Mining

Sample: Mining, Newman: report

Project: Sample: Mining, Newman
Goal: Describe the project Sample: Mining, Newman: run the analyses that tell the most about its data, draw every result, and write a cited report.
Run: e02f959c-4f5a-4566-a3dc-239b3095beaa (running, 11 agent run(s))
Generated: 2026-10-18T05:51:00Z, version 1
Model cost: USD 0.0000 over 0 model call(s)

Data used

Timeline

The timeline counts 1,596 dated events in Prism readings, Drill holes, Production by pit and week per month, from January 2024 to September 2026 1234. By dataset: Prism readings 1,200; Drill holes 320; Production by pit and week 76 1. The trend is +5.81 events per month; the second half of the months has 1,431 against 157 in the first (+811.5%) 1.

Busiest months

• June 2026: 320 events 1
• August 2026: 314 events 1
• May 2026: 258 events 1

Quietest months

• August 2024: 3 events 1
• September 2024: 3 events 1
• June 2024: 5 events 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 5. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 5.

Why it was run on this data. 320 points: Getis-Ord Gi* tests whether the concentrations in Drill holes are statistically significant 6.

What it found. Hot spots on Drill holes: Hot spot analysis: 1 layer saved: Hot spot analysis: Drill holes (50 features) 78. It reported result, bins 50; result, count 50; result, elapsed ms 6; result, cell size m 500; result, incidents used 320; result, distance band m 500 7.

Findings

• Evidenced: Hot spots on Drill holes: Hot spot analysis: 1 layer saved: Hot spot analysis: Drill holes (50 features) 78.
• Evidenced: It reported result, bins 50; result, count 50; result, elapsed ms 6; result, cell size m 500; result, incidents used 320; result, distance band m 500 7.

Tool gp_hot_spots; parameters {"project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Drill holes (3fc6780a)"}, "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 5. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 5.

Why it was run on this data. 1200 points: Getis-Ord Gi* tests whether the concentrations in Prism readings are statistically significant 9.

What it found. Hot spots on Prism readings: Hot spot analysis: 1 layer saved: Hot spot analysis: Prism readings (227 features) 1011. It reported result, bins 227; result, count 227; result, elapsed ms 15; result, cell size m 100; result, incidents used 1,200; result, distance band m 100 10.

Findings

• Evidenced: Hot spots on Prism readings: Hot spot analysis: 1 layer saved: Hot spot analysis: Prism readings (227 features) 1011.
• Evidenced: It reported result, bins 227; result, count 227; result, elapsed ms 15; result, cell size m 100; result, incidents used 1,200; result, distance band m 100 10.

Tool gp_hot_spots; parameters {"project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Prism readings (920e4ae7)"}, "aggregation": "hexagon", "cell_size_m": 100.0, "distance_band_m": 100.0}

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 12. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 12.

Why it was run on this data. Drill holes carry dates in 'drilled_on': emerging hot spot analysis finds new, intensifying and fading hot spots per month 13.

What it found. Space time hot spots on Drill holes: Emerging hot spots: 1 layer saved: Emerging hot spots: Drill holes (30 features) 814. It reported result, count 30; result, elapsed ms 5; result, resolution 8; result, features used 320; result, time neighbors 0; billing, cost 0.116 14.

Findings

• Evidenced: Space time hot spots on Drill holes: Emerging hot spots: 1 layer saved: Emerging hot spots: Drill holes (30 features) 814.
• Evidenced: It reported result, count 30; result, elapsed ms 5; result, resolution 8; result, features used 320; result, time neighbors 0; billing, cost 0.116 14.

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

Tool gp_space_time_hot_spots; parameters {"project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Drill holes (3fc6780a)"}, "interval": "month", "bin_shape": "h3", "resolution": 8, "time_field": "drilled_on"}

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 12. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 12.

Why it was run on this data. Prism readings carry dates in 'observed_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week 15.

What it found. Space time hot spots on Prism readings: Emerging hot spots: 1 layer saved: Emerging hot spots: Prism readings (29 features) 1116. It reported result, count 29; result, elapsed ms 5; result, resolution 9; result, features used 1,200; result, time neighbors 0; billing, cost 0.16 16.

Findings

• Evidenced: Space time hot spots on Prism readings: Emerging hot spots: 1 layer saved: Emerging hot spots: Prism readings (29 features) 1116.
• Evidenced: It reported result, count 29; result, elapsed ms 5; result, resolution 9; result, features used 1,200; result, time neighbors 0; billing, cost 0.16 16.

Tool gp_space_time_hot_spots; parameters {"project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Prism readings (920e4ae7)"}, "interval": "week", "bin_shape": "h3", "resolution": 9, "time_field": "observed_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 17. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator 17.

Why it was run on this data. Near repeat analysis tests whether Drill holes close in space also follow each other within 30 days 18.

What it found. The analysis used an input of Drill holes 8 to classify

Findings

• Evidenced: Near repeat on Drill holes: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Drill holes (320 features) 819.
• Evidenced: It reported result, days 30; result, count 320; result, chains 41; result, distance m 200; result, elapsed ms 8; result, pairs linked 61 19.

Tool gp_near_repeat; parameters {"days": 30, "project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Drill holes (3fc6780a)"}, "distance_m": 200.0, "time_field": "drilled_on"}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 20. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 20.

Why it was run on this data. Count Drill holes inside each feature of Geotechnical domains by type; the counts map as a choropleth (natural breaks, sequential colors) 21.

What it found. Summarize within on Drill holes, Geotechnical domains: Summarize within: 1 layer saved: Summarize within: Geotechnical domains (6 features) 82223. It reported result, count 6; result, elapsed ms 1; billing, cost 0.01654; billing, list price 0.01654; billing, upstream cost 0 22.

Findings

• Evidenced: Summarize within on Drill holes, Geotechnical domains: Summarize within: 1 layer saved: Summarize within: Geotechnical domains (6 features) 82223.
• Evidenced: It reported result, count 6; result, elapsed ms 1; billing, cost 0.01654; billing, list price 0.01654; billing, upstream cost 0 22.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "depth_m"}], "project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Drill holes (3fc6780a)"}, "group_by": "type", "polygons": {"layer": "Geotechnical domains (7e9e4801)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 20. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 20.

Why it was run on this data. Count Drill holes inside each feature of Geotechnical domains by type; the counts map as a choropleth (natural breaks, sequential colors) 21.

What it found. Summarize within on Prism readings, Geotechnical domains: Summarize within: 1 layer saved: Summarize within: Geotechnical domains (6 features) 112324. It reported result, count 6; result, elapsed ms 3; billing, cost 0.03414; billing, list price 0.03414; billing, upstream cost 0 24.

Findings

• Evidenced: Summarize within on Prism readings, Geotechnical domains: Summarize within: 1 layer saved: Summarize within: Geotechnical domains (6 features) 112324.
• Evidenced: It reported result, count 6; result, elapsed ms 3; billing, cost 0.03414; billing, list price 0.03414; billing, upstream cost 0 24.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "displacement_mm"}], "project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Prism readings (920e4ae7)"}, "polygons": {"layer": "Geotechnical domains (7e9e4801)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 20. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 20.

Why it was run on this data. Count Drill holes inside each feature of Geotechnical domains by type; the counts map as a choropleth (natural breaks, sequential colors) 21.

What it found. Summarize within on Monitoring prisms, Geotechnical domains: Summarize within: 1 layer saved: Summarize within: Geotechnical domains (6 features) 232526. It reported result, count 6; result, elapsed ms 1; billing, cost 0.01134; billing, list price 0.01134; billing, upstream cost 0 25.

Findings

• Evidenced: Summarize within on Monitoring prisms, Geotechnical domains: Summarize within: 1 layer saved: Summarize within: Geotechnical domains (6 features) 232526.
• Evidenced: It reported result, count 6; result, elapsed ms 1; billing, cost 0.01134; billing, list price 0.01134; billing, upstream cost 0 25.

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

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "elevation_m"}], "project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Monitoring prisms (6e51a43e)"}, "group_by": "wall", "polygons": {"layer": "Geotechnical domains (7e9e4801)"}}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 27. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 27.

Why it was run on this data. DBSCAN finds dense groups of Drill holes and separates noise 28.

What it found. Find point clusters on Drill holes: Find point clusters: 2 layers saved: Find point clusters: Drill holes (320 features); Find point clusters: cluster hulls (10 features) 829. It reported result, count 320; result, noise 93; result, distance m 200; result, elapsed ms 9; result, min points 8; billing, cost 0.0564 29.

Findings

• Evidenced: Find point clusters on Drill holes: Find point clusters: 2 layers saved: Find point clusters: Drill holes (320 features); Find point clusters: cluster hulls (10 features) 829.
• Evidenced: It reported result, count 320; result, noise 93; result, distance m 200; result, elapsed ms 9; result, min points 8; billing, cost 0.0564 29.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Drill holes (3fc6780a)"}, "distance_m": 200.0, "min_points": 8}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 27. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 27.

Why it was run on this data. DBSCAN finds dense groups of Prism readings and separates noise 30.

What it found. The cluster finding process began using the Prism readings as input 11. The gp_find_point_clusters tool was executed, which saved a total of 2 layers 31.

Findings

• Evidenced: Find point clusters on Prism readings: Find point clusters: 2 layers saved: Find point clusters: Prism readings (1200 features); Find point clusters: cluster hulls (60 features) 1131.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Prism readings (920e4ae7)"}, "distance_m": 50.0, "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 27. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 27.

Why it was run on this data. DBSCAN finds dense groups of Monitoring prisms and separates noise 32.

What it found. Find point clusters on Monitoring prisms: Find point clusters: 2 layers saved: Find point clusters: Monitoring prisms (60 features); Find point clusters: cluster hulls (4 features) 2633. It reported result, count 60; result, noise 5; result, distance m 200; result, elapsed ms 2; result, min points 3; billing, cost 0.0512 33.

Findings

• Evidenced: Find point clusters on Monitoring prisms: Find point clusters: 2 layers saved: Find point clusters: Monitoring prisms (60 features); Find point clusters: cluster hulls (4 features) 2633.
• Evidenced: It reported result, count 60; result, noise 5; result, distance m 200; result, elapsed ms 2; result, min points 3; billing, cost 0.0512 33.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Monitoring prisms (6e51a43e)"}, "distance_m": 200.0, "min_points": 3}

Zonal statistics

What this analysis is. Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON 34. In ArcGIS it is Zonal Statistics as Table (polygons) 34.

Why it was run on this data. Statistics of Mine terrain model (DEM) inside each feature of Geotechnical domains 35.

What it found. The analysis involved Zonal statistics using the rs_zonal_statistics tool 36. The tool summary reports the generation of 6 row(s) 36. It is noted that the analysis involved 6 zones 36.

Findings

• Evidenced: Zonal statistics: Zonal statistics: 6 row(s) 36.
• Evidenced: zones 6 36.

Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Geotechnical domains (7e9e4801)", "raster_id": "Mine terrain model (DEM) (d3bacd4c)"}

Zonal statistics

What this analysis is. Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON 34. In ArcGIS it is Zonal Statistics as Table (polygons) 34.

Why it was run on this data. Statistics of Mine terrain model (DEM) inside each feature of Geotechnical domains 35.

What it found. The analysis utilized the Zonal statistics tool, which is also referenced as rs_zonal_statistics 37. This process yielded 7 row(s) 37. The resulting data analyzed 7 zones 37.

Findings

• Evidenced: Zonal statistics: Zonal statistics: 7 row(s) 37.
• Evidenced: zones 7 37.

Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Mine areas (e1015218)", "raster_id": "Mine terrain model (DEM) (d3bacd4c)"}

Slope

What this analysis is. Slope: Horn slope in degrees or percent, with the share of ground in six grade classes 38. In ArcGIS it is Slope (Spatial Analyst); in QGIS, Slope (GDAL DEM) 38.

Why it was run on this data. Slope of the Mine terrain model (DEM) surface 39.

What it found. The slope analysis was conducted over an area of 48.00 km² 40.

Findings

• Evidenced: Slope on Mine terrain model (DEM): Slope over 48.00 km2: mean 2.68 degrees, max 36.74 degrees; 9% of the ground is steeper than 15% 4041.
• Evidenced: DEM: Mine terrain model (DEM) 4041.
• Evidenced: Verified: 2 checks passed 4041.
• Evidenced: It reported dem, cols 400; dem, rows 300; dem, cell m 20; dem, relief m 320.7; dem, elev max m 630.1; dem, elev min m 309.4 40.

Tool tr_slope; parameters {"dem": {"layer": "Mine terrain model (DEM) (d3bacd4c)"}, "project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e"}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 42. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 42.

Why it was run on this data. 320 points in Drill holes: a density surface shows where they concentrate 43.

What it found. The density calculation was performed on 328 drill holes 44. The resulting maximum density recorded was 81.67 per km2 44. The total elapsed time for the calculation was 11 ms 44. Furthermore, the billing cost associated with this service was 0.0564 44.

Findings

• Evidenced: Calculate density on Drill holes: Calculate density: 1 layer saved: Calculate density: Drill holes (328 features) 844.
• Evidenced: It reported result, count 328; result, elapsed ms 11; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 81.67 44.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Drill holes (3fc6780a)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 42. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 42.

Why it was run on this data. 1200 points in Prism readings: a density surface shows where they concentrate 45.

What it found. Calculate density on Prism readings: Calculate density: 1 layer saved: Calculate density: Prism readings (545 features) 1146. It reported result, count 545; result, elapsed ms 18; result, bandwidth m 100; result, cell size m 50; result, excluded null 0; result, max density per km2 2,781 46.

Findings

• Evidenced: Calculate density on Prism readings: Calculate density: 1 layer saved: Calculate density: Prism readings (545 features) 1146.
• Evidenced: It reported result, count 545; result, elapsed ms 18; result, bandwidth m 100; result, cell size m 50; result, excluded null 0; result, max density per km2 2,781 46.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Prism readings (920e4ae7)"}, "bandwidth_m": 100.0, "cell_size_m": 50.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 42. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 42.

Why it was run on this data. 60 points in Monitoring prisms: a density surface shows where they concentrate 47.

What it found. The gp_calculate_density tool analyzed 134 features from the "Monitoring prisms" layer 48. Key results from this analysis include a maximum density of 32.75 per km² 48. Furthermore, the process utilized a bandwidth of 500 m 48 and completed within 4 elapsed ms 48.

Findings

• Evidenced: Calculate density on Monitoring prisms: Calculate density: 1 layer saved: Calculate density: Monitoring prisms (134 features) 2648.
• Evidenced: It reported result, count 134; result, elapsed ms 4; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 32.75 48.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Monitoring prisms (6e51a43e)"}, "bandwidth_m": 500.0, "cell_size_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 5. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 5.

Why it was run on this data. 60 points: Getis-Ord Gi* tests whether the concentrations in Monitoring prisms are statistically significant 49.

What it found. Hot spots on Monitoring prisms: Hot spot analysis: 1 layer saved: Hot spot analysis: Monitoring prisms (16 features) 2650. It reported result, bins 16; result, count 16; result, elapsed ms 3; result, cell size m 500; result, incidents used 60; result, distance band m 500 50.

Findings

• Evidenced: Hot spots on Monitoring prisms: Hot spot analysis: 1 layer saved: Hot spot analysis: Monitoring prisms (16 features) 2650.
• Evidenced: It reported result, bins 16; result, count 16; result, elapsed ms 3; result, cell size m 500; result, incidents used 60; result, distance band m 500 50.

Tool gp_hot_spots; parameters {"project": "11fd3b70-5f29-4895-b87f-807f2ec1e52e", "features": {"layer": "Monitoring prisms (6e51a43e)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 500.0}

Maps and charts

The run also made 21 map image(s) 515253545556575859606162636465666768697071 and 22 chart(s) 72737475767778798081828384858687888990919293.

What the map shows. The map titled "Emerging hot spots: Drill holes" displays the spatial analysis of drill hole activity over a defined geographical area. According to the legend provided, the analysis concluded that there is "No pattern detected" for the emerging hot spots of drill holes across the mapped region. Visually, the entire area is covered in a continuous network of uniformly shaded polygons, reflecting the absence of a discernible spatial cluster or gradient. Consequently, the image indicates a lack of significant aggregation or concentration of drill hole activity at any specific point. 53

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. 99 rows id d5063e0d-383f-46d6-a583-b42ef86dcb50
2. 2Dataset: Prism readings. vector, 1200 rows id 60f18997-7538-4981-ab32-a2378dbc2332
3. 3Dataset: Drill holes. vector, 320 rows id 2a2433a0-611a-44a4-9f0d-081ef95736f5
4. 4Dataset: Production by pit and week. table, 76 rows id b8016ccc-7a32-41af-83a8-3d85487ce025
5. 5Catalog: 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
6. 6Plan: Hot spots of Drill holes (the Start plan's candidate c_89364af0f3f2). 320 points: Getis-Ord Gi* tests whether the concentrations in Drill holes are statistically significant. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_89364af0f3f2
7. 7Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Drill holes (50 features). id 6d176ba7-ebd6-4f95-82dc-d0c9a4e4146b:0
8. 8Layer: Drill holes. 320 features id 3fc6780a-f84b-41cc-a091-beff7f8fa0d0
9. 9Plan: Hot spots of Prism readings (the Start plan's candidate c_50324c88a44c). 1200 points: Getis-Ord Gi* tests whether the concentrations in Prism readings are statistically significant. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_50324c88a44c
10. 10Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Prism readings (227 features). id 6d176ba7-ebd6-4f95-82dc-d0c9a4e4146b:1
11. 11Layer: Prism readings. 1,200 features id 920e4ae7-03de-45d8-8b1f-df8f2afca685
12. 12Catalog: 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
13. 13Plan: Space time hot spots of Drill holes (the Start plan's candidate c_0ea6ff5db7ae). Drill holes carry dates in 'drilled_on': emerging hot spot analysis finds new, intensifying and fading hot spots per month. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_0ea6ff5db7ae
14. 14Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Drill holes (30 features). id d6b96f46-054d-4ebc-b6ed-8ea1f8115bfb:0
15. 15Plan: Space time hot spots of Prism readings (the Start plan's candidate c_52d4ce8ea935). Prism readings carry dates in 'observed_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_52d4ce8ea935
16. 16Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: Prism readings (29 features). id d6b96f46-054d-4ebc-b6ed-8ea1f8115bfb:1
17. 17Catalog: 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
18. 18Plan: Near repeat of Drill holes (the Start plan's candidate c_817106996954). Near repeat analysis tests whether Drill holes close in space also follow each other within 30 days. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_817106996954
19. 19Tool result: gp_near_repeat, step 2. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Drill holes (320 features). id d6b96f46-054d-4ebc-b6ed-8ea1f8115bfb:2
20. 20Catalog: 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
21. 21Plan: Drill holes per feature of Geotechnical domains (the Start plan's candidate c_755f3a7cd2f9). Count Drill holes inside each feature of Geotechnical domains by type; the counts map as a choropleth (natural breaks, sequential colors). id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_755f3a7cd2f9
22. 22Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Geotechnical domains (6 features). id e68cc066-c112-488f-8008-428c4110a545:0
23. 23Layer: Geotechnical domains. 6 features id 7e9e4801-2390-459b-a556-ee9f4ecc9967
24. 24Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Geotechnical domains (6 features). id e68cc066-c112-488f-8008-428c4110a545:1
25. 25Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Geotechnical domains (6 features). id e68cc066-c112-488f-8008-428c4110a545:2
26. 26Layer: Monitoring prisms. 60 features id 6e51a43e-4dfd-41b4-8730-56c9ba793475
27. 27Catalog: 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
28. 28Plan: Cluster points of Drill holes (the Start plan's candidate c_6300f2dcb36d). DBSCAN finds dense groups of Drill holes and separates noise. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_6300f2dcb36d
29. 29Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Drill holes (320 features); Find point clusters: cluster hulls (10 features). id 5ac0b253-366a-4046-9261-fd5bdc8cf6f3:0
30. 30Plan: Cluster points of Prism readings (the Start plan's candidate c_04a216785510). DBSCAN finds dense groups of Prism readings and separates noise. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_04a216785510
31. 31Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Prism readings (1200 features); Find point clusters: cluster hulls (60 features). id 5ac0b253-366a-4046-9261-fd5bdc8cf6f3:1
32. 32Plan: Cluster points of Monitoring prisms (the Start plan's candidate c_9c591df40051). DBSCAN finds dense groups of Monitoring prisms and separates noise. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_9c591df40051
33. 33Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Monitoring prisms (60 features); Find point clusters: cluster hulls (4 features). id 5ac0b253-366a-4046-9261-fd5bdc8cf6f3:2
34. 34Catalog: 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
35. 35Plan: Zonal statistics of Mine terrain model (DEM) and Geotechnical domains (the Start plan's candidate c_4a873859298f). Statistics of Mine terrain model (DEM) inside each feature of Geotechnical domains. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_4a873859298f
36. 36Tool result: rs_zonal_statistics, step 0. Zonal statistics: 6 row(s). zones 6. id 30c7009a-f86b-4e23-ad74-b8942eb98ecd:0
37. 37Tool result: rs_zonal_statistics, step 1. Zonal statistics: 7 row(s). zones 7. id 30c7009a-f86b-4e23-ad74-b8942eb98ecd:1
38. 38Catalog: 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
39. 39Plan: Slope of Mine terrain model (DEM) (the Start plan's candidate c_c4670a6804ed). Slope of the Mine terrain model (DEM) surface. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_c4670a6804ed
40. 40Tool result: tr_slope, step 2. Slope over 48.00 km2: mean 2.68 degrees, max 36.74 degrees; 9% of the ground is steeper than 15%. DEM: Mine terrain model (DEM). Verified: 2 checks passed. id 30c7009a-f86b-4e23-ad74-b8942eb98ecd:2
41. 41Layer: Mine terrain model (DEM). 0 features id d3bacd4c-de66-4724-8a10-a3ec4d2f4cd4
42. 42Catalog: 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
43. 43Plan: Kernel density of Drill holes (the Start plan's candidate c_2e2e29bf0e3a). 320 points in Drill holes: a density surface shows where they concentrate. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_2e2e29bf0e3a
44. 44Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Drill holes (328 features). id 48907d44-ee48-4d56-88b9-782734e7cb9e:0
45. 45Plan: Kernel density of Prism readings (the Start plan's candidate c_aacfc43e5746). 1200 points in Prism readings: a density surface shows where they concentrate. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_aacfc43e5746
46. 46Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Prism readings (545 features). id 48907d44-ee48-4d56-88b9-782734e7cb9e:1
47. 47Plan: Kernel density of Monitoring prisms (the Start plan's candidate c_f6f8e0a3b55f). 60 points in Monitoring prisms: a density surface shows where they concentrate. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_f6f8e0a3b55f
48. 48Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: Monitoring prisms (134 features). id 48907d44-ee48-4d56-88b9-782734e7cb9e:2
49. 49Plan: Hot spots of Monitoring prisms (the Start plan's candidate c_37960faa4114). 60 points: Getis-Ord Gi* tests whether the concentrations in Monitoring prisms are statistically significant. id 7bc56161-0d78-4299-bc71-0dcafaee2734:c_37960faa4114
50. 50Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Monitoring prisms (16 features). id 85672774-a9a1-438b-b198-fb69481775dd:0
51. 51Rendering: Map: Hot spot analysis: Drill holes id 7aba71c8-1496-40d8-b8eb-42666243d92b
52. 52Rendering: Map: Hot spot analysis: Prism readings id 26ff0f7f-3d97-4039-93a6-123c8eafa5da
53. 53Rendering: Map: Emerging hot spots: Drill holes id e03597b1-0956-4005-94ae-c579b88dc145
54. 54Rendering: Map: Emerging hot spots: Prism readings id c7fd6550-a8b0-43c2-b53f-25a96057ad1f
55. 55Rendering: Map: Repeat and near repeat classification: Drill holes id 64c3b80d-fd24-488e-857c-348e16eef2b7
56. 56Rendering: Map: Summarize within: Geotechnical domains id 46826436-d7ec-40c3-bd26-6442c1f5bce0
57. 57Rendering: Map: Summarize within: Geotechnical domains id a0355267-d17c-48fb-8009-9201561699e5
58. 58Rendering: Map: Summarize within: Geotechnical domains id b5636e87-7844-4fb0-8ed5-f8f518396c87
59. 59Rendering: Map: Find point clusters: Drill holes id db466f95-f4c8-4cd9-af9d-6d9cfcf6d915
60. 60Rendering: Map: Find point clusters: cluster hulls id 6c0777df-5751-4f2a-b90f-28f799dfed17
61. 61Rendering: Map: Find point clusters: Prism readings id 24577f88-171d-4b0e-b865-3e11993d2f9f
62. 62Rendering: Map: Find point clusters: cluster hulls id f6f2da46-1368-4eda-bec6-17e32c7181f8
63. 63Rendering: Map: Find point clusters: Monitoring prisms id a07906f9-8500-4b6c-9113-39a85ad19144
64. 64Rendering: Map: Find point clusters: cluster hulls id 46445a69-778d-4e67-b2ee-b6df575e5ec0
65. 65Rendering: Map: Slope (degrees) id 6e1a5363-e4c6-449d-8033-bd5fd8b184f2
66. 66Rendering: Map: Calculate density: Drill holes id cc413631-8b22-482c-a490-bb94d5d9fcab
67. 67Rendering: Map: Calculate density: Prism readings id 580f357c-fb0a-4e14-adc1-65a8045b73a3
68. 68Rendering: Map: Calculate density: Monitoring prisms id cad3aba8-d7e0-4de8-8c67-d9f79050feae
69. 69Rendering: Map: Geotechnical domains by rmr id c85125a0-4390-476f-8f95-a7f7b2b1772c
70. 70Rendering: Map: Mine areas by depth_m id 7bf2ced2-c815-4869-902d-5189c7dbcbb3
71. 71Rendering: Map: Hot spot analysis: Monitoring prisms id e47105cc-ae94-4c8a-ac4c-801c44b57ad6
72. 72Rendering: Chart: Prism readings over time (velocity_mm_day). 20 rows id f72a208b-643e-404c-b35c-deb12248f756
73. 73Rendering: Chart: Hot spot analysis: Drill holes. 9 rows id 2232e923-0f0f-40a7-bcec-b33f32691a12
74. 74Rendering: Chart: Hot spot analysis: Prism readings. 2 rows id bcc45631-4c2b-4328-b12f-0742986292e6
75. 75Rendering: Chart: Emerging hot spots: Drill holes. 6 rows id 83db89a9-e5f1-43ee-bef4-9607147169ef
76. 76Rendering: Chart: Emerging hot spots: Prism readings. 6 rows id e64dd055-c0fb-427f-a4c1-b3365102d997
77. 77Rendering: Chart: Repeat and near repeat classification: Drill holes. 64 rows id 0487f31b-380e-42f5-8a05-5afcc2dd8033
78. 78Rendering: Chart: Summarize within: Geotechnical domains. 5 rows id 52ed6781-1621-4935-86e6-a4335fb92e26
79. 79Rendering: Chart: Summarize within: Geotechnical domains. 5 rows id 60644b50-b68a-4746-97d0-81795a71efe5
80. 80Rendering: Chart: Summarize within: Geotechnical domains. 5 rows id dc5eabf8-66d5-4b52-ab80-f8fd8a8eb98a
81. 81Rendering: Chart: Find point clusters: Drill holes. 11 rows id c2c8d93a-1510-4b4c-8ceb-eb117952bdd5
82. 82Rendering: Chart: Find point clusters: cluster hulls. 10 rows id 520eb33c-5a35-4111-bf80-cb207542d8d7
83. 83Rendering: Chart: Find point clusters: Prism readings. 60 rows id 23efb700-58b6-48a0-8b27-5d330ea8f265
84. 84Rendering: Chart: Find point clusters: cluster hulls. 60 rows id 471bb2b8-d0f5-4095-8b5a-dc94ede849ca
85. 85Rendering: Chart: Find point clusters: Monitoring prisms. 5 rows id 19c67bbc-96ea-464f-98d9-b8049e398cd2
86. 86Rendering: Chart: Find point clusters: cluster hulls. 4 rows id b959d2a0-9626-4d42-adca-b8bd867773e1
87. 87Rendering: Chart: Calculate density: Drill holes. 10 rows id b2f5fe27-ce96-4a92-b076-cbd54e45c371
88. 88Rendering: Chart: Calculate density: Prism readings. 11 rows id 141cbaa1-7ea2-44fe-a5b7-1bd4079d6a01
89. 89Rendering: Chart: Calculate density: Monitoring prisms. 9 rows id 0f1f5fd1-f50c-4016-ac53-5ae0ec095be2
90. 90Rendering: Chart: Hot spot analysis: Monitoring prisms. 7 rows id 0bacfb00-e39e-4890-93a0-bbdf0d8d797f
91. 91Rendering: Chart: Find point clusters: Prism readings over time. 20 rows id e052e5c9-141f-425d-87c0-88027cefb9e8
92. 92Rendering: Chart: Find point clusters: Drill holes over time. 64 rows id 0501233f-ce86-469a-adb7-88f810d5c2bf
93. 93Rendering: Chart: Repeat and near repeat classification: Drill holes over time. 64 rows id a1201925-0565-4158-a599-5097c6e018b7
94. 94Dataset: Mine areas. vector, 7 rows id 796bc23c-1c01-471f-a816-e4b92b4ad5d2
95. 95Dataset: Haul roads. vector, 38 rows id 98271253-f396-48d5-89e3-e5ec77dd5592
96. 96Dataset: Monitoring prisms. vector, 60 rows id 931b37bf-74ac-40a6-bf1e-5aa284c78b21
97. 97Dataset: Equipment. vector, 40 rows id eb207db2-da08-4413-ab19-e6b7a998dbd4
98. 98Dataset: Geotechnical domains. vector, 6 rows id 767dcbe8-71b3-4319-9ad0-09d448c619f8
99. 99Dataset: Mine terrain model (DEM). raster, ? rows id 963146d0-3d3d-4cbb-a20f-e9c9ef5589ac
100. 100Dataset: Bench design by domain. table, 6 rows id b5190874-406c-48c8-aebb-c9874f6882f0
101. 101Dataset: Geotechnical inspection report. document, ? rows id 80e8ec2d-81ee-46d6-8c45-60a1c26b1f30
102. 102Record: Sample: Mining, Newman id 11fd3b70-5f29-4895-b87f-807f2ec1e52e
103. 103Layer: Hot spot analysis: Drill holes. 50 features id 36abf980-d699-4391-b457-180766c099c0
104. 104Layer: Hot spot analysis: Prism readings. 227 features id 8d411a35-93ea-41e6-ac52-18fa06883b1c
105. 105Layer: Emerging hot spots: Drill holes. 30 features id cc05cc11-2ba3-49cc-afaf-96e5b6d84ea6
106. 106Layer: Emerging hot spots: Prism readings. 29 features id e8e5a0b5-cdd5-4009-9d71-f4cf0a3f9f18
107. 107Layer: Repeat and near repeat classification: Drill holes. 320 features id 8bddb8ce-b2f8-42a4-ab09-fb8113d0c801
108. 108Layer: Summarize within: Geotechnical domains. 6 features id 989fcb0a-59fe-4c99-8349-b5beb2aa041b
109. 109Layer: Summarize within: Geotechnical domains. 6 features id 3a8b71e4-f3a7-48ff-b334-a801a6580ef1
110. 110Layer: Summarize within: Geotechnical domains. 6 features id 6c0a971c-88cb-42dd-b61a-1f207187a16a
111. 111Layer: Find point clusters: Drill holes. 320 features id be76cc0a-2088-45ad-bf48-2d3888e3db73
112. 112Layer: Find point clusters: cluster hulls. 10 features id 2d5bb0ec-3aff-4ae5-8c07-a363aa90d7c0
113. 113Layer: Find point clusters: Prism readings. 1,200 features id 3ed015a7-54f2-48f5-8d53-18be5b1e296f
114. 114Layer: Find point clusters: cluster hulls. 60 features id 879a04ad-6994-4706-b490-5ae1b265ce0c
115. 115Layer: Find point clusters: Monitoring prisms. 60 features id 071cb991-6662-4709-864a-6640d55e879e
116. 116Layer: Find point clusters: cluster hulls. 4 features id e55673cb-1d02-42f4-93f6-1a75d767eade
117. 117Layer: Slope (degrees). 0 features id c5b5f107-7b36-4783-b7a6-737f8f3a02cc
118. 118Layer: Calculate density: Drill holes. 328 features id dd247b38-56c5-4294-964c-b7f44ab1d084
119. 119Layer: Calculate density: Prism readings. 545 features id f30d3b65-9ba2-4542-8fe4-bf9d001bdfdb
120. 120Layer: Calculate density: Monitoring prisms. 134 features id 8f7a5a35-13b6-4d43-bf2b-da32f59529b4
121. 121Layer: Hot spot analysis: Monitoring prisms. 16 features id 4b0bd69c-b2aa-4d02-9ce9-80b98cc051ab