# 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

| Data | Kind | Rows | Time span | Summary | Source |
| --- | --- | --- | --- | --- | --- |
| Schools | vector, Point | 45 |  | 45 point features; 'level' has 3 values (top: elementary 30, middle 8, secondary 7); 'capacity' 350 to 1,597 (mean 663.3); 'enrolment' 266 to 1,355 (mean 603); 'built_year' 1,918 to 2,019 (mean 1965); named by 'name' (e.g. Southgate Elementary School, Eastfield Elementary School, Westbrook Elementary School); extent -79.4521,43.6511 to -79.3505,43.7074. Point locations of Schools by level. | [109] |
| School catchments | vector, Polygon | 45 |  | 45 polygon (area) features; 'children_5_17' 924 to 5,561 (mean 2113); named by 'name' (e.g. Southgate Elementary School catchment, Eastfield Elementary School catchment, Westbrook Elementary School catchment); total area 63.02 km2; extent -79.4566,43.6477 to -79.3433,43.7123. Areas (School catchments) with children_5_17 to compare and map. | [110] |
| Student homes | vector, Point | 2,500 | 2023-08-01 to 2026-09-30 | 2,500 point features; dated by 'enrolled_on' from 2023-08-01 to 2026-09-30; 'transport' has 4 values (top: walk 1125, bus 740, car 497); 'grade' 0 to 12 (mean 6.081); 'distance_km' 0.01 to 1.35 (mean 0.4847); named by 'student_ref' (e.g. ST00001, ST00002, ST00003); extent -79.4552,43.6481 to -79.3453,43.7112. Dated point events by transport: where and when Student homes happen. | [2] |
| Bus stops | vector, Point | 80 |  | 80 point features; 'school_run' has 2 values (top: True 45, False 35); 'routes' 1 to 4 (mean 2.425); extent -79.4545,43.6514 to -79.3453,43.7096. Point locations of Bus stops by school_run. | [111] |
| Childcare centers | vector, Point | 60 |  | 60 point features; 'spaces' 22 to 118 (mean 75.68); 'waitlist' 0 to 79 (mean 43.85); extent -79.4542,43.6525 to -79.3469,43.7106. Point locations of Childcare centers. | [112] |
| Housing developments | vector, Point | 40 | 2023-03-01 to 2027-11-01 | 40 point features; dated by 'completion_month' from 2023-03-01 to 2027-11-01; 'units' 53 to 629 (mean 243.4); 'family_units_pct' 11 to 59 (mean 33.98); extent -79.4441,43.6566 to -79.3486,43.7079. Dated point events: where and when Housing developments happen. | [3] |
| Major roads | vector, LineString | 22 |  | 22 line features; 'lanes' 4 to 4 (mean 4); named by 'name' (e.g. Bloor Nova Street, Dundas Nova Street, Front Street); total length 133.7 km; extent -79.4555,43.6484 to -79.3453,43.7111. Lines (Major roads) such as routes or networks. | [113] |
| Enrollment by school and year | table | 270 | 2021-09-01 to 2026-09-01 | Table of 270 rows and 6 columns without geometry; dated by 'school_year' from 2021-09-01 to 2026-09-01; 'enrolment' 266 to 1,389 (mean 593.7); 'capacity' 350 to 1,597 (mean 663.3); 'utilization_pct' 72 to 134.5 (mean 90.88). A table of Enrollment by school and year without locations. | [4] |
| Staffing | table | 45 |  | Table of 45 rows and 4 columns without geometry; 'teachers' 12 to 61 (mean 26.89); 'support_staff' 2 to 14 (mean 8.267); 'portables' 0 to 8 (mean 0.8667). A table of Staffing without locations. | [114] |
| School capacity review | document |  |  | 1-page md document 'School capacity review 2026'; language en; mentions 4 place(s): Maple Cross Elementary School (1), Parkside Middle School (1), Wren Park Elementary School (1), 43.69843, -79.36600 (1). A document about Maple Cross Elementary School, Parkside Middle School, Wren Park Elementary School. | [115] |
| Sample: Education, Toronto | record |  |  |  | [116] |
| Emerging hot spots: Student homes | layer, Polygon | 96 |  |  | [117] |
| Emerging hot spots: Housing developments | layer, Polygon | 22 |  |  | [118] |
| Repeat and near repeat classification: Student homes | layer, Point | 2,500 |  |  | [119] |
| Summarize within: School catchments | layer, Polygon | 45 |  |  | [120] |
| Summarize within: School catchments | layer, Polygon | 45 |  |  | [121] |
| Summarize within: School catchments | layer, Polygon | 45 |  |  | [122] |
| Hot spot analysis: Student homes | layer, Polygon | 281 |  |  | [123] |
| Hot spot analysis: Childcare centers | layer, Polygon | 20 |  |  | [124] |
| Hot spot analysis: Housing developments | layer, Point | 40 |  |  | [125] |
| Calculate density: Student homes | layer, Polygon | 1,838 |  |  | [126] |
| Calculate density: Childcare centers | layer, Polygon | 112 |  |  | [127] |
| Calculate density: Housing developments | layer, Polygon | 80 |  |  | [128] |
| Find point clusters: Student homes | layer, Point | 2,500 |  |  | [129] |
| Find point clusters: cluster hulls | layer, Polygon | 1 |  |  | [130] |
| Find point clusters: Childcare centers | layer, Point | 60 |  |  | [131] |
| Find point clusters: cluster hulls | layer, Polygon | 8 |  |  | [132] |
| Find point clusters: Housing developments | layer, Point | 40 |  |  | [133] |
| Find point clusters: cluster hulls | layer, Polygon | 1 |  |  | [134] |
| Find outliers: School catchments | layer, Polygon | 45 |  |  | [135] |
| Find nearest: Student homes | layer, Point | 2,500 |  |  | [136] |
| Find nearest: Student homes | layer, Point | 2,500 |  |  | [137] |
| Find nearest: Student homes | layer, Point | 2,251 |  |  | [138] |

## Timeline

![Timeline: events per quarter by dataset](charts/32-timeline-events-per-quarter-by-dataset.png)

*Figure 1. Timeline: events per quarter by dataset [1]. Per quarter, 2021-07-01 to 2027-10-01. Source: Student homes, Housing developments, Enrollment by school and year (2,810 features). The data is in the appendix.*

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 [1][2][3][4]. 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].

| Result | Value |
| --- | --- |
| result, count | 96 |
| result, elapsed ms | 19 |
| result, resolution | 8 |
| result, features used | 2,500 |
| result, time neighbors | 0 |
| billing, cost | 0.225 |
| billing, list price | 0.225 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Student homes: Emerging hot spots: 1 layer saved: Emerging hot spots: Student homes (96 features) [7][8].
- **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].

| Result | Value |
| --- | --- |
| result, count | 22 |
| result, elapsed ms | 4 |
| result, resolution | 7 |
| result, features used | 40 |
| result, time neighbors | 0 |
| billing, cost | 0.102 |
| billing, list price | 0.102 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Housing developments: Emerging hot spots: 1 layer saved: Emerging hot spots: Housing developments (22 features) [10][11].
- **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) [8][14]. 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].

| Result | Value |
| --- | --- |
| result, days | 30 |
| result, count | 2,500 |
| result, chains | 284 |
| result, distance m | 200 |
| result, elapsed ms | 61 |
| result, pairs linked | 1,594 |
| billing, cost | 0.125 |
| billing, list price | 0.125 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Near repeat on Student homes: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Student homes (2500 features) [8][14].
- **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) [8][17][18]. It reported result, count 45; result, elapsed ms 6; billing, cost 0.06092; billing, list price 0.06092; billing, upstream cost 0 [17].

| Result | Value |
| --- | --- |
| result, count | 45 |
| result, elapsed ms | 6 |
| billing, cost | 0.06092 |
| billing, list price | 0.06092 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Student homes, School catchments: Summarize within: 1 layer saved: Summarize within: School catchments (45 features) [8][17][18].
- **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) [18][19][20]. It reported result, count 45; result, elapsed ms 1; billing, cost 0.01212; billing, list price 0.01212; billing, upstream cost 0 [19].

| Result | Value |
| --- | --- |
| result, count | 45 |
| result, elapsed ms | 1 |
| billing, cost | 0.01212 |
| billing, list price | 0.01212 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Childcare centers, School catchments: Summarize within: 1 layer saved: Summarize within: School catchments (45 features) [18][19][20].
- **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) [11][18][21]. It reported result, count 45; result, elapsed ms 1; billing, cost 0.01172; billing, list price 0.01172; billing, upstream cost 0 [21].

| Result | Value |
| --- | --- |
| result, count | 45 |
| result, elapsed ms | 1 |
| billing, cost | 0.01172 |
| billing, list price | 0.01172 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Housing developments, School catchments: Summarize within: 1 layer saved: Summarize within: School catchments (45 features) [11][18][21].
- **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) [8][24]. 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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.1 |
| billing, list price | 0.1 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Student homes: Hot spot analysis: 1 layer saved: Hot spot analysis: Student homes (281 features) [8][24].
- **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) [20][26]. 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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.0512 |
| billing, list price | 0.0512 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Childcare centers: Hot spot analysis: 1 layer saved: Hot spot analysis: Childcare centers (20 features) [20][26].
- **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].

| Result | Value |
| --- | --- |
| 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 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Housing developments: Hot spot analysis: 1 layer saved: Hot spot analysis: Housing developments (40 features) [11][28].
- **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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.1 |
| billing, list price | 0.1 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Student homes: Calculate density: 1 layer saved: Calculate density: Student homes (1838 features) [8][31].
- **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) [20][33]. 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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.0512 |
| billing, list price | 0.0512 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Childcare centers: Calculate density: 1 layer saved: Calculate density: Childcare centers (112 features) [20][33].
- **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) [11][35]. 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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.0508 |
| billing, list price | 0.0508 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Housing developments: Calculate density: 1 layer saved: Calculate density: Housing developments (80 features) [11][35].
- **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) [8][38]. 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].

| Result | Value |
| --- | --- |
| result, count | 2,500 |
| result, noise | 1,490 |
| result, distance m | 200 |
| result, elapsed ms | 32 |
| result, min points | 20 |
| billing, cost | 0.1 |
| billing, list price | 0.1 |
| billing, upstream cost | 0 |

**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) [8][38].
- **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) [20][40]. 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].

| Result | Value |
| --- | --- |
| result, count | 60 |
| result, noise | 1 |
| result, distance m | 1,000 |
| result, elapsed ms | 3 |
| result, min points | 3 |
| billing, cost | 0.0512 |
| billing, list price | 0.0512 |
| billing, upstream cost | 0 |

**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) [20][40].
- **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) [11][42]. 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].

| Result | Value |
| --- | --- |
| result, count | 40 |
| result, noise | 3 |
| result, distance m | 2,000 |
| result, elapsed ms | 1 |
| result, min points | 3 |
| billing, cost | 0.0508 |
| billing, list price | 0.0508 |
| billing, upstream cost | 0 |

**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) [11][42].
- **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].

| Result | Value |
| --- | --- |
| result, count | 45 |
| result, elapsed ms | 4 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 1,430 |
| billing, cost | 0.05135 |
| billing, list price | 0.05135 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on School catchments: Find outliers: 1 layer saved: Find outliers: School catchments (45 features) [18][45].
- **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 [18][48]. 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].

| Result | Value |
| --- | --- |
| 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 |
| result, expected i | -0.02273 |
| result, excluded null | 0 |
| result, distance band m | 1,430 |
| result, p value normality | 0.5075 |
| result, z score normality | 0.6627 |
| result, variance normality | 0.01228 |
| billing, cost | 0.02 |
| billing, list price | 0.02 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Spatial autocorrelation on School catchments: Spatial autocorrelation: done; no map output [18][48].
- **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) [8][51][52]. 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].

| Result | Value |
| --- | --- |
| result, count | 2,500 |
| result, elapsed ms | 1,073 |
| billing, cost | 0.06044 |
| billing, list price | 0.06044 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find nearest on Major roads, Student homes: Find nearest: 1 layer saved: Find nearest: Student homes (2500 features) [8][51][52].
- **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) [8][20][53]. It reported result, count 2,500; result, elapsed ms 129; billing, cost 0.0612; billing, list price 0.0612; billing, upstream cost 0 [53].

| Result | Value |
| --- | --- |
| result, count | 2,500 |
| result, elapsed ms | 129 |
| billing, cost | 0.0612 |
| billing, list price | 0.0612 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find nearest on Childcare centers, Student homes: Find nearest: 1 layer saved: Find nearest: Student homes (2500 features) [8][20][53].
- **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].

| Result | Value |
| --- | --- |
| result, count | 2,251 |
| result, elapsed ms | 118 |
| billing, cost | 0.0608 |
| billing, list price | 0.0608 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find nearest on Housing developments, Student homes: Find nearest: 1 layer saved: Find nearest: Student homes (2251 features) [8][11][54].
- **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) [55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77] and 31 chart(s) [78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108].

![Emerging hot spots: Student homes](maps/01-emerging-hot-spots-student-homes.png)

*Figure 2. Emerging hot spots: Student homes [55]*

![Emerging hot spots: Housing developments](maps/02-emerging-hot-spots-housing-developments.png)

*Figure 3. Emerging hot spots: Housing developments [56]*

![Repeat and near repeat classification: Student homes](maps/03-repeat-and-near-repeat-classification-student-homes.png)

*Figure 4. Repeat and near repeat classification: Student homes [57]*

![Summarize within: School catchments](maps/04-summarize-within-school-catchments.png)

*Figure 5. Summarize within: School catchments [58]*

![Summarize within: School catchments](maps/05-summarize-within-school-catchments.png)

*Figure 6. Summarize within: School catchments [59]*

![Summarize within: School catchments](maps/06-summarize-within-school-catchments.png)

*Figure 7. Summarize within: School catchments [60]*

![Hot spot analysis: Student homes](maps/07-hot-spot-analysis-student-homes.png)

*Figure 8. Hot spot analysis: Student homes [61]*

![Hot spot analysis: Childcare centers](maps/08-hot-spot-analysis-childcare-centers.png)

*Figure 9. Hot spot analysis: Childcare centers [62]*

![Hot spot analysis: Housing developments](maps/09-hot-spot-analysis-housing-developments.png)

*Figure 10. Hot spot analysis: Housing developments [63]*

![Calculate density: Student homes](maps/10-calculate-density-student-homes.png)

*Figure 11. Calculate density: Student homes [64]*

![Calculate density: Childcare centers](maps/11-calculate-density-childcare-centers.png)

*Figure 12. Calculate density: Childcare centers [65]*

![Calculate density: Housing developments](maps/12-calculate-density-housing-developments.png)

*Figure 13. Calculate density: Housing developments [66]*

![Find point clusters: Student homes](maps/13-find-point-clusters-student-homes.png)

*Figure 14. Find point clusters: Student homes [67]*

![Find point clusters: cluster hulls](maps/14-find-point-clusters-cluster-hulls.png)

*Figure 15. Find point clusters: cluster hulls [68]*

![Find point clusters: Childcare centers](maps/15-find-point-clusters-childcare-centers.png)

*Figure 16. Find point clusters: Childcare centers [69]*

![Find point clusters: cluster hulls](maps/16-find-point-clusters-cluster-hulls.png)

*Figure 17. Find point clusters: cluster hulls [70]*

![Find point clusters: Housing developments](maps/17-find-point-clusters-housing-developments.png)

*Figure 18. Find point clusters: Housing developments [71]*

![Find point clusters: cluster hulls](maps/18-find-point-clusters-cluster-hulls.png)

*Figure 19. Find point clusters: cluster hulls [72]*

![School catchments by children_5_17](maps/19-school-catchments-by-children-5-17.png)

*Figure 20. School catchments by children_5_17 [73]*

![Find outliers: School catchments](maps/20-find-outliers-school-catchments.png)

*Figure 21. Find outliers: School catchments [74]*

![Find nearest: Student homes](maps/21-find-nearest-student-homes.png)

*Figure 22. Find nearest: Student homes [75]*

![Find nearest: Student homes](maps/22-find-nearest-student-homes.png)

*Figure 23. Find nearest: Student homes [76]*

![Find nearest: Student homes](maps/23-find-nearest-student-homes.png)

*Figure 24. Find nearest: Student homes [77]*

**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.*

![Schools over time (built_year)](charts/01-schools-over-time-built-year.png)

*Figure 25. Schools over time (built_year) [78]. Source: Schools (45 features). The data is in the appendix.*

![Housing developments over time (completion_month)](charts/02-housing-developments-over-time-completion-month.png)

*Figure 26. Housing developments over time (completion_month) [79]. Per quarter, 2023-01-01 to 2027-10-01. Source: Housing developments (40 features). The data is in the appendix.*

![Enrollment by school and year over time (school_year)](charts/03-enrollment-by-school-and-year-over-time-school-year.png)

*Figure 27. Enrollment by school and year over time (school_year) [80]. Per quarter, 2021-07-01 to 2026-07-01. Source: Enrollment by school and year (270 features). The data is in the appendix.*

![Emerging hot spots: Student homes](charts/04-emerging-hot-spots-student-homes.png)

*Figure 28. Emerging hot spots: Student homes [81]. Source: Emerging hot spots: Student homes (96 features). The data is in the appendix.*

![Emerging hot spots: Housing developments](charts/05-emerging-hot-spots-housing-developments.png)

*Figure 29. Emerging hot spots: Housing developments [82]. Source: Emerging hot spots: Housing developments (22 features). The data is in the appendix.*

![Repeat and near repeat classification: Student homes](charts/06-repeat-and-near-repeat-classification-student-homes.png)

*Figure 30. Repeat and near repeat classification: Student homes [83]. Period: 2023-08-01 to 2026-09-30 (enrolled_on). Source: Repeat and near repeat classification: Student homes (2,500 features). The data is in the appendix.*

![Summarize within: School catchments](charts/07-summarize-within-school-catchments.png)

*Figure 31. Summarize within: School catchments [84]. Source: Summarize within: School catchments (45 features). The data is in the appendix.*

![Summarize within: School catchments](charts/08-summarize-within-school-catchments.png)

*Figure 32. Summarize within: School catchments [85]. Source: Summarize within: School catchments (45 features). The data is in the appendix.*

![Summarize within: School catchments](charts/09-summarize-within-school-catchments.png)

*Figure 33. Summarize within: School catchments [86]. Source: Summarize within: School catchments (45 features). The data is in the appendix.*

![Hot spot analysis: Student homes](charts/10-hot-spot-analysis-student-homes.png)

*Figure 34. Hot spot analysis: Student homes [87]. Source: Hot spot analysis: Student homes (281 features). The data is in the appendix.*

![Hot spot analysis: Childcare centers](charts/11-hot-spot-analysis-childcare-centers.png)

*Figure 35. Hot spot analysis: Childcare centers [88]. Source: Hot spot analysis: Childcare centers (20 features). The data is in the appendix.*

![Hot spot analysis: Housing developments](charts/12-hot-spot-analysis-housing-developments.png)

*Figure 36. Hot spot analysis: Housing developments [89]. Period: 2023-03-01 to 2027-11-01 (completion_month). Source: Hot spot analysis: Housing developments (40 features). The data is in the appendix.*

![Calculate density: Student homes](charts/13-calculate-density-student-homes.png)

*Figure 37. Calculate density: Student homes [90]. Source: Calculate density: Student homes (1,838 features). The data is in the appendix.*

![Calculate density: Childcare centers](charts/14-calculate-density-childcare-centers.png)

*Figure 38. Calculate density: Childcare centers [91]. Source: Calculate density: Childcare centers (112 features). The data is in the appendix.*

![Calculate density: Housing developments](charts/15-calculate-density-housing-developments.png)

*Figure 39. Calculate density: Housing developments [92]. Source: Calculate density: Housing developments (80 features). The data is in the appendix.*

![Find point clusters: Student homes](charts/16-find-point-clusters-student-homes.png)

*Figure 40. Find point clusters: Student homes [93]. Period: 2023-08-01 to 2026-09-30 (enrolled_on). Source: Find point clusters: Student homes (2,500 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/17-find-point-clusters-cluster-hulls.png)

*Figure 41. Find point clusters: cluster hulls [94]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.*

![Find point clusters: Childcare centers](charts/18-find-point-clusters-childcare-centers.png)

*Figure 42. Find point clusters: Childcare centers [95]. Source: Find point clusters: Childcare centers (60 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/19-find-point-clusters-cluster-hulls.png)

*Figure 43. Find point clusters: cluster hulls [96]. Source: Find point clusters: cluster hulls (8 features). The data is in the appendix.*

![Find point clusters: Housing developments](charts/20-find-point-clusters-housing-developments.png)

*Figure 44. Find point clusters: Housing developments [97]. Period: 2023-03-01 to 2027-11-01 (completion_month). Source: Find point clusters: Housing developments (40 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/21-find-point-clusters-cluster-hulls.png)

*Figure 45. Find point clusters: cluster hulls [98]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.*

![Find outliers: School catchments](charts/22-find-outliers-school-catchments.png)

*Figure 46. Find outliers: School catchments [99]. Source: Find outliers: School catchments (45 features). The data is in the appendix.*

![Find nearest: Student homes](charts/23-find-nearest-student-homes.png)

*Figure 47. Find nearest: Student homes [100]. Period: 2023-08-01 to 2026-09-30 (from_enrolled_on). Source: Find nearest: Student homes (2,500 features). The data is in the appendix.*

![Find nearest: Student homes](charts/24-find-nearest-student-homes.png)

*Figure 48. Find nearest: Student homes [101]. Period: 2023-08-01 to 2026-09-30 (from_enrolled_on). Source: Find nearest: Student homes (2,500 features). The data is in the appendix.*

![Find nearest: Student homes over time](charts/25-find-nearest-student-homes-over-time.png)

*Figure 49. Find nearest: Student homes over time [102]. Per month, 2023-08-01 to 2026-09-01. Source: Find nearest: Student homes (2,251 features). The data is in the appendix.*

![Find nearest: Student homes over time](charts/26-find-nearest-student-homes-over-time.png)

*Figure 50. Find nearest: Student homes over time [103]. Per month, 2023-08-01 to 2026-09-01. Source: Find nearest: Student homes (2,500 features). The data is in the appendix.*

![Find nearest: Student homes over time](charts/27-find-nearest-student-homes-over-time.png)

*Figure 51. Find nearest: Student homes over time [104]. Per month, 2023-08-01 to 2026-09-01. Source: Find nearest: Student homes (2,500 features). The data is in the appendix.*

![Find point clusters: Housing developments over time](charts/28-find-point-clusters-housing-developments-over-time.png)

*Figure 52. Find point clusters: Housing developments over time [105]. Per quarter, 2023-01-01 to 2027-10-01. Source: Find point clusters: Housing developments (40 features). The data is in the appendix.*

![Find point clusters: Student homes over time](charts/29-find-point-clusters-student-homes-over-time.png)

*Figure 53. Find point clusters: Student homes over time [106]. Per month, 2023-08-01 to 2026-09-01. Source: Find point clusters: Student homes (2,500 features). The data is in the appendix.*

![Hot spot analysis: Housing developments over time](charts/30-hot-spot-analysis-housing-developments-over-time.png)

*Figure 54. Hot spot analysis: Housing developments over time [107]. Per quarter, 2023-01-01 to 2027-10-01. Source: Hot spot analysis: Housing developments (40 features). The data is in the appendix.*

![Repeat and near repeat classification: Student homes over time](charts/31-repeat-and-near-repeat-classification-student-homes-over-tim.png)

*Figure 55. Repeat and near repeat classification: Student homes over time [108]. Per month, 2023-08-01 to 2026-09-01. Source: Repeat and near repeat classification: Student homes (2,500 features). The data is in the appendix.*

## Changes since the last run

This is the first report of this project.

## Sources

[1] Rendering: Chart: Timeline: events per quarter by dataset. 78 rows (id 79388dbd-a74a-40df-b300-c9c05078ac9c)  
[2] Dataset: Student homes. vector, 2500 rows (id 28ef32ad-fdbf-4e64-879d-ef97c69d99c8)  
[3] Dataset: Housing developments. vector, 40 rows (id 9073798d-0507-463a-99b0-0f74516b77ea)  
[4] Dataset: Enrollment by school and year. table, 270 rows (id af8cf345-1c02-4892-beda-cb8f239c4cd1)  
[5] Catalog: 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] Plan: 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] Tool 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] Layer: Student homes. 2,500 features (id 05789208-552f-4f37-be96-0e77b9279a4e)  
[9] Plan: 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] Tool 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] Layer: Housing developments. 40 features (id afc1332b-5445-4ac2-9d98-ebb6b1520bd2)  
[12] Catalog: 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] Plan: 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] Tool 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] Catalog: 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] Plan: 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] Tool 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] Layer: School catchments. 45 features (id 534622c6-56d8-4348-94c9-c424e61a442d)  
[19] Tool 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] Layer: Childcare centers. 60 features (id 9489916f-d13f-4283-b308-ff5bcae62a0d)  
[21] Tool 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] Catalog: 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] Plan: 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] Tool 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] Plan: 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] Tool 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] Plan: 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] Tool 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] Catalog: 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] Plan: 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] Tool 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] Plan: 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] Tool 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] Plan: 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] Tool 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] Catalog: 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] Plan: 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] Tool 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] Plan: 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] Tool 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] Plan: 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] Tool 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] Catalog: 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] Plan: 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] Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: School catchments (45 features). (id 2d7fd4ea-3928-4276-820c-93cbaa5d8370:0)  
[46] Catalog: 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] Plan: 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] Tool result: gp_spatial_autocorrelation, step 1. Spatial autocorrelation: done; no map output. (id 2d7fd4ea-3928-4276-820c-93cbaa5d8370:1)  
[49] Catalog: 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] Plan: 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] Tool 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] Layer: Major roads. 22 features (id f801bcde-56b2-4e61-ba19-14d0273cc0a8)  
[53] Tool 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] Tool 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] Rendering: Map: Emerging hot spots: Student homes (id 99865f9e-61ae-4a54-8e71-4f24fa9e045e)  
[56] Rendering: Map: Emerging hot spots: Housing developments (id c6a807f2-4f43-4cf7-bbdd-0d94e82be22b)  
[57] Rendering: Map: Repeat and near repeat classification: Student homes (id f7b40372-ed14-4b51-9886-d46e8380e886)  
[58] Rendering: Map: Summarize within: School catchments (id 16071de3-2db4-4d2a-9f22-069707921f74)  
[59] Rendering: Map: Summarize within: School catchments (id 6880f2c7-fb2a-402c-b96c-562b236be148)  
[60] Rendering: Map: Summarize within: School catchments (id 7370b20e-f371-4cfd-b692-ca80e8c8b321)  
[61] Rendering: Map: Hot spot analysis: Student homes (id 81a795ec-10a3-41fe-8418-bb2735586194)  
[62] Rendering: Map: Hot spot analysis: Childcare centers (id d79041bc-d5c3-40bc-9a8b-1efee804ea40)  
[63] Rendering: Map: Hot spot analysis: Housing developments (id 53ecf3c1-e4a9-4b9b-9614-b876299daf76)  
[64] Rendering: Map: Calculate density: Student homes (id bfebe2b5-1bfa-44c5-b02d-d31e232f58c2)  
[65] Rendering: Map: Calculate density: Childcare centers (id 4d64bc05-4b18-466d-9916-7cbaaba82672)  
[66] Rendering: Map: Calculate density: Housing developments (id 9939a26d-3d46-4228-a88e-6276ccbbfce9)  
[67] Rendering: Map: Find point clusters: Student homes (id 61cf32ac-295f-43d1-b073-76ae493477dd)  
[68] Rendering: Map: Find point clusters: cluster hulls (id a6559fb3-821b-4fe4-ba92-a09934cf2f3c)  
[69] Rendering: Map: Find point clusters: Childcare centers (id ece220ce-fe57-4bdc-a842-471ae4c94bed)  
[70] Rendering: Map: Find point clusters: cluster hulls (id f519a030-b3a6-484c-93cf-8681144e4a8b)  
[71] Rendering: Map: Find point clusters: Housing developments (id d14109fd-c380-468c-85e3-d7337bbf109e)  
[72] Rendering: Map: Find point clusters: cluster hulls (id 078cb00b-7d37-4f3e-a204-57bd1a525b99)  
[73] Rendering: Map: School catchments by children_5_17 (id a27302ad-817f-4696-a735-0a3a66fbb8ef)  
[74] Rendering: Map: Find outliers: School catchments (id da831d0f-f5f9-4acb-b0cd-29083c443756)  
[75] Rendering: Map: Find nearest: Student homes (id 40e2475f-79fa-4fc0-b95d-0920bd10ac82)  
[76] Rendering: Map: Find nearest: Student homes (id 70ebd047-6948-4216-9cf9-75b35e49c830)  
[77] Rendering: Map: Find nearest: Student homes (id 5b49f3b5-9a72-4fd4-96f3-f2c2703027ab)  
[78] Rendering: Chart: Schools over time (built_year). 7 rows (id 880207dc-7868-40f4-b250-d08848fe4f7c)  
[79] Rendering: Chart: Housing developments over time (completion_month). 20 rows (id ee6bae39-81ad-4908-93f3-48e4b8dab2f3)  
[80] Rendering: Chart: Enrollment by school and year over time (school_year). 21 rows (id 3c99d290-2ab6-4f32-bf72-5e28a47a0305)  
[81] Rendering: Chart: Emerging hot spots: Student homes. 8 rows (id c2759717-bd77-4ab7-80f2-0ff5624d1d0d)  
[82] Rendering: Chart: Emerging hot spots: Housing developments. 6 rows (id 9da321f7-d9db-457c-b398-aadc13a51b1d)  
[83] Rendering: Chart: Repeat and near repeat classification: Student homes. 20 rows (id e9d40fe4-2dcb-4274-a637-c4492395395e)  
[84] Rendering: Chart: Summarize within: School catchments. 7 rows (id ed117d61-c2b8-4711-8e6b-6162a4f09bdd)  
[85] Rendering: Chart: Summarize within: School catchments. 7 rows (id 538dd834-8935-4acf-aa77-c6b3e48cbffe)  
[86] Rendering: Chart: Summarize within: School catchments. 5 rows (id eb2e00b9-1335-4b02-9b76-1e583e9b01a0)  
[87] Rendering: Chart: Hot spot analysis: Student homes. 15 rows (id 12fec60a-bec0-4c49-8e09-a74a94fa49f5)  
[88] Rendering: Chart: Hot spot analysis: Childcare centers. 7 rows (id 40554f68-7964-4f45-a1ac-19d17066ae79)  
[89] Rendering: Chart: Hot spot analysis: Housing developments. 8 rows (id 01cab3a4-6f58-49af-8dd9-3f246085fc6b)  
[90] Rendering: Chart: Calculate density: Student homes. 12 rows (id e75022eb-77cc-4f13-ae4e-64fa1d81fa6d)  
[91] Rendering: Chart: Calculate density: Childcare centers. 8 rows (id 6c204cf8-7a68-44be-80f5-4f7cde56072e)  
[92] Rendering: Chart: Calculate density: Housing developments. 8 rows (id ca34157f-9a27-457d-81e1-29b86f4f275e)  
[93] Rendering: Chart: Find point clusters: Student homes. 2 rows (id e0334377-b051-4859-a2cd-6fbb987551b4)  
[94] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id ef7a2223-1d85-448d-b093-0c2513affca5)  
[95] Rendering: Chart: Find point clusters: Childcare centers. 9 rows (id 29ef1518-618b-4ea4-a5ee-2c96e1ec5827)  
[96] Rendering: Chart: Find point clusters: cluster hulls. 8 rows (id 208e1927-bbe9-4755-a5ef-4f46a24d4689)  
[97] Rendering: Chart: Find point clusters: Housing developments. 2 rows (id 40ab1fbd-7eab-4e95-ba32-5f9e4781e9d4)  
[98] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id e4c8befa-2e05-41d0-bb8c-8fe8283edd90)  
[99] Rendering: Chart: Find outliers: School catchments. 45 rows (id a50e1dab-42b9-4ff9-ae70-a9480fc0b696)  
[100] Rendering: Chart: Find nearest: Student homes. 20 rows (id 4fc59395-9d71-4f81-958d-f88bf5cb6659)  
[101] Rendering: Chart: Find nearest: Student homes. 20 rows (id 0852c0c8-a586-4261-b871-7d8105dd7e1b)  
[102] Rendering: Chart: Find nearest: Student homes over time. 152 rows (id 75d40924-ed16-40d7-b9b0-3c4a873dd613)  
[103] Rendering: Chart: Find nearest: Student homes over time. 152 rows (id b06f7c1c-a6b8-4ca2-8124-9b931b6d16b9)  
[104] Rendering: Chart: Find nearest: Student homes over time. 228 rows (id 1007cf6f-af9a-4bc4-a5e5-aba4c425e396)  
[105] Rendering: Chart: Find point clusters: Housing developments over time. 40 rows (id 96ec5cd7-6530-4341-a797-33f6a56e2fb7)  
[106] Rendering: Chart: Find point clusters: Student homes over time. 76 rows (id 8bc71da9-95da-46d6-9855-df0cfc359e93)  
[107] Rendering: Chart: Hot spot analysis: Housing developments over time. 40 rows (id 9cfa00e8-1d3c-4faa-95d0-f0720805090f)  
[108] Rendering: Chart: Repeat and near repeat classification: Student homes over time. 114 rows (id b240dc06-1b76-4e65-bbc3-8914c9a85bb6)  
[109] Dataset: Schools. vector, 45 rows (id febb26e1-84d6-4ddb-9a3f-f5e1f0b7f795)  
[110] Dataset: School catchments. vector, 45 rows (id ad5953fb-dbca-4073-bc92-0e08c51e1651)  
[111] Dataset: Bus stops. vector, 80 rows (id bb1e7508-4720-407c-b71c-a26b23a4f915)  
[112] Dataset: Childcare centers. vector, 60 rows (id 4fef3ba9-e30d-4b7d-a5d6-605ce87f7d10)  
[113] Dataset: Major roads. vector, 22 rows (id 2a9a4d23-28ed-4b9f-9748-91ea14603b7a)  
[114] Dataset: Staffing. table, 45 rows (id afb73c89-d4b1-4d19-a074-f8a824e6cbbc)  
[115] Dataset: School capacity review. document, ? rows (id 4d0bbaf0-8f67-45ac-b564-ab860018c60d)  
[116] Record: Sample: Education, Toronto (id 89129455-9e05-4edb-9cc1-dcfb440cc5ba)  
[117] Layer: Emerging hot spots: Student homes. 96 features (id 7dda31be-8b88-4be6-9357-f49b99e0ad3f)  
[118] Layer: Emerging hot spots: Housing developments. 22 features (id 967c6193-6c0c-406a-a2c6-546a83bbbc65)  
[119] Layer: Repeat and near repeat classification: Student homes. 2,500 features (id 4b593664-c2f4-4a80-95fe-f240335119a6)  
[120] Layer: Summarize within: School catchments. 45 features (id d0384b31-144b-4c4f-a178-ec4c53caae50)  
[121] Layer: Summarize within: School catchments. 45 features (id 478d8495-5caf-48d5-b6fa-eb6fb6b4293d)  
[122] Layer: Summarize within: School catchments. 45 features (id 975d2d0e-1edb-42ad-b028-67b76335136f)  
[123] Layer: Hot spot analysis: Student homes. 281 features (id a502e8fe-ffa2-4e21-9f58-9ec29380391f)  
[124] Layer: Hot spot analysis: Childcare centers. 20 features (id 1b0dfd64-2343-44f2-94d6-e1605b181217)  
[125] Layer: Hot spot analysis: Housing developments. 40 features (id e4d2879f-df41-457c-becc-2143c520d6c0)  
[126] Layer: Calculate density: Student homes. 1,838 features (id 0a6f63b2-6a88-4993-a597-e2734a63f13a)  
[127] Layer: Calculate density: Childcare centers. 112 features (id 9a43b28a-a83c-403a-89b3-276f72a30159)  
[128] Layer: Calculate density: Housing developments. 80 features (id db78cd56-46ef-4a6b-8256-28f6febc19e9)  
[129] Layer: Find point clusters: Student homes. 2,500 features (id 6619977a-546f-41cf-a5d9-f9fa92133533)  
[130] Layer: Find point clusters: cluster hulls. 1 features (id 93e7b932-a3f4-42f8-9d0a-b4d8266c9326)  
[131] Layer: Find point clusters: Childcare centers. 60 features (id 394eb585-c105-412b-8add-e49c4579ae41)  
[132] Layer: Find point clusters: cluster hulls. 8 features (id eac0e935-66fc-4c67-b7eb-367618a58b3c)  
[133] Layer: Find point clusters: Housing developments. 40 features (id e8272a5f-e728-40d3-9f70-cc41284df4b6)  
[134] Layer: Find point clusters: cluster hulls. 1 features (id e315ede4-4e6c-4399-b511-158bba19ae68)  
[135] Layer: Find outliers: School catchments. 45 features (id c01d8927-0a79-40f8-ac32-3d74384c6f2c)  
[136] Layer: Find nearest: Student homes. 2,500 features (id 9df5c370-c651-40e7-be15-05353e74ecf7)  
[137] Layer: Find nearest: Student homes. 2,500 features (id a2c749a0-1ba4-4b20-92aa-88c388a66e07)  
[138] Layer: Find nearest: Student homes. 2,251 features (id f6524eb3-a994-493f-8273-4c78a1381cac)  

## Appendix: the data behind the charts

### Figure 1. Timeline: events per quarter by dataset [1]

| period | series | value |
| --- | --- | --- |
| 2021-07-01 | Student homes | 0 |
| 2021-07-01 | Enrollment by school and year | 45 |
| 2021-07-01 | Housing developments | 0 |
| 2021-10-01 | Student homes | 0 |
| 2021-10-01 | Enrollment by school and year | 0 |
| 2021-10-01 | Housing developments | 0 |
| 2022-01-01 | Student homes | 0 |
| 2022-01-01 | Enrollment by school and year | 0 |
| 2022-01-01 | Housing developments | 0 |
| 2022-04-01 | Student homes | 0 |
| 2022-04-01 | Enrollment by school and year | 0 |
| 2022-04-01 | Housing developments | 0 |
| 2022-07-01 | Student homes | 0 |
| 2022-07-01 | Enrollment by school and year | 45 |
| 2022-07-01 | Housing developments | 0 |
| 2022-10-01 | Student homes | 0 |
| 2022-10-01 | Enrollment by school and year | 0 |
| 2022-10-01 | Housing developments | 0 |
| 2023-01-01 | Student homes | 0 |
| 2023-01-01 | Enrollment by school and year | 0 |
| 2023-01-01 | Housing developments | 2 |
| 2023-04-01 | Student homes | 0 |
| 2023-04-01 | Enrollment by school and year | 0 |
| 2023-04-01 | Housing developments | 0 |
| 2023-07-01 | Student homes | 224 |
| 2023-07-01 | Enrollment by school and year | 45 |
| 2023-07-01 | Housing developments | 2 |
| 2023-10-01 | Student homes | 264 |
| 2023-10-01 | Enrollment by school and year | 0 |
| 2023-10-01 | Housing developments | 1 |
| 2024-01-01 | Student homes | 19 |
| 2024-01-01 | Enrollment by school and year | 0 |
| 2024-01-01 | Housing developments | 0 |
| 2024-04-01 | Student homes | 68 |
| 2024-04-01 | Enrollment by school and year | 0 |
| 2024-04-01 | Housing developments | 3 |
| 2024-07-01 | Student homes | 335 |
| 2024-07-01 | Enrollment by school and year | 45 |
| 2024-07-01 | Housing developments | 3 |
| 2024-10-01 | Student homes | 263 |
| 2024-10-01 | Enrollment by school and year | 0 |
| 2024-10-01 | Housing developments | 4 |
| 2025-01-01 | Student homes | 25 |
| 2025-01-01 | Enrollment by school and year | 0 |
| 2025-01-01 | Housing developments | 2 |
| 2025-04-01 | Student homes | 86 |
| 2025-04-01 | Enrollment by school and year | 0 |
| 2025-04-01 | Housing developments | 2 |
| 2025-07-01 | Student homes | 385 |
| 2025-07-01 | Enrollment by school and year | 45 |
| 2025-07-01 | Housing developments | 3 |
| 2025-10-01 | Student homes | 284 |
| 2025-10-01 | Enrollment by school and year | 0 |
| 2025-10-01 | Housing developments | 1 |
| 2026-01-01 | Student homes | 36 |
| 2026-01-01 | Enrollment by school and year | 0 |
| 2026-01-01 | Housing developments | 3 |
| 2026-04-01 | Student homes | 102 |
| 2026-04-01 | Enrollment by school and year | 0 |
| 2026-04-01 | Housing developments | 2 |
| 2026-07-01 | Student homes | 409 |
| 2026-07-01 | Enrollment by school and year | 45 |
| 2026-07-01 | Housing developments | 5 |
| 2026-10-01 | Student homes | 0 |
| 2026-10-01 | Enrollment by school and year | 0 |
| 2026-10-01 | Housing developments | 0 |
| 2027-01-01 | Student homes | 0 |
| 2027-01-01 | Enrollment by school and year | 0 |
| 2027-01-01 | Housing developments | 0 |
| 2027-04-01 | Student homes | 0 |
| 2027-04-01 | Enrollment by school and year | 0 |
| 2027-04-01 | Housing developments | 3 |
| 2027-07-01 | Student homes | 0 |
| 2027-07-01 | Enrollment by school and year | 0 |
| 2027-07-01 | Housing developments | 2 |
| 2027-10-01 | Student homes | 0 |
| 2027-10-01 | Enrollment by school and year | 0 |
| 2027-10-01 | Housing developments | 2 |

### Figure 25. Schools over time (built_year) [78]

| bin_start | bin_end | count |
| --- | --- | --- |
| 350 | 528.1 | 14 |
| 528.1 | 706.3 | 21 |
| 706.3 | 884.4 | 3 |
| 884.4 | 1,063 | 0 |
| 1,063 | 1,241 | 3 |
| 1,241 | 1,419 | 2 |
| 1,419 | 1,597 | 2 |

### Figure 26. Housing developments over time (completion_month) [79]

| period | value |
| --- | --- |
| 2023-01-01 | 164 |
| 2023-04-01 | 0 |
| 2023-07-01 | 621 |
| 2023-10-01 | 137 |
| 2024-01-01 | 0 |
| 2024-04-01 | 579 |
| 2024-07-01 | 775 |
| 2024-10-01 | 883 |
| 2025-01-01 | 515 |
| 2025-04-01 | 994 |
| 2025-07-01 | 583 |
| 2025-10-01 | 509 |
| 2026-01-01 | 484 |
| 2026-04-01 | 424 |
| 2026-07-01 | 1,297 |
| 2026-10-01 | 0 |
| 2027-01-01 | 0 |
| 2027-04-01 | 767 |
| 2027-07-01 | 337 |
| 2027-10-01 | 669 |

### Figure 27. Enrollment by school and year over time (school_year) [80]

| period | value |
| --- | --- |
| 2021-07-01 | 45 |
| 2021-10-01 | 0 |
| 2022-01-01 | 0 |
| 2022-04-01 | 0 |
| 2022-07-01 | 45 |
| 2022-10-01 | 0 |
| 2023-01-01 | 0 |
| 2023-04-01 | 0 |
| 2023-07-01 | 45 |
| 2023-10-01 | 0 |
| 2024-01-01 | 0 |
| 2024-04-01 | 0 |
| 2024-07-01 | 45 |
| 2024-10-01 | 0 |
| 2025-01-01 | 0 |
| 2025-04-01 | 0 |
| 2025-07-01 | 45 |
| 2025-10-01 | 0 |
| 2026-01-01 | 0 |
| 2026-04-01 | 0 |
| 2026-07-01 | 45 |

### Figure 28. Emerging hot spots: Student homes [81]

| bin_start | bin_end | count |
| --- | --- | --- |
| -0.896 | 2.069 | 77 |
| 2.069 | 5.033 | 7 |
| 5.033 | 7.998 | 1 |
| 7.998 | 10.96 | 5 |
| 10.96 | 13.93 | 0 |
| 13.93 | 16.89 | 3 |
| 16.89 | 19.86 | 0 |
| 19.86 | 22.82 | 3 |

### Figure 29. Emerging hot spots: Housing developments [82]

| bin_start | bin_end | count |
| --- | --- | --- |
| -0.379 | 0.3548 | 15 |
| 0.3548 | 1.089 | 0 |
| 1.089 | 1.823 | 0 |
| 1.823 | 2.556 | 0 |
| 2.556 | 3.29 | 0 |
| 3.29 | 4.024 | 7 |

### Figure 30. Repeat and near repeat classification: Student homes [83]

| rank | name | distance_km |
| --- | --- | --- |
| 1 | near_repeat | 0.01 |
| 2 | isolated | 0.02 |
| 3 | isolated | 0.02 |
| 4 | isolated | 0.02 |
| 5 | originator | 0.03 |
| 6 | isolated | 0.03 |
| 7 | near_repeat | 0.03 |
| 8 | isolated | 0.03 |
| 9 | near_repeat | 0.04 |
| 10 | near_repeat | 0.04 |
| 11 | originator | 0.05 |
| 12 | isolated | 0.05 |
| 13 | originator | 0.05 |
| 14 | near_repeat | 0.05 |
| 15 | isolated | 0.05 |
| 16 | originator | 0.05 |
| 17 | near_repeat | 0.05 |
| 18 | near_repeat | 0.06 |
| 19 | near_repeat | 0.06 |
| 20 | isolated | 0.06 |

### Figure 31. Summarize within: School catchments [84]

| bin_start | bin_end | count |
| --- | --- | --- |
| 12 | 56.14 | 37 |
| 56.14 | 100.3 | 3 |
| 100.3 | 144.4 | 2 |
| 144.4 | 188.6 | 0 |
| 188.6 | 232.7 | 1 |
| 232.7 | 276.9 | 0 |
| 276.9 | 321 | 2 |

### Figure 32. Summarize within: School catchments [85]

| bin_start | bin_end | count |
| --- | --- | --- |
| 25 | 66 | 7 |
| 66 | 107 | 6 |
| 107 | 148 | 8 |
| 148 | 189 | 5 |
| 189 | 230 | 5 |
| 230 | 271 | 1 |
| 271 | 312 | 2 |

### Figure 33. Summarize within: School catchments [86]

| bin_start | bin_end | count |
| --- | --- | --- |
| 137 | 475.6 | 9 |
| 475.6 | 814.2 | 3 |
| 814.2 | 1,153 | 1 |
| 1,153 | 1,491 | 1 |
| 1,491 | 1,830 | 2 |

### Figure 34. Hot spot analysis: Student homes [87]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 52 |
| -0.5 | 0 | not_significant | Not significant | 164 |
| 0 | 0.5 | not_significant | Not significant | 37 |
| 0.5 | 1 | not_significant | Not significant | 7 |
| 1 | 1.5 | not_significant | Not significant | 2 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 2 |
| 1.5 | 2 | not_significant | Not significant | 1 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 3 |
| 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 3 |
| 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 3 |
| 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 3 |
| 4 | 4.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 5 | 5.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 5.5 | 6 | hot_99 | Hot spot, 99% confidence | 1 |
| 6.5 | 7 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 35. Hot spot analysis: Childcare centers [88]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -2 | -1.5 | cold_90 | Cold spot, 90% confidence | 3 |
| -1.5 | -1 | not_significant | Not significant | 1 |
| -1 | -0.5 | not_significant | Not significant | 3 |
| 0 | 0.5 | not_significant | Not significant | 4 |
| 0.5 | 1 | not_significant | Not significant | 6 |
| 1 | 1.5 | not_significant | Not significant | 1 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 2 |

### Figure 36. Hot spot analysis: Housing developments [89]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -2 | -1.5 | not_significant | Not significant | 1 |
| -1.5 | -1 | not_significant | Not significant | 14 |
| -1 | -0.5 | not_significant | Not significant | 8 |
| -0.5 | 0 | not_significant | Not significant | 7 |
| 0 | 0.5 | not_significant | Not significant | 5 |
| 0.5 | 1 | not_significant | Not significant | 2 |
| 1 | 1.5 | not_significant | Not significant | 1 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 2 |

### Figure 37. Calculate density: Student homes [90]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 4 | 121 |
| 4 | 8 | 153 |
| 8 | 12 | 156 |
| 12 | 16 | 154 |
| 16 | 20 | 153 |
| 20 | 24 | 151 |
| 24 | 28 | 152 |
| 28 | 32 | 152 |
| 32 | 36 | 153 |
| 36 | 40 | 154 |
| 40 | 44 | 156 |
| 44 | 48 | 183 |

### Figure 38. Calculate density: Childcare centers [91]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 1.5 | 11 |
| 1.5 | 3 | 10 |
| 3 | 4.5 | 20 |
| 4.5 | 6 | 10 |
| 6 | 7.5 | 19 |
| 7.5 | 9 | 10 |
| 9 | 10.5 | 20 |
| 10.5 | 12 | 12 |

### Figure 39. Calculate density: Housing developments [92]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0.0577 | 110.2 | 55 |
| 110.2 | 220.4 | 13 |
| 220.4 | 330.6 | 1 |
| 330.6 | 440.8 | 3 |
| 440.8 | 551 | 3 |
| 551 | 661.2 | 3 |
| 661.2 | 771.4 | 0 |
| 771.4 | 881.6 | 2 |

### Figure 40. Find point clusters: Student homes [93]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 1,490 | 0.596 | 59.6% |
| 0 | 1,010 | 0.404 | 40.4% |

### Figure 41. Find point clusters: cluster hulls [94]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 1 | 100.0% |

### Figure 42. Find point clusters: Childcare centers [95]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 1 | 14 | 0.2333 | 23.3% |
| 0 | 11 | 0.1833 | 18.3% |
| 6 | 9 | 0.15 | 15.0% |
| 3 | 7 | 0.1167 | 11.7% |
| 4 | 6 | 0.1 | 10.0% |
| 2 | 5 | 0.08333 | 8.3% |
| 5 | 4 | 0.06667 | 6.7% |
| 7 | 3 | 0.05 | 5.0% |
| Noise | 1 | 0.01667 | 1.7% |

### Figure 43. Find point clusters: cluster hulls [96]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.125 | 12.5% |
| 1 | 1 | 0.125 | 12.5% |
| 2 | 1 | 0.125 | 12.5% |
| 3 | 1 | 0.125 | 12.5% |
| 4 | 1 | 0.125 | 12.5% |
| 5 | 1 | 0.125 | 12.5% |
| 6 | 1 | 0.125 | 12.5% |
| 7 | 1 | 0.125 | 12.5% |

### Figure 44. Find point clusters: Housing developments [97]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 37 | 0.925 | 92.5% |
| Noise | 3 | 0.075 | 7.5% |

### Figure 45. Find point clusters: cluster hulls [98]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 1 | 100.0% |

### Figure 46. Find outliers: School catchments [99]

| value | lag | type |
| --- | --- | --- |
| -0.9672 | -0.1255 | NS |
| 1.383 | 0.255 | NS |
| -0.6288 | -0.6039 | NS |
| -0.0459 | -0.6442 | NS |
| 0.7193 | 0.1212 | NS |
| 1.317 | 0.8302 | NS |
| -0.0572 | 0.3557 | NS |
| 0.0175 | -0.639 | NS |
| -0.5212 | -0.3817 | NS |
| -0.356 | -0.459 | NS |
| 0.4873 | 0.0922 | NS |
| 0.2043 | 0.0226 | NS |
| -0.9095 | 0.2148 | NS |
| -0.2881 | 0.7917 | NS |
| 0.9559 | 0.2419 | NS |
| -0.2405 | -0.4586 | NS |
| -0.6061 | -0.1115 | NS |
| -0.2824 | 0.1801 | NS |
| -1.147 | 0.1129 | NS |
| -1.243 | 0.0056 | NS |
| -1.18 | 0.049 | NS |
| -1.044 | -0.5665 | NS |
| 1.131 | -0.9146 | NS |
| -0.0311 | -0.7442 | NS |
| 0.2201 | -0.5934 | NS |
| -0.3798 | -0.8665 | NS |
| 1.49 | -0.2094 | NS |
| 1.824 | -0.5705 | NS |
| -0.0198 | -0.3756 | NS |
| 0.8348 | -0.5589 | NS |
| 0.0322 | -0.2103 | NS |
| -0.5473 | 0.169 | NS |
| -1.234 | -0.6621 | NS |
| -0.5416 | 0.7018 | NS |
| -0.4816 | -0.4831 | NS |
| -1.345 | -0.8861 | NS |
| -0.3617 | 0.0937 | NS |
| -0.8529 | 0.1562 | NS |
| -0.2711 | -0.4635 | NS |
| 3.903 | -0.5416 | NS |
| -0.6831 | -0.1341 | NS |
| 0.8302 | 0.8808 | NS |
| 1.119 | -0.2824 | NS |
| -0.682 | 0.1734 | NS |
| 0.4805 | -0.3526 | NS |

### Figure 47. Find nearest: Student homes [100]

| rank | name |
| --- | --- |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |

### Figure 48. Find nearest: Student homes [101]

| rank | name |
| --- | --- |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |

### Figure 49. Find nearest: Student homes over time [102]

| period | series | value |
| --- | --- | --- |
| 2023-08-01 | walk | 44 |
| 2023-08-01 | bus | 25 |
| 2023-08-01 | car | 15 |
| 2023-08-01 | bike | 7 |
| 2023-09-01 | walk | 50 |
| 2023-09-01 | bus | 26 |
| 2023-09-01 | car | 24 |
| 2023-09-01 | bike | 5 |
| 2023-10-01 | walk | 48 |
| 2023-10-01 | bus | 28 |
| 2023-10-01 | car | 29 |
| 2023-10-01 | bike | 8 |
| 2023-11-01 | walk | 31 |
| 2023-11-01 | bus | 23 |
| 2023-11-01 | car | 19 |
| 2023-11-01 | bike | 3 |
| 2023-12-01 | walk | 17 |
| 2023-12-01 | bus | 12 |
| 2023-12-01 | car | 8 |
| 2023-12-01 | bike | 5 |
| 2024-01-01 | walk | 6 |
| 2024-01-01 | bus | 4 |
| 2024-01-01 | car | 1 |
| 2024-01-01 | bike | 1 |
| 2024-02-01 | walk | 2 |
| 2024-02-01 | bus | 0 |
| 2024-02-01 | car | 0 |
| 2024-02-01 | bike | 0 |
| 2024-03-01 | walk | 1 |
| 2024-03-01 | bus | 1 |
| 2024-03-01 | car | 0 |
| 2024-03-01 | bike | 0 |
| 2024-04-01 | walk | 1 |
| 2024-04-01 | bus | 0 |
| 2024-04-01 | car | 1 |
| 2024-04-01 | bike | 0 |
| 2024-05-01 | walk | 5 |
| 2024-05-01 | bus | 2 |
| 2024-05-01 | car | 6 |
| 2024-05-01 | bike | 1 |
| 2024-06-01 | walk | 12 |
| 2024-06-01 | bus | 15 |
| 2024-06-01 | car | 15 |
| 2024-06-01 | bike | 2 |
| 2024-07-01 | walk | 36 |
| 2024-07-01 | bus | 20 |
| 2024-07-01 | car | 15 |
| 2024-07-01 | bike | 5 |
| 2024-08-01 | walk | 56 |
| 2024-08-01 | bus | 26 |
| 2024-08-01 | car | 19 |
| 2024-08-01 | bike | 1 |
| 2024-09-01 | walk | 64 |
| 2024-09-01 | bus | 31 |
| 2024-09-01 | car | 25 |
| 2024-09-01 | bike | 3 |
| 2024-10-01 | walk | 48 |
| 2024-10-01 | bus | 44 |
| 2024-10-01 | car | 26 |
| 2024-10-01 | bike | 3 |
| 2024-11-01 | walk | 34 |
| 2024-11-01 | bus | 17 |
| 2024-11-01 | car | 15 |
| 2024-11-01 | bike | 4 |
| 2024-12-01 | walk | 28 |
| 2024-12-01 | bus | 16 |
| 2024-12-01 | car | 3 |
| 2024-12-01 | bike | 2 |
| 2025-01-01 | walk | 11 |
| 2025-01-01 | bus | 9 |
| 2025-01-01 | car | 3 |
| 2025-01-01 | bike | 0 |
| 2025-02-01 | walk | 0 |
| 2025-02-01 | bus | 0 |
| 2025-02-01 | car | 0 |
| 2025-02-01 | bike | 0 |
| 2025-03-01 | walk | 0 |
| 2025-03-01 | bus | 0 |
| 2025-03-01 | car | 0 |
| 2025-03-01 | bike | 0 |
| 2025-04-01 | walk | 2 |
| 2025-04-01 | bus | 1 |
| 2025-04-01 | car | 0 |
| 2025-04-01 | bike | 0 |
| 2025-05-01 | walk | 7 |
| 2025-05-01 | bus | 8 |
| 2025-05-01 | car | 1 |
| 2025-05-01 | bike | 1 |
| 2025-06-01 | walk | 25 |
| 2025-06-01 | bus | 13 |
| 2025-06-01 | car | 16 |
| 2025-06-01 | bike | 3 |
| 2025-07-01 | walk | 42 |
| 2025-07-01 | bus | 26 |
| 2025-07-01 | car | 20 |
| 2025-07-01 | bike | 3 |
| 2025-08-01 | walk | 55 |
| 2025-08-01 | bus | 38 |
| 2025-08-01 | car | 21 |
| 2025-08-01 | bike | 8 |
| 2025-09-01 | walk | 62 |
| 2025-09-01 | bus | 39 |
| 2025-09-01 | car | 27 |
| 2025-09-01 | bike | 9 |
| 2025-10-01 | walk | 49 |
| 2025-10-01 | bus | 38 |
| 2025-10-01 | car | 17 |
| 2025-10-01 | bike | 7 |
| 2025-11-01 | walk | 30 |
| 2025-11-01 | bus | 31 |
| 2025-11-01 | car | 16 |
| 2025-11-01 | bike | 6 |
| 2025-12-01 | walk | 29 |
| 2025-12-01 | bus | 13 |
| 2025-12-01 | car | 14 |
| 2025-12-01 | bike | 5 |
| 2026-01-01 | walk | 7 |
| 2026-01-01 | bus | 8 |
| 2026-01-01 | car | 7 |
| 2026-01-01 | bike | 3 |
| 2026-02-01 | walk | 1 |
| 2026-02-01 | bus | 3 |
| 2026-02-01 | car | 2 |
| 2026-02-01 | bike | 0 |
| 2026-03-01 | walk | 1 |
| 2026-03-01 | bus | 0 |
| 2026-03-01 | car | 3 |
| 2026-03-01 | bike | 0 |
| 2026-04-01 | walk | 2 |
| 2026-04-01 | bus | 1 |
| 2026-04-01 | car | 2 |
| 2026-04-01 | bike | 0 |
| 2026-05-01 | walk | 13 |
| 2026-05-01 | bus | 7 |
| 2026-05-01 | car | 7 |
| 2026-05-01 | bike | 0 |
| 2026-06-01 | walk | 27 |
| 2026-06-01 | bus | 21 |
| 2026-06-01 | car | 8 |
| 2026-06-01 | bike | 5 |
| 2026-07-01 | walk | 47 |
| 2026-07-01 | bus | 23 |
| 2026-07-01 | car | 13 |
| 2026-07-01 | bike | 4 |
| 2026-08-01 | walk | 60 |
| 2026-08-01 | bus | 47 |
| 2026-08-01 | car | 20 |
| 2026-08-01 | bike | 12 |
| 2026-09-01 | walk | 55 |
| 2026-09-01 | bus | 48 |
| 2026-09-01 | car | 33 |
| 2026-09-01 | bike | 12 |

### Figure 50. Find nearest: Student homes over time [103]

| period | series | value |
| --- | --- | --- |
| 2023-08-01 | walk | 48 |
| 2023-08-01 | bus | 29 |
| 2023-08-01 | car | 18 |
| 2023-08-01 | bike | 8 |
| 2023-09-01 | walk | 58 |
| 2023-09-01 | bus | 31 |
| 2023-09-01 | car | 26 |
| 2023-09-01 | bike | 6 |
| 2023-10-01 | walk | 54 |
| 2023-10-01 | bus | 34 |
| 2023-10-01 | car | 33 |
| 2023-10-01 | bike | 9 |
| 2023-11-01 | walk | 35 |
| 2023-11-01 | bus | 26 |
| 2023-11-01 | car | 20 |
| 2023-11-01 | bike | 4 |
| 2023-12-01 | walk | 21 |
| 2023-12-01 | bus | 14 |
| 2023-12-01 | car | 9 |
| 2023-12-01 | bike | 5 |
| 2024-01-01 | walk | 7 |
| 2024-01-01 | bus | 4 |
| 2024-01-01 | car | 1 |
| 2024-01-01 | bike | 1 |
| 2024-02-01 | walk | 2 |
| 2024-02-01 | bus | 0 |
| 2024-02-01 | car | 1 |
| 2024-02-01 | bike | 0 |
| 2024-03-01 | walk | 1 |
| 2024-03-01 | bus | 2 |
| 2024-03-01 | car | 0 |
| 2024-03-01 | bike | 0 |
| 2024-04-01 | walk | 1 |
| 2024-04-01 | bus | 0 |
| 2024-04-01 | car | 1 |
| 2024-04-01 | bike | 0 |
| 2024-05-01 | walk | 8 |
| 2024-05-01 | bus | 3 |
| 2024-05-01 | car | 6 |
| 2024-05-01 | bike | 1 |
| 2024-06-01 | walk | 14 |
| 2024-06-01 | bus | 16 |
| 2024-06-01 | car | 15 |
| 2024-06-01 | bike | 3 |
| 2024-07-01 | walk | 40 |
| 2024-07-01 | bus | 25 |
| 2024-07-01 | car | 19 |
| 2024-07-01 | bike | 5 |
| 2024-08-01 | walk | 59 |
| 2024-08-01 | bus | 32 |
| 2024-08-01 | car | 20 |
| 2024-08-01 | bike | 1 |
| 2024-09-01 | walk | 70 |
| 2024-09-01 | bus | 35 |
| 2024-09-01 | car | 25 |
| 2024-09-01 | bike | 4 |
| 2024-10-01 | walk | 53 |
| 2024-10-01 | bus | 48 |
| 2024-10-01 | car | 27 |
| 2024-10-01 | bike | 4 |
| 2024-11-01 | walk | 39 |
| 2024-11-01 | bus | 21 |
| 2024-11-01 | car | 17 |
| 2024-11-01 | bike | 4 |
| 2024-12-01 | walk | 28 |
| 2024-12-01 | bus | 16 |
| 2024-12-01 | car | 4 |
| 2024-12-01 | bike | 2 |
| 2025-01-01 | walk | 12 |
| 2025-01-01 | bus | 9 |
| 2025-01-01 | car | 3 |
| 2025-01-01 | bike | 1 |
| 2025-02-01 | walk | 0 |
| 2025-02-01 | bus | 0 |
| 2025-02-01 | car | 0 |
| 2025-02-01 | bike | 0 |
| 2025-03-01 | walk | 0 |
| 2025-03-01 | bus | 0 |
| 2025-03-01 | car | 0 |
| 2025-03-01 | bike | 0 |
| 2025-04-01 | walk | 2 |
| 2025-04-01 | bus | 1 |
| 2025-04-01 | car | 0 |
| 2025-04-01 | bike | 0 |
| 2025-05-01 | walk | 8 |
| 2025-05-01 | bus | 8 |
| 2025-05-01 | car | 2 |
| 2025-05-01 | bike | 1 |
| 2025-06-01 | walk | 29 |
| 2025-06-01 | bus | 15 |
| 2025-06-01 | car | 17 |
| 2025-06-01 | bike | 3 |
| 2025-07-01 | walk | 47 |
| 2025-07-01 | bus | 30 |
| 2025-07-01 | car | 20 |
| 2025-07-01 | bike | 3 |
| 2025-08-01 | walk | 62 |
| 2025-08-01 | bus | 41 |
| 2025-08-01 | car | 25 |
| 2025-08-01 | bike | 8 |
| 2025-09-01 | walk | 69 |
| 2025-09-01 | bus | 41 |
| 2025-09-01 | car | 29 |
| 2025-09-01 | bike | 10 |
| 2025-10-01 | walk | 58 |
| 2025-10-01 | bus | 40 |
| 2025-10-01 | car | 21 |
| 2025-10-01 | bike | 7 |
| 2025-11-01 | walk | 34 |
| 2025-11-01 | bus | 34 |
| 2025-11-01 | car | 17 |
| 2025-11-01 | bike | 6 |
| 2025-12-01 | walk | 31 |
| 2025-12-01 | bus | 14 |
| 2025-12-01 | car | 16 |
| 2025-12-01 | bike | 6 |
| 2026-01-01 | walk | 8 |
| 2026-01-01 | bus | 8 |
| 2026-01-01 | car | 7 |
| 2026-01-01 | bike | 3 |
| 2026-02-01 | walk | 1 |
| 2026-02-01 | bus | 3 |
| 2026-02-01 | car | 2 |
| 2026-02-01 | bike | 0 |
| 2026-03-01 | walk | 1 |
| 2026-03-01 | bus | 0 |
| 2026-03-01 | car | 3 |
| 2026-03-01 | bike | 0 |
| 2026-04-01 | walk | 2 |
| 2026-04-01 | bus | 1 |
| 2026-04-01 | car | 2 |
| 2026-04-01 | bike | 0 |
| 2026-05-01 | walk | 13 |
| 2026-05-01 | bus | 8 |
| 2026-05-01 | car | 7 |
| 2026-05-01 | bike | 0 |
| 2026-06-01 | walk | 31 |
| 2026-06-01 | bus | 22 |
| 2026-06-01 | car | 11 |
| 2026-06-01 | bike | 5 |
| 2026-07-01 | walk | 54 |
| 2026-07-01 | bus | 25 |
| 2026-07-01 | car | 13 |
| 2026-07-01 | bike | 4 |
| 2026-08-01 | walk | 62 |
| 2026-08-01 | bus | 51 |
| 2026-08-01 | car | 25 |
| 2026-08-01 | bike | 12 |
| 2026-09-01 | walk | 63 |
| 2026-09-01 | bus | 53 |
| 2026-09-01 | car | 35 |
| 2026-09-01 | bike | 12 |

### Figure 51. Find nearest: Student homes over time [104]

| period | series | value |
| --- | --- | --- |
| 2023-08-01 | Dundas Nova Street | 16 |
| 2023-08-01 | Lakeview Avenue | 15 |
| 2023-08-01 | Forest Hill Avenue | 17 |
| 2023-08-01 | Front Street | 15 |
| 2023-08-01 | Bloor Nova Street | 8 |
| 2023-08-01 | Other | 32 |
| 2023-09-01 | Dundas Nova Street | 18 |
| 2023-09-01 | Lakeview Avenue | 23 |
| 2023-09-01 | Forest Hill Avenue | 14 |
| 2023-09-01 | Front Street | 17 |
| 2023-09-01 | Bloor Nova Street | 10 |
| 2023-09-01 | Other | 39 |
| 2023-10-01 | Dundas Nova Street | 19 |
| 2023-10-01 | Lakeview Avenue | 16 |
| 2023-10-01 | Forest Hill Avenue | 14 |
| 2023-10-01 | Front Street | 20 |
| 2023-10-01 | Bloor Nova Street | 21 |
| 2023-10-01 | Other | 40 |
| 2023-11-01 | Dundas Nova Street | 13 |
| 2023-11-01 | Lakeview Avenue | 15 |
| 2023-11-01 | Forest Hill Avenue | 6 |
| 2023-11-01 | Front Street | 17 |
| 2023-11-01 | Bloor Nova Street | 10 |
| 2023-11-01 | Other | 24 |
| 2023-12-01 | Dundas Nova Street | 8 |
| 2023-12-01 | Lakeview Avenue | 7 |
| 2023-12-01 | Forest Hill Avenue | 12 |
| 2023-12-01 | Front Street | 8 |
| 2023-12-01 | Bloor Nova Street | 4 |
| 2023-12-01 | Other | 10 |
| 2024-01-01 | Dundas Nova Street | 4 |
| 2024-01-01 | Lakeview Avenue | 3 |
| 2024-01-01 | Forest Hill Avenue | 0 |
| 2024-01-01 | Front Street | 1 |
| 2024-01-01 | Bloor Nova Street | 1 |
| 2024-01-01 | Other | 4 |
| 2024-02-01 | Dundas Nova Street | 0 |
| 2024-02-01 | Lakeview Avenue | 1 |
| 2024-02-01 | Forest Hill Avenue | 0 |
| 2024-02-01 | Front Street | 0 |
| 2024-02-01 | Bloor Nova Street | 1 |
| 2024-02-01 | Other | 1 |
| 2024-03-01 | Dundas Nova Street | 1 |
| 2024-03-01 | Lakeview Avenue | 0 |
| 2024-03-01 | Forest Hill Avenue | 0 |
| 2024-03-01 | Front Street | 2 |
| 2024-03-01 | Bloor Nova Street | 0 |
| 2024-03-01 | Other | 0 |
| 2024-04-01 | Dundas Nova Street | 0 |
| 2024-04-01 | Lakeview Avenue | 0 |
| 2024-04-01 | Forest Hill Avenue | 0 |
| 2024-04-01 | Front Street | 0 |
| 2024-04-01 | Bloor Nova Street | 1 |
| 2024-04-01 | Other | 1 |
| 2024-05-01 | Dundas Nova Street | 4 |
| 2024-05-01 | Lakeview Avenue | 4 |
| 2024-05-01 | Forest Hill Avenue | 1 |
| 2024-05-01 | Front Street | 1 |
| 2024-05-01 | Bloor Nova Street | 4 |
| 2024-05-01 | Other | 4 |
| 2024-06-01 | Dundas Nova Street | 8 |
| 2024-06-01 | Lakeview Avenue | 3 |
| 2024-06-01 | Forest Hill Avenue | 10 |
| 2024-06-01 | Front Street | 7 |
| 2024-06-01 | Bloor Nova Street | 7 |
| 2024-06-01 | Other | 13 |
| 2024-07-01 | Dundas Nova Street | 17 |
| 2024-07-01 | Lakeview Avenue | 11 |
| 2024-07-01 | Forest Hill Avenue | 14 |
| 2024-07-01 | Front Street | 19 |
| 2024-07-01 | Bloor Nova Street | 11 |
| 2024-07-01 | Other | 17 |
| 2024-08-01 | Dundas Nova Street | 25 |
| 2024-08-01 | Lakeview Avenue | 22 |
| 2024-08-01 | Forest Hill Avenue | 15 |
| 2024-08-01 | Front Street | 11 |
| 2024-08-01 | Bloor Nova Street | 13 |
| 2024-08-01 | Other | 26 |
| 2024-09-01 | Dundas Nova Street | 24 |
| 2024-09-01 | Lakeview Avenue | 18 |
| 2024-09-01 | Forest Hill Avenue | 22 |
| 2024-09-01 | Front Street | 21 |
| 2024-09-01 | Bloor Nova Street | 10 |
| 2024-09-01 | Other | 39 |
| 2024-10-01 | Dundas Nova Street | 19 |
| 2024-10-01 | Lakeview Avenue | 21 |
| 2024-10-01 | Forest Hill Avenue | 18 |
| 2024-10-01 | Front Street | 15 |
| 2024-10-01 | Bloor Nova Street | 25 |
| 2024-10-01 | Other | 34 |
| 2024-11-01 | Dundas Nova Street | 14 |
| 2024-11-01 | Lakeview Avenue | 12 |
| 2024-11-01 | Forest Hill Avenue | 10 |
| 2024-11-01 | Front Street | 8 |
| 2024-11-01 | Bloor Nova Street | 14 |
| 2024-11-01 | Other | 23 |
| 2024-12-01 | Dundas Nova Street | 8 |
| 2024-12-01 | Lakeview Avenue | 8 |
| 2024-12-01 | Forest Hill Avenue | 12 |
| 2024-12-01 | Front Street | 4 |
| 2024-12-01 | Bloor Nova Street | 6 |
| 2024-12-01 | Other | 12 |
| 2025-01-01 | Dundas Nova Street | 7 |
| 2025-01-01 | Lakeview Avenue | 4 |
| 2025-01-01 | Forest Hill Avenue | 2 |
| 2025-01-01 | Front Street | 2 |
| 2025-01-01 | Bloor Nova Street | 5 |
| 2025-01-01 | Other | 5 |
| 2025-02-01 | Dundas Nova Street | 0 |
| 2025-02-01 | Lakeview Avenue | 0 |
| 2025-02-01 | Forest Hill Avenue | 0 |
| 2025-02-01 | Front Street | 0 |
| 2025-02-01 | Bloor Nova Street | 0 |
| 2025-02-01 | Other | 0 |
| 2025-03-01 | Dundas Nova Street | 0 |
| 2025-03-01 | Lakeview Avenue | 0 |
| 2025-03-01 | Forest Hill Avenue | 0 |
| 2025-03-01 | Front Street | 0 |
| 2025-03-01 | Bloor Nova Street | 0 |
| 2025-03-01 | Other | 0 |
| 2025-04-01 | Dundas Nova Street | 0 |
| 2025-04-01 | Lakeview Avenue | 0 |
| 2025-04-01 | Forest Hill Avenue | 2 |
| 2025-04-01 | Front Street | 0 |
| 2025-04-01 | Bloor Nova Street | 0 |
| 2025-04-01 | Other | 1 |
| 2025-05-01 | Dundas Nova Street | 5 |
| 2025-05-01 | Lakeview Avenue | 7 |
| 2025-05-01 | Forest Hill Avenue | 1 |
| 2025-05-01 | Front Street | 3 |
| 2025-05-01 | Bloor Nova Street | 2 |
| 2025-05-01 | Other | 1 |
| 2025-06-01 | Dundas Nova Street | 12 |
| 2025-06-01 | Lakeview Avenue | 8 |
| 2025-06-01 | Forest Hill Avenue | 15 |
| 2025-06-01 | Front Street | 10 |
| 2025-06-01 | Bloor Nova Street | 7 |
| 2025-06-01 | Other | 12 |
| 2025-07-01 | Dundas Nova Street | 15 |
| 2025-07-01 | Lakeview Avenue | 18 |
| 2025-07-01 | Forest Hill Avenue | 13 |
| 2025-07-01 | Front Street | 12 |
| 2025-07-01 | Bloor Nova Street | 12 |
| 2025-07-01 | Other | 30 |
| 2025-08-01 | Dundas Nova Street | 24 |
| 2025-08-01 | Lakeview Avenue | 22 |
| 2025-08-01 | Forest Hill Avenue | 26 |
| 2025-08-01 | Front Street | 18 |
| 2025-08-01 | Bloor Nova Street | 17 |
| 2025-08-01 | Other | 29 |
| 2025-09-01 | Dundas Nova Street | 37 |
| 2025-09-01 | Lakeview Avenue | 21 |
| 2025-09-01 | Forest Hill Avenue | 21 |
| 2025-09-01 | Front Street | 16 |
| 2025-09-01 | Bloor Nova Street | 21 |
| 2025-09-01 | Other | 33 |
| 2025-10-01 | Dundas Nova Street | 20 |
| 2025-10-01 | Lakeview Avenue | 26 |
| 2025-10-01 | Forest Hill Avenue | 18 |
| 2025-10-01 | Front Street | 12 |
| 2025-10-01 | Bloor Nova Street | 18 |
| 2025-10-01 | Other | 32 |
| 2025-11-01 | Dundas Nova Street | 16 |
| 2025-11-01 | Lakeview Avenue | 23 |
| 2025-11-01 | Forest Hill Avenue | 11 |
| 2025-11-01 | Front Street | 13 |
| 2025-11-01 | Bloor Nova Street | 7 |
| 2025-11-01 | Other | 21 |
| 2025-12-01 | Dundas Nova Street | 20 |
| 2025-12-01 | Lakeview Avenue | 7 |
| 2025-12-01 | Forest Hill Avenue | 13 |
| 2025-12-01 | Front Street | 8 |
| 2025-12-01 | Bloor Nova Street | 10 |
| 2025-12-01 | Other | 9 |
| 2026-01-01 | Dundas Nova Street | 5 |
| 2026-01-01 | Lakeview Avenue | 4 |
| 2026-01-01 | Forest Hill Avenue | 3 |
| 2026-01-01 | Front Street | 3 |
| 2026-01-01 | Bloor Nova Street | 6 |
| 2026-01-01 | Other | 5 |
| 2026-02-01 | Dundas Nova Street | 1 |
| 2026-02-01 | Lakeview Avenue | 1 |
| 2026-02-01 | Forest Hill Avenue | 2 |
| 2026-02-01 | Front Street | 0 |
| 2026-02-01 | Bloor Nova Street | 1 |
| 2026-02-01 | Other | 1 |
| 2026-03-01 | Dundas Nova Street | 0 |
| 2026-03-01 | Lakeview Avenue | 1 |
| 2026-03-01 | Forest Hill Avenue | 0 |
| 2026-03-01 | Front Street | 1 |
| 2026-03-01 | Bloor Nova Street | 0 |
| 2026-03-01 | Other | 2 |
| 2026-04-01 | Dundas Nova Street | 1 |
| 2026-04-01 | Lakeview Avenue | 0 |
| 2026-04-01 | Forest Hill Avenue | 0 |
| 2026-04-01 | Front Street | 1 |
| 2026-04-01 | Bloor Nova Street | 1 |
| 2026-04-01 | Other | 2 |
| 2026-05-01 | Dundas Nova Street | 4 |
| 2026-05-01 | Lakeview Avenue | 4 |
| 2026-05-01 | Forest Hill Avenue | 4 |
| 2026-05-01 | Front Street | 2 |
| 2026-05-01 | Bloor Nova Street | 6 |
| 2026-05-01 | Other | 8 |
| 2026-06-01 | Dundas Nova Street | 15 |
| 2026-06-01 | Lakeview Avenue | 14 |
| 2026-06-01 | Forest Hill Avenue | 15 |
| 2026-06-01 | Front Street | 11 |
| 2026-06-01 | Bloor Nova Street | 7 |
| 2026-06-01 | Other | 7 |
| 2026-07-01 | Dundas Nova Street | 22 |
| 2026-07-01 | Lakeview Avenue | 19 |
| 2026-07-01 | Forest Hill Avenue | 18 |
| 2026-07-01 | Front Street | 6 |
| 2026-07-01 | Bloor Nova Street | 8 |
| 2026-07-01 | Other | 23 |
| 2026-08-01 | Dundas Nova Street | 33 |
| 2026-08-01 | Lakeview Avenue | 28 |
| 2026-08-01 | Forest Hill Avenue | 24 |
| 2026-08-01 | Front Street | 18 |
| 2026-08-01 | Bloor Nova Street | 21 |
| 2026-08-01 | Other | 26 |
| 2026-09-01 | Dundas Nova Street | 31 |
| 2026-09-01 | Lakeview Avenue | 38 |
| 2026-09-01 | Forest Hill Avenue | 26 |
| 2026-09-01 | Front Street | 18 |
| 2026-09-01 | Bloor Nova Street | 17 |
| 2026-09-01 | Other | 33 |

### Figure 52. Find point clusters: Housing developments over time [105]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | False | 2 |
| 2023-01-01 | True | 0 |
| 2023-04-01 | False | 0 |
| 2023-04-01 | True | 0 |
| 2023-07-01 | False | 2 |
| 2023-07-01 | True | 0 |
| 2023-10-01 | False | 1 |
| 2023-10-01 | True | 0 |
| 2024-01-01 | False | 0 |
| 2024-01-01 | True | 0 |
| 2024-04-01 | False | 3 |
| 2024-04-01 | True | 0 |
| 2024-07-01 | False | 3 |
| 2024-07-01 | True | 0 |
| 2024-10-01 | False | 4 |
| 2024-10-01 | True | 0 |
| 2025-01-01 | False | 2 |
| 2025-01-01 | True | 0 |
| 2025-04-01 | False | 1 |
| 2025-04-01 | True | 1 |
| 2025-07-01 | False | 3 |
| 2025-07-01 | True | 0 |
| 2025-10-01 | False | 1 |
| 2025-10-01 | True | 0 |
| 2026-01-01 | False | 3 |
| 2026-01-01 | True | 0 |
| 2026-04-01 | False | 1 |
| 2026-04-01 | True | 1 |
| 2026-07-01 | False | 5 |
| 2026-07-01 | True | 0 |
| 2026-10-01 | False | 0 |
| 2026-10-01 | True | 0 |
| 2027-01-01 | False | 0 |
| 2027-01-01 | True | 0 |
| 2027-04-01 | False | 3 |
| 2027-04-01 | True | 0 |
| 2027-07-01 | False | 2 |
| 2027-07-01 | True | 0 |
| 2027-10-01 | False | 1 |
| 2027-10-01 | True | 1 |

### Figure 53. Find point clusters: Student homes over time [106]

| period | series | value |
| --- | --- | --- |
| 2023-08-01 | True | 78 |
| 2023-08-01 | False | 25 |
| 2023-09-01 | True | 94 |
| 2023-09-01 | False | 27 |
| 2023-10-01 | True | 95 |
| 2023-10-01 | False | 35 |
| 2023-11-01 | True | 63 |
| 2023-11-01 | False | 22 |
| 2023-12-01 | True | 29 |
| 2023-12-01 | False | 20 |
| 2024-01-01 | True | 9 |
| 2024-01-01 | False | 4 |
| 2024-02-01 | True | 3 |
| 2024-02-01 | False | 0 |
| 2024-03-01 | True | 2 |
| 2024-03-01 | False | 1 |
| 2024-04-01 | True | 1 |
| 2024-04-01 | False | 1 |
| 2024-05-01 | True | 12 |
| 2024-05-01 | False | 6 |
| 2024-06-01 | True | 30 |
| 2024-06-01 | False | 18 |
| 2024-07-01 | True | 60 |
| 2024-07-01 | False | 29 |
| 2024-08-01 | True | 70 |
| 2024-08-01 | False | 42 |
| 2024-09-01 | True | 81 |
| 2024-09-01 | False | 53 |
| 2024-10-01 | True | 82 |
| 2024-10-01 | False | 50 |
| 2024-11-01 | True | 58 |
| 2024-11-01 | False | 23 |
| 2024-12-01 | True | 27 |
| 2024-12-01 | False | 23 |
| 2025-01-01 | True | 12 |
| 2025-01-01 | False | 13 |
| 2025-02-01 | True | 0 |
| 2025-02-01 | False | 0 |
| 2025-03-01 | True | 0 |
| 2025-03-01 | False | 0 |
| 2025-04-01 | True | 2 |
| 2025-04-01 | False | 1 |
| 2025-05-01 | True | 9 |
| 2025-05-01 | False | 10 |
| 2025-06-01 | True | 34 |
| 2025-06-01 | False | 30 |
| 2025-07-01 | True | 53 |
| 2025-07-01 | False | 47 |
| 2025-08-01 | True | 73 |
| 2025-08-01 | False | 63 |
| 2025-09-01 | True | 90 |
| 2025-09-01 | False | 59 |
| 2025-10-01 | True | 74 |
| 2025-10-01 | False | 52 |
| 2025-11-01 | True | 59 |
| 2025-11-01 | False | 32 |
| 2025-12-01 | True | 35 |
| 2025-12-01 | False | 32 |
| 2026-01-01 | True | 14 |
| 2026-01-01 | False | 12 |
| 2026-02-01 | True | 3 |
| 2026-02-01 | False | 3 |
| 2026-03-01 | True | 2 |
| 2026-03-01 | False | 2 |
| 2026-04-01 | True | 3 |
| 2026-04-01 | False | 2 |
| 2026-05-01 | True | 14 |
| 2026-05-01 | False | 14 |
| 2026-06-01 | True | 32 |
| 2026-06-01 | False | 37 |
| 2026-07-01 | True | 41 |
| 2026-07-01 | False | 55 |
| 2026-08-01 | True | 77 |
| 2026-08-01 | False | 73 |
| 2026-09-01 | True | 69 |
| 2026-09-01 | False | 94 |

### Figure 54. Hot spot analysis: Housing developments over time [107]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | not_significant | 2 |
| 2023-01-01 | hot_90 | 0 |
| 2023-04-01 | not_significant | 0 |
| 2023-04-01 | hot_90 | 0 |
| 2023-07-01 | not_significant | 2 |
| 2023-07-01 | hot_90 | 0 |
| 2023-10-01 | not_significant | 1 |
| 2023-10-01 | hot_90 | 0 |
| 2024-01-01 | not_significant | 0 |
| 2024-01-01 | hot_90 | 0 |
| 2024-04-01 | not_significant | 3 |
| 2024-04-01 | hot_90 | 0 |
| 2024-07-01 | not_significant | 3 |
| 2024-07-01 | hot_90 | 0 |
| 2024-10-01 | not_significant | 4 |
| 2024-10-01 | hot_90 | 0 |
| 2025-01-01 | not_significant | 2 |
| 2025-01-01 | hot_90 | 0 |
| 2025-04-01 | not_significant | 1 |
| 2025-04-01 | hot_90 | 1 |
| 2025-07-01 | not_significant | 3 |
| 2025-07-01 | hot_90 | 0 |
| 2025-10-01 | not_significant | 1 |
| 2025-10-01 | hot_90 | 0 |
| 2026-01-01 | not_significant | 3 |
| 2026-01-01 | hot_90 | 0 |
| 2026-04-01 | not_significant | 1 |
| 2026-04-01 | hot_90 | 1 |
| 2026-07-01 | not_significant | 5 |
| 2026-07-01 | hot_90 | 0 |
| 2026-10-01 | not_significant | 0 |
| 2026-10-01 | hot_90 | 0 |
| 2027-01-01 | not_significant | 0 |
| 2027-01-01 | hot_90 | 0 |
| 2027-04-01 | not_significant | 3 |
| 2027-04-01 | hot_90 | 0 |
| 2027-07-01 | not_significant | 2 |
| 2027-07-01 | hot_90 | 0 |
| 2027-10-01 | not_significant | 2 |
| 2027-10-01 | hot_90 | 0 |

### Figure 55. Repeat and near repeat classification: Student homes over time [108]

| period | series | value |
| --- | --- | --- |
| 2023-08-01 | isolated | 66 |
| 2023-08-01 | near_repeat | 15 |
| 2023-08-01 | originator | 22 |
| 2023-09-01 | isolated | 65 |
| 2023-09-01 | near_repeat | 33 |
| 2023-09-01 | originator | 23 |
| 2023-10-01 | isolated | 66 |
| 2023-10-01 | near_repeat | 36 |
| 2023-10-01 | originator | 28 |
| 2023-11-01 | isolated | 55 |
| 2023-11-01 | near_repeat | 24 |
| 2023-11-01 | originator | 6 |
| 2023-12-01 | isolated | 29 |
| 2023-12-01 | near_repeat | 18 |
| 2023-12-01 | originator | 2 |
| 2024-01-01 | isolated | 11 |
| 2024-01-01 | near_repeat | 2 |
| 2024-01-01 | originator | 0 |
| 2024-02-01 | isolated | 3 |
| 2024-02-01 | near_repeat | 0 |
| 2024-02-01 | originator | 0 |
| 2024-03-01 | isolated | 3 |
| 2024-03-01 | near_repeat | 0 |
| 2024-03-01 | originator | 0 |
| 2024-04-01 | isolated | 2 |
| 2024-04-01 | near_repeat | 0 |
| 2024-04-01 | originator | 0 |
| 2024-05-01 | isolated | 14 |
| 2024-05-01 | near_repeat | 1 |
| 2024-05-01 | originator | 3 |
| 2024-06-01 | isolated | 28 |
| 2024-06-01 | near_repeat | 10 |
| 2024-06-01 | originator | 10 |
| 2024-07-01 | isolated | 43 |
| 2024-07-01 | near_repeat | 27 |
| 2024-07-01 | originator | 19 |
| 2024-08-01 | isolated | 55 |
| 2024-08-01 | near_repeat | 35 |
| 2024-08-01 | originator | 22 |
| 2024-09-01 | isolated | 52 |
| 2024-09-01 | near_repeat | 59 |
| 2024-09-01 | originator | 23 |
| 2024-10-01 | isolated | 54 |
| 2024-10-01 | near_repeat | 51 |
| 2024-10-01 | originator | 27 |
| 2024-11-01 | isolated | 42 |
| 2024-11-01 | near_repeat | 32 |
| 2024-11-01 | originator | 7 |
| 2024-12-01 | isolated | 29 |
| 2024-12-01 | near_repeat | 13 |
| 2024-12-01 | originator | 8 |
| 2025-01-01 | isolated | 20 |
| 2025-01-01 | near_repeat | 4 |
| 2025-01-01 | originator | 1 |
| 2025-02-01 | isolated | 0 |
| 2025-02-01 | near_repeat | 0 |
| 2025-02-01 | originator | 0 |
| 2025-03-01 | isolated | 0 |
| 2025-03-01 | near_repeat | 0 |
| 2025-03-01 | originator | 0 |
| 2025-04-01 | isolated | 3 |
| 2025-04-01 | near_repeat | 0 |
| 2025-04-01 | originator | 0 |
| 2025-05-01 | isolated | 12 |
| 2025-05-01 | near_repeat | 2 |
| 2025-05-01 | originator | 5 |
| 2025-06-01 | isolated | 35 |
| 2025-06-01 | near_repeat | 14 |
| 2025-06-01 | originator | 15 |
| 2025-07-01 | isolated | 46 |
| 2025-07-01 | near_repeat | 35 |
| 2025-07-01 | originator | 19 |
| 2025-08-01 | isolated | 56 |
| 2025-08-01 | near_repeat | 51 |
| 2025-08-01 | originator | 29 |
| 2025-09-01 | isolated | 63 |
| 2025-09-01 | near_repeat | 67 |
| 2025-09-01 | originator | 19 |
| 2025-10-01 | isolated | 42 |
| 2025-10-01 | near_repeat | 65 |
| 2025-10-01 | originator | 19 |
| 2025-11-01 | isolated | 53 |
| 2025-11-01 | near_repeat | 30 |
| 2025-11-01 | originator | 8 |
| 2025-12-01 | isolated | 39 |
| 2025-12-01 | near_repeat | 21 |
| 2025-12-01 | originator | 7 |
| 2026-01-01 | isolated | 17 |
| 2026-01-01 | near_repeat | 8 |
| 2026-01-01 | originator | 1 |
| 2026-02-01 | isolated | 5 |
| 2026-02-01 | near_repeat | 1 |
| 2026-02-01 | originator | 0 |
| 2026-03-01 | isolated | 4 |
| 2026-03-01 | near_repeat | 0 |
| 2026-03-01 | originator | 0 |
| 2026-04-01 | isolated | 5 |
| 2026-04-01 | near_repeat | 0 |
| 2026-04-01 | originator | 0 |
| 2026-05-01 | isolated | 16 |
| 2026-05-01 | near_repeat | 4 |
| 2026-05-01 | originator | 8 |
| 2026-06-01 | isolated | 37 |
| 2026-06-01 | near_repeat | 17 |
| 2026-06-01 | originator | 15 |
| 2026-07-01 | isolated | 34 |
| 2026-07-01 | near_repeat | 49 |
| 2026-07-01 | originator | 13 |
| 2026-08-01 | isolated | 48 |
| 2026-08-01 | near_repeat | 77 |
| 2026-08-01 | originator | 25 |
| 2026-09-01 | isolated | 49 |
| 2026-09-01 | near_repeat | 99 |
| 2026-09-01 | originator | 15 |


*GeoBit Large Spatial Model. Findings are labeled Evidenced (computed by a tool), Inferred (a reading or reasoning) or Unverified (no evidence yet).*