Oil and gas pipelines

Sample: Oil and gas pipelines, Midland: report

Project: Sample: Oil and gas pipelines, Midland
Goal: Describe the project Sample: Oil and gas pipelines, Midland: run the analyses that tell the most about its data, draw every result, and write a cited report.
Run: debf2e20-caf9-45f0-b26f-597c66cae39f (running, 11 agent run(s))
Generated: 2026-10-18T05:56:24Z, version 1
Model cost: USD 0.0000 over 0 model call(s)

Data used

Timeline

The timeline counts 1,356 dated events in In-line inspection anomalies, Incidents, Cathodic protection readings per year, from 2016 to 2026 1234. By dataset: In-line inspection anomalies 1,118; Cathodic protection readings 168; Incidents 70 1. The trend is +25 events per year; the second half of the years has 902 against 26 in the first (+3369.2%) 1.

Busiest years

• 2021: 428 events 1
• 2025: 423 events 1
• 2023: 377 events 1

Quietest years

• 2016: 1 event 1
• 2017: 4 events 1
• 2018: 5 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 5. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 5.

Why it was run on this data. Incidents carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per year 6.

What it found. The analysis generated an output layer titled "Emerging hot spots: Incidents" which contained 13 features 7. The input data for this analysis was the Incidents layer 8. The process completed quickly, generating the result in 3 milliseconds 7.

Findings

• Evidenced: Space time hot spots on Incidents: Emerging hot spots: 1 layer saved: Emerging hot spots: Incidents (13 features) 78.
• Evidenced: It reported result, count 13; result, elapsed ms 3; result, resolution 6; result, features used 70; result, time neighbors 0; billing, cost 0.1035 7.

Tool gp_space_time_hot_spots; parameters {"agg": "sum", "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "Incidents (f9c94228)"}, "interval": "year", "bin_shape": "h3", "resolution": 6, "time_field": "date", "value_field": "station_m"}

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 5. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 5.

Why it was run on this data. In-line inspection anomalies carry dates in 'inspected_on': emerging hot spot analysis finds new, intensifying and fading hot spots per month 9.

What it found. The tool utilized the In-line inspection anomalies data as input 11. The analysis identified 56 features for emerging hot spots, saving a layer named Emerging hot spots: In-line inspection anomalies 10.

Findings

• Evidenced: Space time hot spots on In-line inspection anomalies: Emerging hot spots: 1 layer saved: Emerging hot spots: In-line inspection anomalies (56 features) 1011.
• Evidenced: It reported result, count 56; result, elapsed ms 13; result, resolution 7; result, features used 1,118; result, time neighbors 0; billing, cost 0.1559 10.

Tool gp_space_time_hot_spots; parameters {"agg": "sum", "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "In-line inspection anomalies (47cf738b)"}, "interval": "month", "bin_shape": "h3", "resolution": 7, "time_field": "inspected_on", "value_field": "station_m"}

Hot spots

What this analysis is. Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins 12. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 12.

Why it was run on this data. 70 points: Getis-Ord Gi* tests whether the concentrations in Incidents are statistically significant 13.

What it found. Hot spots on Incidents: Hot spot analysis: 1 layer saved: Hot spot analysis: Incidents (70 features) 814. It reported result, count 70; result, elapsed ms 2; result, excluded null 0; result, distance band m 5,000; billing, cost 0.0514; billing, list price 0.0514 14.

Findings

• Evidenced: Hot spots on Incidents: Hot spot analysis: 1 layer saved: Hot spot analysis: Incidents (70 features) 814.
• Evidenced: It reported result, count 70; result, elapsed ms 2; result, excluded null 0; result, distance band m 5,000; billing, cost 0.0514; billing, list price 0.0514 14.

Tool gp_hot_spots; parameters {"field": "station_m", "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "Incidents (f9c94228)"}, "aggregation": "hexagon", "cell_size_m": 5000.0, "distance_band_m": 5000.0}

Hot spots

What this analysis is. Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins 12. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 12.

Why it was run on this data. 1118 points: Getis-Ord Gi* tests whether the concentrations in In-line inspection anomalies are statistically significant 15.

What it found. The hot spot analysis was performed using the In-line inspection anomalies data 11.

Findings

• Evidenced: Hot spots on In-line inspection anomalies: Hot spot analysis: 1 layer saved: Hot spot analysis: In-line inspection anomalies (1118 features) 1116.
• Evidenced: It reported result, count 1,118; result, elapsed ms 22; result, excluded null 0; result, distance band m 2,000; billing, cost 0.07236; billing, list price 0.07236 16.

Tool gp_hot_spots; parameters {"field": "station_m", "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "In-line inspection anomalies (47cf738b)"}, "aggregation": "hexagon", "cell_size_m": 1000.0, "distance_band_m": 2000.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 17. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 17.

Why it was run on this data. 25 points in Valves and stations: a density surface shows where they concentrate 18.

What it found. Calculate density on Valves and stations: Calculate density: 1 layer saved: Calculate density: Valves and stations (41 features) 1920. It reported result, count 41; result, elapsed ms 2; result, bandwidth m 10,000; result, cell size m 5,000; result, excluded null 0; result, max density per km2 1,474 19.

Findings

• Evidenced: Calculate density on Valves and stations: Calculate density: 1 layer saved: Calculate density: Valves and stations (41 features) 1920.
• Evidenced: It reported result, count 41; result, elapsed ms 2; result, bandwidth m 10,000; result, cell size m 5,000; result, excluded null 0; result, max density per km2 1,474 19.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "Valves and stations (db114367)"}, "bandwidth_m": 10000.0, "cell_size_m": 5000.0, "weight_field": "station_m"}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 17. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 17.

Why it was run on this data. 70 points in Incidents: a density surface shows where they concentrate 21.

What it found. Calculate density on Incidents: Calculate density: 1 layer saved: Calculate density: Incidents (144 features) 822. It reported result, count 144; result, elapsed ms 4; result, bandwidth m 5,000; result, cell size m 2,000; result, excluded null 0; result, max density per km2 1.124e+04 22.

Findings

• Evidenced: Calculate density on Incidents: Calculate density: 1 layer saved: Calculate density: Incidents (144 features) 822.
• Evidenced: It reported result, count 144; result, elapsed ms 4; result, bandwidth m 5,000; result, cell size m 2,000; result, excluded null 0; result, max density per km2 1.124e+04 22.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "Incidents (f9c94228)"}, "bandwidth_m": 5000.0, "cell_size_m": 2000.0, "weight_field": "station_m"}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 17. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 17.

Why it was run on this data. 1118 points in In-line inspection anomalies: a density surface shows where they concentrate 23.

What it found. The density calculation processed 1,039 features from the in-line inspection anomalies data 24. The resulting maximum density per km2 was 2.813e+05 24. This analysis took an elapsed time of 35 ms 24. Furthermore, the associated billing cost for this tool was 0.07236 24.

Findings

• Evidenced: Calculate density on In-line inspection anomalies: Calculate density: 1 layer saved: Calculate density: In-line inspection anomalies (1039 features) 1124.
• Evidenced: It reported result, count 1,039; result, elapsed ms 35; result, bandwidth m 2,000; result, cell size m 500; result, excluded null 0; result, max density per km2 2.813e+05 24.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "In-line inspection anomalies (47cf738b)"}, "bandwidth_m": 2000.0, "cell_size_m": 500.0, "weight_field": "station_m"}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 25. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 25.

Why it was run on this data. Count Valves and stations inside each feature of Right of way by type; the counts map as a choropleth (natural breaks, sequential colors) 26.

What it found. Summarize within on Valves and stations, Right of way: Summarize within: 1 layer saved: Summarize within: Right of way (3 features) 202728. It reported result, count 3; result, elapsed ms 2; billing, cost 0.01058; billing, list price 0.01058; billing, upstream cost 0 27.

Findings

• Evidenced: Summarize within on Valves and stations, Right of way: Summarize within: 1 layer saved: Summarize within: Right of way (3 features) 202728.
• Evidenced: It reported result, count 3; result, elapsed ms 2; billing, cost 0.01058; billing, list price 0.01058; billing, upstream cost 0 27.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "station_m"}], "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "Valves and stations (db114367)"}, "group_by": "type", "polygons": {"layer": "Right of way (73b1c8fe)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 25. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 25.

Why it was run on this data. Count Valves and stations inside each feature of Right of way by type; the counts map as a choropleth (natural breaks, sequential colors) 26.

What it found. A summary within of right of way was generated using the gp_summarize_within tool 29. The inputs for this process included incidents 8 and right of way data 28 29. The result produced contained 3 entries 29, and the process required 2 elapsed milliseconds 29.

Findings

• Evidenced: Summarize within on Incidents, Right of way: Summarize within: 1 layer saved: Summarize within: Right of way (3 features) 82829.
• Evidenced: It reported result, count 3; result, elapsed ms 2; billing, cost 0.01148; billing, list price 0.01148; billing, upstream cost 0 29.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "station_m"}], "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "Incidents (f9c94228)"}, "group_by": "cause", "polygons": {"layer": "Right of way (73b1c8fe)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 25. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 25.

Why it was run on this data. Count Valves and stations inside each feature of Right of way by type; the counts map as a choropleth (natural breaks, sequential colors) 26.

What it found. Summarize within on Crossings, Right of way: Summarize within: 1 layer saved: Summarize within: Right of way (3 features) 283031. It reported result, count 3; result, elapsed ms 2; billing, cost 0.01036; billing, list price 0.01036; billing, upstream cost 0 30.

Findings

• Evidenced: Summarize within on Crossings, Right of way: Summarize within: 1 layer saved: Summarize within: Right of way (3 features) 283031.
• Evidenced: It reported result, count 3; result, elapsed ms 2; billing, cost 0.01036; billing, list price 0.01036; billing, upstream cost 0 30.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "station_m"}], "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "Crossings (d8fae91e)"}, "group_by": "type", "polygons": {"layer": "Right of way (73b1c8fe)"}}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 32. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 32.

Why it was run on this data. DBSCAN finds dense groups of Valves and stations and separates noise 33.

What it found. Find point clusters on Valves and stations: Find point clusters: 2 layers saved: Find point clusters: Valves and stations (25 features); Find point clusters: cluster hulls (1 feature) 2034. It reported result, count 25; result, noise 0; result, distance m 5,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0505 34.

Findings

• Evidenced: Find point clusters on Valves and stations: Find point clusters: 2 layers saved: Find point clusters: Valves and stations (25 features); Find point clusters: cluster hulls (1 feature) 2034.
• Evidenced: It reported result, count 25; result, noise 0; result, distance m 5,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0505 34.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "Valves and stations (db114367)"}, "distance_m": 5000.0, "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 32. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 32.

Why it was run on this data. DBSCAN finds dense groups of Incidents and separates noise 35.

What it found. Find point clusters on Incidents: Find point clusters: 2 layers saved: Find point clusters: Incidents (70 features); Find point clusters: cluster hulls (1 feature) 836. It reported result, count 70; result, noise 2; result, distance m 5,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0514 36.

Findings

• Evidenced: Find point clusters on Incidents: Find point clusters: 2 layers saved: Find point clusters: Incidents (70 features); Find point clusters: cluster hulls (1 feature) 836.
• Evidenced: It reported result, count 70; result, noise 2; result, distance m 5,000; result, elapsed ms 2; result, min points 3; billing, cost 0.0514 36.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "Incidents (f9c94228)"}, "distance_m": 5000.0, "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 32. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 32.

Why it was run on this data. DBSCAN finds dense groups of Crossings and separates noise 37.

What it found. Find point clusters on Crossings: Find point clusters: 2 layers saved: Find point clusters: Crossings (14 features); Find point clusters: cluster hulls (1 feature) 3138. It reported result, count 14; result, noise 4; result, distance m 5,000; result, elapsed ms 1; result, min points 3; billing, cost 0.05028 38.

Findings

• Evidenced: Find point clusters on Crossings: Find point clusters: 2 layers saved: Find point clusters: Crossings (14 features); Find point clusters: cluster hulls (1 feature) 3138.
• Evidenced: It reported result, count 14; result, noise 4; result, distance m 5,000; result, elapsed ms 1; result, min points 3; billing, cost 0.05028 38.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "Crossings (d8fae91e)"}, "distance_m": 5000.0, "min_points": 3}

Find nearest

What this analysis is. Find nearest: The k nearest features for each input, with geodesic distance and bearing 39. In ArcGIS it is Find Nearest or Near; in QGIS, native:joinbynearest or native:nearestneighbouranalysis 39.

Why it was run on this data. The nearest of Crossings to each of Valves and stations, with distances 40.

What it found. Find nearest on Crossings, Valves and stations: Find nearest: 1 layer saved: Find nearest: Valves and stations (24 features) 203141. It reported result, count 24; result, elapsed ms 2; billing, cost 0.01078; billing, list price 0.01078; billing, upstream cost 0 41.

Findings

• Evidenced: Find nearest on Crossings, Valves and stations: Find nearest: 1 layer saved: Find nearest: Valves and stations (24 features) 203141.
• Evidenced: It reported result, count 24; result, elapsed ms 2; billing, cost 0.01078; billing, list price 0.01078; billing, upstream cost 0 41.

Tool gp_find_nearest; parameters {"k": 1, "to": {"layer": "Crossings (d8fae91e)"}, "from": {"layer": "Valves and stations (db114367)"}, "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "max_distance_m": 5000.0}

Find nearest

What this analysis is. Find nearest: The k nearest features for each input, with geodesic distance and bearing 39. In ArcGIS it is Find Nearest or Near; in QGIS, native:joinbynearest or native:nearestneighbouranalysis 39.

Why it was run on this data. The nearest of Crossings to each of Valves and stations, with distances 40.

What it found. The gp_find_nearest tool was utilized, taking Valves and stations 20 and Pipeline segments 43 as input 42. The function successfully returned 25 results 42. The operation completed with an elapsed time of 12 ms 42, and the associated costs were reported as 0.01248 42.

Findings

• Evidenced: Find nearest on Pipeline segments, Valves and stations: Find nearest: 1 layer saved: Find nearest: Valves and stations (25 features) 204243.
• Evidenced: It reported result, count 25; result, elapsed ms 12; billing, cost 0.01248; billing, list price 0.01248; billing, upstream cost 0 42.

Tool gp_find_nearest; parameters {"k": 1, "to": {"layer": "Pipeline segments (07d2778e)"}, "from": {"layer": "Valves and stations (db114367)"}, "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "max_distance_m": 5000.0}

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 44. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator 44.

Why it was run on this data. Near repeat analysis tests whether Incidents close in space also follow each other within 30 days 45.

What it found. The Near repeat classification tool was run using Incidents data 8 and generated a layer containing 70

Findings

• Evidenced: Near repeat on Incidents: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Incidents (70 features) 846.
• Evidenced: It reported result, days 30; result, count 70; result, chains 3; result, distance m 2,000; result, elapsed ms 4; result, pairs linked 3 46.

Tool gp_near_repeat; parameters {"days": 30, "project": "814309a7-f84e-4437-a8ec-209438a3f0a6", "features": {"layer": "Incidents (f9c94228)"}, "distance_m": 2000.0, "time_field": "date"}

Maps and charts

The run also made 20 map image(s) 4748495051525354555657585960616263646566 and 27 chart(s) 676869707172737475767778798081828384858687888990919293.

What the map shows. This map visualizes the analysis of emerging hot spots for incidents across the mapped area. The legend explicitly states that no pattern was detected for this category of incidents. Graphically, the map is drawn using a tessellation of hexagonal polygons, all of which are uniformly filled with a solid grey shade. Because no pattern was detected, the map does not display any visible clusters, significant gradients, or distinguishable high or low activity areas. 47

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 year by dataset. 33 rows id 27fa3a89-f6f1-4b50-8932-e49cafec5ffa
2. 2Dataset: In-line inspection anomalies. vector, 1118 rows id ab2540a1-9c20-423b-b653-0cf763c5cc8e
3. 3Dataset: Incidents. vector, 70 rows id 663eca22-c04e-4522-8f28-ddc430b2a25c
4. 4Dataset: Cathodic protection readings. table, 168 rows id aa287ab3-e04d-46e8-9c49-c47c5badeaec
5. 5Catalog: 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
6. 6Plan: Space time hot spots of Incidents (the Start plan's candidate c_4bb802ff0ca3). Incidents carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per year. id 448d8646-960a-4050-9835-47097cdccdec:c_4bb802ff0ca3
7. 7Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Incidents (13 features). id 23f4a12e-8bc2-4bed-8142-0185e3507423:0
8. 8Layer: Incidents. 70 features id f9c94228-f003-4199-ac8c-9f7c7ce6573e
9. 9Plan: Space time hot spots of In-line inspection anomalies (the Start plan's candidate c_ae37f1059eb0). In-line inspection anomalies carry dates in 'inspected_on': emerging hot spot analysis finds new, intensifying and fading hot spots per month. id 448d8646-960a-4050-9835-47097cdccdec:c_ae37f1059eb0
10. 10Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: In-line inspection anomalies (56 features). id 23f4a12e-8bc2-4bed-8142-0185e3507423:1
11. 11Layer: In-line inspection anomalies. 1,118 features id 47cf738b-1c03-4bc9-bfb4-57cf3a587582
12. 12Catalog: 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
13. 13Plan: Hot spots of Incidents (the Start plan's candidate c_08af186146f2). 70 points: Getis-Ord Gi* tests whether the concentrations in Incidents are statistically significant. id 448d8646-960a-4050-9835-47097cdccdec:c_08af186146f2
14. 14Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Incidents (70 features). id 20eab220-57ff-40eb-9e8f-f1ba9a425785:0
15. 15Plan: Hot spots of In-line inspection anomalies (the Start plan's candidate c_5c37a51e83ac). 1118 points: Getis-Ord Gi* tests whether the concentrations in In-line inspection anomalies are statistically significant. id 448d8646-960a-4050-9835-47097cdccdec:c_5c37a51e83ac
16. 16Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: In-line inspection anomalies (1118 features). id 20eab220-57ff-40eb-9e8f-f1ba9a425785:1
17. 17Catalog: 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
18. 18Plan: Kernel density of Valves and stations (the Start plan's candidate c_43c88752b5a9). 25 points in Valves and stations: a density surface shows where they concentrate. id 448d8646-960a-4050-9835-47097cdccdec:c_43c88752b5a9
19. 19Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Valves and stations (41 features). id b394d06b-54ec-4401-9565-ef318584fd16:0
20. 20Layer: Valves and stations. 25 features id db114367-2f18-40d1-b793-29e91297403d
21. 21Plan: Kernel density of Incidents (the Start plan's candidate c_d907267d52f7). 70 points in Incidents: a density surface shows where they concentrate. id 448d8646-960a-4050-9835-47097cdccdec:c_d907267d52f7
22. 22Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Incidents (144 features). id b394d06b-54ec-4401-9565-ef318584fd16:1
23. 23Plan: Kernel density of In-line inspection anomalies (the Start plan's candidate c_61f2c99818e2). 1118 points in In-line inspection anomalies: a density surface shows where they concentrate. id 448d8646-960a-4050-9835-47097cdccdec:c_61f2c99818e2
24. 24Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: In-line inspection anomalies (1039 features). id b394d06b-54ec-4401-9565-ef318584fd16:2
25. 25Catalog: Summarize within (operation catalog, gp_summarize_within). Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons. ArcGIS: Summarize Within, Aggregate Points. QGIS: native:countpointsinpolygon, native:sumlinelengths. id gp_summarize_within
26. 26Plan: Valves and stations per feature of Right of way (the Start plan's candidate c_b6eafe06ef22). Count Valves and stations inside each feature of Right of way by type; the counts map as a choropleth (natural breaks, sequential colors). id 448d8646-960a-4050-9835-47097cdccdec:c_b6eafe06ef22
27. 27Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Right of way (3 features). id ea2f3752-846d-448f-8548-5b0a1c92e970:0
28. 28Layer: Right of way. 3 features id 73b1c8fe-ccbe-4a85-be57-8b42d4cb2f98
29. 29Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Right of way (3 features). id ea2f3752-846d-448f-8548-5b0a1c92e970:1
30. 30Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Right of way (3 features). id ea2f3752-846d-448f-8548-5b0a1c92e970:2
31. 31Layer: Crossings. 14 features id d8fae91e-ebf5-4f86-914b-576c03ec8036
32. 32Catalog: 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
33. 33Plan: Cluster points of Valves and stations (the Start plan's candidate c_7d2ecc0e9b43). DBSCAN finds dense groups of Valves and stations and separates noise. id 448d8646-960a-4050-9835-47097cdccdec:c_7d2ecc0e9b43
34. 34Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Valves and stations (25 features); Find point clusters: cluster hulls (1 feature). id 507d8dab-fdbf-4108-a054-5cc8d7218340:0
35. 35Plan: Cluster points of Incidents (the Start plan's candidate c_4583222e078a). DBSCAN finds dense groups of Incidents and separates noise. id 448d8646-960a-4050-9835-47097cdccdec:c_4583222e078a
36. 36Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Incidents (70 features); Find point clusters: cluster hulls (1 feature). id 507d8dab-fdbf-4108-a054-5cc8d7218340:1
37. 37Plan: Cluster points of Crossings (the Start plan's candidate c_8f718b3a3142). DBSCAN finds dense groups of Crossings and separates noise. id 448d8646-960a-4050-9835-47097cdccdec:c_8f718b3a3142
38. 38Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Crossings (14 features); Find point clusters: cluster hulls (1 feature). id 507d8dab-fdbf-4108-a054-5cc8d7218340:2
39. 39Catalog: 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
40. 40Plan: Find nearest of Valves and stations and Crossings (the Start plan's candidate c_e87a78f2fa16). The nearest of Crossings to each of Valves and stations, with distances. id 448d8646-960a-4050-9835-47097cdccdec:c_e87a78f2fa16
41. 41Tool result: gp_find_nearest, step 0. Find nearest: 1 layer saved: Find nearest: Valves and stations (24 features). id 98d845c3-a74a-4d63-8fbd-35eec3976f40:0
42. 42Tool result: gp_find_nearest, step 1. Find nearest: 1 layer saved: Find nearest: Valves and stations (25 features). id 98d845c3-a74a-4d63-8fbd-35eec3976f40:1
43. 43Layer: Pipeline segments. 99 features id 07d2778e-6a97-4ea5-9c49-141bc281a75e
44. 44Catalog: 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
45. 45Plan: Near repeat of Incidents (the Start plan's candidate c_4d0990e5511d). Near repeat analysis tests whether Incidents close in space also follow each other within 30 days. id 448d8646-960a-4050-9835-47097cdccdec:c_4d0990e5511d
46. 46Tool result: gp_near_repeat, step 0. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Incidents (70 features). id 17f2580a-7f80-4b8b-8777-ba9b5e830194:0
47. 47Rendering: Map: Emerging hot spots: Incidents id 9d77b25c-7721-4e11-a5b8-69e26ace1947
48. 48Rendering: Map: Emerging hot spots: In-line inspection anomalies id 90c2582b-d73c-4982-b407-80c5f9808033
49. 49Rendering: Map: Hot spot analysis: Incidents id 0b24f3d0-b06f-47f1-b372-43fc67cd8551
50. 50Rendering: Map: Hot spot analysis: In-line inspection anomalies id 646edb97-f9c0-4bb5-8f51-a08c09d511e1
51. 51Rendering: Map: Calculate density: Valves and stations id fa84664f-57a4-4a31-9378-cdfa74fa2ba5
52. 52Rendering: Map: Calculate density: Incidents id b5fc9dfb-5cc7-4f8a-949d-ff6deb264068
53. 53Rendering: Map: Calculate density: In-line inspection anomalies id 91e61349-3f8f-4df0-b85a-43246e0fba55
54. 54Rendering: Map: Summarize within: Right of way id 43517d52-4c91-4a8a-b917-7c411a38d5ca
55. 55Rendering: Map: Summarize within: Right of way id df7b3a24-22a5-4690-8eb3-f3af1d42b294
56. 56Rendering: Map: Summarize within: Right of way id 6a8fcbbf-760e-42ab-86f6-975c6006fbff
57. 57Rendering: Map: Find point clusters: Valves and stations id 39caaf58-743b-4be4-920b-cf81ce83ab6f
58. 58Rendering: Map: Find point clusters: cluster hulls id 1c7d602b-58b5-4916-ae6b-1c8f781c42bc
59. 59Rendering: Map: Find point clusters: Incidents id 23a515ac-6326-42d9-9b84-da366f111f0e
60. 60Rendering: Map: Find point clusters: cluster hulls id 747a058e-9c8c-4dba-b805-099266717c23
61. 61Rendering: Map: Find point clusters: Crossings id 880dba8e-22b4-42a1-bc63-5b6f17eba956
62. 62Rendering: Map: Find point clusters: cluster hulls id 98f83533-fdad-4c1e-acfc-495e89277355
63. 63Rendering: Map: High consequence areas by population id be0e9735-069a-4144-bcc3-4a5076868b7c
64. 64Rendering: Map: Find nearest: Valves and stations id bc01fc55-62d8-4244-a6d8-2793a3a96a49
65. 65Rendering: Map: Find nearest: Valves and stations id 389dedf9-c64f-4e07-bdee-1671f0aac289
66. 66Rendering: Map: Repeat and near repeat classification: Incidents id 406a8b02-086d-430d-8e92-300ea2f58ebd
67. 67Rendering: Chart: Pipeline segments over time (install_year). 8 rows id deca339b-546d-42f4-b999-c2fdb81c2551
68. 68Rendering: Chart: Incidents over time (date). 55 rows id 28e9d8db-0969-4b8d-83d8-bd378ada30cc
69. 69Rendering: Chart: Cathodic protection readings over time (date). 33 rows id 725b8aba-f6b3-4275-a079-6901dd4884a9
70. 70Rendering: Chart: Segment operating limits over time (last_hydrotest_year). 8 rows id e6a700bd-e844-4aab-9f11-fb5d6ff8d9ac
71. 71Rendering: Chart: Emerging hot spots: Incidents. 5 rows id ed2a12a0-1331-4e78-9b34-65bbc3466482
72. 72Rendering: Chart: Emerging hot spots: In-line inspection anomalies. 7 rows id 568b890a-b0be-491f-b8c3-f5baca0de437
73. 73Rendering: Chart: Hot spot analysis: Incidents. 22 rows id d61118a9-f614-4df7-96ea-d1853818f5cf
74. 74Rendering: Chart: Hot spot analysis: In-line inspection anomalies. 60 rows id 8cecacc9-055e-4d3f-9f80-ad5581542518
75. 75Rendering: Chart: Calculate density: Valves and stations. 7 rows id bbe9d65a-31d2-4828-a906-2d7744159241
76. 76Rendering: Chart: Calculate density: Incidents. 9 rows id 648c7e0d-4975-4e6e-a88d-8be424d7ce73
77. 77Rendering: Chart: Calculate density: In-line inspection anomalies. 12 rows id 45573bae-3b31-4aab-9b04-e8beccc02c66
78. 78Rendering: Chart: Summarize within: Right of way. 5 rows id 80a42914-05e4-4d36-8d7a-4764e8358c4f
79. 79Rendering: Chart: Summarize within: Right of way. 5 rows id 3fdf97cb-7ffa-42e4-a600-ff2a1546d530
80. 80Rendering: Chart: Summarize within: Right of way. 5 rows id a91d845a-309d-4fb0-b09d-704693b7a33b
81. 81Rendering: Chart: Find point clusters: Valves and stations. 1 rows id d116571e-86a6-4d39-89f0-a9b3ba40672e
82. 82Rendering: Chart: Find point clusters: cluster hulls. 1 rows id a9ae9300-1702-49d4-bd47-7cc40008cadb
83. 83Rendering: Chart: Find point clusters: Incidents. 2 rows id 6038f799-99b3-4a72-971a-4641713d3bc3
84. 84Rendering: Chart: Find point clusters: cluster hulls. 1 rows id 01936942-9161-4210-a318-1e6ed3863d39
85. 85Rendering: Chart: Find point clusters: Crossings. 2 rows id 708723bf-3747-4536-aa03-97d38d05bdf3
86. 86Rendering: Chart: Find point clusters: cluster hulls. 1 rows id 0f980414-a050-4d48-9c30-1718d1a53d68
87. 87Rendering: Chart: Find nearest: Valves and stations. 20 rows id ab68370c-1fcf-4a5a-bbf1-95daec887c63
88. 88Rendering: Chart: Find nearest: Valves and stations. 20 rows id 03a124ee-3f3c-47e6-8427-457bea343092
89. 89Rendering: Chart: Repeat and near repeat classification: Incidents. 55 rows id e0888086-d628-40b1-8e42-a28e2ad03314
90. 90Rendering: Chart: Repeat and near repeat classification: Incidents over time. 55 rows id f7464338-c561-4ab3-8d45-b9664fa68c68
91. 91Rendering: Chart: Find point clusters: Incidents over time. 55 rows id 3f3f2588-8f02-47f7-8ee8-b3b05074a4c0
92. 92Rendering: Chart: Hot spot analysis: In-line inspection anomalies over time. 294 rows id 9615d7aa-ad69-493e-bcb4-301f153345a1
93. 93Rendering: Chart: Hot spot analysis: Incidents over time. 55 rows id 5ed77f86-ae16-45fc-a707-ffe729229767
94. 94Dataset: Pipeline segments. vector, 99 rows id aa24fa37-25a4-401a-aa0f-50268903f48d
95. 95Dataset: Valves and stations. vector, 25 rows id 3d5c604b-238e-40fa-bcba-7a9411f150fb
96. 96Dataset: High consequence areas. vector, 6 rows id c293e365-7a24-403b-9a39-f76e3b3326f1
97. 97Dataset: Crossings. vector, 14 rows id 782618e2-11ce-41b2-b4fd-45a0e8fece3e
98. 98Dataset: Right of way. vector, 3 rows id bb3c91ac-eea5-46a8-8560-01a305ee1a0d
99. 99Dataset: Segment operating limits. table, 99 rows id 09899115-5eca-4e48-a25a-e02a48f1f415
100. 100Dataset: Integrity management summary. document, ? rows id e4123571-4649-4ac8-85d6-bae9373cef36
101. 101Record: Sample: Oil and gas pipelines, Midland id 814309a7-f84e-4437-a8ec-209438a3f0a6
102. 102Layer: Emerging hot spots: Incidents. 13 features id 5ccafcff-cff1-4e9d-b2e3-765c77156339
103. 103Layer: Emerging hot spots: In-line inspection anomalies. 56 features id 353953a9-5c13-479e-8f1b-6501da1582af
104. 104Layer: Hot spot analysis: Incidents. 70 features id ec8a6c4b-e103-4cfc-9121-a54f3cec6e6e
105. 105Layer: Hot spot analysis: In-line inspection anomalies. 1,118 features id bacc7686-f7e9-4955-a0cf-9db64aa4870e
106. 106Layer: Calculate density: Valves and stations. 41 features id cc1d20be-5dca-4029-8f6b-33c27e79db1c
107. 107Layer: Calculate density: Incidents. 144 features id 2e0cc790-5fb1-484a-9922-bc678772fb7d
108. 108Layer: Calculate density: In-line inspection anomalies. 1,039 features id 6b4b4463-7e80-40f9-93a0-0cb62e92dcba
109. 109Layer: Summarize within: Right of way. 3 features id 13538db0-7d38-497b-94d1-40cf60ad245d
110. 110Layer: Summarize within: Right of way. 3 features id 227062b6-aa48-4440-a0f6-277e670c7170
111. 111Layer: Summarize within: Right of way. 3 features id 06a7dd75-70ab-43d0-8420-66697a994dcc
112. 112Layer: Find point clusters: Valves and stations. 25 features id 3110df92-7371-4f5e-8e6c-b97b894bb024
113. 113Layer: Find point clusters: cluster hulls. 1 features id 060ccd5c-fd41-45f5-a05f-e6acaf40d13a
114. 114Layer: Find point clusters: Incidents. 70 features id 6114c590-c23e-4ea0-a8b5-64c271fd3ab1
115. 115Layer: Find point clusters: cluster hulls. 1 features id 3b9663d6-98db-48e7-91e9-6c73f7269773
116. 116Layer: Find point clusters: Crossings. 14 features id d6bec857-d779-4b62-a669-ed072d7ff82e
117. 117Layer: Find point clusters: cluster hulls. 1 features id 97d9231b-03fd-4842-802d-d3268005cde9
118. 118Layer: Find nearest: Valves and stations. 24 features id 93cedf1e-ed61-4ee9-81a7-acea61799ce3
119. 119Layer: Find nearest: Valves and stations. 25 features id 07d06af9-ca9c-459d-905a-591ff5b8a7b4
120. 120Layer: Repeat and near repeat classification: Incidents. 70 features id 190a3e17-cf0e-4dd6-b885-45720a8e06f1