4689-a539-31a71b8348eb"}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 49. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 49.

Why it was run on this data. DBSCAN finds dense groups of Inspections and defects and separates noise 50.

What it found. Find point clusters on Inspections and defects: Find point clusters: 2 layers saved: Find point clusters: Inspections and defects (900 features); Find point clusters: cluster hulls (7 features) 951. It reported result, count 900; result, noise 43; result, distance m 100; result, elapsed ms 18; result, min points 20; billing, cost 0.068 51.

Findings

• Evidenced: Find point clusters on Inspections and defects: Find point clusters: 2 layers saved: Find point clusters: Inspections and defects (900 features); Find point clusters: cluster hulls (7 features) 951.
• Evidenced: It reported result, count 900; result, noise 43; result, distance m 100; result, elapsed ms 18; result, min points 20; billing, cost 0.068 51.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Inspections and defects (2d513b03)"}, "distance_m": 100.0, "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 49. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 49.

Why it was run on this data. DBSCAN finds dense groups of Settlement monitoring and separates noise 52.

What it found. Find point clusters on Settlement monitoring: Find point clusters: 2 layers saved: Find point clusters: Settlement monitoring (270 features); Find point clusters: cluster hulls (19 features) 1253. It reported result, count 270; result, noise 0; result, distance m 50; result, elapsed ms 8; result, min points 7; billing, cost 0.0554 53.

Findings

• Evidenced: Find point clusters on Settlement monitoring: Find point clusters: 2 layers saved: Find point clusters: Settlement monitoring (270 features); Find point clusters: cluster hulls (19 features) 1253.
• Evidenced: It reported result, count 270; result, noise 0; result, distance m 50; result, elapsed ms 8; result, min points 7; billing, cost 0.0554 53.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Settlement monitoring (425841af)"}, "distance_m": 50.0, "min_points": 7}

Nearest neighbor index

What this analysis is. Average nearest neighbor: Clark-Evans ratio: clustered, dispersed or random 54. In ArcGIS it is Average Nearest Neighbor; in QGIS, native:nearestneighbouranalysis 54.

Why it was run on this data. The average nearest neighbor ratio says whether Inspections and defects are clustered, random or dispersed 55.

What it found. Nearest neighbor index on Inspections and defects: Average nearest neighbor: done; no map output 956. It reported result, n 900; result, p value 0; result, z score -36.26; result, elapsed ms 4; result, study area m2 2,804,596; result, nearest neighbor ratio 0.3682 56.

Findings

• Evidenced: Nearest neighbor index on Inspections and defects: Average nearest neighbor: done; no map output 956.
• Evidenced: It reported result, n 900; result, p value 0; result, z score -36.26; result, elapsed ms 4; result, study area m2 2,804,596; result, nearest neighbor ratio 0.3682 56.

Tool gp_nearest_neighbor_index; parameters {"project": "d4e5404f-06e4-4689-a539-31a71b8348eb", "features": {"layer": "Inspections and defects (2d513b03)"}}

Zonal statistics

What this analysis is. Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON 41. In ArcGIS it is Zonal Statistics as Table (polygons) 41.

Why it was run on this data. Statistics of Site progress scene 2026-09-01 inside each feature of Work zones 57.

What it found. The analysis utilized the Zonal statistics tool, specifically rs_zonal_statistics 58. The summary generated by the tool states the completion of zonal statistics 58. These results indicate there are 8 row(s) recorded 58. Moreover, the analysis covered a total of 8 zones 58.

Findings

• Evidenced: Zonal statistics: Zonal statistics: 8 row(s) 58.
• Evidenced: zones 8 58.

Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Work zones (a87c9da1)", "raster_id": "Site progress scene 2026-09-01 (0e506696)"}

Zonal statistics

What this analysis is. Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON 41. In ArcGIS it is Zonal Statistics as Table (polygons) 41.

Why it was run on this data. Statistics of Site progress scene 2026-09-01 inside each feature of Work zones 57.

What it found. The analysis section utilized the rs_zonal_statistics tool for the first step 59. The zonal statistics report indicates 1 row(s) analyzed across 1 zone 59.

Findings

• Evidenced: Zonal statistics: Zonal statistics: 1 row(s) 59.
• Evidenced: zones 1 59.
• Evidenced: It reported stats, zones 1; features, count 1; elapsed ms 35 59.

Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Site boundary (3902997c)", "raster_id": "Site progress scene 2026-09-01 (0e506696)"}

Zonal statistics

What this analysis is. Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON 41. In ArcGIS it is Zonal Statistics as Table (polygons) 41.

Why it was run on this data. Statistics of Site progress scene 2026-09-01 inside each feature of Work zones 57.

What it found. Zonal statistics: Zonal statistics: 8 row(s) 60. zones 8 60.

Findings

• Evidenced: Zonal statistics: Zonal statistics: 8 row(s) 60.
• Evidenced: zones 8 60.

Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Work zones (a87c9da1)", "raster_id": "Site progress scene 2026-03-01 (44bcba49)"}

Index change

What this analysis is. Spectral index change: Change of a spectral index (NDVI, NDWI, NDBI, NBR …) or a band expression between two images: before, after and difference rasters, decrease / increase classes beyond the threshold with polygons and areas; NBR also gives dNBR with burn severity classes 24.

Why it was run on this data. Index change between Site progress scene 2026-03-01 (2026-03-01) and Site progress scene 2026-09-01 (2026-09-01): the changed area, with the changed places as polygons 25.

What it found. Index change: Spectral index change: created NDVI change Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m) 61. plus 1 more raster layer(s) 61. and layer NDVI change polygons with 1078 feature(s) 61. mean before 0.08239; mean after 0.08535; mean difference 0.002955; decrease ha 9.00; increase ha 15.70 61. It reported stats, polygons 1,078; stats, mean after 0.08535; stats, decrease ha 9; stats, increase ha 15.7; stats, mean before 0.08239; stats, masked pixels 0 61.

Findings

• Evidenced: Index change: Spectral index change: created NDVI change Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m) 61.
• Evidenced: plus 1 more raster layer(s) 61.
• Evidenced: and layer NDVI change polygons with 1078 feature(s) 61.
• Evidenced: mean before 0.08239; mean after 0.08535; mean difference 0.002955; decrease ha 9.00; increase ha 15.70 61.
• Evidenced: It reported stats, polygons 1,078; stats, mean after 0.08535; stats, decrease ha 9; stats, increase ha 15.7; stats, mean before 0.08239; stats, masked pixels 0 61.

Tool im_index_change; parameters {"after_id": "Site progress scene 2026-09-01 (0e506696)", "polygons": true, "before_id": "Site progress scene 2026-03-01 (44bcba49)"}

Compute change raster

What this analysis is. Compute change raster: Pixel value or spectral change between two co-registered images: difference, relative difference, absolute difference, log ratio (radar, dB), Euclidean spectral distance, spectral angle (radians) or the band with most change 27.

Why it was run on this data. A change raster between Site progress scene 2026-03-01 (2026-03-01) and Site progress scene 2026-09-01 (2026-09-01): where and how much the pixels changed 28.

What it found. Compute change raster: Compute change raster: created Difference Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m) 62. mean -0.001083; std 0.01804; min -0.134; max 0.091; masked pixels 0 62. It reported stats, max 0.091; stats, min -0.134; stats, std 0.01804; stats, mean -0.001083; stats, masked pixels 0; elapsed ms 151 62.

Findings

• Evidenced: Compute change raster: Compute change raster: created Difference Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m) 62.
• Evidenced: mean -0.001083; std 0.01804; min -0.134; max 0.091; masked pixels 0 62.
• Evidenced: It reported stats, max 0.091; stats, min -0.134; stats, std 0.01804; stats, mean -0.001083; stats, masked pixels 0; elapsed ms 151 62.

Tool im_compute_change_raster; parameters {"method": "difference", "after_id": "Site progress scene 2026-09-01 (0e506696)", "before_id": "Site progress scene 2026-03-01 (44bcba49)"}

Maps and charts

The run also made 24 map image(s) 636465666768697071727374757677787980818283848586 and 23 chart(s) 87888990919293949596979899100101102103104105106107108109.

What the map shows. The map is titled "Emerging spots: Inspections and defects" and appears to be a spatial analysis designed to identify unusual clustering of inspections or defects. According to the legend, the analysis result indicates that "No pattern detected" for emerging hot spots in this area. Though the map displays various adjacent polygons filled with gray tones, the formal analysis provided does not show any specific hot spots or significant geographic patterns of defects. Overall, the image reports a lack of detectable spatial 63

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 week by dataset. 160 rows id 9f2bb274-af82-407c-9900-4bc77773b75b
2. 2Dataset: Inspections and defects. vector, 900 rows id 3a360dc7-0b8b-4224-98e8-aba6b17ab2e0
3. 3Dataset: Settlement monitoring. vector, 270 rows id 806d8b39-f228-4065-a394-d600f3812c49
4. 4Dataset: Daily progress by zone. table, 1264 rows id 85f34f63-e5d5-45af-b627-04240488c239
5. 5Dataset: Deliveries log. table, 400 rows id 25ca0509-fbcd-465c-ad7c-3d5ff8495ec0
6. 6Catalog: 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
7. 7Plan: Space time hot spots of Inspections and defects (the Start plan's candidate c_daa895129d49). Inspections and defects carry dates in 'inspected_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_daa895129d49
8. 8Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Inspections and defects (28 features). id c3736b48-18ef-4b5f-917c-a3f44e49df09:0
9. 9Layer: Inspections and defects. 900 features id 2d513b03-8124-4d94-9ddd-e0e8d35ed9ae
10. 10Plan: Space time hot spots of Settlement monitoring (the Start plan's candidate c_a657847729a2). Settlement monitoring carry dates in 'month': emerging hot spot analysis finds new, intensifying and fading hot spots per week. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_a657847729a2
11. 11Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: Settlement monitoring (10 features). id c3736b48-18ef-4b5f-917c-a3f44e49df09:1
12. 12Layer: Settlement monitoring. 270 features id 425841af-4239-4ea3-bb9a-66322270d86d
13. 13Catalog: 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
14. 14Plan: Near repeat of Inspections and defects (the Start plan's candidate c_dab5834d48f7). Near repeat analysis tests whether Inspections and defects close in space also follow each other within 21 days. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_dab5834d48f7
15. 15Tool result: gp_near_repeat, step 2. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Inspections and defects (900 features). id c3736b48-18ef-4b5f-917c-a3f44e49df09:2
16. 16Catalog: 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
17. 17Plan: Hot spots of Inspections and defects (the Start plan's candidate c_a14382cda44d). 900 points: Getis-Ord Gi* tests whether the concentrations in Inspections and defects are statistically significant. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_a14382cda44d
18. 18Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Inspections and defects (317 features). id b3ca19ef-101a-41ac-92ce-bbbc2c003a80:0
19. 19Plan: Hot spots of Settlement monitoring (the Start plan's candidate c_40b66c08f429). 270 points: Getis-Ord Gi* tests whether the concentrations in Settlement monitoring are statistically significant. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_40b66c08f429
20. 20Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Settlement monitoring (87 features). id b3ca19ef-101a-41ac-92ce-bbbc2c003a80:1
21. 21Plan: Hot spots of Work zones (the Start plan's candidate c_c596ca1fffb2). Gi* hot spots of 'pct_complete' across the areas. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_c596ca1fffb2
22. 22Tool result: gp_hot_spots, step 2. Hot spot analysis: 1 layer saved: Hot spot analysis: Work zones (8 features). id b3ca19ef-101a-41ac-92ce-bbbc2c003a80:2
23. 23Layer: Work zones. 8 features id a87c9da1-d753-4f2a-ab7d-b864dabbbe8f
24. 24Catalog: Spectral index change (analysis catalog, im_index_change). Change of a spectral index (NDVI, NDWI, NDBI, NBR ...) or a band expression between two images: before, after and difference rasters, decrease / increase classes beyond the threshold with polygons and areas; NBR also gives dNBR with burn severity classes. id im_index_change
25. 25Plan: Index change of Site progress scene 2026-03-01 and Site progress scene 2026-09-01 (the Start plan's candidate c_be7290b1b94a). Index change between Site progress scene 2026-03-01 (2026-03-01) and Site progress scene 2026-09-01 (2026-09-01): the changed area, with the changed places as polygons. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_be7290b1b94a
26. 26Tool result: im_index_change, step 0. Spectral index change: created NDVI change Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m). plus 1 more raster layer(s). and layer NDVI change polygons with 1078 feature(s). mean before 0.08239; mean after 0.08535; mean difference 0.002955; decrease ha 9.0 id 7ffbeb87-0a83-4448-a1e2-6acaf335bc0c:0
27. 27Catalog: Compute change raster (analysis catalog, im_compute_change_raster). Pixel value or spectral change between two co-registered images: difference, relative difference, absolute difference, log ratio (radar, dB), Euclidean spectral distance, spectral angle (radians) or the band with most change. Clouds and shadows are masked when the images have id im_compute_change_raster
28. 28Plan: Compute change raster of Site progress scene 2026-03-01 and Site progress scene 2026-09-01 (the Start plan's candidate c_7e48e7dad7f6). A change raster between Site progress scene 2026-03-01 (2026-03-01) and Site progress scene 2026-09-01 (2026-09-01): where and how much the pixels changed. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_7e48e7dad7f6
29. 29Tool result: im_compute_change_raster, step 1. Compute change raster: created Difference Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m). mean -0.001083; std 0.01804; min -0.134; max 0.091; masked pixels 0. id 7ffbeb87-0a83-4448-a1e2-6acaf335bc0c:1
30. 30Catalog: 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
31. 31Plan: Kernel density of Inspections and defects (the Start plan's candidate c_b027c874c560). 900 points in Inspections and defects: a density surface shows where they concentrate. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_b027c874c560
32. 32Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Inspections and defects (976 features). id b4af9d8c-897b-4cb3-a492-826d8edde822:0
33. 33Plan: Kernel density of Settlement monitoring (the Start plan's candidate c_d14b64b8c3f9). 270 points in Settlement monitoring: a density surface shows where they concentrate. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_d14b64b8c3f9
34. 34Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Settlement monitoring (806 features). id b4af9d8c-897b-4cb3-a492-826d8edde822:1
35. 35Catalog: 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
36. 36Plan: Inspections and defects per feature of Work zones (the Start plan's candidate c_9b02c6bcc3d7). Count Inspections and defects inside each feature of Work zones by type; the counts map as a choropleth (natural breaks, sequential colors). id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_9b02c6bcc3d7
37. 37Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Work zones (8 features). id c58995ca-42b1-4b4e-86fb-a88fb13acf0b:0
38. 38Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Work zones (8 features). id c58995ca-42b1-4b4e-86fb-a88fb13acf0b:1
39. 39Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Work zones (8 features). id c58995ca-42b1-4b4e-86fb-a88fb13acf0b:2
40. 40Layer: Tower cranes. 4 features id a6101de1-0f97-42bb-a976-2736f8fc42cd
41. 41Catalog: 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
42. 42Plan: Zonal statistics of Site terrain model (DEM) and Work zones (the Start plan's candidate c_2be6fba28be6). Statistics of Site terrain model (DEM) inside each feature of Work zones. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_2be6fba28be6
43. 43Tool result: rs_zonal_statistics, step 0. Zonal statistics: 8 row(s). zones 8. id b9751382-9c63-45f7-aeeb-df532c540b48:0
44. 44Tool result: rs_zonal_statistics, step 1. Zonal statistics: 1 row(s). zones 1. id b9751382-9c63-45f7-aeeb-df532c540b48:1
45. 45Catalog: 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
46. 46Plan: Slope of Site terrain model (DEM) (the Start plan's candidate c_9bcc9b88376e). Slope of the Site terrain model (DEM) surface. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_9bcc9b88376e
47. 47Tool result: tr_slope, step 2. Slope over 7.50 km2: mean 0.36 degrees, max 18.73 degrees; 1% of the ground is steeper than 15%. DEM: Site terrain model (DEM). Verified: 2 checks passed. id b9751382-9c63-45f7-aeeb-df532c540b48:2
48. 48Layer: Site terrain model (DEM). 0 features id 0c6d4acd-4d8d-4cf4-b654-e096f06f71ff
49. 49Catalog: 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
50. 50Plan: Cluster points of Inspections and defects (the Start plan's candidate c_6339b071c51c). DBSCAN finds dense groups of Inspections and defects and separates noise. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_6339b071c51c
51. 51Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Inspections and defects (900 features); Find point clusters: cluster hulls (7 features). id a18ea698-d2a7-4e8a-b749-f525e603592f:0
52. 52Plan: Cluster points of Settlement monitoring (the Start plan's candidate c_4199c930b59b). DBSCAN finds dense groups of Settlement monitoring and separates noise. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_4199c930b59b
53. 53Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Settlement monitoring (270 features); Find point clusters: cluster hulls (19 features). id a18ea698-d2a7-4e8a-b749-f525e603592f:1
54. 54Catalog: 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
55. 55Plan: Nearest neighbor index of Inspections and defects (the Start plan's candidate c_16c101371f91). The average nearest neighbor ratio says whether Inspections and defects are clustered, random or dispersed. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_16c101371f91
56. 56Tool result: gp_nearest_neighbor_index, step 2. Average nearest neighbor: done; no map output. id a18ea698-d2a7-4e8a-b749-f525e603592f:2
57. 57Plan: Zonal statistics of Site progress scene 2026-09-01 and Work zones (the Start plan's candidate c_fbbb0c518f13). Statistics of Site progress scene 2026-09-01 inside each feature of Work zones. id a0327f1f-63f3-4f6a-b834-207b8f9700a5:c_fbbb0c518f13
58. 58Tool result: rs_zonal_statistics, step 0. Zonal statistics: 8 row(s). zones 8. id f697e2f1-f28e-4342-8c96-78425e7ef30c:0
59. 59Tool result: rs_zonal_statistics, step 1. Zonal statistics: 1 row(s). zones 1. id f697e2f1-f28e-4342-8c96-78425e7ef30c:1
60. 60Tool result: rs_zonal_statistics, step 2. Zonal statistics: 8 row(s). zones 8. id f697e2f1-f28e-4342-8c96-78425e7ef30c:2
61. 61Tool result: im_index_change, step 0. Spectral index change: created NDVI change Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m). plus 1 more raster layer(s). and layer NDVI change polygons with 1078 feature(s). mean before 0.08239; mean after 0.08535; mean difference 0.002955; decrease ha 9.0 id a50cf479-ac3c-4eaf-ba13-51b573e1a936:0
62. 62Tool result: im_compute_change_raster, step 1. Compute change raster: created Difference Site progress scene 2026-03-01 to Site progress scene 2026-09-01 (300 x 250 cells, 10.00 m). mean -0.001083; std 0.01804; min -0.134; max 0.091; masked pixels 0. id a50cf479-ac3c-4eaf-ba13-51b573e1a936:1
63. 63Rendering: Map: Emerging hot spots: Inspections and defects id 44c54df1-3ef4-4f48-bfdf-373e1dcae072
64. 64Rendering: Map: Emerging hot spots: Settlement monitoring id cc8b26b5-2189-437a-ae60-bb5505e01bb3
65. 65Rendering: Map: Repeat and near repeat classification: Inspections and defects id fa93e6d7-4351-4807-954d-94f0348da021
66. 66Rendering: Map: Hot spot analysis: Inspections and defects id f6d190a7-d4f9-4493-be19-1be9956826b5
67. 67Rendering: Map: Hot spot analysis: Settlement monitoring id fc9ccdba-f38f-47eb-997b-8a386b4ddb71
68. 68Rendering: Map: Hot spot analysis: Work zones id efda6f0a-8900-45e7-97f8-c2c08b7ee7a9
69. 69Rendering: Map: NDVI change Site progress scene 2026-03-01 to Site progress scene 2026-09-01 id 33bfa5c4-92ac-43df-86aa-b50106359dd2
70. 70Rendering: Map: NDVI decrease / increase id f22e9812-824b-4eb6-8554-f8a7732bce15
71. 71Rendering: Map: NDVI change polygons id 4fe5d947-63bf-4602-a7df-f7fceecf47aa
72. 72Rendering: Map: Difference Site progress scene 2026-03-01 to Site progress scene 2026-09-01 id 53be4756-2fc4-4c8f-8dff-b832cdf6d17a
73. 73Rendering: Map: Calculate density: Inspections and defects id 397fd56d-b01c-410e-84ef-a8e0aa7bcc27
74. 74Rendering: Map: Calculate density: Settlement monitoring id f12560e9-420f-49e4-a19c-4fe1b9459ba8
75. 75Rendering: Map: Summarize within: Work zones id 7d4caaef-9484-48dc-9123-64a0072860b6
76. 76Rendering: Map: Summarize within: Work zones id f7131359-06b3-4304-8a0c-6106da84afdc
77. 77Rendering: Map: Summarize within: Work zones id 43631e4b-9ad3-4475-8715-3bf283b8f106
78. 78Rendering: Map: Slope (degrees) id c3664af4-8bb7-47c6-abf3-7aceea809124
79. 79Rendering: Map: Find point clusters: Inspections and defects id cc3d7893-ab41-4034-a602-437ec73a0b31
80. 80Rendering: Map: Find point clusters: cluster hulls id 7d94af92-e519-4c82-8a96-ffde80cf991c
81. 81Rendering: Map: Find point clusters: Settlement monitoring id 96e2071a-d82a-40e3-9a4b-6cf33279bbbb
82. 82Rendering: Map: Find point clusters: cluster hulls id 2c3db94d-30bb-47b3-9454-ae7ef2846a1d
83. 83Rendering: Map: Site Progress Scene Index Analysis NDVI change Site progress scene 2026-03-01 to Site progress scene 2026-09-01 id 292b34c5-61b7-475e-96fe-f7b859c107ed
84. 84Rendering: Map: Site Progress Scene Index Analysis NDVI decrease / increase id 01e5e6a9-49bd-4ac6-b2e8-ddda771b5582
85. 85Rendering: Map: Site Progress Scene Index Analysis NDVI change polygons id 86d28d82-9108-431d-9ad3-a2feeec950c7
86. 86Rendering: Map: Site Progress Scene Index Analysis Difference Site progress scene 2026-03-01 to Site progress scene 2026-09-01 id 7a29f585-6966-47f6-9e4c-058f8691bb28
87. 87Rendering: Chart: Settlement monitoring over time (month). 36 rows id 9ead02da-348b-44ba-8ee7-269c1c2d63f3
88. 88Rendering: Chart: Daily progress by zone over time (date). 28 rows id 39d65924-a112-4224-9302-9115ac3dd0da
89. 89Rendering: Chart: Deliveries log over time (date). 140 rows id 8e7bf61d-a5a0-42f3-b502-875288bf1e29
90. 90Rendering: Chart: Emerging hot spots: Inspections and defects. 6 rows id ef7d42dd-f3fa-4e99-a910-15f7ec83297b
91. 91Rendering: Chart: Emerging hot spots: Settlement monitoring. 5 rows id dd35971b-2787-4a8d-9a43-aba57f75d47e
92. 92Rendering: Chart: Repeat and near repeat classification: Inspections and defects. 96 rows id 8ca20871-10d1-40b1-834b-38cd1849b4b8
93. 93Rendering: Chart: Hot spot analysis: Inspections and defects. 15 rows id 12698a56-ae20-4fae-aedd-bd8d3179f94f
94. 94Rendering: Chart: Hot spot analysis: Settlement monitoring. 13 rows id 02c89ac1-1d31-4844-914d-6077abf66479
95. 95Rendering: Chart: Hot spot analysis: Work zones. 5 rows id 9177a78d-8b65-410f-af6d-d56ceda4a636
96. 96Rendering: Chart: NDVI change polygons. 12 rows id 61c75041-5982-429d-ac74-d9605047def1
97. 97Rendering: Chart: Calculate density: Inspections and defects. 11 rows id abcf7e57-ebc5-4630-98dd-65228e550aa6
98. 98Rendering: Chart: Calculate density: Settlement monitoring. 11 rows id 0aa266b1-0ecb-4312-8fb6-a3063dc7c7e5
99. 99Rendering: Chart: Summarize within: Work zones. 5 rows id 5ee14e18-ab1d-46fd-8640-7feb7d508da7
100. 100Rendering: Chart: Summarize within: Work zones. 5 rows id 67e6d938-9903-48f6-9a48-91d47bee8a02
101. 101Rendering: Chart: Summarize within: Work zones. 5 rows id 2a7e5e5b-93c2-4898-8a7b-d8fb4045b928
102. 102Rendering: Chart: Find point clusters: Inspections and defects. 8 rows id 5fd0ca87-fab6-488d-b580-707c3e546506
103. 103Rendering: Chart: Find point clusters: cluster hulls. 7 rows id 0bbf6b08-675a-421e-a2b9-10bda913071e
104. 104Rendering: Chart: Find point clusters: Settlement monitoring. 19 rows id 668ea7e9-018e-4e3a-8dc8-d454151ecdba
105. 105Rendering: Chart: Find point clusters: cluster hulls. 19 rows id cce16250-17dc-44a0-90b3-1effbf06eee0
106. 106Rendering: Chart: NDVI change polygons. 12 rows id bf7e96fd-8ac6-4ea5-abba-531dddd6c242
107. 107Rendering: Chart: Find point clusters: Settlement monitoring over time. 36 rows id 8741d3df-a209-4ea2-b13f-828908da90f7
108. 108Rendering: Chart: Find point clusters: Inspections and defects over time. 96 rows id 1dd42be8-67ac-47fa-bdce-1ee0deeb71f1
109. 109Rendering: Chart: Repeat and near repeat classification: Inspections and defects over time. 96 rows id b8a63915-574e-487c-bc0e-b1f8099863a6
110. 110Dataset: Site boundary. vector, 1 rows id bb559260-1c80-48a4-b1cf-f8d45912c652
111. 111Dataset: Work zones. vector, 8 rows id 0e3a49ca-6c53-4c6a-9921-e5f541953931
112. 112Dataset: Site progress scene 2026-03-01. raster, ? rows id d5b68dc5-16d9-4cfd-968a-c8903d998eed
113. 113Dataset: Site progress scene 2026-09-01. raster, ? rows id 4032b2ca-3632-4bc3-b9ad-60d3a59b951f
114. 114Dataset: Site terrain model (DEM). raster, ? rows id 454c0b82-bbf9-4c73-9e4c-e939f6a3fb53
115. 115Dataset: Site roads. vector, 3 rows id 0f387307-aec2-41f8-92eb-4838eb3282bb
116. 116Dataset: Tower cranes. vector, 4 rows id 9f4ea7c1-9e1c-47e8-8184-8cb05533c4ac
117. 117Dataset: Monthly progress report. document, ? rows id 026a6911-73c9-4c20-aaf9-809a17446401
118. 118Record: Sample: Construction, Al Reem Island id d4e5404f-06e4-4689-a539-31a71b8348eb
119. 119Layer: Emerging hot spots: Inspections and defects. 28 features id b6e6a183-1b27-496c-a678-9f8df4e4b081
120. 120Layer: Emerging hot spots: Settlement monitoring. 10 features id 97e34fec-6ba0-4f5b-aa10-f76b28b24d30
121. 121Layer: Repeat and near repeat classification: Inspections and defects. 900 features id f23bce2c-8508-4e9c-8458-fd8faf8ec029
122. 122Layer: Hot spot analysis: Inspections and defects. 317 features id ef4eb056-e817-462a-9733-a58fd43d00cb
123. 123Layer: Hot spot analysis: Settlement monitoring. 87 features id cfbaa203-6871-498f-b688-d75ec0db6fa9
124. 124Layer: Hot spot analysis: Work zones. 8 features id 006e9ce1-b483-4c73-acfb-5f48e1852b8f
125. 125Layer: NDVI change Site progress scene 2026-03-01 to Site progress scene 2026-09-01. 0 features id 771ca68b-d2ee-4ff2-bbba-c4f0576cbae9
126. 126Layer: NDVI decrease / increase. 0 features id 15acb616-2cef-42cf-a5e4-15164cafb21e
127. 127Layer: NDVI change polygons. 1,078 features id 96ff870f-9163-461c-9f71-1cd79c835ecc
128. 128Layer: Difference Site progress scene 2026-03-01 to Site progress scene 2026-09-01. 0 features id b963eb25-56c3-4309-af9d-4e029ccf90c5
129. 129Layer: Calculate density: Inspections and defects. 976 features id 21b40807-b901-4510-b690-3a72503d53bb
130. 130Layer: Calculate density: Settlement monitoring. 806 features id 4c96ec2a-2ff3-470f-a6f4-a4fe2e2c0f31
131. 131Layer: Summarize within: Work zones. 8 features id 61db1d60-2cda-4ce2-ab31-ab3c66cf5242
132. 132Layer: Summarize within: Work zones. 8 features id b32e8596-c2a2-453b-9f24-50829120080e
133. 133Layer: Summarize within: Work zones. 8 features id 9b7216b5-5c39-4fa7-95fc-419178fd4681
134. 134Layer: Slope (degrees). 0 features id 04fe23a2-183b-4b40-99ed-00c0a1c62545
135. 135Layer: Find point clusters: Inspections and defects. 900 features id 483f389f-6f5f-4686-9358-d8ba8dfecc0c
136. 136Layer: Find point clusters: cluster hulls. 7 features id a3ed91a3-93ec-4c1d-8f39-d00038f33711
137. 137Layer: Find point clusters: Settlement monitoring. 270 features id e11e55d7-847b-4fb0-9ce1-b78d3ec3f87c
138. 138Layer: Find point clusters: cluster hulls. 19 features id b5149260-c6f8-4546-8616-94dd97d60029
139. 139Layer: NDVI change polygons. 1,078 features id ac97061d-6bed-44e0-9705-e3ab6f002d53