Education

Sample: Education, Toronto: report

Project: Sample: Education, Toronto
Goal: Describe the project Sample: Education, Toronto: run the analyses that tell the most about its data, draw every result, and write a cited report.
Run: 95849996-3ab1-4ed0-9250-b76a5d0b816f (running, 11 agent run(s))
Generated: 2026-10-18T06:06:03Z, version 1
Model cost: USD 0.0000 over 0 model call(s)

Data used

Timeline

The timeline counts 2,810 dated events in Student homes, Housing developments, Enrollment by school and year per quarter, from Q3 2021 to Q4 2027 1234. By dataset: Student homes 2,500; Enrollment by school and year 270; Housing developments 40 1. The trend is +2.84 events per quarter; the second half of the quarters has 1,709 against 1,101 in the first (+55.2%) 1. 7 of the 26 quarters had no events 1.

Busiest quarters

• Q3 2026: 459 events 1
• Q3 2025: 433 events 1
• Q3 2024: 383 events 1

Quietest quarters with events

• Q1 2023: 2 events 1
• Q3 2027: 2 events 1
• Q4 2027: 2 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. Student homes carry dates in 'enrolled_on': emerging hot spot analysis finds new, intensifying and fading hot spots per month 6.

What it found. The analysis generated 96 features for the "Emerging hot spots: Student homes" layer 7.

Findings

• Evidenced: Space time hot spots on Student homes: Emerging hot spots: 1 layer saved: Emerging hot spots: Student homes (96 features) 78.
• Evidenced: It reported result, count 96; result, elapsed ms 19; result, resolution 8; result, features used 2,500; result, time neighbors 0; billing, cost 0.225 7.

Tool gp_space_time_hot_spots; parameters {"project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Student homes (05789208)"}, "interval": "month", "bin_shape": "h3", "resolution": 8, "time_field": "enrolled_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 5. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 5.

Why it was run on this data. Housing developments carry dates in 'completion_month': emerging hot spot analysis finds new, intensifying and fading hot spots per month 9.

What it found. The analysis processed the Housing developments layer 11, resulting in an Emerging hot spots layer containing 22 features 10.

Findings

• Evidenced: Space time hot spots on Housing developments: Emerging hot spots: 1 layer saved: Emerging hot spots: Housing developments (22 features) 1011.
• Evidenced: It reported result, count 22; result, elapsed ms 4; result, resolution 7; result, features used 40; result, time neighbors 0; billing, cost 0.102 10.

Tool gp_space_time_hot_spots; parameters {"agg": "sum", "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Housing developments (afc1332b)"}, "interval": "month", "bin_shape": "h3", "resolution": 7, "time_field": "completion_month", "value_field": "units"}

Near repeat

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

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

What it found. Near repeat on Student homes: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Student homes (2500 features) 814. It reported result, days 30; result, count 2,500; result, chains 284; result, distance m 200; result, elapsed ms 61; result, pairs linked 1,594 14.

Findings

• Evidenced: Near repeat on Student homes: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Student homes (2500 features) 814.
• Evidenced: It reported result, days 30; result, count 2,500; result, chains 284; result, distance m 200; result, elapsed ms 61; result, pairs linked 1,594 14.

Tool gp_near_repeat; parameters {"days": 30, "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Student homes (05789208)"}, "distance_m": 200.0, "time_field": "enrolled_on"}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 15. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 15.

Why it was run on this data. Count Student homes inside each feature of School catchments by transport; the counts map as a choropleth (natural breaks, sequential colors) 16.

What it found. Summarize within on Student homes, School catchments: Summarize within: 1 layer saved: Summarize within: School catchments (45 features) 81718. It reported result, count 45; result, elapsed ms 6; billing, cost 0.06092; billing, list price 0.06092; billing, upstream cost 0 17.

Findings

• Evidenced: Summarize within on Student homes, School catchments: Summarize within: 1 layer saved: Summarize within: School catchments (45 features) 81718.
• Evidenced: It reported result, count 45; result, elapsed ms 6; billing, cost 0.06092; billing, list price 0.06092; billing, upstream cost 0 17.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "grade"}], "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Student homes (05789208)"}, "group_by": "transport", "polygons": {"layer": "School catchments (534622c6)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 15. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 15.

Why it was run on this data. Count Student homes inside each feature of School catchments by transport; the counts map as a choropleth (natural breaks, sequential colors) 16.

What it found. Summarize within on Childcare centers, School catchments: Summarize within: 1 layer saved: Summarize within: School catchments (45 features) 181920. It reported result, count 45; result, elapsed ms 1; billing, cost 0.01212; billing, list price 0.01212; billing, upstream cost 0 19.

Findings

• Evidenced: Summarize within on Childcare centers, School catchments: Summarize within: 1 layer saved: Summarize within: School catchments (45 features) 181920.
• Evidenced: It reported result, count 45; result, elapsed ms 1; billing, cost 0.01212; billing, list price 0.01212; billing, upstream cost 0 19.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "spaces"}], "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Childcare centers (9489916f)"}, "polygons": {"layer": "School catchments (534622c6)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 15. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 15.

Why it was run on this data. Count Student homes inside each feature of School catchments by transport; the counts map as a choropleth (natural breaks, sequential colors) 16.

What it found. Summarize within on Housing developments, School catchments: Summarize within: 1 layer saved: Summarize within: School catchments (45 features) 111821. It reported result, count 45; result, elapsed ms 1; billing, cost 0.01172; billing, list price 0.01172; billing, upstream cost 0 21.

Findings

• Evidenced: Summarize within on Housing developments, School catchments: Summarize within: 1 layer saved: Summarize within: School catchments (45 features) 111821.
• Evidenced: It reported result, count 45; result, elapsed ms 1; billing, cost 0.01172; billing, list price 0.01172; billing, upstream cost 0 21.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "units"}], "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Housing developments (afc1332b)"}, "polygons": {"layer": "School catchments (534622c6)"}}

Hot spots

What this analysis is. Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins 22. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 22.

Why it was run on this data. 2500 points: Getis-Ord Gi* tests whether the concentrations in Student homes are statistically significant 23.

What it found. Hot spots on Student homes: Hot spot analysis: 1 layer saved: Hot spot analysis: Student homes (281 features) 824. It reported result, bins 281; result, count 281; result, elapsed ms 23; result, cell size m 500; result, incidents used 2,500; result, distance band m 500 24.

Findings

• Evidenced: Hot spots on Student homes: Hot spot analysis: 1 layer saved: Hot spot analysis: Student homes (281 features) 824.
• Evidenced: It reported result, bins 281; result, count 281; result, elapsed ms 23; result, cell size m 500; result, incidents used 2,500; result, distance band m 500 24.

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

Tool gp_hot_spots; parameters {"project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Student homes (05789208)"}, "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 22. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 22.

Why it was run on this data. 60 points: Getis-Ord Gi* tests whether the concentrations in Childcare centers are statistically significant 25.

What it found. Hot spots on Childcare centers: Hot spot analysis: 1 layer saved: Hot spot analysis: Childcare centers (20 features) 2026. It reported result, bins 20; result, count 20; result, elapsed ms 2; result, cell size m 2,000; result, incidents used 60; result, distance band m 2,000 26.

Findings

• Evidenced: Hot spots on Childcare centers: Hot spot analysis: 1 layer saved: Hot spot analysis: Childcare centers (20 features) 2026.
• Evidenced: It reported result, bins 20; result, count 20; result, elapsed ms 2; result, cell size m 2,000; result, incidents used 60; result, distance band m 2,000 26.

Tool gp_hot_spots; parameters {"project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Childcare centers (9489916f)"}, "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 22. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 22.

Why it was run on this data. 40 points: Getis-Ord Gi* tests whether the concentrations in Housing developments are statistically significant 27.

What it found. The hot spot analysis was performed using the Housing developments layer 11 and saved one layer, "Hot spot analysis: Housing developments," containing 40 features 28. This execution step resulted in an elapsed time of 1 ms 28. The analysis also generated a result set with 0 excluded null values 28, and total billing costs for the process amounted to 0.0508 28.

Findings

• Evidenced: Hot spots on Housing developments: Hot spot analysis: 1 layer saved: Hot spot analysis: Housing developments (40 features) 1128.
• Evidenced: It reported result, count 40; result, elapsed ms 1; result, excluded null 0; result, distance band m 2,000; billing, cost 0.0508; billing, list price 0.0508 28.

Tool gp_hot_spots; parameters {"field": "units", "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Housing developments (afc1332b)"}, "aggregation": "hexagon", "cell_size_m": 2000.0, "distance_band_m": 2000.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 29. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 29.

Why it was run on this data. 2500 points in Student homes: a density surface shows where they concentrate 30.

What it found. The density calculation was performed using the Student homes dataset 31. This process analyzed 1,838 features and reported a maximum density per km2 of 388.8 31.

Findings

• Evidenced: Calculate density on Student homes: Calculate density: 1 layer saved: Calculate density: Student homes (1838 features) 831.
• Evidenced: It reported result, count 1,838; result, elapsed ms 59; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 388.8 31.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Student homes (05789208)"}, "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 29. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 29.

Why it was run on this data. 60 points in Childcare centers: a density surface shows where they concentrate 32.

What it found. Calculate density on Childcare centers: Calculate density: 1 layer saved: Calculate density: Childcare centers (112 features) 2033. It reported result, count 112; result, elapsed ms 3; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 1.453 33.

Findings

• Evidenced: Calculate density on Childcare centers: Calculate density: 1 layer saved: Calculate density: Childcare centers (112 features) 2033.
• Evidenced: It reported result, count 112; result, elapsed ms 3; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 1.453 33.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Childcare centers (9489916f)"}, "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 29. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 29.

Why it was run on this data. 40 points in Housing developments: a density surface shows where they concentrate 34.

What it found. Calculate density on Housing developments: Calculate density: 1 layer saved: Calculate density: Housing developments (80 features) 1135. It reported result, count 80; result, elapsed ms 3; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 881.6 35.

Findings

• Evidenced: Calculate density on Housing developments: Calculate density: 1 layer saved: Calculate density: Housing developments (80 features) 1135.
• Evidenced: It reported result, count 80; result, elapsed ms 3; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 881.6 35.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Housing developments (afc1332b)"}, "bandwidth_m": 2000.0, "cell_size_m": 1000.0, "weight_field": "units"}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 36. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 36.

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

What it found. Find point clusters on Student homes: Find point clusters: 2 layers saved: Find point clusters: Student homes (2500 features); Find point clusters: cluster hulls (1 feature) 838. It reported result, count 2,500; result, noise 1,490; result, distance m 200; result, elapsed ms 32; result, min points 20; billing, cost 0.1 38.

Findings

• Evidenced: Find point clusters on Student homes: Find point clusters: 2 layers saved: Find point clusters: Student homes (2500 features); Find point clusters: cluster hulls (1 feature) 838.
• Evidenced: It reported result, count 2,500; result, noise 1,490; result, distance m 200; result, elapsed ms 32; result, min points 20; billing, cost 0.1 38.

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

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Student homes (05789208)"}, "distance_m": 200.0, "min_points": 20}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 36. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 36.

Why it was run on this data. DBSCAN finds dense groups of Childcare centers and separates noise 39.

What it found. Find point clusters on Childcare centers: Find point clusters: 2 layers saved: Find point clusters: Childcare centers (60 features); Find point clusters: cluster hulls (8 features) 2040. It reported result, count 60; result, noise 1; result, distance m 1,000; result, elapsed ms 3; result, min points 3; billing, cost 0.0512 40.

Findings

• Evidenced: Find point clusters on Childcare centers: Find point clusters: 2 layers saved: Find point clusters: Childcare centers (60 features); Find point clusters: cluster hulls (8 features) 2040.
• Evidenced: It reported result, count 60; result, noise 1; result, distance m 1,000; result, elapsed ms 3; result, min points 3; billing, cost 0.0512 40.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Childcare centers (9489916f)"}, "distance_m": 1000.0, "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 36. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 36.

Why it was run on this data. DBSCAN finds dense groups of Housing developments and separates noise 41.

What it found. Find point clusters on Housing developments: Find point clusters: 2 layers saved: Find point clusters: Housing developments (40 features); Find point clusters: cluster hulls (1 feature) 1142. It reported result, count 40; result, noise 3; result, distance m 2,000; result, elapsed ms 1; result, min points 3; billing, cost 0.0508 42.

Findings

• Evidenced: Find point clusters on Housing developments: Find point clusters: 2 layers saved: Find point clusters: Housing developments (40 features); Find point clusters: cluster hulls (1 feature) 1142.
• Evidenced: It reported result, count 40; result, noise 3; result, distance m 2,000; result, elapsed ms 1; result, min points 3; billing, cost 0.0508 42.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "Housing developments (afc1332b)"}, "distance_m": 2000.0, "min_points": 3}

Outliers

What this analysis is. Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers 43. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) 43.

Why it was run on this data. Local Moran's I maps clusters and outliers of 'children_5_17' 44.

What it found. The gp_outliers tool was executed using the School catchments layer 18. The process generated 45 results 45, completing with an elapsed time of 4 ms 45. Furthermore, the analysis utilized 199 permutations 45 and established a distance band of 1,430 m 45.

Findings

• Evidenced: Outliers on School catchments: Find outliers: 1 layer saved: Find outliers: School catchments (45 features) 1845.
• Evidenced: It reported result, count 45; result, elapsed ms 4; result, permutations 199; result, excluded null 0; result, distance band m 1,430; billing, cost 0.05135 45.

Tool gp_outliers; parameters {"field": "children_5_17", "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "School catchments (534622c6)"}, "aggregation": "hexagon"}

Spatial autocorrelation

What this analysis is. Spatial autocorrelation: Global Moran’s I: is the pattern clustered, dispersed or random 46. In ArcGIS it is Spatial Autocorrelation (Global Moran’s I) 46.

Why it was run on this data. Global Moran's I says whether similar values of 'children_5_17' sit next to each other 47.

What it found. Spatial autocorrelation on School catchments: Spatial autocorrelation: done; no map output 1848. It reported result, n 45; result, p value 0.4896; result, z score 0.6909; result, morans i 0.05071; result, variance 0.0113; result, elapsed ms 2 48.

Findings

• Evidenced: Spatial autocorrelation on School catchments: Spatial autocorrelation: done; no map output 1848.
• Evidenced: It reported result, n 45; result, p value 0.4896; result, z score 0.6909; result, morans i 0.05071; result, variance 0.0113; result, elapsed ms 2 48.

Tool gp_spatial_autocorrelation; parameters {"field": "children_5_17", "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "features": {"layer": "School catchments (534622c6)"}}

Find nearest

What this analysis is. Find nearest: The k nearest features for each input, with geodesic distance and bearing 49. In ArcGIS it is Find Nearest or Near; in QGIS, native:joinbynearest or native:nearestneighbouranalysis 49.

Why it was run on this data. The nearest of Major roads to each of Student homes, with distances 50.

What it found. Find nearest on Major roads, Student homes: Find nearest: 1 layer saved: Find nearest: Student homes (2500 features) 85152. It reported result, count 2,500; result, elapsed ms 1,073; billing, cost 0.06044; billing, list price 0.06044; billing, upstream cost 0 51.

Findings

• Evidenced: Find nearest on Major roads, Student homes: Find nearest: 1 layer saved: Find nearest: Student homes (2500 features) 85152.
• Evidenced: It reported result, count 2,500; result, elapsed ms 1,073; billing, cost 0.06044; billing, list price 0.06044; billing, upstream cost 0 51.

Tool gp_find_nearest; parameters {"k": 1, "to": {"layer": "Major roads (f801bcde)"}, "from": {"layer": "Student homes (05789208)"}, "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "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 49. In ArcGIS it is Find Nearest or Near; in QGIS, native:joinbynearest or native:nearestneighbouranalysis 49.

Why it was run on this data. The nearest of Major roads to each of Student homes, with distances 50.

What it found. Find nearest on Childcare centers, Student homes: Find nearest: 1 layer saved: Find nearest: Student homes (2500 features) 82053. It reported result, count 2,500; result, elapsed ms 129; billing, cost 0.0612; billing, list price 0.0612; billing, upstream cost 0 53.

Findings

• Evidenced: Find nearest on Childcare centers, Student homes: Find nearest: 1 layer saved: Find nearest: Student homes (2500 features) 82053.
• Evidenced: It reported result, count 2,500; result, elapsed ms 129; billing, cost 0.0612; billing, list price 0.0612; billing, upstream cost 0 53.

Tool gp_find_nearest; parameters {"k": 1, "to": {"layer": "Childcare centers (9489916f)"}, "from": {"layer": "Student homes (05789208)"}, "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "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 49. In ArcGIS it is Find Nearest or Near; in QGIS, native:joinbynearest or native:nearestneighbouranalysis 49.

Why it was run on this data. The nearest of Major roads to each of Student homes, with distances 50.

What it found. The gp_find_nearest tool was utilized with Housing developments 11 and Student homes 8 as inputs 54. The operation successfully generated 2,251 results 54. Specific performance metrics showed the elapsed time was 118 ms 54. Furthermore, the recorded billing cost for this process was 0.0608 54.

Findings

• Evidenced: Find nearest on Housing developments, Student homes: Find nearest: 1 layer saved: Find nearest: Student homes (2251 features) 81154.
• Evidenced: It reported result, count 2,251; result, elapsed ms 118; billing, cost 0.0608; billing, list price 0.0608; billing, upstream cost 0 54.

Tool gp_find_nearest; parameters {"k": 1, "to": {"layer": "Housing developments (afc1332b)"}, "from": {"layer": "Student homes (05789208)"}, "project": "89129455-9e05-4edb-9cc1-dcfb440cc5ba", "max_distance_m": 2000.0}

Maps and charts

The run also made 23 map image(s) 5556575859606162636465666768697071727374757677 and 31 chart(s) 78798081828384858687888990919293949596979899100101102103104105106107108.

What the map shows. The map displays the emerging hot spots for student homes across a grid area. A distinct, concentrated cluster of "Sporadic hot spot" areas, colored orange, is visible in the upper-central portion of the map. The majority of the geographical area is colored gray, corresponding to 55

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 quarter by dataset. 78 rows id 79388dbd-a74a-40df-b300-c9c05078ac9c
2. 2Dataset: Student homes. vector, 2500 rows id 28ef32ad-fdbf-4e64-879d-ef97c69d99c8
3. 3Dataset: Housing developments. vector, 40 rows id 9073798d-0507-463a-99b0-0f74516b77ea
4. 4Dataset: Enrollment by school and year. table, 270 rows id af8cf345-1c02-4892-beda-cb8f239c4cd1
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 Student homes (the Start plan's candidate c_7f1ecb93fe03). Student homes carry dates in 'enrolled_on': emerging hot spot analysis finds new, intensifying and fading hot spots per month. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_7f1ecb93fe03
7. 7Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Student homes (96 features). id 9aff1884-9259-4a26-929a-88629da03a75:0
8. 8Layer: Student homes. 2,500 features id 05789208-552f-4f37-be96-0e77b9279a4e
9. 9Plan: Space time hot spots of Housing developments (the Start plan's candidate c_e422a5866706). Housing developments carry dates in 'completion_month': emerging hot spot analysis finds new, intensifying and fading hot spots per month. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_e422a5866706
10. 10Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: Housing developments (22 features). id 9aff1884-9259-4a26-929a-88629da03a75:1
11. 11Layer: Housing developments. 40 features id afc1332b-5445-4ac2-9d98-ebb6b1520bd2
12. 12Catalog: Repeat and near repeat classification (operation catalog, gp_near_repeat). Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table. ArcGIS: Repeat and Near Repeat Classification, Near Repeat Calculator. id gp_near_repeat
13. 13Plan: Near repeat of Student homes (the Start plan's candidate c_a7cb51bb85b3). Near repeat analysis tests whether Student homes close in space also follow each other within 30 days. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_a7cb51bb85b3
14. 14Tool result: gp_near_repeat, step 2. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Student homes (2500 features). id 9aff1884-9259-4a26-929a-88629da03a75:2
15. 15Catalog: 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
16. 16Plan: Student homes per feature of School catchments (the Start plan's candidate c_525cc89e9df7). Count Student homes inside each feature of School catchments by transport; the counts map as a choropleth (natural breaks, sequential colors). id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_525cc89e9df7
17. 17Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: School catchments (45 features). id 0c3a09c9-8654-4d86-b171-2c58372abb46:0
18. 18Layer: School catchments. 45 features id 534622c6-56d8-4348-94c9-c424e61a442d
19. 19Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: School catchments (45 features). id 0c3a09c9-8654-4d86-b171-2c58372abb46:1
20. 20Layer: Childcare centers. 60 features id 9489916f-d13f-4283-b308-ff5bcae62a0d
21. 21Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: School catchments (45 features). id 0c3a09c9-8654-4d86-b171-2c58372abb46:2
22. 22Catalog: 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
23. 23Plan: Hot spots of Student homes (the Start plan's candidate c_7a39d826a935). 2500 points: Getis-Ord Gi* tests whether the concentrations in Student homes are statistically significant. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_7a39d826a935
24. 24Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Student homes (281 features). id 9c081bf6-6d02-4b46-93ef-d4823a0030c1:0
25. 25Plan: Hot spots of Childcare centers (the Start plan's candidate c_0bdc4dd83b93). 60 points: Getis-Ord Gi* tests whether the concentrations in Childcare centers are statistically significant. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_0bdc4dd83b93
26. 26Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Childcare centers (20 features). id 9c081bf6-6d02-4b46-93ef-d4823a0030c1:1
27. 27Plan: Hot spots of Housing developments (the Start plan's candidate c_6f57933191fe). 40 points: Getis-Ord Gi* tests whether the concentrations in Housing developments are statistically significant. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_6f57933191fe
28. 28Tool result: gp_hot_spots, step 2. Hot spot analysis: 1 layer saved: Hot spot analysis: Housing developments (40 features). id 9c081bf6-6d02-4b46-93ef-d4823a0030c1:2
29. 29Catalog: 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
30. 30Plan: Kernel density of Student homes (the Start plan's candidate c_4c71448e6d05). 2500 points in Student homes: a density surface shows where they concentrate. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_4c71448e6d05
31. 31Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Student homes (1838 features). id a4a004d3-3ba6-400d-932e-b8110651d1ff:0
32. 32Plan: Kernel density of Childcare centers (the Start plan's candidate c_069e6a20b03a). 60 points in Childcare centers: a density surface shows where they concentrate. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_069e6a20b03a
33. 33Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Childcare centers (112 features). id a4a004d3-3ba6-400d-932e-b8110651d1ff:1
34. 34Plan: Kernel density of Housing developments (the Start plan's candidate c_441f3db47c68). 40 points in Housing developments: a density surface shows where they concentrate. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_441f3db47c68
35. 35Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: Housing developments (80 features). id a4a004d3-3ba6-400d-932e-b8110651d1ff:2
36. 36Catalog: 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
37. 37Plan: Cluster points of Student homes (the Start plan's candidate c_e4e6b0b009c4). DBSCAN finds dense groups of Student homes and separates noise. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_e4e6b0b009c4
38. 38Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Student homes (2500 features); Find point clusters: cluster hulls (1 feature). id d27977c5-a8cd-4f40-9af6-797ca9424ec9:0
39. 39Plan: Cluster points of Childcare centers (the Start plan's candidate c_43ed026ce5e0). DBSCAN finds dense groups of Childcare centers and separates noise. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_43ed026ce5e0
40. 40Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Childcare centers (60 features); Find point clusters: cluster hulls (8 features). id d27977c5-a8cd-4f40-9af6-797ca9424ec9:1
41. 41Plan: Cluster points of Housing developments (the Start plan's candidate c_ac70f2a99d9b). DBSCAN finds dense groups of Housing developments and separates noise. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_ac70f2a99d9b
42. 42Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Housing developments (40 features); Find point clusters: cluster hulls (1 feature). id d27977c5-a8cd-4f40-9af6-797ca9424ec9:2
43. 43Catalog: 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
44. 44Plan: Local morans i of School catchments (the Start plan's candidate c_98e5a5704b62). Local Moran's I maps clusters and outliers of 'children_5_17'. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_98e5a5704b62
45. 45Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: School catchments (45 features). id 2d7fd4ea-3928-4276-820c-93cbaa5d8370:0
46. 46Catalog: 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
47. 47Plan: Global morans i of School catchments (the Start plan's candidate c_62c90091b007). Global Moran's I says whether similar values of 'children_5_17' sit next to each other. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_62c90091b007
48. 48Tool result: gp_spatial_autocorrelation, step 1. Spatial autocorrelation: done; no map output. id 2d7fd4ea-3928-4276-820c-93cbaa5d8370:1
49. 49Catalog: 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
50. 50Plan: Find nearest of Student homes and Major roads (the Start plan's candidate c_b5e7bf8242db). The nearest of Major roads to each of Student homes, with distances. id da4c2c71-fa66-4f6d-b51a-fc46626d4137:c_b5e7bf8242db
51. 51Tool result: gp_find_nearest, step 0. Find nearest: 1 layer saved: Find nearest: Student homes (2500 features). id 96f791ed-0415-4640-9ce5-4a248513b9be:0
52. 52Layer: Major roads. 22 features id f801bcde-56b2-4e61-ba19-14d0273cc0a8
53. 53Tool result: gp_find_nearest, step 1. Find nearest: 1 layer saved: Find nearest: Student homes (2500 features). id 96f791ed-0415-4640-9ce5-4a248513b9be:1
54. 54Tool result: gp_find_nearest, step 2. Find nearest: 1 layer saved: Find nearest: Student homes (2251 features). id 96f791ed-0415-4640-9ce5-4a248513b9be:2
55. 55Rendering: Map: Emerging hot spots: Student homes id 99865f9e-61ae-4a54-8e71-4f24fa9e045e
56. 56Rendering: Map: Emerging hot spots: Housing developments id c6a807f2-4f43-4cf7-bbdd-0d94e82be22b
57. 57Rendering: Map: Repeat and near repeat classification: Student homes id f7b40372-ed14-4b51-9886-d46e8380e886
58. 58Rendering: Map: Summarize within: School catchments id 16071de3-2db4-4d2a-9f22-069707921f74
59. 59Rendering: Map: Summarize within: School catchments id 6880f2c7-fb2a-402c-b96c-562b236be148
60. 60Rendering: Map: Summarize within: School catchments id 7370b20e-f371-4cfd-b692-ca80e8c8b321
61. 61Rendering: Map: Hot spot analysis: Student homes id 81a795ec-10a3-41fe-8418-bb2735586194
62. 62Rendering: Map: Hot spot analysis: Childcare centers id d79041bc-d5c3-40bc-9a8b-1efee804ea40
63. 63Rendering: Map: Hot spot analysis: Housing developments id 53ecf3c1-e4a9-4b9b-9614-b876299daf76
64. 64Rendering: Map: Calculate density: Student homes id bfebe2b5-1bfa-44c5-b02d-d31e232f58c2
65. 65Rendering: Map: Calculate density: Childcare centers id 4d64bc05-4b18-466d-9916-7cbaaba82672
66. 66Rendering: Map: Calculate density: Housing developments id 9939a26d-3d46-4228-a88e-6276ccbbfce9
67. 67Rendering: Map: Find point clusters: Student homes id 61cf32ac-295f-43d1-b073-76ae493477dd
68. 68Rendering: Map: Find point clusters: cluster hulls id a6559fb3-821b-4fe4-ba92-a09934cf2f3c
69. 69Rendering: Map: Find point clusters: Childcare centers id ece220ce-fe57-4bdc-a842-471ae4c94bed
70. 70Rendering: Map: Find point clusters: cluster hulls id f519a030-b3a6-484c-93cf-8681144e4a8b
71. 71Rendering: Map: Find point clusters: Housing developments id d14109fd-c380-468c-85e3-d7337bbf109e
72. 72Rendering: Map: Find point clusters: cluster hulls id 078cb00b-7d37-4f3e-a204-57bd1a525b99
73. 73Rendering: Map: School catchments by children_5_17 id a27302ad-817f-4696-a735-0a3a66fbb8ef
74. 74Rendering: Map: Find outliers: School catchments id da831d0f-f5f9-4acb-b0cd-29083c443756
75. 75Rendering: Map: Find nearest: Student homes id 40e2475f-79fa-4fc0-b95d-0920bd10ac82
76. 76Rendering: Map: Find nearest: Student homes id 70ebd047-6948-4216-9cf9-75b35e49c830
77. 77Rendering: Map: Find nearest: Student homes id 5b49f3b5-9a72-4fd4-96f3-f2c2703027ab
78. 78Rendering: Chart: Schools over time (built_year). 7 rows id 880207dc-7868-40f4-b250-d08848fe4f7c
79. 79Rendering: Chart: Housing developments over time (completion_month). 20 rows id ee6bae39-81ad-4908-93f3-48e4b8dab2f3
80. 80Rendering: Chart: Enrollment by school and year over time (school_year). 21 rows id 3c99d290-2ab6-4f32-bf72-5e28a47a0305
81. 81Rendering: Chart: Emerging hot spots: Student homes. 8 rows id c2759717-bd77-4ab7-80f2-0ff5624d1d0d
82. 82Rendering: Chart: Emerging hot spots: Housing developments. 6 rows id 9da321f7-d9db-457c-b398-aadc13a51b1d
83. 83Rendering: Chart: Repeat and near repeat classification: Student homes. 20 rows id e9d40fe4-2dcb-4274-a637-c4492395395e
84. 84Rendering: Chart: Summarize within: School catchments. 7 rows id ed117d61-c2b8-4711-8e6b-6162a4f09bdd
85. 85Rendering: Chart: Summarize within: School catchments. 7 rows id 538dd834-8935-4acf-aa77-c6b3e48cbffe
86. 86Rendering: Chart: Summarize within: School catchments. 5 rows id eb2e00b9-1335-4b02-9b76-1e583e9b01a0
87. 87Rendering: Chart: Hot spot analysis: Student homes. 15 rows id 12fec60a-bec0-4c49-8e09-a74a94fa49f5
88. 88Rendering: Chart: Hot spot analysis: Childcare centers. 7 rows id 40554f68-7964-4f45-a1ac-19d17066ae79
89. 89Rendering: Chart: Hot spot analysis: Housing developments. 8 rows id 01cab3a4-6f58-49af-8dd9-3f246085fc6b
90. 90Rendering: Chart: Calculate density: Student homes. 12 rows id e75022eb-77cc-4f13-ae4e-64fa1d81fa6d
91. 91Rendering: Chart: Calculate density: Childcare centers. 8 rows id 6c204cf8-7a68-44be-80f5-4f7cde56072e
92. 92Rendering: Chart: Calculate density: Housing developments. 8 rows id ca34157f-9a27-457d-81e1-29b86f4f275e
93. 93Rendering: Chart: Find point clusters: Student homes. 2 rows id e0334377-b051-4859-a2cd-6fbb987551b4
94. 94Rendering: Chart: Find point clusters: cluster hulls. 1 rows id ef7a2223-1d85-448d-b093-0c2513affca5
95. 95Rendering: Chart: Find point clusters: Childcare centers. 9 rows id 29ef1518-618b-4ea4-a5ee-2c96e1ec5827
96. 96Rendering: Chart: Find point clusters: cluster hulls. 8 rows id 208e1927-bbe9-4755-a5ef-4f46a24d4689
97. 97Rendering: Chart: Find point clusters: Housing developments. 2 rows id 40ab1fbd-7eab-4e95-ba32-5f9e4781e9d4
98. 98Rendering: Chart: Find point clusters: cluster hulls. 1 rows id e4c8befa-2e05-41d0-bb8c-8fe8283edd90
99. 99Rendering: Chart: Find outliers: School catchments. 45 rows id a50e1dab-42b9-4ff9-ae70-a9480fc0b696
100. 100Rendering: Chart: Find nearest: Student homes. 20 rows id 4fc59395-9d71-4f81-958d-f88bf5cb6659
101. 101Rendering: Chart: Find nearest: Student homes. 20 rows id 0852c0c8-a586-4261-b871-7d8105dd7e1b
102. 102Rendering: Chart: Find nearest: Student homes over time. 152 rows id 75d40924-ed16-40d7-b9b0-3c4a873dd613
103. 103Rendering: Chart: Find nearest: Student homes over time. 152 rows id b06f7c1c-a6b8-4ca2-8124-9b931b6d16b9
104. 104Rendering: Chart: Find nearest: Student homes over time. 228 rows id 1007cf6f-af9a-4bc4-a5e5-aba4c425e396
105. 105Rendering: Chart: Find point clusters: Housing developments over time. 40 rows id 96ec5cd7-6530-4341-a797-33f6a56e2fb7
106. 106Rendering: Chart: Find point clusters: Student homes over time. 76 rows id 8bc71da9-95da-46d6-9855-df0cfc359e93
107. 107Rendering: Chart: Hot spot analysis: Housing developments over time. 40 rows id 9cfa00e8-1d3c-4faa-95d0-f0720805090f
108. 108Rendering: Chart: Repeat and near repeat classification: Student homes over time. 114 rows id b240dc06-1b76-4e65-bbc3-8914c9a85bb6
109. 109Dataset: Schools. vector, 45 rows id febb26e1-84d6-4ddb-9a3f-f5e1f0b7f795
110. 110Dataset: School catchments. vector, 45 rows id ad5953fb-dbca-4073-bc92-0e08c51e1651
111. 111Dataset: Bus stops. vector, 80 rows id bb1e7508-4720-407c-b71c-a26b23a4f915
112. 112Dataset: Childcare centers. vector, 60 rows id 4fef3ba9-e30d-4b7d-a5d6-605ce87f7d10
113. 113Dataset: Major roads. vector, 22 rows id 2a9a4d23-28ed-4b9f-9748-91ea14603b7a
114. 114Dataset: Staffing. table, 45 rows id afb73c89-d4b1-4d19-a074-f8a824e6cbbc
115. 115Dataset: School capacity review. document, ? rows id 4d0bbaf0-8f67-45ac-b564-ab860018c60d
116. 116Record: Sample: Education, Toronto id 89129455-9e05-4edb-9cc1-dcfb440cc5ba
117. 117Layer: Emerging hot spots: Student homes. 96 features id 7dda31be-8b88-4be6-9357-f49b99e0ad3f
118. 118Layer: Emerging hot spots: Housing developments. 22 features id 967c6193-6c0c-406a-a2c6-546a83bbbc65
119. 119Layer: Repeat and near repeat classification: Student homes. 2,500 features id 4b593664-c2f4-4a80-95fe-f240335119a6
120. 120Layer: Summarize within: School catchments. 45 features id d0384b31-144b-4c4f-a178-ec4c53caae50
121. 121Layer: Summarize within: School catchments. 45 features id 478d8495-5caf-48d5-b6fa-eb6fb6b4293d
122. 122Layer: Summarize within: School catchments. 45 features id 975d2d0e-1edb-42ad-b028-67b76335136f
123. 123Layer: Hot spot analysis: Student homes. 281 features id a502e8fe-ffa2-4e21-9f58-9ec29380391f
124. 124Layer: Hot spot analysis: Childcare centers. 20 features id 1b0dfd64-2343-44f2-94d6-e1605b181217
125. 125Layer: Hot spot analysis: Housing developments. 40 features id e4d2879f-df41-457c-becc-2143c520d6c0
126. 126Layer: Calculate density: Student homes. 1,838 features id 0a6f63b2-6a88-4993-a597-e2734a63f13a
127. 127Layer: Calculate density: Childcare centers. 112 features id 9a43b28a-a83c-403a-89b3-276f72a30159
128. 128Layer: Calculate density: Housing developments. 80 features id db78cd56-46ef-4a6b-8256-28f6febc19e9
129. 129Layer: Find point clusters: Student homes. 2,500 features id 6619977a-546f-41cf-a5d9-f9fa92133533
130. 130Layer: Find point clusters: cluster hulls. 1 features id 93e7b932-a3f4-42f8-9d0a-b4d8266c9326
131. 131Layer: Find point clusters: Childcare centers. 60 features id 394eb585-c105-412b-8add-e49c4579ae41
132. 132Layer: Find point clusters: cluster hulls. 8 features id eac0e935-66fc-4c67-b7eb-367618a58b3c
133. 133Layer: Find point clusters: Housing developments. 40 features id e8272a5f-e728-40d3-9f70-cc41284df4b6
134. 134Layer: Find point clusters: cluster hulls. 1 features id e315ede4-4e6c-4399-b511-158bba19ae68
135. 135Layer: Find outliers: School catchments. 45 features id c01d8927-0a79-40f8-ac32-3d74384c6f2c
136. 136Layer: Find nearest: Student homes. 2,500 features id 9df5c370-c651-40e7-be15-05353e74ecf7
137. 137Layer: Find nearest: Student homes. 2,500 features id a2c749a0-1ba4-4b20-92aa-88c388a66e07
138. 138Layer: Find nearest: Student homes. 2,251 features id f6524eb3-a994-493f-8273-4c78a1381cac