Corporate security and travel risk

Sample: Corporate security and travel risk, Mexico City: report

Project: Sample: Corporate security and travel risk, Mexico City
Goal: Describe the project Sample: Corporate security and travel risk, Mexico City: run the analyses that tell the most about its data, draw every result, and write a cited report.
Run: 294d550b-9eea-4398-b429-6280256992ba (running, 11 agent run(s))
Generated: 2026-10-18T05:29:33Z, version 1
Model cost: USD 0.0000 over 0 model call(s)

Data used

Timeline

The timeline counts 2,180 dated events in Security incidents, Protest events, Travel log per week, from week of 2025-09-29 to week of 2026-09-28 1234. By dataset: Security incidents 1,700; Travel log 400; Protest events 80 1. The trend is +0.105 events per week; the second half of the weeks has 1,079 against 1,078 in the first (+0.1%) 1.

Busiest weeks

• week of 2026-03-02: 123 events 1
• week of 2026-06-08: 120 events 1
• week of 2026-04-27: 110 events 1

Quietest weeks

• week of 2026-09-28: 19 events 1
• week of 2025-10-20: 21 events 1
• week of 2026-07-20: 22 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. 80 points: Getis-Ord Gi* tests whether the concentrations in Protest events are statistically significant 6.

What it found. Hot spots on Protest events: Hot spot analysis: 1 layer saved: Hot spot analysis: Protest events (29 features) 78. It reported result, bins 29; result, count 29; result, elapsed ms 5; result, cell size m 500; result, incidents used 80; result, distance band m 1,000 7.

Findings

• Evidenced: Hot spots on Protest events: Hot spot analysis: 1 layer saved: Hot spot analysis: Protest events (29 features) 78.
• Evidenced: It reported result, bins 29; result, count 29; result, elapsed ms 5; result, cell size m 500; result, incidents used 80; result, distance band m 1,000 7.

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

Tool gp_hot_spots; parameters {"project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Protest events (cf75e6b1)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 1000.0}

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. 30 points: Getis-Ord Gi* tests whether the concentrations in Hotels are statistically significant 9.

What it found. The hot spot analysis generated 14 features 10 after processing the Hotels layer 11. The analysis used 30 incidents and set both the distance band and cell size to 2,000 m 10. The process completed quickly, taking 2 ms 10. The billing for this analysis was 0.0506 10.

Findings

• Evidenced: Hot spots on Hotels: Hot spot analysis: 1 layer saved: Hot spot analysis: Hotels (14 features) 1011.
• Evidenced: It reported result, bins 14; result, count 14; result, elapsed ms 2; result, cell size m 2,000; result, incidents used 30; result, distance band m 2,000 10.

Tool gp_hot_spots; parameters {"project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Hotels (28842e9c)"}, "aggregation": "hexagon", "cell_size_m": 2000.0, "distance_band_m": 2000.0}

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. 1700 points: Getis-Ord Gi* tests whether the concentrations in Security incidents are statistically significant 12.

What it found. Hot spots on Security incidents: Hot spot analysis: 1 layer saved: Hot spot analysis: Security incidents (353 features) 1314. It reported result, bins 353; result, count 353; result, elapsed ms 18; result, cell size m 500; result, incidents used 1,700; result, distance band m 500 13.

Findings

• Evidenced: Hot spots on Security incidents: Hot spot analysis: 1 layer saved: Hot spot analysis: Security incidents (353 features) 1314.
• Evidenced: It reported result, bins 353; result, count 353; result, elapsed ms 18; result, cell size m 500; result, incidents used 1,700; result, distance band m 500 13.

Tool gp_hot_spots; parameters {"project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Security incidents (afb5d90c)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 500.0}

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 15. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 15.

Why it was run on this data. Protest events carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per week 16.

What it found. Space time hot spots on Protest events: Emerging hot spots: 1 layer saved: Emerging hot spots: Protest events (14 features) 817. It reported result, count 14; result, elapsed ms 4; result, resolution 8; result, features used 80; result, time neighbors 0; billing, cost 0.104 17.

Findings

• Evidenced: Space time hot spots on Protest events: Emerging hot spots: 1 layer saved: Emerging hot spots: Protest events (14 features) 817.
• Evidenced: It reported result, count 14; result, elapsed ms 4; result, resolution 8; result, features used 80; result, time neighbors 0; billing, cost 0.104 17.

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

Tool gp_space_time_hot_spots; parameters {"project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Protest events (cf75e6b1)"}, "interval": "week", "bin_shape": "h3", "resolution": 8, "time_field": "date"}

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 15. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 15.

Why it was run on this data. Security incidents carry dates in 'occurred_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week 18.

What it found. Space time hot spots on Security incidents: Emerging hot spots: 1 layer saved: Emerging hot spots: Security incidents (108 features) 1419. It reported result, count 108; result, elapsed ms 21; result, resolution 8; result, features used 1,700; result, time neighbors 0; billing, cost 0.185 19.

Findings

• Evidenced: Space time hot spots on Security incidents: Emerging hot spots: 1 layer saved: Emerging hot spots: Security incidents (108 features) 1419.
• Evidenced: It reported result, count 108; result, elapsed ms 21; result, resolution 8; result, features used 1,700; result, time neighbors 0; billing, cost 0.185 19.

Tool gp_space_time_hot_spots; parameters {"project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Security incidents (afb5d90c)"}, "interval": "week", "bin_shape": "h3", "resolution": 8, "time_field": "occurred_at"}

Near repeat

What this analysis is. Repeat and near repeat classification: Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table 20. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator 20.

Why it was run on this data. Near repeat analysis tests whether Protest events close in space also follow each other within 27 days 21.

What it found. The tool executed used the Protest events data 8 to classify near repeats 22. The resulting dataset contained a count of 80 pairs 22, with 11 total chains identified 22. Furthermore, 15 pairs were linked, and the average distance was reported as 200 m 22.

Findings

• Evidenced: Near repeat on Protest events: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Protest events (80 features) 822.
• Evidenced: It reported result, days 27; result, count 80; result, chains 11; result, distance m 200; result, elapsed ms 4; result, pairs linked 15 22.

Tool gp_near_repeat; parameters {"days": 27, "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Protest events (cf75e6b1)"}, "distance_m": 200.0, "time_field": "date"}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 23. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 23.

Why it was run on this data. Count Hospitals inside each feature of Risk zones by english_speaking; the counts map as a choropleth (natural breaks, sequential colors) 24.

What it found. Summarize within on Hospitals, Risk zones: Summarize within: 1 layer saved: Summarize within: Risk zones (14 features) 252627. It reported result, count 14; result, elapsed ms 0; billing, cost 0.0106; billing, list price 0.0106; billing, upstream cost 0 25.

Findings

• Evidenced: Summarize within on Hospitals, Risk zones: Summarize within: 1 layer saved: Summarize within: Risk zones (14 features) 252627.
• Evidenced: It reported result, count 14; result, elapsed ms 0; billing, cost 0.0106; billing, list price 0.0106; billing, upstream cost 0 25.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "trauma_level"}], "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Hospitals (8a96a3d4)"}, "group_by": "english_speaking", "polygons": {"layer": "Risk zones (a4d2e294)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 23. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 23.

Why it was run on this data. Count Hospitals inside each feature of Risk zones by english_speaking; the counts map as a choropleth (natural breaks, sequential colors) 24.

What it found. Summarize within on Safe havens, Risk zones: Summarize within: 1 layer saved: Summarize within: Risk zones (14 features) 272829. It reported result, count 14; result, elapsed ms 0; billing, cost 0.01052; billing, list price 0.01052; billing, upstream cost 0 28.

Findings

• Evidenced: Summarize within on Safe havens, Risk zones: Summarize within: 1 layer saved: Summarize within: Risk zones (14 features) 272829.
• Evidenced: It reported result, count 14; result, elapsed ms 0; billing, cost 0.01052; billing, list price 0.01052; billing, upstream cost 0 28.

Tool gp_summarize_within; parameters {"stats": [], "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Safe havens (d7ec6bb0)"}, "group_by": "type", "polygons": {"layer": "Risk zones (a4d2e294)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 23. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 23.

Why it was run on this data. Count Hospitals inside each feature of Risk zones by english_speaking; the counts map as a choropleth (natural breaks, sequential colors) 24.

What it found. The analysis employed the gp_summarize_within tool 30, accepting Protest events 8 and Risk zones 27 as inputs 30. This process yielded a result count of 14 30.

Findings

• Evidenced: Summarize within on Protest events, Risk zones: Summarize within: 1 layer saved: Summarize within: Risk zones (14 features) 82730.
• Evidenced: It reported result, count 14; result, elapsed ms 0; billing, cost 0.01188; billing, list price 0.01188; billing, upstream cost 0 30.

Tool gp_summarize_within; parameters {"stats": [], "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Protest events (cf75e6b1)"}, "group_by": "size", "polygons": {"layer": "Risk zones (a4d2e294)"}}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 31. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 31.

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

What it found. Find point clusters on Hospitals: Find point clusters: 2 layers saved: Find point clusters: Hospitals (15 features); Find point clusters: cluster hulls (1 feature) 2633. It reported result, count 15; result, noise 4; result, distance m 2,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0503 33.

Findings

• Evidenced: Find point clusters on Hospitals: Find point clusters: 2 layers saved: Find point clusters: Hospitals (15 features); Find point clusters: cluster hulls (1 feature) 2633.
• Evidenced: It reported result, count 15; result, noise 4; result, distance m 2,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0503 33.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Hospitals (8a96a3d4)"}, "distance_m": 2000.0, "min_points": 3}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 31. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 31.

Why it was run on this data. DBSCAN finds dense groups of Safe havens and separates noise 34.

What it found. The tool saved 2 layers: Find point clusters: Safe havens found 12 features, and Find point clusters: cluster hulls found 2 features 35. The process resulted in 12 points, with 4 points categorized as noise 35.

Findings

• Evidenced: Find point clusters on Safe havens: Find point clusters: 2 layers saved: Find point clusters: Safe havens (12 features); Find point clusters: cluster hulls (2 features) 2935.
• Evidenced: It reported result, count 12; result, noise 4; result, distance m 2,000; result, elapsed ms 1; result, min points 3; billing, cost 0.05024 35.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Safe havens (d7ec6bb0)"}, "distance_m": 2000.0, "min_points": 3}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 31. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 31.

Why it was run on this data. DBSCAN finds dense groups of Protest events and separates noise 36.

What it found. Find point clusters on Protest events: Find point clusters: 2 layers saved: Find point clusters: Protest events (80 features); Find point clusters: cluster hulls (1 feature) 837. It reported result, count 80; result, noise 3; result, distance m 500; result, elapsed ms 2; result, min points 3; billing, cost 0.0516 37.

Findings

• Evidenced: Find point clusters on Protest events: Find point clusters: 2 layers saved: Find point clusters: Protest events (80 features); Find point clusters: cluster hulls (1 feature) 837.
• Evidenced: It reported result, count 80; result, noise 3; result, distance m 500; result, elapsed ms 2; result, min points 3; billing, cost 0.0516 37.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Protest events (cf75e6b1)"}, "distance_m": 500.0, "min_points": 3}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 38. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 38.

Why it was run on this data. 80 points in Protest events: a density surface shows where they concentrate 39.

What it found. Calculate density on Protest events: Calculate density: 1 layer saved: Calculate density: Protest events (386 features) 840. It reported result, count 386; result, elapsed ms 11; result, bandwidth m 1,000; result, cell size m 200; result, excluded null 0; result, max density per km2 29.94 40.

Findings

• Evidenced: Calculate density on Protest events: Calculate density: 1 layer saved: Calculate density: Protest events (386 features) 840.
• Evidenced: It reported result, count 386; result, elapsed ms 11; result, bandwidth m 1,000; result, cell size m 200; result, excluded null 0; result, max density per km2 29.94 40.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Protest events (cf75e6b1)"}, "bandwidth_m": 1000.0, "cell_size_m": 200.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 38. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 38.

Why it was run on this data. 30 points in Hotels: a density surface shows where they concentrate 41.

What it found. The density calculation was run on the Hotels layer 11 by utilizing the gp_calculate_density tool 42.

Findings

• Evidenced: Calculate density on Hotels: Calculate density: 1 layer saved: Calculate density: Hotels (82 features) 1142.
• Evidenced: It reported result, count 82; result, elapsed ms 3; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 1.819 42.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Hotels (28842e9c)"}, "bandwidth_m": 2000.0, "cell_size_m": 1000.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 38. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 38.

Why it was run on this data. 1700 points in Security incidents: a density surface shows where they concentrate 43.

What it found. Calculate density on Security incidents: Calculate density: 1 layer saved: Calculate density: Security incidents (2240 features) 1444. It reported result, count 2,240; result, elapsed ms 60; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 267.9 44.

Findings

• Evidenced: Calculate density on Security incidents: Calculate density: 1 layer saved: Calculate density: Security incidents (2240 features) 1444.
• Evidenced: It reported result, count 2,240; result, elapsed ms 60; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 267.9 44.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Security incidents (afb5d90c)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0}

Outliers

What this analysis is. Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers 45. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) 45.

Why it was run on this data. Local Moran's I maps clusters and outliers of 'risk_score' 46.

What it found. Outliers on Risk zones: Find outliers: 1 layer saved: Find outliers: Risk zones (14 features) 2747. It reported result, count 14; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 2,548; billing, cost 0.05042 47.

Findings

• Evidenced: Outliers on Risk zones: Find outliers: 1 layer saved: Find outliers: Risk zones (14 features) 2747.
• Evidenced: It reported result, count 14; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 2,548; billing, cost 0.05042 47.

Tool gp_outliers; parameters {"field": "risk_score", "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Risk zones (a4d2e294)"}, "aggregation": "hexagon"}

Spatial autocorrelation

What this analysis is. Spatial autocorrelation: Global Moran’s I: is the pattern clustered, dispersed or random 48. In ArcGIS it is Spatial Autocorrelation (Global Moran’s I) 48.

Why it was run on this data. Global Moran's I says whether similar values of 'risk_score' sit next to each other 49.

What it found. The spatial autocorrelation tool was run using the Risk zones input 27, completing without generating a map output 50.

Findings

• Evidenced: Spatial autocorrelation on Risk zones: Spatial autocorrelation: done; no map output 2750.
• Evidenced: It reported result, n 14; result, p value 0.6185; result, z score -0.498; result, morans i -0.2235; result, variance 0.08664; result, elapsed ms 1 50.

Tool gp_spatial_autocorrelation; parameters {"field": "risk_score", "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "features": {"layer": "Risk zones (a4d2e294)"}}

Find nearest

What this analysis is. Find nearest: The k nearest features for each input, with geodesic distance and bearing 51. In ArcGIS it is Find Nearest or Near; in QGIS, native:joinbynearest or native:nearestneighbouranalysis 51.

Why it was run on this data. The nearest of Travel routes to each of Hospitals, with distances 52.

What it found. Find nearest on Travel routes, Hospitals: Find nearest: 1 layer saved: Find nearest: Hospitals (14 features) 265354. It reported result, count 14; result, elapsed ms 10; billing, cost 0.0105; billing, list price 0.0105; billing, upstream cost 0 53.

Findings

• Evidenced: Find nearest on Travel routes, Hospitals: Find nearest: 1 layer saved: Find nearest: Hospitals (14 features) 265354.
• Evidenced: It reported result, count 14; result, elapsed ms 10; billing, cost 0.0105; billing, list price 0.0105; billing, upstream cost 0 53.

Tool gp_find_nearest; parameters {"k": 1, "to": {"layer": "Travel routes (17118d2e)"}, "from": {"layer": "Hospitals (8a96a3d4)"}, "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "max_distance_m": 2000.0}

Find nearest

What this analysis is. Find nearest: The k nearest features for each input, with geodesic distance and bearing 51. In ArcGIS it is Find Nearest or Near; in QGIS, native:joinbynearest or native:nearestneighbouranalysis 51.

Why it was run on this data. The nearest of Travel routes to each of Hospitals, with distances 52.

What it found. Find nearest on Safe havens, Hospitals: Find nearest: 1 layer saved: Find nearest: Hospitals (14 features) 262955. It reported result, count 14; result, elapsed ms 1; billing, cost 0.01054; billing, list price 0.01054; billing, upstream cost 0 55.

Findings

• Evidenced: Find nearest on Safe havens, Hospitals: Find nearest: 1 layer saved: Find nearest: Hospitals (14 features) 262955.
• Evidenced: It reported result, count 14; result, elapsed ms 1; billing, cost 0.01054; billing, list price 0.01054; billing, upstream cost 0 55.

Tool gp_find_nearest; parameters {"k": 1, "to": {"layer": "Safe havens (d7ec6bb0)"}, "from": {"layer": "Hospitals (8a96a3d4)"}, "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "max_distance_m": 2000.0}

Find nearest

What this analysis is. Find nearest: The k nearest features for each input, with geodesic distance and bearing 51. In ArcGIS it is Find Nearest or Near; in QGIS, native:joinbynearest or native:nearestneighbouranalysis 51.

Why it was run on this data. The nearest of Travel routes to each of Hospitals, with distances 52.

What it found. The gp_find_nearest tool was utilized with Protest events 8 and Hospitals 26 56. This process found 6 results 56. The execution was quick, completing in 1 elapsed ms 56. The billing cost associated with this operation was 0.0119 56.

Findings

• Evidenced: Find nearest on Protest events, Hospitals: Find nearest: 1 layer saved: Find nearest: Hospitals (6 features) 82656.
• Evidenced: It reported result, count 6; result, elapsed ms 1; billing, cost 0.0119; billing, list price 0.0119; billing, upstream cost 0 56.

Tool gp_find_nearest; parameters {"k": 1, "to": {"layer": "Protest events (cf75e6b1)"}, "from": {"layer": "Hospitals (8a96a3d4)"}, "project": "188436ed-c19a-4e7c-ab43-b58fe7291e56", "max_distance_m": 2000.0}

Maps and charts

The run also made 23 map image(s) 5758596061626364656667686970717273747576777879 and 27 chart(s) 8081828384858687888990919293949596979899100101102103104105106.

What the map shows. This map, titled "Emerging hot spots: Protest events," analyzes a specific geographic area running from longitude -99.15818 to -99.10583 and latitude 19.41925 to 19.45709. The analysis indicates that there is no identifiable pattern of "Emerging 60

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. 159 rows id f07ed5b6-9486-4c24-a7ad-e2c1b9a45323
2. 2Dataset: Security incidents. vector, 1700 rows id e157aa87-0056-48e2-affe-2abe8c8f035b
3. 3Dataset: Protest events. vector, 80 rows id 61b11bf6-a94e-410d-bc75-a441daea9e2e
4. 4Dataset: Travel log. table, 400 rows id 91769657-5256-4993-90ae-bea93e5d9277
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 Protest events (the Start plan's candidate c_e4c60efa6297). 80 points: Getis-Ord Gi* tests whether the concentrations in Protest events are statistically significant. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_e4c60efa6297
7. 7Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Protest events (29 features). id cb954b77-a9f1-40f9-ba2a-8936e0981670:0
8. 8Layer: Protest events. 80 features id cf75e6b1-838e-4795-85d8-20e39702882e
9. 9Plan: Hot spots of Hotels (the Start plan's candidate c_d9214e0b2e9c). 30 points: Getis-Ord Gi* tests whether the concentrations in Hotels are statistically significant. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_d9214e0b2e9c
10. 10Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Hotels (14 features). id cb954b77-a9f1-40f9-ba2a-8936e0981670:1
11. 11Layer: Hotels. 30 features id 28842e9c-fa0f-4fa0-ab60-50616b38e2eb
12. 12Plan: Hot spots of Security incidents (the Start plan's candidate c_2cf7cb199846). 1700 points: Getis-Ord Gi* tests whether the concentrations in Security incidents are statistically significant. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_2cf7cb199846
13. 13Tool result: gp_hot_spots, step 2. Hot spot analysis: 1 layer saved: Hot spot analysis: Security incidents (353 features). id cb954b77-a9f1-40f9-ba2a-8936e0981670:2
14. 14Layer: Security incidents. 1,700 features id afb5d90c-b04a-45b5-86de-02dd48a97b15
15. 15Catalog: 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
16. 16Plan: Space time hot spots of Protest events (the Start plan's candidate c_7097f355a4d8). Protest events carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per week. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_7097f355a4d8
17. 17Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Protest events (14 features). id 4ce981f8-e618-4a52-ab5e-deb572cf8a18:0
18. 18Plan: Space time hot spots of Security incidents (the Start plan's candidate c_333ac7b0a6a6). Security incidents carry dates in 'occurred_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_333ac7b0a6a6
19. 19Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: Security incidents (108 features). id 4ce981f8-e618-4a52-ab5e-deb572cf8a18:1
20. 20Catalog: Repeat and near repeat classification (operation catalog, gp_near_repeat). Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table. ArcGIS: Repeat and Near Repeat Classification, Near Repeat Calculator. id gp_near_repeat
21. 21Plan: Near repeat of Protest events (the Start plan's candidate c_f95b4db5e496). Near repeat analysis tests whether Protest events close in space also follow each other within 27 days. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_f95b4db5e496
22. 22Tool result: gp_near_repeat, step 2. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Protest events (80 features). id 4ce981f8-e618-4a52-ab5e-deb572cf8a18:2
23. 23Catalog: 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
24. 24Plan: Hospitals per feature of Risk zones (the Start plan's candidate c_6520524d187a). Count Hospitals inside each feature of Risk zones by english_speaking; the counts map as a choropleth (natural breaks, sequential colors). id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_6520524d187a
25. 25Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Risk zones (14 features). id 4db35232-a32c-47e1-9154-89fbc3620126:0
26. 26Layer: Hospitals. 15 features id 8a96a3d4-1867-4c29-8033-2a6817b92b2c
27. 27Layer: Risk zones. 14 features id a4d2e294-c13c-4572-b6fc-2b8ef4e9c955
28. 28Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Risk zones (14 features). id 4db35232-a32c-47e1-9154-89fbc3620126:1
29. 29Layer: Safe havens. 12 features id d7ec6bb0-8b36-474d-8157-931bdc40122a
30. 30Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Risk zones (14 features). id 4db35232-a32c-47e1-9154-89fbc3620126:2
31. 31Catalog: 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
32. 32Plan: Cluster points of Hospitals (the Start plan's candidate c_65a1f33e9e5e). DBSCAN finds dense groups of Hospitals and separates noise. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_65a1f33e9e5e
33. 33Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Hospitals (15 features); Find point clusters: cluster hulls (1 feature). id 86c36cc2-95cb-4bfa-aac6-db9cc2913cc1:0
34. 34Plan: Cluster points of Safe havens (the Start plan's candidate c_5ba3c571cd04). DBSCAN finds dense groups of Safe havens and separates noise. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_5ba3c571cd04
35. 35Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Safe havens (12 features); Find point clusters: cluster hulls (2 features). id 86c36cc2-95cb-4bfa-aac6-db9cc2913cc1:1
36. 36Plan: Cluster points of Protest events (the Start plan's candidate c_ae60a45625a8). DBSCAN finds dense groups of Protest events and separates noise. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_ae60a45625a8
37. 37Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Protest events (80 features); Find point clusters: cluster hulls (1 feature). id 86c36cc2-95cb-4bfa-aac6-db9cc2913cc1:2
38. 38Catalog: 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
39. 39Plan: Kernel density of Protest events (the Start plan's candidate c_7c8acce6dd51). 80 points in Protest events: a density surface shows where they concentrate. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_7c8acce6dd51
40. 40Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Protest events (386 features). id 72e7e894-08fe-429b-84d0-f973eac006a1:0
41. 41Plan: Kernel density of Hotels (the Start plan's candidate c_fb39ef5f9446). 30 points in Hotels: a density surface shows where they concentrate. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_fb39ef5f9446
42. 42Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Hotels (82 features). id 72e7e894-08fe-429b-84d0-f973eac006a1:1
43. 43Plan: Kernel density of Security incidents (the Start plan's candidate c_8b75d0e8f5bf). 1700 points in Security incidents: a density surface shows where they concentrate. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_8b75d0e8f5bf
44. 44Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: Security incidents (2240 features). id 72e7e894-08fe-429b-84d0-f973eac006a1:2
45. 45Catalog: Find outliers (operation catalog, gp_outliers). Anselin local Moran’s I: HH, LL clusters and HL, LH outliers. ArcGIS: Find Outliers, Cluster and Outlier Analysis (Anselin Local Moran’s I). id gp_outliers
46. 46Plan: Local morans i of Risk zones (the Start plan's candidate c_b57bb9ae247a). Local Moran's I maps clusters and outliers of 'risk_score'. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_b57bb9ae247a
47. 47Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: Risk zones (14 features). id 8804c2f2-0669-4232-a6ea-33b246fdbe10:0
48. 48Catalog: Spatial autocorrelation (operation catalog, gp_spatial_autocorrelation). Global Moran’s I: is the pattern clustered, dispersed or random. ArcGIS: Spatial Autocorrelation (Global Moran’s I). id gp_spatial_autocorrelation
49. 49Plan: Global morans i of Risk zones (the Start plan's candidate c_e8c49b66ee35). Global Moran's I says whether similar values of 'risk_score' sit next to each other. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_e8c49b66ee35
50. 50Tool result: gp_spatial_autocorrelation, step 1. Spatial autocorrelation: done; no map output. id 8804c2f2-0669-4232-a6ea-33b246fdbe10:1
51. 51Catalog: Find nearest (operation catalog, gp_find_nearest). The k nearest features for each input, with geodesic distance and bearing. ArcGIS: Find Nearest, Near, Generate Near Table. QGIS: native:joinbynearest, native:nearestneighbouranalysis. id gp_find_nearest
52. 52Plan: Find nearest of Hospitals and Travel routes (the Start plan's candidate c_6c9de7d7d3b4). The nearest of Travel routes to each of Hospitals, with distances. id 2f94775f-544a-4c5f-9cbe-3a9e3ee974e2:c_6c9de7d7d3b4
53. 53Tool result: gp_find_nearest, step 0. Find nearest: 1 layer saved: Find nearest: Hospitals (14 features). id d72a65fe-570d-4b7e-bb1f-3397ddd3dff0:0
54. 54Layer: Travel routes. 10 features id 17118d2e-4c8a-4e58-a365-56ed0dccddeb
55. 55Tool result: gp_find_nearest, step 1. Find nearest: 1 layer saved: Find nearest: Hospitals (14 features). id d72a65fe-570d-4b7e-bb1f-3397ddd3dff0:1
56. 56Tool result: gp_find_nearest, step 2. Find nearest: 1 layer saved: Find nearest: Hospitals (6 features). id d72a65fe-570d-4b7e-bb1f-3397ddd3dff0:2
57. 57Rendering: Map: Hot spot analysis: Protest events id 500df2bf-0773-4df4-997b-e52faf1d7b86
58. 58Rendering: Map: Hot spot analysis: Hotels id a9b20642-0e6f-4981-a664-da575f4c0f47
59. 59Rendering: Map: Hot spot analysis: Security incidents id dfc0443f-8eec-4850-bcf5-b167e4b25bb2
60. 60Rendering: Map: Emerging hot spots: Protest events id 0099fe22-7871-4a15-a0e8-1feae00a5bfe
61. 61Rendering: Map: Emerging hot spots: Security incidents id ac12c726-afc7-4f73-bcad-180032253879
62. 62Rendering: Map: Repeat and near repeat classification: Protest events id 88da8834-5528-4120-a9c9-c2d8f343794a
63. 63Rendering: Map: Summarize within: Risk zones id 00bc1476-064e-4f9d-b9df-58b7176702f7
64. 64Rendering: Map: Summarize within: Risk zones id 097e7003-7459-4810-8b27-5f7a0070cb99
65. 65Rendering: Map: Summarize within: Risk zones id 0f0f8452-fff5-40e3-9da5-e4b1a4e0c9c3
66. 66Rendering: Map: Find point clusters: Hospitals id 47eb8a01-1990-4474-b495-5bf2f8a9ea58
67. 67Rendering: Map: Find point clusters: cluster hulls id 9199cde6-d3cd-466f-bbdb-c545c614f5d9
68. 68Rendering: Map: Find point clusters: Safe havens id b7163651-cc15-45dd-85df-b9a1e1a0c2ab
69. 69Rendering: Map: Find point clusters: cluster hulls id b07c64b4-3212-4751-b9fe-884c92e2af0b
70. 70Rendering: Map: Find point clusters: Protest events id 09c181e5-fd50-4a4d-8fb9-c372f05b7087
71. 71Rendering: Map: Find point clusters: cluster hulls id 837965dd-bd8f-4eda-8fc0-9ce4211449eb
72. 72Rendering: Map: Calculate density: Protest events id 26c2dd95-9903-4e3a-9502-b9d59a00d91f
73. 73Rendering: Map: Calculate density: Hotels id 87642b35-e16d-4168-a04d-24944d0ea7c8
74. 74Rendering: Map: Calculate density: Security incidents id db714770-2476-4110-af9b-826c0fda7266
75. 75Rendering: Map: Risk zones by risk_score id acd51039-768b-4034-9aa3-0fa9355f4884
76. 76Rendering: Map: Find outliers: Risk zones id 5323e5c1-28e1-44b3-8f9c-44f34efbf17f
77. 77Rendering: Map: Find nearest: Hospitals id a1567a83-3fb0-4dcf-8d6c-71ba8b4ed7d7
78. 78Rendering: Map: Find nearest: Hospitals id b16977bf-56c6-43a5-bf88-4d5b01a2648c
79. 79Rendering: Map: Find nearest: Hospitals id 11bfd2f1-7879-4693-a32a-915be104a231
80. 80Rendering: Chart: Security incidents over time (occurred_at). 318 rows id e4bbd9ee-6938-4976-afea-98b10b22455f
81. 81Rendering: Chart: Protest events over time (date). 117 rows id f828e7a2-d14d-4bc3-b257-85cd520ee1ef
82. 82Rendering: Chart: Travel log over time (date). 106 rows id 66c88c61-6c69-4dc0-a55d-5c961369ab06
83. 83Rendering: Chart: Hot spot analysis: Protest events. 11 rows id 66439a0c-ac69-4219-b24e-6f7ff88f70d2
84. 84Rendering: Chart: Hot spot analysis: Hotels. 6 rows id c52e7b32-d25d-4aed-abe8-9ebb7f603bfd
85. 85Rendering: Chart: Hot spot analysis: Security incidents. 16 rows id ca9f7204-df1a-4b6c-b63a-414bc07961a3
86. 86Rendering: Chart: Emerging hot spots: Protest events. 5 rows id a0edbd15-0e36-4d00-9145-33631fafb0d4
87. 87Rendering: Chart: Emerging hot spots: Security incidents. 8 rows id 0b5d890b-d25b-4e32-8200-951dc01149f7
88. 88Rendering: Chart: Repeat and near repeat classification: Protest events. 117 rows id 2306aa49-d791-4cd3-8d4c-28e6660067db
89. 89Rendering: Chart: Summarize within: Risk zones. 5 rows id c94ab553-318c-4a5e-bfe3-5ceb6088a6d2
90. 90Rendering: Chart: Summarize within: Risk zones. 5 rows id 67037acd-9bb9-4174-b327-02e297418724
91. 91Rendering: Chart: Summarize within: Risk zones. 5 rows id 923af0d1-ab6e-4ce5-b3d9-81d29ab8cc03
92. 92Rendering: Chart: Find point clusters: Hospitals. 2 rows id 20d9b93a-9f38-4502-8832-0c3847923a24
93. 93Rendering: Chart: Find point clusters: cluster hulls. 1 rows id f9da01d9-7689-4ba1-b714-0a48053eb1ae
94. 94Rendering: Chart: Find point clusters: Safe havens. 3 rows id b1d7e3a3-1643-4aea-87c6-3d0616a865de
95. 95Rendering: Chart: Find point clusters: cluster hulls. 2 rows id 4093c918-1c9f-481a-8b57-2adef8395ab8
96. 96Rendering: Chart: Find point clusters: Protest events. 2 rows id 83853866-50d0-468b-8c71-7faa0aa71c50
97. 97Rendering: Chart: Find point clusters: cluster hulls. 1 rows id e5bfd36e-59ac-4d44-805f-ba7f25c9358d
98. 98Rendering: Chart: Calculate density: Protest events. 10 rows id 73671b82-b4a3-42c0-a8ae-b1d27cba0ec2
99. 99Rendering: Chart: Calculate density: Hotels. 8 rows id c0aefbfb-9735-4f03-a2e6-d27fabd30313
100. 100Rendering: Chart: Calculate density: Security incidents. 13 rows id a8231907-9048-4318-9575-1e7364e4238a
101. 101Rendering: Chart: Find outliers: Risk zones. 14 rows id 491c699f-46c0-4622-98ad-1183374f76e1
102. 102Rendering: Chart: Find nearest: Hospitals. 14 rows id 9cc7d0a7-efe8-4e46-9c45-e42681c0944f
103. 103Rendering: Chart: Find nearest: Hospitals. 14 rows id e56bd3f0-499e-402f-a74a-cca2f35b3bc9
104. 104Rendering: Chart: Find nearest: Hospitals over time. 66 rows id 51a6266c-78a0-4146-87ab-d79e4d6ba260
105. 105Rendering: Chart: Find point clusters: Protest events over time. 117 rows id 86bdb528-cab1-4fff-aa57-5ee0311a2b43
106. 106Rendering: Chart: Repeat and near repeat classification: Protest events over time. 117 rows id 4dfde3d2-01b3-45b2-90c2-09129de0912c
107. 107Dataset: Company sites. vector, 8 rows id a57e5e2e-a25d-409d-9272-6ca1c1ae23ab
108. 108Dataset: Hotels. vector, 30 rows id 960da6f9-a119-47d2-8c4c-73faa6425317
109. 109Dataset: Travel routes. vector, 10 rows id 1008679c-3d12-45ea-a8ce-be53355c90d8
110. 110Dataset: Risk zones. vector, 14 rows id a6555125-2ca1-45ce-8a48-57ce5705423b
111. 111Dataset: Hospitals. vector, 15 rows id 03c9ffea-5a0a-4d80-8965-c0709759aff3
112. 112Dataset: Safe havens. vector, 12 rows id 511f2fd6-d838-46cc-a47c-c29f431a83c0
113. 113Dataset: Site guard roster. table, 16 rows id 19276383-dfed-4594-b706-1f9d7ba7b072
114. 114Dataset: Country security briefing. document, ? rows id f20d3f16-9057-4bc4-a33a-b2d8b53657a0
115. 115Dataset: Hotel security assessment. document, ? rows id e794bded-1be4-41a5-bbf2-086e1a0791f3
116. 116Record: Sample: Corporate security and travel risk, Mexico City id 188436ed-c19a-4e7c-ab43-b58fe7291e56
117. 117Layer: Hot spot analysis: Protest events. 29 features id db1e7e50-0b84-4c23-a7f1-70d712d84483
118. 118Layer: Hot spot analysis: Hotels. 14 features id 236b49e6-718b-468e-9414-fec05e2fd046
119. 119Layer: Hot spot analysis: Security incidents. 353 features id 354043da-a305-407a-bbe0-c2d077f68fed
120. 120Layer: Emerging hot spots: Protest events. 14 features id d4e930d7-f5cc-45e0-b105-ee6ff35846d6
121. 121Layer: Emerging hot spots: Security incidents. 108 features id 7d258e9e-7357-4428-8775-a97375d60dd3
122. 122Layer: Repeat and near repeat classification: Protest events. 80 features id c15ea26d-f7bf-43bf-987f-05e0d75a4a76
123. 123Layer: Summarize within: Risk zones. 14 features id 46424976-b4de-430a-93e8-53723336e3a7
124. 124Layer: Summarize within: Risk zones. 14 features id 66d57619-49ef-4eb7-831b-bedc47b571d1
125. 125Layer: Summarize within: Risk zones. 14 features id 705e651a-1e75-493d-a532-0c8927aa40cd
126. 126Layer: Find point clusters: Hospitals. 15 features id 991c0297-f207-49c1-9361-29e0d01cd1f5
127. 127Layer: Find point clusters: cluster hulls. 1 features id e06a131f-7989-4c9a-bad5-98f65d8d13ee
128. 128Layer: Find point clusters: Safe havens. 12 features id 1da076c5-d608-4691-b2c6-0458f1989d7e
129. 129Layer: Find point clusters: cluster hulls. 2 features id cb9419c5-0ee1-4dba-a62a-5b39176bab51
130. 130Layer: Find point clusters: Protest events. 80 features id 84d6cdb7-cfe3-4875-8ab3-4c85027ca7be
131. 131Layer: Find point clusters: cluster hulls. 1 features id baa8dcc7-143d-4f94-a316-947abdb534cf
132. 132Layer: Calculate density: Protest events. 386 features id 54008367-d33a-4511-b5e1-8c4a55915ad0
133. 133Layer: Calculate density: Hotels. 82 features id c4eba0a4-6712-445b-bc6c-cf0c26bb9cac
134. 134Layer: Calculate density: Security incidents. 2,240 features id 57bb3eed-15a3-4c3d-b425-89ac6f7edeaf
135. 135Layer: Find outliers: Risk zones. 14 features id e8ee4af5-2649-4990-8578-af9a7ca2a1e6
136. 136Layer: Find nearest: Hospitals. 14 features id cc3abbbe-dedb-4bf4-a08f-10d4882a63d2
137. 137Layer: Find nearest: Hospitals. 14 features id 39cac909-2a3f-47b4-9123-524c237e05eb
138. 138Layer: Find nearest: Hospitals. 6 features id 5b28ce74-706d-460a-b867-4b1ce5f6ed98