# Sample: Real estate and urban planning, Lisbon: report

Project: Sample: Real estate and urban planning, Lisbon  
Goal: Describe the project Sample: Real estate and urban planning, Lisbon: run the analyses that tell the most about its data, draw every result, and write a cited report.  
Run: 76926ded-9cc8-4578-9472-14b00b3eb370 (running, 11 agent run(s))  
Generated: 2026-10-18T06:25:20Z, version 1  
Model cost: USD 0.0000 over 0 model call(s)  

## Data used

| Data | Kind | Rows | Time span | Summary | Source |
| --- | --- | --- | --- | --- | --- |
| Zoning districts | vector, Polygon | 10 |  | 10 polygon (area) features; 'use' has 4 values (top: residential 5, mixed use 3, commercial 1); 'max_far' 1.2 to 4 (mean 2.32); 'max_height_m' 15 to 40 (mean 23.5); named by 'name' (e.g. Vila Norte, Jardim Primavera, Alto do Moinho); total area 20.02 km2; extent -9.1715,38.7188 to -9.1139,38.7550. Areas (Zoning districts) with max_far to compare and map. | [101] |
| Parcels | vector, Polygon | 1,100 |  | 1,100 polygon (area) features; 'zone_code' has 6 values (top: I07 282, R09 189, R04 188); 'vacant' has 2 values (top: False 1051, True 49); 'built_far' 0 to 3.87 (mean 1.459); 'area_m2' 752 to 13,841 (mean 4667); 'slope_deg' 0 to 6.8 (mean 2.608); total area 5.138 km2; extent -9.1709,38.7193 to -9.1450,38.7544. Areas (Parcels) with built_far to compare and map. | [102] |
| Property sales | vector, Point | 950 | 2022-01-01 to 2026-09-30 | 950 point features; dated by 'date' from 2022-01-01 to 2026-09-30; 'type' has 3 values (top: apartment 431, office 268, house 251); 'price_eur_m2' 2,476 to 8,706 (mean 4799); 'price_eur' 78,920 to 1,343,084 (mean 4.309e+05); 'area_m2' 24 to 242 (mean 89.94); extent -9.1704,38.7199 to -9.1457,38.7539. Dated point events by type: where and when Property sales happen. | [2] |
| Building permits | vector, Point | 380 | 2023-01-01 to 2026-09-01 | 380 point features; dated by 'decision_month' from 2023-01-01 to 2026-09-01; 'type' has 4 values (top: extension 130, new build 127, change of use 88); 'units' 0 to 40 (mean 20.4); extent -9.1702,38.7204 to -9.1457,38.7539. Dated point events by type: where and when Building permits happen. | [3] |
| Transit stations | vector, Point | 12 |  | 12 point features; 'lines' 1 to 2 (mean 1.333); 'daily_entries' 4,708 to 38,148 (mean 1.733e+04); named by 'name' (e.g. Metro station Jardim Primavera, Metro station Alto do Moinho, Metro station Bairro da Estacao); extent -9.1699,38.7198 to -9.1176,38.7485. Point locations of Transit stations. | [103] |
| Green spaces | vector, Polygon | 10 |  | 10 polygon (area) features; 'type' has 3 values (top: garden 7, square 2, park 1); 'area_ha' 1.04 to 1.4 (mean 1.219); named by 'name' (e.g. Jardim Jardim Primavera, Jardim Alto do Moinho, Jardim Bairro da Estacao); total area 0.122 km2; extent -9.1688,38.7218 to -9.1188,38.7511. Areas (Green spaces) with area_ha to compare and map. | [104] |
| Streets | vector, LineString | 151 |  | 151 line features; 'class' has 2 values (top: street 132, avenue 19); 'one_way' has 2 values (top: False 94, True 57); named by 'name' (e.g. Avenida Central, Avenida da Liberdade, Avenida do Mar); total length 272.2 km; extent -9.1710,38.7192 to -9.1145,38.7546. Lines (Streets) such as routes or networks. | [105] |
| City terrain model (DEM) | raster |  |  | 1-band float32 raster (dem), 333 x 267 cells; resolution 15; band 1 12.08 to 130.7; extent -9.1715,38.7188 to -9.1140,38.7550. An elevation surface for terrain analysis. | [106] |
| Land values by zone and year | table | 90 | 2018-01-01 to 2026-01-01 | Table of 90 rows and 3 columns without geometry; dated by 'year' from 2018-01-01 to 2026-01-01; 'zone_code' has 10 values (top: R01 9, R02 9, R03 9); 'value_eur_m2' 1,319 to 4,341 (mean 1891). A table of Land values by zone and year without locations. | [4] |
| Planning applications | table | 160 | 2025-01-01 to 2025-12-28 | Table of 160 rows and 6 columns without geometry; dated by 'submitted' from 2025-01-01 to 2025-12-28; 'use' has 4 values (top: residential 47, mixed 41, hotel 37); 'status' has 3 values (top: approved 84, pending 61, refused 15); 'units' 1 to 120 (mean 55.81). A table of Planning applications without locations. | [5] |
| Planning committee brief | document |  |  | 1-page md document 'Planning committee brief: riverside regeneration'; language en; mentions 4 place(s): Jardim dos Pinheiros (1), Jardim (1), Ribeira Sul (1), Fonte Nova (1). A document about Jardim dos Pinheiros, Jardim, Ribeira Sul. | [107] |
| Sample: Real estate and urban planning, Lisbon | record |  |  |  | [108] |
| Emerging hot spots: Property sales | layer, Polygon | 81 |  |  | [109] |
| Emerging hot spots: Building permits | layer, Polygon | 90 |  |  | [110] |
| Repeat and near repeat classification: Property sales | layer, Point | 950 |  |  | [111] |
| Summarize within: Parcels | layer, Polygon | 1,100 |  |  | [112] |
| Summarize within: Parcels | layer, Polygon | 1,100 |  |  | [113] |
| Summarize within: Parcels | layer, Polygon | 1,100 |  |  | [114] |
| Hot spot analysis: Property sales | layer, Polygon | 213 |  |  | [115] |
| Hot spot analysis: Building permits | layer, Point | 380 |  |  | [116] |
| Calculate density: Property sales | layer, Polygon | 867 |  |  | [117] |
| Calculate density: Building permits | layer, Polygon | 1,211 |  |  | [118] |
| Find point clusters: Transit stations | layer, Point | 12 |  |  | [119] |
| Find point clusters: cluster hulls | layer, Polygon | 1 |  |  | [120] |
| Find point clusters: Property sales | layer, Point | 950 |  |  | [121] |
| Find point clusters: Building permits | layer, Point | 380 |  |  | [122] |
| Find point clusters: cluster hulls | layer, Polygon | 14 |  |  | [123] |
| Find outliers: Parcels | layer, Polygon | 1,100 |  |  | [124] |
| Find outliers: Green spaces | layer, Polygon | 10 |  |  | [125] |
| Find outliers: Zoning districts | layer, Polygon | 10 |  |  | [126] |

## Timeline

![Timeline: events per quarter by dataset](charts/27-timeline-events-per-quarter-by-dataset.png)

*Figure 1. Timeline: events per quarter by dataset [1]. Per quarter, 2018-01-01 to 2026-07-01. Source: Property sales, Building permits, Land values by zone and year, Planning applications (1,580 features). The data is in the appendix.*

The timeline counts 1,580 dated events in Property sales, Building permits, Land values by zone and year and 1 more per quarter, from Q1 2018 to Q3 2026 [1][2][3][4][5]. By dataset: Property sales 950; Building permits 380; Planning applications 160; Land values by zone and year 90 [1]. The trend is +3.87 events per quarter; the second half of the quarters has 1,438 against 96 in the first (+1397.9%) [1]. 12 of the 35 quarters had no events [1].

**Busiest quarters**

- Q2 2025: 133 events [1]
- Q4 2025: 125 events [1]
- Q1 2025: 122 events [1]

**Quietest quarters with events**

- Q1 2018: 10 events [1]
- Q1 2019: 10 events [1]
- Q1 2020: 10 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 [6]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [6].

**Why it was run on this data.** Property sales carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per month [7].

**What it found.** The emerging hot spots layer, titled "Emerging hot spots: Property sales," was generated with 9 properties per resolution and involved 950 features [8]. This analysis resulted in 81 features [8]. The process took 21 elapsed milliseconds [8], and the total cost incurred was 0.1475 [8].

| Result | Value |
| --- | --- |
| result, count | 81 |
| result, elapsed ms | 21 |
| result, resolution | 9 |
| result, features used | 950 |
| result, time neighbors | 0 |
| billing, cost | 0.1475 |
| billing, list price | 0.1475 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Property sales: Emerging hot spots: 1 layer saved: Emerging hot spots: Property sales (81 features) [8][9].
- **Evidenced:** It reported result, count 81; result, elapsed ms 21; result, resolution 9; result, features used 950; result, time neighbors 0; billing, cost 0.1475 [8].

*Tool gp_space_time_hot_spots; parameters {"project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Property sales (a421b4df)"}, "interval": "month", "bin_shape": "h3", "resolution": 9, "time_field": "date"}*

## Space time hot spots

**What this analysis is.** Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time [6]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [6].

**Why it was run on this data.** Building permits carry dates in 'decision_month': emerging hot spot analysis finds new, intensifying and fading hot spots per month [10].

**What it found.** The `gp_space_time_hot_spots` tool was run using `Building permits` data [12], resulting in the saving of emerging hot spots containing 90 features [11].

| Result | Value |
| --- | --- |
| result, count | 90 |
| result, elapsed ms | 19 |
| result, resolution | 9 |
| result, features used | 380 |
| result, time neighbors | 0 |
| billing, cost | 0.119 |
| billing, list price | 0.119 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Building permits: Emerging hot spots: 1 layer saved: Emerging hot spots: Building permits (90 features) [11][12].
- **Evidenced:** It reported result, count 90; result, elapsed ms 19; result, resolution 9; result, features used 380; result, time neighbors 0; billing, cost 0.119 [11].

*Tool gp_space_time_hot_spots; parameters {"agg": "sum", "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Building permits (c9732c3b)"}, "interval": "month", "bin_shape": "h3", "resolution": 9, "time_field": "decision_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 [13]. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator [13].

**Why it was run on this data.** Near repeat analysis tests whether Property sales close in space also follow each other within 30 days [14].

**What it found.** Near repeat on Property sales: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Property sales (950 features) [9][15]. It reported result, days 30; result, count 950; result, chains 43; result, distance m 100; result, elapsed ms 23; result, pairs linked 51 [15].

| Result | Value |
| --- | --- |
| result, days | 30 |
| result, count | 950 |
| result, chains | 43 |
| result, distance m | 100 |
| result, elapsed ms | 23 |
| result, pairs linked | 51 |
| billing, cost | 0.0785 |
| billing, list price | 0.0785 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Near repeat on Property sales: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Property sales (950 features) [9][15].
- **Evidenced:** It reported result, days 30; result, count 950; result, chains 43; result, distance m 100; result, elapsed ms 23; result, pairs linked 51 [15].

*Tool gp_near_repeat; parameters {"days": 30, "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Property sales (a421b4df)"}, "distance_m": 100.0, "time_field": "date"}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [16]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [16].

**Why it was run on this data.** Count Transit stations inside each feature of Parcels; the counts map as a choropleth (natural breaks, sequential colors) [17].

**What it found.** Summarize within on Transit stations, Parcels: Summarize within: 1 layer saved: Summarize within: Parcels (1100 features) [18][19][20]. It reported result, count 1,100; result, elapsed ms 43; billing, cost 0.03226; billing, list price 0.03226; billing, upstream cost 0 [18].

| Result | Value |
| --- | --- |
| result, count | 1,100 |
| result, elapsed ms | 43 |
| billing, cost | 0.03226 |
| billing, list price | 0.03226 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Transit stations, Parcels: Summarize within: 1 layer saved: Summarize within: Parcels (1100 features) [18][19][20].
- **Evidenced:** It reported result, count 1,100; result, elapsed ms 43; billing, cost 0.03226; billing, list price 0.03226; billing, upstream cost 0 [18].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "lines"}], "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Transit stations (400e1561)"}, "polygons": {"layer": "Parcels (ccc22cc1)"}}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [16]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [16].

**Why it was run on this data.** Count Transit stations inside each feature of Parcels; the counts map as a choropleth (natural breaks, sequential colors) [17].

**What it found.** Summarize within on Property sales, Parcels: Summarize within: 1 layer saved: Summarize within: Parcels (1100 features) [9][20][21]. It reported result, count 1,100; result, elapsed ms 471; billing, cost 0.05102; billing, list price 0.05102; billing, upstream cost 0 [21].

| Result | Value |
| --- | --- |
| result, count | 1,100 |
| result, elapsed ms | 471 |
| billing, cost | 0.05102 |
| billing, list price | 0.05102 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Property sales, Parcels: Summarize within: 1 layer saved: Summarize within: Parcels (1100 features) [9][20][21].
- **Evidenced:** It reported result, count 1,100; result, elapsed ms 471; billing, cost 0.05102; billing, list price 0.05102; billing, upstream cost 0 [21].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "price_eur_m2"}], "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Property sales (a421b4df)"}, "group_by": "type", "polygons": {"layer": "Parcels (ccc22cc1)"}}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [16]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [16].

**Why it was run on this data.** Count Transit stations inside each feature of Parcels; the counts map as a choropleth (natural breaks, sequential colors) [17].

**What it found.** Summarize within on Building permits, Parcels: Summarize within: 1 layer saved: Summarize within: Parcels (1100 features) [12][20][22]. It reported result, count 1,100; result, elapsed ms 49; billing, cost 0.03962; billing, list price 0.03962; billing, upstream cost 0 [22].

| Result | Value |
| --- | --- |
| result, count | 1,100 |
| result, elapsed ms | 49 |
| billing, cost | 0.03962 |
| billing, list price | 0.03962 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Building permits, Parcels: Summarize within: 1 layer saved: Summarize within: Parcels (1100 features) [12][20][22].
- **Evidenced:** It reported result, count 1,100; result, elapsed ms 49; billing, cost 0.03962; billing, list price 0.03962; billing, upstream cost 0 [22].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "units"}], "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Building permits (c9732c3b)"}, "group_by": "type", "polygons": {"layer": "Parcels (ccc22cc1)"}}*

## Zonal statistics

**What this analysis is.** Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON [23]. In ArcGIS it is Zonal Statistics as Table (polygons) [23].

**Why it was run on this data.** Statistics of City terrain model (DEM) inside each feature of Parcels [24].

**What it found.** The Zonal statistics tool (rs_zonal_statistics) was used for the analysis [25].

**Findings**

- **Evidenced:** Zonal statistics: Zonal statistics: 1100 row(s) [25].
- **Evidenced:** zones 1100 [25].

*Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Parcels (ccc22cc1)", "raster_id": "City terrain model (DEM) (45e73a8a)"}*

## Zonal statistics

**What this analysis is.** Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON [23]. In ArcGIS it is Zonal Statistics as Table (polygons) [23].

**Why it was run on this data.** Statistics of City terrain model (DEM) inside each feature of Parcels [24].

**What it found.** The zonal statistics tool was utilized for this analysis [26]. A total of 10 row(s) were generated for 10 zones [26]. This calculation was performed as part of the `rs_zonal_statistics` process during step 1 [26].

**Findings**

- **Evidenced:** Zonal statistics: Zonal statistics: 10 row(s) [26].
- **Evidenced:** zones 10 [26].

*Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Green spaces (448e3c1b)", "raster_id": "City terrain model (DEM) (45e73a8a)"}*

## Zonal statistics

**What this analysis is.** Zonal statistics: count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON [23]. In ArcGIS it is Zonal Statistics as Table (polygons) [23].

**Why it was run on this data.** Statistics of City terrain model (DEM) inside each feature of Parcels [24].

**What it found.** The rs_zonal_statistics tool produced results for 10 rows and 10 zones [27]. This indicator provides zonal statistics for the given dataset [27].

**Findings**

- **Evidenced:** Zonal statistics: Zonal statistics: 10 row(s) [27].
- **Evidenced:** zones 10 [27].

*Tool rs_zonal_statistics; parameters {"stats": ["mean", "min", "max", "std"], "zones": "Zoning districts (f84e0e45)", "raster_id": "City terrain model (DEM) (45e73a8a)"}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [28]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [28].

**Why it was run on this data.** 950 points: Getis-Ord Gi* tests whether the concentrations in Property sales are statistically significant [29].

**What it found.** Hot spots on Property sales: Hot spot analysis: 1 layer saved: Hot spot analysis: Property sales (213 features) [9][30]. It reported result, bins 213; result, count 213; result, elapsed ms 37; result, cell size m 200; result, incidents used 950; result, distance band m 200 [30].

| Result | Value |
| --- | --- |
| result, bins | 213 |
| result, count | 213 |
| result, elapsed ms | 37 |
| result, cell size m | 200 |
| result, incidents used | 950 |
| result, distance band m | 200 |
| billing, cost | 0.069 |
| billing, list price | 0.069 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Property sales: Hot spot analysis: 1 layer saved: Hot spot analysis: Property sales (213 features) [9][30].
- **Evidenced:** It reported result, bins 213; result, count 213; result, elapsed ms 37; result, cell size m 200; result, incidents used 950; result, distance band m 200 [30].

*Tool gp_hot_spots; parameters {"project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Property sales (a421b4df)"}, "aggregation": "hexagon", "cell_size_m": 200.0, "distance_band_m": 200.0}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [28]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [28].

**Why it was run on this data.** 380 points: Getis-Ord Gi* tests whether the concentrations in Building permits are statistically significant [31].

**What it found.** Hot spots on Building permits: Hot spot analysis: 1 layer saved: Hot spot analysis: Building permits (380 features) [12][32]. It reported result, count 380; result, elapsed ms 10; result, excluded null 0; result, distance band m 500; billing, cost 0.0576; billing, list price 0.0576 [32].

| Result | Value |
| --- | --- |
| result, count | 380 |
| result, elapsed ms | 10 |
| result, excluded null | 0 |
| result, distance band m | 500 |
| billing, cost | 0.0576 |
| billing, list price | 0.0576 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Building permits: Hot spot analysis: 1 layer saved: Hot spot analysis: Building permits (380 features) [12][32].
- **Evidenced:** It reported result, count 380; result, elapsed ms 10; result, excluded null 0; result, distance band m 500; billing, cost 0.0576; billing, list price 0.0576 [32].

*Tool gp_hot_spots; parameters {"field": "units", "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Building permits (c9732c3b)"}, "aggregation": "hexagon", "cell_size_m": 200.0, "distance_band_m": 500.0}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [33]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [33].

**Why it was run on this data.** 950 points in Property sales: a density surface shows where they concentrate [34].

**What it found.** The calculation utilized the Property sales data [9] and produced a total count of

| Result | Value |
| --- | --- |
| result, count | 867 |
| result, elapsed ms | 31 |
| result, bandwidth m | 200 |
| result, cell size m | 100 |
| result, excluded null | 0 |
| result, max density per km2 | 364 |
| billing, cost | 0.069 |
| billing, list price | 0.069 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Property sales: Calculate density: 1 layer saved: Calculate density: Property sales (867 features) [9][35].
- **Evidenced:** It reported result, count 867; result, elapsed ms 31; result, bandwidth m 200; result, cell size m 100; result, excluded null 0; result, max density per km2 364 [35].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Property sales (a421b4df)"}, "bandwidth_m": 200.0, "cell_size_m": 100.0}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [33]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [33].

**Why it was run on this data.** 380 points in Building permits: a density surface shows where they concentrate [36].

**What it found.** The density calculation used the Building permits layer [12].  The process resulted in 1,211 features [37]. This calculation required 41 milliseconds and generated a bandwidth of 500 meters [37]. The resulting max density per $\text{km}^2$ was 2,113 [37].

| Result | Value |
| --- | --- |
| result, count | 1,211 |
| result, elapsed ms | 41 |
| result, bandwidth m | 500 |
| result, cell size m | 100 |
| result, excluded null | 0 |
| result, max density per km2 | 2,113 |
| billing, cost | 0.0576 |
| billing, list price | 0.0576 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Building permits: Calculate density: 1 layer saved: Calculate density: Building permits (1211 features) [12][37].
- **Evidenced:** It reported result, count 1,211; result, elapsed ms 41; result, bandwidth m 500; result, cell size m 100; result, excluded null 0; result, max density per km2 2,113 [37].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Building permits (c9732c3b)"}, "bandwidth_m": 500.0, "cell_size_m": 100.0, "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 [38]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [38].

**Why it was run on this data.** DBSCAN finds dense groups of Transit stations and separates noise [39].

**What it found.** The `gp_find_point_clusters` tool processed the `Transit stations` layer [19]. This process yielded $12$ point clusters [40]. The resulting noise count was $0$ [40], and the maximum cluster distance was $2,000$ meters [40]. The operation was completed in $5$ milliseconds [40] and included $12$ features in the final output [40].

| Result | Value |
| --- | --- |
| result, count | 12 |
| result, noise | 0 |
| result, distance m | 2,000 |
| result, elapsed ms | 5 |
| result, min points | 3 |
| billing, cost | 0.05024 |
| billing, list price | 0.05024 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Transit stations: Find point clusters: 2 layers saved: Find point clusters: Transit stations (12 features); Find point clusters: cluster hulls (1 feature) [19][40].
- **Evidenced:** It reported result, count 12; result, noise 0; result, distance m 2,000; result, elapsed ms 5; result, min points 3; billing, cost 0.05024 [40].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Transit stations (400e1561)"}, "distance_m": 2000.0, "min_points": 3}*

## Find point clusters

**What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [38]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [38].

**Why it was run on this data.** DBSCAN finds dense groups of Property sales and separates noise [41].

**What it found.** Find point clusters on Property sales: Find point clusters: 1 layer saved: Find point clusters: Property sales (950 features) [9][42]. It reported result, count 950; result, noise 950; result, distance m 100; result, elapsed ms 12; result, min points 20; billing, cost 0.069 [42].

| Result | Value |
| --- | --- |
| result, count | 950 |
| result, noise | 950 |
| result, distance m | 100 |
| result, elapsed ms | 12 |
| result, min points | 20 |
| billing, cost | 0.069 |
| billing, list price | 0.069 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Property sales: Find point clusters: 1 layer saved: Find point clusters: Property sales (950 features) [9][42].
- **Evidenced:** It reported result, count 950; result, noise 950; result, distance m 100; result, elapsed ms 12; result, min points 20; billing, cost 0.069 [42].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Property sales (a421b4df)"}, "distance_m": 100.0, "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 [38]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [38].

**Why it was run on this data.** DBSCAN finds dense groups of Building permits and separates noise [43].

**What it found.** Building permits served as the input data for the point cluster analysis [12].

| Result | Value |
| --- | --- |
| result, count | 380 |
| result, noise | 126 |
| result, distance m | 200 |
| result, elapsed ms | 11 |
| result, min points | 10 |
| billing, cost | 0.0576 |
| billing, list price | 0.0576 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Building permits: Find point clusters: 2 layers saved: Find point clusters: Building permits (380 features); Find point clusters: cluster hulls (14 features) [12][44].
- **Evidenced:** It reported result, count 380; result, noise 126; result, distance m 200; result, elapsed ms 11; result, min points 10; billing, cost 0.0576 [44].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Building permits (c9732c3b)"}, "distance_m": 200.0, "min_points": 10}*

## Outliers

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

**Why it was run on this data.** Local Moran's I maps clusters and outliers of 'built_far' [46].

**What it found.** The `gp_outliers` tool was utilized on the Parcels layer [20]. The analysis result reported a count of 1,100 features [47]. This task was completed with an elapsed time of 74 ms [47]. Key metrics generated included a distance band of 86.92 m [47], and the billing cost was 0.083 [47].

| Result | Value |
| --- | --- |
| result, count | 1,100 |
| result, elapsed ms | 74 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 86.92 |
| billing, cost | 0.083 |
| billing, list price | 0.083 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Parcels: Find outliers: 1 layer saved: Find outliers: Parcels (1100 features) [20][47].
- **Evidenced:** It reported result, count 1,100; result, elapsed ms 74; result, permutations 199; result, excluded null 0; result, distance band m 86.92; billing, cost 0.083 [47].

*Tool gp_outliers; parameters {"field": "built_far", "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Parcels (ccc22cc1)"}, "aggregation": "hexagon"}*

## Outliers

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

**Why it was run on this data.** Local Moran's I maps clusters and outliers of 'area_ha' [48].

**What it found.** Outliers on Green spaces: Find outliers: 1 layer saved: Find outliers: Green spaces (10 features) [49][50]. It reported result, count 10; result, elapsed ms 3; result, permutations 199; result, excluded null 0; result, distance band m 1,747; billing, cost 0.0503 [49].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 3 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 1,747 |
| billing, cost | 0.0503 |
| billing, list price | 0.0503 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Green spaces: Find outliers: 1 layer saved: Find outliers: Green spaces (10 features) [49][50].
- **Evidenced:** It reported result, count 10; result, elapsed ms 3; result, permutations 199; result, excluded null 0; result, distance band m 1,747; billing, cost 0.0503 [49].

*Tool gp_outliers; parameters {"field": "area_ha", "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Green spaces (448e3c1b)"}, "aggregation": "hexagon"}*

## Outliers

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

**Why it was run on this data.** Local Moran's I maps clusters and outliers of 'max_far' [51].

**What it found.** Outliers on Zoning districts: Find outliers: 1 layer saved: Find outliers: Zoning districts (10 features) [52][53]. It reported result, count 10; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 1,435; billing, cost 0.0503 [52].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 2 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 1,435 |
| billing, cost | 0.0503 |
| billing, list price | 0.0503 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Zoning districts: Find outliers: 1 layer saved: Find outliers: Zoning districts (10 features) [52][53].
- **Evidenced:** It reported result, count 10; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 1,435; billing, cost 0.0503 [52].

*Tool gp_outliers; parameters {"field": "max_far", "project": "acfed0f6-9df9-4775-9325-30c0bbed70e9", "features": {"layer": "Zoning districts (f84e0e45)"}, "aggregation": "hexagon"}*

## Maps and charts

The run also made 21 map image(s) [54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74] and 26 chart(s) [75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100].

![Emerging hot spots: Property sales](maps/01-emerging-hot-spots-property-sales.png)

*Figure 2. Emerging hot spots: Property sales [54]*

![Emerging hot spots: Building permits](maps/02-emerging-hot-spots-building-permits.png)

*Figure 3. Emerging hot spots: Building permits [55]*

![Repeat and near repeat classification: Property sales](maps/03-repeat-and-near-repeat-classification-property-sales.png)

*Figure 4. Repeat and near repeat classification: Property sales [56]*

![Summarize within: Parcels](maps/04-summarize-within-parcels.png)

*Figure 5. Summarize within: Parcels [57]*

![Summarize within: Parcels](maps/05-summarize-within-parcels.png)

*Figure 6. Summarize within: Parcels [58]*

![Summarize within: Parcels](maps/06-summarize-within-parcels.png)

*Figure 7. Summarize within: Parcels [59]*

![Parcels by built_far](maps/07-parcels-by-built-far.png)

*Figure 8. Parcels by built_far [60]*

![Green spaces by area_ha](maps/08-green-spaces-by-area-ha.png)

*Figure 9. Green spaces by area_ha [61]*

![Zoning districts by max_far](maps/09-zoning-districts-by-max-far.png)

*Figure 10. Zoning districts by max_far [62]*

![Hot spot analysis: Property sales](maps/10-hot-spot-analysis-property-sales.png)

*Figure 11. Hot spot analysis: Property sales [63]*

![Hot spot analysis: Building permits](maps/11-hot-spot-analysis-building-permits.png)

*Figure 12. Hot spot analysis: Building permits [64]*

![Calculate density: Property sales](maps/12-calculate-density-property-sales.png)

*Figure 13. Calculate density: Property sales [65]*

![Calculate density: Building permits](maps/13-calculate-density-building-permits.png)

*Figure 14. Calculate density: Building permits [66]*

![Find point clusters: Transit stations](maps/14-find-point-clusters-transit-stations.png)

*Figure 15. Find point clusters: Transit stations [67]*

![Find point clusters: cluster hulls](maps/15-find-point-clusters-cluster-hulls.png)

*Figure 16. Find point clusters: cluster hulls [68]*

![Find point clusters: Property sales](maps/16-find-point-clusters-property-sales.png)

*Figure 17. Find point clusters: Property sales [69]*

![Find point clusters: Building permits](maps/17-find-point-clusters-building-permits.png)

*Figure 18. Find point clusters: Building permits [70]*

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

*Figure 19. Find point clusters: cluster hulls [71]*

![Find outliers: Parcels](maps/19-find-outliers-parcels.png)

*Figure 20. Find outliers: Parcels [72]*

![Find outliers: Green spaces](maps/20-find-outliers-green-spaces.png)

*Figure 21. Find outliers: Green spaces [73]*

![Find outliers: Zoning districts](maps/21-find-outliers-zoning-districts.png)

*Figure 22. Find outliers: Zoning districts [74]*

**What the map shows.** The map, titled "Emerging hot spots: Property sales," displays the pattern of property sales across a hexagonal grid. The majority of the area is colored grey, indicating that no pattern was detected in these regions. A cluster [54]

*Inferred: read from the image by gemma4:e4b, not evidence.*

![Parcels over time (year_built)](charts/01-parcels-over-time-year-built.png)

*Figure 23. Parcels over time (year_built) [75]. Source: Parcels (1,100 features). The data is in the appendix.*

![Property sales over time (date)](charts/02-property-sales-over-time-date.png)

*Figure 24. Property sales over time (date) [76]. Per quarter, 2022-01-01 to 2026-07-01. Source: Property sales (950 features). The data is in the appendix.*

![Building permits over time (decision_month)](charts/03-building-permits-over-time-decision-month.png)

*Figure 25. Building permits over time (decision_month) [77]. Per month, 2023-01-01 to 2026-09-01. Source: Building permits (380 features). The data is in the appendix.*

![Land values by zone and year over time (year)](charts/04-land-values-by-zone-and-year-over-time-year.png)

*Figure 26. Land values by zone and year over time (year) [78]. Per quarter, 2018-01-01 to 2026-01-01. Source: Land values by zone and year (90 features). The data is in the appendix.*

![Emerging hot spots: Property sales](charts/05-emerging-hot-spots-property-sales.png)

*Figure 27. Emerging hot spots: Property sales [79]. Source: Emerging hot spots: Property sales (81 features). The data is in the appendix.*

![Emerging hot spots: Building permits](charts/06-emerging-hot-spots-building-permits.png)

*Figure 28. Emerging hot spots: Building permits [80]. Source: Emerging hot spots: Building permits (90 features). The data is in the appendix.*

![Repeat and near repeat classification: Property sales](charts/07-repeat-and-near-repeat-classification-property-sales.png)

*Figure 29. Repeat and near repeat classification: Property sales [81]. Per quarter, 2022-01-01 to 2026-07-01. Source: Repeat and near repeat classification: Property sales (950 features). The data is in the appendix.*

![Summarize within: Parcels](charts/08-summarize-within-parcels.png)

*Figure 30. Summarize within: Parcels [82]. Source: Summarize within: Parcels (1,100 features). The data is in the appendix.*

![Summarize within: Parcels](charts/09-summarize-within-parcels.png)

*Figure 31. Summarize within: Parcels [83]. Source: Summarize within: Parcels (1,100 features). The data is in the appendix.*

![Summarize within: Parcels](charts/10-summarize-within-parcels.png)

*Figure 32. Summarize within: Parcels [84]. Source: Summarize within: Parcels (1,100 features). The data is in the appendix.*

![Hot spot analysis: Property sales](charts/11-hot-spot-analysis-property-sales.png)

*Figure 33. Hot spot analysis: Property sales [85]. Source: Hot spot analysis: Property sales (213 features). The data is in the appendix.*

![Hot spot analysis: Building permits](charts/12-hot-spot-analysis-building-permits.png)

*Figure 34. Hot spot analysis: Building permits [86]. Period: 2023-01-01 to 2026-09-01 (decision_month). Source: Hot spot analysis: Building permits (380 features). The data is in the appendix.*

![Calculate density: Property sales](charts/13-calculate-density-property-sales.png)

*Figure 35. Calculate density: Property sales [87]. Source: Calculate density: Property sales (867 features). The data is in the appendix.*

![Calculate density: Building permits](charts/14-calculate-density-building-permits.png)

*Figure 36. Calculate density: Building permits [88]. Source: Calculate density: Building permits (1,211 features). The data is in the appendix.*

![Find point clusters: Transit stations](charts/15-find-point-clusters-transit-stations.png)

*Figure 37. Find point clusters: Transit stations [89]. Source: Find point clusters: Transit stations (12 features). The data is in the appendix.*

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

*Figure 38. Find point clusters: cluster hulls [90]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.*

![Find point clusters: Property sales](charts/17-find-point-clusters-property-sales.png)

*Figure 39. Find point clusters: Property sales [91]. Period: 2022-01-01 to 2026-09-30 (date). Source: Find point clusters: Property sales (950 features). The data is in the appendix.*

![Find point clusters: Building permits](charts/18-find-point-clusters-building-permits.png)

*Figure 40. Find point clusters: Building permits [92]. Period: 2023-01-01 to 2026-09-01 (decision_month). Source: Find point clusters: Building permits (380 features). The data is in the appendix.*

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

*Figure 41. Find point clusters: cluster hulls [93]. Source: Find point clusters: cluster hulls (14 features). The data is in the appendix.*

![Find outliers: Parcels](charts/20-find-outliers-parcels.png)

*Figure 42. Find outliers: Parcels [94]. Source: Find outliers: Parcels (1,100 features). The data is in the appendix.*

![Find outliers: Green spaces](charts/21-find-outliers-green-spaces.png)

*Figure 43. Find outliers: Green spaces [95]. Source: Find outliers: Green spaces (10 features). The data is in the appendix.*

![Find outliers: Zoning districts](charts/22-find-outliers-zoning-districts.png)

*Figure 44. Find outliers: Zoning districts [96]. Source: Find outliers: Zoning districts (10 features). The data is in the appendix.*

![Find point clusters: Building permits over time](charts/23-find-point-clusters-building-permits-over-time.png)

*Figure 45. Find point clusters: Building permits over time [97]. Per month, 2023-01-01 to 2026-09-01. Source: Find point clusters: Building permits (380 features). The data is in the appendix.*

![Find point clusters: Property sales over time](charts/24-find-point-clusters-property-sales-over-time.png)

*Figure 46. Find point clusters: Property sales over time [98]. Per quarter, 2022-01-01 to 2026-07-01. Source: Find point clusters: Property sales (950 features). The data is in the appendix.*

![Hot spot analysis: Building permits over time](charts/25-hot-spot-analysis-building-permits-over-time.png)

*Figure 47. Hot spot analysis: Building permits over time [99]. Per month, 2023-01-01 to 2026-09-01. Source: Hot spot analysis: Building permits (380 features). The data is in the appendix.*

![Repeat and near repeat classification: Property sales over time](charts/26-repeat-and-near-repeat-classification-property-sales-over-ti.png)

*Figure 48. Repeat and near repeat classification: Property sales over time [100]. Per quarter, 2022-01-01 to 2026-07-01. Source: Repeat and near repeat classification: Property sales (950 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. 140 rows (id efe17a86-8fd3-4c8e-aed1-29ebb025af6d)  
[2] Dataset: Property sales. vector, 950 rows (id 8f893170-c6db-4826-83e8-09af6dc89e27)  
[3] Dataset: Building permits. vector, 380 rows (id aa79bba6-c73f-4b2f-a79e-176493aa4621)  
[4] Dataset: Land values by zone and year. table, 90 rows (id 7cee65a2-b1e0-47a4-98b2-10d181fb8423)  
[5] Dataset: Planning applications. table, 160 rows (id ae83bb58-05b5-4b51-b3f8-ca020020f1d5)  
[6] 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)  
[7] Plan: Space time hot spots of Property sales (the Start plan's candidate c_37c352605313). Property sales carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per month. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_37c352605313)  
[8] Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Property sales (81 features). (id 3b547b3d-4c3c-4444-9020-8c97986c7155:0)  
[9] Layer: Property sales. 950 features (id a421b4df-eece-4b35-a40f-e8daf4e77a1e)  
[10] Plan: Space time hot spots of Building permits (the Start plan's candidate c_cd867438faaa). Building permits carry dates in 'decision_month': emerging hot spot analysis finds new, intensifying and fading hot spots per month. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_cd867438faaa)  
[11] Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: Building permits (90 features). (id 3b547b3d-4c3c-4444-9020-8c97986c7155:1)  
[12] Layer: Building permits. 380 features (id c9732c3b-7527-455c-bfff-16e282af4bf8)  
[13] 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)  
[14] Plan: Near repeat of Property sales (the Start plan's candidate c_71042b703d0d). Near repeat analysis tests whether Property sales close in space also follow each other within 30 days. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_71042b703d0d)  
[15] Tool result: gp_near_repeat, step 2. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Property sales (950 features). (id 3b547b3d-4c3c-4444-9020-8c97986c7155:2)  
[16] 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)  
[17] Plan: Transit stations per feature of Parcels (the Start plan's candidate c_2a92a76984e1). Count Transit stations inside each feature of Parcels; the counts map as a choropleth (natural breaks, sequential colors). (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_2a92a76984e1)  
[18] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Parcels (1100 features). (id 029a787d-7030-40ee-8bec-78edb55a4233:0)  
[19] Layer: Transit stations. 12 features (id 400e1561-f1f1-481c-b9bc-1ac817459feb)  
[20] Layer: Parcels. 1,100 features (id ccc22cc1-2735-4cb4-aa2f-5cc33be687bf)  
[21] Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Parcels (1100 features). (id 029a787d-7030-40ee-8bec-78edb55a4233:1)  
[22] Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Parcels (1100 features). (id 029a787d-7030-40ee-8bec-78edb55a4233:2)  
[23] Catalog: Zonal statistics (analysis catalog, rs_zonal_statistics). count/min/max/mean/std/sum/median/majority per polygon as a table and as GeoJSON. ArcGIS: Zonal Statistics as Table (polygons). (id rs_zonal_statistics)  
[24] Plan: Zonal statistics of City terrain model (DEM) and Parcels (the Start plan's candidate c_223f208b883c). Statistics of City terrain model (DEM) inside each feature of Parcels. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_223f208b883c)  
[25] Tool result: rs_zonal_statistics, step 0. Zonal statistics: 1100 row(s). zones 1100. (id e1b6768a-3792-454a-b224-e267bd9b2d98:0)  
[26] Tool result: rs_zonal_statistics, step 1. Zonal statistics: 10 row(s). zones 10. (id e1b6768a-3792-454a-b224-e267bd9b2d98:1)  
[27] Tool result: rs_zonal_statistics, step 2. Zonal statistics: 10 row(s). zones 10. (id e1b6768a-3792-454a-b224-e267bd9b2d98:2)  
[28] 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)  
[29] Plan: Hot spots of Property sales (the Start plan's candidate c_acf84d8d58c2). 950 points: Getis-Ord Gi* tests whether the concentrations in Property sales are statistically significant. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_acf84d8d58c2)  
[30] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Property sales (213 features). (id ba41b9e6-74d7-45fb-bad3-3f51fe7fca0d:0)  
[31] Plan: Hot spots of Building permits (the Start plan's candidate c_3746fcbd31f7). 380 points: Getis-Ord Gi* tests whether the concentrations in Building permits are statistically significant. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_3746fcbd31f7)  
[32] Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Building permits (380 features). (id ba41b9e6-74d7-45fb-bad3-3f51fe7fca0d:1)  
[33] 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)  
[34] Plan: Kernel density of Property sales (the Start plan's candidate c_18bfb4d14078). 950 points in Property sales: a density surface shows where they concentrate. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_18bfb4d14078)  
[35] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Property sales (867 features). (id 9ca81de6-468a-4ffe-86a2-5163eb3bafa7:0)  
[36] Plan: Kernel density of Building permits (the Start plan's candidate c_2c9614b6c24c). 380 points in Building permits: a density surface shows where they concentrate. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_2c9614b6c24c)  
[37] Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Building permits (1211 features). (id 9ca81de6-468a-4ffe-86a2-5163eb3bafa7:1)  
[38] 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)  
[39] Plan: Cluster points of Transit stations (the Start plan's candidate c_5a6f63bcd73b). DBSCAN finds dense groups of Transit stations and separates noise. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_5a6f63bcd73b)  
[40] Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Transit stations (12 features); Find point clusters: cluster hulls (1 feature). (id f445c7d5-e1f8-40ba-b66a-b6f77ff14617:0)  
[41] Plan: Cluster points of Property sales (the Start plan's candidate c_4f023019863a). DBSCAN finds dense groups of Property sales and separates noise. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_4f023019863a)  
[42] Tool result: gp_find_point_clusters, step 1. Find point clusters: 1 layer saved: Find point clusters: Property sales (950 features). (id f445c7d5-e1f8-40ba-b66a-b6f77ff14617:1)  
[43] Plan: Cluster points of Building permits (the Start plan's candidate c_8d2b732415a9). DBSCAN finds dense groups of Building permits and separates noise. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_8d2b732415a9)  
[44] Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Building permits (380 features); Find point clusters: cluster hulls (14 features). (id f445c7d5-e1f8-40ba-b66a-b6f77ff14617:2)  
[45] 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)  
[46] Plan: Local morans i of Parcels (the Start plan's candidate c_c7bf78360ddf). Local Moran's I maps clusters and outliers of 'built_far'. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_c7bf78360ddf)  
[47] Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: Parcels (1100 features). (id 60b5f86e-cffb-436f-9438-0ad7618a186f:0)  
[48] Plan: Local morans i of Green spaces (the Start plan's candidate c_f5f393a93740). Local Moran's I maps clusters and outliers of 'area_ha'. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_f5f393a93740)  
[49] Tool result: gp_outliers, step 1. Find outliers: 1 layer saved: Find outliers: Green spaces (10 features). (id 60b5f86e-cffb-436f-9438-0ad7618a186f:1)  
[50] Layer: Green spaces. 10 features (id 448e3c1b-11d3-4dc1-ad29-15c041e2af35)  
[51] Plan: Local morans i of Zoning districts (the Start plan's candidate c_c8c9fa0162f0). Local Moran's I maps clusters and outliers of 'max_far'. (id f8db5acb-8be1-4e6d-8904-9ee8c04e791a:c_c8c9fa0162f0)  
[52] Tool result: gp_outliers, step 2. Find outliers: 1 layer saved: Find outliers: Zoning districts (10 features). (id 60b5f86e-cffb-436f-9438-0ad7618a186f:2)  
[53] Layer: Zoning districts. 10 features (id f84e0e45-37e6-4f41-b7d9-ceb30bbc840a)  
[54] Rendering: Map: Emerging hot spots: Property sales (id be66919c-5ac4-46fc-9303-3b6ae32d215e)  
[55] Rendering: Map: Emerging hot spots: Building permits (id 10e5f0c6-f9d0-4087-bebf-3a342415f755)  
[56] Rendering: Map: Repeat and near repeat classification: Property sales (id 3c1c183a-14b4-4c01-b623-ce0905573f25)  
[57] Rendering: Map: Summarize within: Parcels (id f5aabbb2-9d66-4cde-936d-0fd21fa0e47f)  
[58] Rendering: Map: Summarize within: Parcels (id cf2850e2-e68b-4fe5-9668-bc39c1717ddc)  
[59] Rendering: Map: Summarize within: Parcels (id c7c3fa96-6cbc-43c6-9c0d-fe80010b2209)  
[60] Rendering: Map: Parcels by built_far (id 225b0a21-d925-4a9c-830d-a8ff465d85f3)  
[61] Rendering: Map: Green spaces by area_ha (id d835f68f-d836-45cb-bc1c-330e22a1a5a5)  
[62] Rendering: Map: Zoning districts by max_far (id 5c925f5e-357f-42ad-a25b-84fd46efdb28)  
[63] Rendering: Map: Hot spot analysis: Property sales (id 27148a64-8d81-466d-ba61-2ae85cfd1868)  
[64] Rendering: Map: Hot spot analysis: Building permits (id 5b6f2580-842f-4994-a302-a3bb1b0bf448)  
[65] Rendering: Map: Calculate density: Property sales (id 9c3290f3-cc57-4600-9529-82ee062fb61c)  
[66] Rendering: Map: Calculate density: Building permits (id 91b2edc2-2bf7-4663-bb16-12595287149f)  
[67] Rendering: Map: Find point clusters: Transit stations (id 5608ea02-b2ac-4e51-a075-0e201a1bc4ed)  
[68] Rendering: Map: Find point clusters: cluster hulls (id 9864ff42-febc-4252-a0ba-b1ca69720112)  
[69] Rendering: Map: Find point clusters: Property sales (id 0d51172d-775f-4364-998b-1f2614c6d6c6)  
[70] Rendering: Map: Find point clusters: Building permits (id 450b8dd1-0c4b-4bbf-8e03-6cff33e001f4)  
[71] Rendering: Map: Find point clusters: cluster hulls (id 0eabad24-1821-4dea-a840-3e1ce29b5fcb)  
[72] Rendering: Map: Find outliers: Parcels (id 32f0c9a9-217e-4701-b234-c3234c26ff02)  
[73] Rendering: Map: Find outliers: Green spaces (id bbac1884-3dfd-41d5-99a3-a3dc22b9372a)  
[74] Rendering: Map: Find outliers: Zoning districts (id 1ed7248f-0b15-4174-8c9d-e4fcc7ad2031)  
[75] Rendering: Chart: Parcels over time (year_built). 12 rows (id a56a4586-993b-4959-8f12-cda5bdc8c1bc)  
[76] Rendering: Chart: Property sales over time (date). 57 rows (id d7dcbaa6-e88d-4c73-b1bc-4c18320c9e9e)  
[77] Rendering: Chart: Building permits over time (decision_month). 180 rows (id 803ff097-0db7-4dd0-bf88-08718e9b6106)  
[78] Rendering: Chart: Land values by zone and year over time (year). 198 rows (id a51cb267-2b59-4303-a275-1b299891614b)  
[79] Rendering: Chart: Emerging hot spots: Property sales. 8 rows (id cddb02b3-4f62-47c6-9df5-dead122811ae)  
[80] Rendering: Chart: Emerging hot spots: Building permits. 8 rows (id 9b5c350e-1c14-438a-9d63-489450edc2a3)  
[81] Rendering: Chart: Repeat and near repeat classification: Property sales. 57 rows (id e0196faa-807f-4c10-88ff-a378a575498f)  
[82] Rendering: Chart: Summarize within: Parcels. 12 rows (id 1dbcaea9-a5e4-4882-a88f-ea90a1a7bd7b)  
[83] Rendering: Chart: Summarize within: Parcels. 12 rows (id ed9ee8e2-4579-417f-8013-81e91e4a0afb)  
[84] Rendering: Chart: Summarize within: Parcels. 12 rows (id 73d109a5-5d83-481d-981e-0be9ffa57bd5)  
[85] Rendering: Chart: Hot spot analysis: Property sales. 12 rows (id 517b4c71-81b6-42fe-9408-26c588875b42)  
[86] Rendering: Chart: Hot spot analysis: Building permits. 13 rows (id 7a0cc108-0105-4152-b9f4-1796dc1e0c64)  
[87] Rendering: Chart: Calculate density: Property sales. 11 rows (id 201493fb-714c-40d2-9a76-ddb22527f9a5)  
[88] Rendering: Chart: Calculate density: Building permits. 12 rows (id 741b541a-642f-4d11-af05-b4f10e12c13e)  
[89] Rendering: Chart: Find point clusters: Transit stations. 1 rows (id df5f4fbc-f176-4563-8792-c439140abf7d)  
[90] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id ec35aed0-af92-4b16-987b-bc0bb9edc445)  
[91] Rendering: Chart: Find point clusters: Property sales. 1 rows (id 420829ca-242a-4203-a03e-1d3c30f92be2)  
[92] Rendering: Chart: Find point clusters: Building permits. 15 rows (id a75725d0-4a42-4c03-b775-4841e49a974a)  
[93] Rendering: Chart: Find point clusters: cluster hulls. 14 rows (id 19c0352e-e8ce-4b43-b68c-e5d9e727c6d8)  
[94] Rendering: Chart: Find outliers: Parcels. 1100 rows (id 22928ec9-70d1-47be-8138-15ef9ace47fc)  
[95] Rendering: Chart: Find outliers: Green spaces. 10 rows (id 986b9bba-697e-48c7-ad92-e4319618143d)  
[96] Rendering: Chart: Find outliers: Zoning districts. 10 rows (id 74b1b0e7-13ca-4b67-abac-f0beab687357)  
[97] Rendering: Chart: Find point clusters: Building permits over time. 180 rows (id f5fafb9d-4abc-4967-900a-fe4195579060)  
[98] Rendering: Chart: Find point clusters: Property sales over time. 57 rows (id 633faacd-1c67-43fc-92f7-c5b129144335)  
[99] Rendering: Chart: Hot spot analysis: Building permits over time. 180 rows (id 63728d45-bd6d-408e-b13d-19cba683b84d)  
[100] Rendering: Chart: Repeat and near repeat classification: Property sales over time. 57 rows (id c2cc74df-5240-4117-b8b3-c351a9f1deb5)  
[101] Dataset: Zoning districts. vector, 10 rows (id e41da161-19b5-48ad-9ac1-ec14aba11071)  
[102] Dataset: Parcels. vector, 1100 rows (id 334e7daf-16c0-41ea-bad4-945a00366679)  
[103] Dataset: Transit stations. vector, 12 rows (id 26d28a13-4955-415e-8c92-76eac9333571)  
[104] Dataset: Green spaces. vector, 10 rows (id 3c7e642e-5612-4d39-9522-1784d7abf668)  
[105] Dataset: Streets. vector, 151 rows (id 4db290a4-42c5-4245-8ecd-06ec0d874f2a)  
[106] Dataset: City terrain model (DEM). raster, ? rows (id b4ace474-faad-4e75-8a4c-267a0401808c)  
[107] Dataset: Planning committee brief. document, ? rows (id b40a0012-69f3-4624-bf05-59a0379f41f9)  
[108] Record: Sample: Real estate and urban planning, Lisbon (id acfed0f6-9df9-4775-9325-30c0bbed70e9)  
[109] Layer: Emerging hot spots: Property sales. 81 features (id dc34ccf4-38d7-4643-8ab2-86b694e35b9c)  
[110] Layer: Emerging hot spots: Building permits. 90 features (id a26af55a-a693-4e98-b7a5-ccf061c4b3dd)  
[111] Layer: Repeat and near repeat classification: Property sales. 950 features (id e13509b5-d34e-404a-8352-c51480e7869b)  
[112] Layer: Summarize within: Parcels. 1,100 features (id d5217ead-227b-4720-8179-db14e5edc246)  
[113] Layer: Summarize within: Parcels. 1,100 features (id 7342e67c-cd62-400b-8ac8-e6893f4004f8)  
[114] Layer: Summarize within: Parcels. 1,100 features (id 1cffb229-6c6c-459d-afd2-b27e02cafa7a)  
[115] Layer: Hot spot analysis: Property sales. 213 features (id e5e5e574-adff-45f1-a500-025bf5057582)  
[116] Layer: Hot spot analysis: Building permits. 380 features (id ce1fd16e-c432-424b-8b6e-95c7442a25ea)  
[117] Layer: Calculate density: Property sales. 867 features (id f054cd23-8647-44c1-ad3f-df8046f553c7)  
[118] Layer: Calculate density: Building permits. 1,211 features (id a54dd906-1fba-477a-b3b4-4b2b57a69d8c)  
[119] Layer: Find point clusters: Transit stations. 12 features (id 712ec0c3-8dd4-4826-b637-a5959f8a8bc2)  
[120] Layer: Find point clusters: cluster hulls. 1 features (id 8f02c57f-8442-4514-8c69-ad2c271b02b5)  
[121] Layer: Find point clusters: Property sales. 950 features (id 060168a0-b1c4-4161-8cf4-2e5e7e616489)  
[122] Layer: Find point clusters: Building permits. 380 features (id 1828d446-6bee-4fc7-8d2a-1e9162eab56b)  
[123] Layer: Find point clusters: cluster hulls. 14 features (id 1280d1a9-f52c-4411-9b1a-bfe208cf113c)  
[124] Layer: Find outliers: Parcels. 1,100 features (id 83bec88a-063d-4158-a3b1-83369d40e2c4)  
[125] Layer: Find outliers: Green spaces. 10 features (id e9005658-d31f-457a-bf22-d38615856ba0)  
[126] Layer: Find outliers: Zoning districts. 10 features (id 940a4302-ccf6-4dc6-ae17-c77fc0e39f96)  

## Appendix: the data behind the charts

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

| period | series | value |
| --- | --- | --- |
| 2018-01-01 | Property sales | 0 |
| 2018-01-01 | Building permits | 0 |
| 2018-01-01 | Planning applications | 0 |
| 2018-01-01 | Land values by zone and year | 10 |
| 2018-04-01 | Property sales | 0 |
| 2018-04-01 | Building permits | 0 |
| 2018-04-01 | Planning applications | 0 |
| 2018-04-01 | Land values by zone and year | 0 |
| 2018-07-01 | Property sales | 0 |
| 2018-07-01 | Building permits | 0 |
| 2018-07-01 | Planning applications | 0 |
| 2018-07-01 | Land values by zone and year | 0 |
| 2018-10-01 | Property sales | 0 |
| 2018-10-01 | Building permits | 0 |
| 2018-10-01 | Planning applications | 0 |
| 2018-10-01 | Land values by zone and year | 0 |
| 2019-01-01 | Property sales | 0 |
| 2019-01-01 | Building permits | 0 |
| 2019-01-01 | Planning applications | 0 |
| 2019-01-01 | Land values by zone and year | 10 |
| 2019-04-01 | Property sales | 0 |
| 2019-04-01 | Building permits | 0 |
| 2019-04-01 | Planning applications | 0 |
| 2019-04-01 | Land values by zone and year | 0 |
| 2019-07-01 | Property sales | 0 |
| 2019-07-01 | Building permits | 0 |
| 2019-07-01 | Planning applications | 0 |
| 2019-07-01 | Land values by zone and year | 0 |
| 2019-10-01 | Property sales | 0 |
| 2019-10-01 | Building permits | 0 |
| 2019-10-01 | Planning applications | 0 |
| 2019-10-01 | Land values by zone and year | 0 |
| 2020-01-01 | Property sales | 0 |
| 2020-01-01 | Building permits | 0 |
| 2020-01-01 | Planning applications | 0 |
| 2020-01-01 | Land values by zone and year | 10 |
| 2020-04-01 | Property sales | 0 |
| 2020-04-01 | Building permits | 0 |
| 2020-04-01 | Planning applications | 0 |
| 2020-04-01 | Land values by zone and year | 0 |
| 2020-07-01 | Property sales | 0 |
| 2020-07-01 | Building permits | 0 |
| 2020-07-01 | Planning applications | 0 |
| 2020-07-01 | Land values by zone and year | 0 |
| 2020-10-01 | Property sales | 0 |
| 2020-10-01 | Building permits | 0 |
| 2020-10-01 | Planning applications | 0 |
| 2020-10-01 | Land values by zone and year | 0 |
| 2021-01-01 | Property sales | 0 |
| 2021-01-01 | Building permits | 0 |
| 2021-01-01 | Planning applications | 0 |
| 2021-01-01 | Land values by zone and year | 10 |
| 2021-04-01 | Property sales | 0 |
| 2021-04-01 | Building permits | 0 |
| 2021-04-01 | Planning applications | 0 |
| 2021-04-01 | Land values by zone and year | 0 |
| 2021-07-01 | Property sales | 0 |
| 2021-07-01 | Building permits | 0 |
| 2021-07-01 | Planning applications | 0 |
| 2021-07-01 | Land values by zone and year | 0 |
| 2021-10-01 | Property sales | 0 |
| 2021-10-01 | Building permits | 0 |
| 2021-10-01 | Planning applications | 0 |
| 2021-10-01 | Land values by zone and year | 0 |
| 2022-01-01 | Property sales | 46 |
| 2022-01-01 | Building permits | 0 |
| 2022-01-01 | Planning applications | 0 |
| 2022-01-01 | Land values by zone and year | 10 |
| 2022-04-01 | Property sales | 46 |
| 2022-04-01 | Building permits | 0 |
| 2022-04-01 | Planning applications | 0 |
| 2022-04-01 | Land values by zone and year | 0 |
| 2022-07-01 | Property sales | 42 |
| 2022-07-01 | Building permits | 0 |
| 2022-07-01 | Planning applications | 0 |
| 2022-07-01 | Land values by zone and year | 0 |
| 2022-10-01 | Property sales | 58 |
| 2022-10-01 | Building permits | 0 |
| 2022-10-01 | Planning applications | 0 |
| 2022-10-01 | Land values by zone and year | 0 |
| 2023-01-01 | Property sales | 40 |
| 2023-01-01 | Building permits | 14 |
| 2023-01-01 | Planning applications | 0 |
| 2023-01-01 | Land values by zone and year | 10 |
| 2023-04-01 | Property sales | 49 |
| 2023-04-01 | Building permits | 24 |
| 2023-04-01 | Planning applications | 0 |
| 2023-04-01 | Land values by zone and year | 0 |
| 2023-07-01 | Property sales | 61 |
| 2023-07-01 | Building permits | 19 |
| 2023-07-01 | Planning applications | 0 |
| 2023-07-01 | Land values by zone and year | 0 |
| 2023-10-01 | Property sales | 48 |
| 2023-10-01 | Building permits | 24 |
| 2023-10-01 | Planning applications | 0 |
| 2023-10-01 | Land values by zone and year | 0 |
| 2024-01-01 | Property sales | 50 |
| 2024-01-01 | Building permits | 12 |
| 2024-01-01 | Planning applications | 0 |
| 2024-01-01 | Land values by zone and year | 10 |
| 2024-04-01 | Property sales | 56 |
| 2024-04-01 | Building permits | 23 |
| 2024-04-01 | Planning applications | 0 |
| 2024-04-01 | Land values by zone and year | 0 |
| 2024-07-01 | Property sales | 46 |
| 2024-07-01 | Building permits | 30 |
| 2024-07-01 | Planning applications | 0 |
| 2024-07-01 | Land values by zone and year | 0 |
| 2024-10-01 | Property sales | 40 |
| 2024-10-01 | Building permits | 29 |
| 2024-10-01 | Planning applications | 0 |
| 2024-10-01 | Land values by zone and year | 0 |
| 2025-01-01 | Property sales | 47 |
| 2025-01-01 | Building permits | 14 |
| 2025-01-01 | Planning applications | 51 |
| 2025-01-01 | Land values by zone and year | 10 |
| 2025-04-01 | Property sales | 60 |
| 2025-04-01 | Building permits | 28 |
| 2025-04-01 | Planning applications | 45 |
| 2025-04-01 | Land values by zone and year | 0 |
| 2025-07-01 | Property sales | 51 |
| 2025-07-01 | Building permits | 31 |
| 2025-07-01 | Planning applications | 28 |
| 2025-07-01 | Land values by zone and year | 0 |
| 2025-10-01 | Property sales | 55 |
| 2025-10-01 | Building permits | 34 |
| 2025-10-01 | Planning applications | 36 |
| 2025-10-01 | Land values by zone and year | 0 |
| 2026-01-01 | Property sales | 55 |
| 2026-01-01 | Building permits | 34 |
| 2026-01-01 | Planning applications | 0 |
| 2026-01-01 | Land values by zone and year | 10 |
| 2026-04-01 | Property sales | 49 |
| 2026-04-01 | Building permits | 28 |
| 2026-04-01 | Planning applications | 0 |
| 2026-04-01 | Land values by zone and year | 0 |
| 2026-07-01 | Property sales | 51 |
| 2026-07-01 | Building permits | 36 |
| 2026-07-01 | Planning applications | 0 |
| 2026-07-01 | Land values by zone and year | 0 |

### Figure 23. Parcels over time (year_built) [75]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 0.3225 | 82 |
| 0.3225 | 0.645 | 63 |
| 0.645 | 0.9675 | 166 |
| 0.9675 | 1.29 | 200 |
| 1.29 | 1.613 | 194 |
| 1.613 | 1.935 | 103 |
| 1.935 | 2.257 | 92 |
| 2.257 | 2.58 | 71 |
| 2.58 | 2.902 | 62 |
| 2.902 | 3.225 | 45 |
| 3.225 | 3.547 | 18 |
| 3.547 | 3.87 | 4 |

### Figure 24. Property sales over time (date) [76]

| period | series | value |
| --- | --- | --- |
| 2022-01-01 | apartment | 21 |
| 2022-01-01 | office | 15 |
| 2022-01-01 | house | 10 |
| 2022-04-01 | apartment | 24 |
| 2022-04-01 | office | 10 |
| 2022-04-01 | house | 12 |
| 2022-07-01 | apartment | 19 |
| 2022-07-01 | office | 13 |
| 2022-07-01 | house | 10 |
| 2022-10-01 | apartment | 33 |
| 2022-10-01 | office | 12 |
| 2022-10-01 | house | 13 |
| 2023-01-01 | apartment | 14 |
| 2023-01-01 | office | 14 |
| 2023-01-01 | house | 12 |
| 2023-04-01 | apartment | 26 |
| 2023-04-01 | office | 12 |
| 2023-04-01 | house | 11 |
| 2023-07-01 | apartment | 29 |
| 2023-07-01 | office | 15 |
| 2023-07-01 | house | 17 |
| 2023-10-01 | apartment | 19 |
| 2023-10-01 | office | 19 |
| 2023-10-01 | house | 10 |
| 2024-01-01 | apartment | 22 |
| 2024-01-01 | office | 14 |
| 2024-01-01 | house | 14 |
| 2024-04-01 | apartment | 25 |
| 2024-04-01 | office | 15 |
| 2024-04-01 | house | 16 |
| 2024-07-01 | apartment | 22 |
| 2024-07-01 | office | 12 |
| 2024-07-01 | house | 12 |
| 2024-10-01 | apartment | 15 |
| 2024-10-01 | office | 13 |
| 2024-10-01 | house | 12 |
| 2025-01-01 | apartment | 23 |
| 2025-01-01 | office | 13 |
| 2025-01-01 | house | 11 |
| 2025-04-01 | apartment | 27 |
| 2025-04-01 | office | 18 |
| 2025-04-01 | house | 15 |
| 2025-07-01 | apartment | 21 |
| 2025-07-01 | office | 13 |
| 2025-07-01 | house | 17 |
| 2025-10-01 | apartment | 26 |
| 2025-10-01 | office | 15 |
| 2025-10-01 | house | 14 |
| 2026-01-01 | apartment | 24 |
| 2026-01-01 | office | 21 |
| 2026-01-01 | house | 10 |
| 2026-04-01 | apartment | 20 |
| 2026-04-01 | office | 14 |
| 2026-04-01 | house | 15 |
| 2026-07-01 | apartment | 21 |
| 2026-07-01 | office | 10 |
| 2026-07-01 | house | 20 |

### Figure 25. Building permits over time (decision_month) [77]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | extension | 31 |
| 2023-01-01 | new build | 14 |
| 2023-01-01 | change of use | 22 |
| 2023-01-01 | demolition | 0 |
| 2023-02-01 | extension | 54 |
| 2023-02-01 | new build | 28 |
| 2023-02-01 | change of use | 0 |
| 2023-02-01 | demolition | 0 |
| 2023-03-01 | extension | 34 |
| 2023-03-01 | new build | 47 |
| 2023-03-01 | change of use | 28 |
| 2023-03-01 | demolition | 0 |
| 2023-04-01 | extension | 65 |
| 2023-04-01 | new build | 13 |
| 2023-04-01 | change of use | 0 |
| 2023-04-01 | demolition | 41 |
| 2023-05-01 | extension | 82 |
| 2023-05-01 | new build | 53 |
| 2023-05-01 | change of use | 45 |
| 2023-05-01 | demolition | 0 |
| 2023-06-01 | extension | 26 |
| 2023-06-01 | new build | 84 |
| 2023-06-01 | change of use | 3 |
| 2023-06-01 | demolition | 0 |
| 2023-07-01 | extension | 15 |
| 2023-07-01 | new build | 43 |
| 2023-07-01 | change of use | 0 |
| 2023-07-01 | demolition | 8 |
| 2023-08-01 | extension | 32 |
| 2023-08-01 | new build | 102 |
| 2023-08-01 | change of use | 8 |
| 2023-08-01 | demolition | 24 |
| 2023-09-01 | extension | 57 |
| 2023-09-01 | new build | 57 |
| 2023-09-01 | change of use | 0 |
| 2023-09-01 | demolition | 31 |
| 2023-10-01 | extension | 70 |
| 2023-10-01 | new build | 3 |
| 2023-10-01 | change of use | 37 |
| 2023-10-01 | demolition | 17 |
| 2023-11-01 | extension | 42 |
| 2023-11-01 | new build | 39 |
| 2023-11-01 | change of use | 36 |
| 2023-11-01 | demolition | 0 |
| 2023-12-01 | extension | 94 |
| 2023-12-01 | new build | 67 |
| 2023-12-01 | change of use | 65 |
| 2023-12-01 | demolition | 2 |
| 2024-01-01 | extension | 0 |
| 2024-01-01 | new build | 19 |
| 2024-01-01 | change of use | 34 |
| 2024-01-01 | demolition | 30 |
| 2024-02-01 | extension | 99 |
| 2024-02-01 | new build | 21 |
| 2024-02-01 | change of use | 32 |
| 2024-02-01 | demolition | 39 |
| 2024-03-01 | extension | 23 |
| 2024-03-01 | new build | 0 |
| 2024-03-01 | change of use | 0 |
| 2024-03-01 | demolition | 0 |
| 2024-04-01 | extension | 12 |
| 2024-04-01 | new build | 0 |
| 2024-04-01 | change of use | 45 |
| 2024-04-01 | demolition | 0 |
| 2024-05-01 | extension | 105 |
| 2024-05-01 | new build | 110 |
| 2024-05-01 | change of use | 7 |
| 2024-05-01 | demolition | 6 |
| 2024-06-01 | extension | 46 |
| 2024-06-01 | new build | 0 |
| 2024-06-01 | change of use | 58 |
| 2024-06-01 | demolition | 0 |
| 2024-07-01 | extension | 69 |
| 2024-07-01 | new build | 28 |
| 2024-07-01 | change of use | 71 |
| 2024-07-01 | demolition | 32 |
| 2024-08-01 | extension | 0 |
| 2024-08-01 | new build | 81 |
| 2024-08-01 | change of use | 89 |
| 2024-08-01 | demolition | 8 |
| 2024-09-01 | extension | 0 |
| 2024-09-01 | new build | 96 |
| 2024-09-01 | change of use | 98 |
| 2024-09-01 | demolition | 39 |
| 2024-10-01 | extension | 43 |
| 2024-10-01 | new build | 77 |
| 2024-10-01 | change of use | 32 |
| 2024-10-01 | demolition | 56 |
| 2024-11-01 | extension | 175 |
| 2024-11-01 | new build | 176 |
| 2024-11-01 | change of use | 26 |
| 2024-11-01 | demolition | 0 |
| 2024-12-01 | extension | 49 |
| 2024-12-01 | new build | 80 |
| 2024-12-01 | change of use | 15 |
| 2024-12-01 | demolition | 41 |
| 2025-01-01 | extension | 41 |
| 2025-01-01 | new build | 64 |
| 2025-01-01 | change of use | 0 |
| 2025-01-01 | demolition | 0 |
| 2025-02-01 | extension | 24 |
| 2025-02-01 | new build | 28 |
| 2025-02-01 | change of use | 53 |
| 2025-02-01 | demolition | 0 |
| 2025-03-01 | extension | 0 |
| 2025-03-01 | new build | 32 |
| 2025-03-01 | change of use | 28 |
| 2025-03-01 | demolition | 0 |
| 2025-04-01 | extension | 41 |
| 2025-04-01 | new build | 33 |
| 2025-04-01 | change of use | 55 |
| 2025-04-01 | demolition | 0 |
| 2025-05-01 | extension | 67 |
| 2025-05-01 | new build | 76 |
| 2025-05-01 | change of use | 57 |
| 2025-05-01 | demolition | 27 |
| 2025-06-01 | extension | 87 |
| 2025-06-01 | new build | 47 |
| 2025-06-01 | change of use | 73 |
| 2025-06-01 | demolition | 4 |
| 2025-07-01 | extension | 16 |
| 2025-07-01 | new build | 47 |
| 2025-07-01 | change of use | 30 |
| 2025-07-01 | demolition | 0 |
| 2025-08-01 | extension | 62 |
| 2025-08-01 | new build | 69 |
| 2025-08-01 | change of use | 43 |
| 2025-08-01 | demolition | 26 |
| 2025-09-01 | extension | 116 |
| 2025-09-01 | new build | 90 |
| 2025-09-01 | change of use | 63 |
| 2025-09-01 | demolition | 65 |
| 2025-10-01 | extension | 70 |
| 2025-10-01 | new build | 54 |
| 2025-10-01 | change of use | 119 |
| 2025-10-01 | demolition | 18 |
| 2025-11-01 | extension | 55 |
| 2025-11-01 | new build | 64 |
| 2025-11-01 | change of use | 80 |
| 2025-11-01 | demolition | 10 |
| 2025-12-01 | extension | 108 |
| 2025-12-01 | new build | 23 |
| 2025-12-01 | change of use | 23 |
| 2025-12-01 | demolition | 0 |
| 2026-01-01 | extension | 130 |
| 2026-01-01 | new build | 117 |
| 2026-01-01 | change of use | 39 |
| 2026-01-01 | demolition | 0 |
| 2026-02-01 | extension | 108 |
| 2026-02-01 | new build | 22 |
| 2026-02-01 | change of use | 28 |
| 2026-02-01 | demolition | 0 |
| 2026-03-01 | extension | 80 |
| 2026-03-01 | new build | 35 |
| 2026-03-01 | change of use | 85 |
| 2026-03-01 | demolition | 3 |
| 2026-04-01 | extension | 21 |
| 2026-04-01 | new build | 140 |
| 2026-04-01 | change of use | 0 |
| 2026-04-01 | demolition | 32 |
| 2026-05-01 | extension | 55 |
| 2026-05-01 | new build | 65 |
| 2026-05-01 | change of use | 17 |
| 2026-05-01 | demolition | 0 |
| 2026-06-01 | extension | 63 |
| 2026-06-01 | new build | 112 |
| 2026-06-01 | change of use | 93 |
| 2026-06-01 | demolition | 0 |
| 2026-07-01 | extension | 167 |
| 2026-07-01 | new build | 112 |
| 2026-07-01 | change of use | 9 |
| 2026-07-01 | demolition | 14 |
| 2026-08-01 | extension | 66 |
| 2026-08-01 | new build | 65 |
| 2026-08-01 | change of use | 67 |
| 2026-08-01 | demolition | 104 |
| 2026-09-01 | extension | 130 |
| 2026-09-01 | new build | 34 |
| 2026-09-01 | change of use | 66 |
| 2026-09-01 | demolition | 0 |

### Figure 26. Land values by zone and year over time (year) [78]

| period | series | value |
| --- | --- | --- |
| 2018-01-01 | C06 | 1 |
| 2018-01-01 | I07 | 1 |
| 2018-01-01 | M05 | 1 |
| 2018-01-01 | M08 | 1 |
| 2018-01-01 | M10 | 1 |
| 2018-01-01 | Other | 5 |
| 2018-04-01 | C06 | 0 |
| 2018-04-01 | I07 | 0 |
| 2018-04-01 | M05 | 0 |
| 2018-04-01 | M08 | 0 |
| 2018-04-01 | M10 | 0 |
| 2018-04-01 | Other | 0 |
| 2018-07-01 | C06 | 0 |
| 2018-07-01 | I07 | 0 |
| 2018-07-01 | M05 | 0 |
| 2018-07-01 | M08 | 0 |
| 2018-07-01 | M10 | 0 |
| 2018-07-01 | Other | 0 |
| 2018-10-01 | C06 | 0 |
| 2018-10-01 | I07 | 0 |
| 2018-10-01 | M05 | 0 |
| 2018-10-01 | M08 | 0 |
| 2018-10-01 | M10 | 0 |
| 2018-10-01 | Other | 0 |
| 2019-01-01 | C06 | 1 |
| 2019-01-01 | I07 | 1 |
| 2019-01-01 | M05 | 1 |
| 2019-01-01 | M08 | 1 |
| 2019-01-01 | M10 | 1 |
| 2019-01-01 | Other | 5 |
| 2019-04-01 | C06 | 0 |
| 2019-04-01 | I07 | 0 |
| 2019-04-01 | M05 | 0 |
| 2019-04-01 | M08 | 0 |
| 2019-04-01 | M10 | 0 |
| 2019-04-01 | Other | 0 |
| 2019-07-01 | C06 | 0 |
| 2019-07-01 | I07 | 0 |
| 2019-07-01 | M05 | 0 |
| 2019-07-01 | M08 | 0 |
| 2019-07-01 | M10 | 0 |
| 2019-07-01 | Other | 0 |
| 2019-10-01 | C06 | 0 |
| 2019-10-01 | I07 | 0 |
| 2019-10-01 | M05 | 0 |
| 2019-10-01 | M08 | 0 |
| 2019-10-01 | M10 | 0 |
| 2019-10-01 | Other | 0 |
| 2020-01-01 | C06 | 1 |
| 2020-01-01 | I07 | 1 |
| 2020-01-01 | M05 | 1 |
| 2020-01-01 | M08 | 1 |
| 2020-01-01 | M10 | 1 |
| 2020-01-01 | Other | 5 |
| 2020-04-01 | C06 | 0 |
| 2020-04-01 | I07 | 0 |
| 2020-04-01 | M05 | 0 |
| 2020-04-01 | M08 | 0 |
| 2020-04-01 | M10 | 0 |
| 2020-04-01 | Other | 0 |
| 2020-07-01 | C06 | 0 |
| 2020-07-01 | I07 | 0 |
| 2020-07-01 | M05 | 0 |
| 2020-07-01 | M08 | 0 |
| 2020-07-01 | M10 | 0 |
| 2020-07-01 | Other | 0 |
| 2020-10-01 | C06 | 0 |
| 2020-10-01 | I07 | 0 |
| 2020-10-01 | M05 | 0 |
| 2020-10-01 | M08 | 0 |
| 2020-10-01 | M10 | 0 |
| 2020-10-01 | Other | 0 |
| 2021-01-01 | C06 | 1 |
| 2021-01-01 | I07 | 1 |
| 2021-01-01 | M05 | 1 |
| 2021-01-01 | M08 | 1 |
| 2021-01-01 | M10 | 1 |
| 2021-01-01 | Other | 5 |
| 2021-04-01 | C06 | 0 |
| 2021-04-01 | I07 | 0 |
| 2021-04-01 | M05 | 0 |
| 2021-04-01 | M08 | 0 |
| 2021-04-01 | M10 | 0 |
| 2021-04-01 | Other | 0 |
| 2021-07-01 | C06 | 0 |
| 2021-07-01 | I07 | 0 |
| 2021-07-01 | M05 | 0 |
| 2021-07-01 | M08 | 0 |
| 2021-07-01 | M10 | 0 |
| 2021-07-01 | Other | 0 |
| 2021-10-01 | C06 | 0 |
| 2021-10-01 | I07 | 0 |
| 2021-10-01 | M05 | 0 |
| 2021-10-01 | M08 | 0 |
| 2021-10-01 | M10 | 0 |
| 2021-10-01 | Other | 0 |
| 2022-01-01 | C06 | 1 |
| 2022-01-01 | I07 | 1 |
| 2022-01-01 | M05 | 1 |
| 2022-01-01 | M08 | 1 |
| 2022-01-01 | M10 | 1 |
| 2022-01-01 | Other | 5 |
| 2022-04-01 | C06 | 0 |
| 2022-04-01 | I07 | 0 |
| 2022-04-01 | M05 | 0 |
| 2022-04-01 | M08 | 0 |
| 2022-04-01 | M10 | 0 |
| 2022-04-01 | Other | 0 |
| 2022-07-01 | C06 | 0 |
| 2022-07-01 | I07 | 0 |
| 2022-07-01 | M05 | 0 |
| 2022-07-01 | M08 | 0 |
| 2022-07-01 | M10 | 0 |
| 2022-07-01 | Other | 0 |
| 2022-10-01 | C06 | 0 |
| 2022-10-01 | I07 | 0 |
| 2022-10-01 | M05 | 0 |
| 2022-10-01 | M08 | 0 |
| 2022-10-01 | M10 | 0 |
| 2022-10-01 | Other | 0 |
| 2023-01-01 | C06 | 1 |
| 2023-01-01 | I07 | 1 |
| 2023-01-01 | M05 | 1 |
| 2023-01-01 | M08 | 1 |
| 2023-01-01 | M10 | 1 |
| 2023-01-01 | Other | 5 |
| 2023-04-01 | C06 | 0 |
| 2023-04-01 | I07 | 0 |
| 2023-04-01 | M05 | 0 |
| 2023-04-01 | M08 | 0 |
| 2023-04-01 | M10 | 0 |
| 2023-04-01 | Other | 0 |
| 2023-07-01 | C06 | 0 |
| 2023-07-01 | I07 | 0 |
| 2023-07-01 | M05 | 0 |
| 2023-07-01 | M08 | 0 |
| 2023-07-01 | M10 | 0 |
| 2023-07-01 | Other | 0 |
| 2023-10-01 | C06 | 0 |
| 2023-10-01 | I07 | 0 |
| 2023-10-01 | M05 | 0 |
| 2023-10-01 | M08 | 0 |
| 2023-10-01 | M10 | 0 |
| 2023-10-01 | Other | 0 |
| 2024-01-01 | C06 | 1 |
| 2024-01-01 | I07 | 1 |
| 2024-01-01 | M05 | 1 |
| 2024-01-01 | M08 | 1 |
| 2024-01-01 | M10 | 1 |
| 2024-01-01 | Other | 5 |
| 2024-04-01 | C06 | 0 |
| 2024-04-01 | I07 | 0 |
| 2024-04-01 | M05 | 0 |
| 2024-04-01 | M08 | 0 |
| 2024-04-01 | M10 | 0 |
| 2024-04-01 | Other | 0 |
| 2024-07-01 | C06 | 0 |
| 2024-07-01 | I07 | 0 |
| 2024-07-01 | M05 | 0 |
| 2024-07-01 | M08 | 0 |
| 2024-07-01 | M10 | 0 |
| 2024-07-01 | Other | 0 |
| 2024-10-01 | C06 | 0 |
| 2024-10-01 | I07 | 0 |
| 2024-10-01 | M05 | 0 |
| 2024-10-01 | M08 | 0 |
| 2024-10-01 | M10 | 0 |
| 2024-10-01 | Other | 0 |
| 2025-01-01 | C06 | 1 |
| 2025-01-01 | I07 | 1 |
| 2025-01-01 | M05 | 1 |
| 2025-01-01 | M08 | 1 |
| 2025-01-01 | M10 | 1 |
| 2025-01-01 | Other | 5 |
| 2025-04-01 | C06 | 0 |
| 2025-04-01 | I07 | 0 |
| 2025-04-01 | M05 | 0 |
| 2025-04-01 | M08 | 0 |
| 2025-04-01 | M10 | 0 |
| 2025-04-01 | Other | 0 |
| 2025-07-01 | C06 | 0 |
| 2025-07-01 | I07 | 0 |
| 2025-07-01 | M05 | 0 |
| 2025-07-01 | M08 | 0 |
| 2025-07-01 | M10 | 0 |
| 2025-07-01 | Other | 0 |
| 2025-10-01 | C06 | 0 |
| 2025-10-01 | I07 | 0 |
| 2025-10-01 | M05 | 0 |
| 2025-10-01 | M08 | 0 |
| 2025-10-01 | M10 | 0 |
| 2025-10-01 | Other | 0 |
| 2026-01-01 | C06 | 1 |
| 2026-01-01 | I07 | 1 |
| 2026-01-01 | M05 | 1 |
| 2026-01-01 | M08 | 1 |
| 2026-01-01 | M10 | 1 |
| 2026-01-01 | Other | 5 |

### Figure 27. Emerging hot spots: Property sales [79]

| bin_start | bin_end | count |
| --- | --- | --- |
| -0.963 | -0.1466 | 23 |
| -0.1466 | 0.6697 | 17 |
| 0.6697 | 1.486 | 14 |
| 1.486 | 2.303 | 13 |
| 2.303 | 3.119 | 4 |
| 3.119 | 3.935 | 6 |
| 3.935 | 4.752 | 3 |
| 4.752 | 5.568 | 1 |

### Figure 28. Emerging hot spots: Building permits [80]

| bin_start | bin_end | count |
| --- | --- | --- |
| -0.609 | 0.08612 | 42 |
| 0.08612 | 0.7812 | 21 |
| 0.7812 | 1.476 | 9 |
| 1.476 | 2.171 | 9 |
| 2.171 | 2.867 | 5 |
| 2.867 | 3.562 | 3 |
| 3.562 | 4.257 | 0 |
| 4.257 | 4.952 | 1 |

### Figure 29. Repeat and near repeat classification: Property sales [81]

| period | series | value |
| --- | --- | --- |
| 2022-01-01 | apartment | 21 |
| 2022-01-01 | office | 15 |
| 2022-01-01 | house | 10 |
| 2022-04-01 | apartment | 24 |
| 2022-04-01 | office | 10 |
| 2022-04-01 | house | 12 |
| 2022-07-01 | apartment | 19 |
| 2022-07-01 | office | 13 |
| 2022-07-01 | house | 10 |
| 2022-10-01 | apartment | 33 |
| 2022-10-01 | office | 12 |
| 2022-10-01 | house | 13 |
| 2023-01-01 | apartment | 14 |
| 2023-01-01 | office | 14 |
| 2023-01-01 | house | 12 |
| 2023-04-01 | apartment | 26 |
| 2023-04-01 | office | 12 |
| 2023-04-01 | house | 11 |
| 2023-07-01 | apartment | 29 |
| 2023-07-01 | office | 15 |
| 2023-07-01 | house | 17 |
| 2023-10-01 | apartment | 19 |
| 2023-10-01 | office | 19 |
| 2023-10-01 | house | 10 |
| 2024-01-01 | apartment | 22 |
| 2024-01-01 | office | 14 |
| 2024-01-01 | house | 14 |
| 2024-04-01 | apartment | 25 |
| 2024-04-01 | office | 15 |
| 2024-04-01 | house | 16 |
| 2024-07-01 | apartment | 22 |
| 2024-07-01 | office | 12 |
| 2024-07-01 | house | 12 |
| 2024-10-01 | apartment | 15 |
| 2024-10-01 | office | 13 |
| 2024-10-01 | house | 12 |
| 2025-01-01 | apartment | 23 |
| 2025-01-01 | office | 13 |
| 2025-01-01 | house | 11 |
| 2025-04-01 | apartment | 27 |
| 2025-04-01 | office | 18 |
| 2025-04-01 | house | 15 |
| 2025-07-01 | apartment | 21 |
| 2025-07-01 | office | 13 |
| 2025-07-01 | house | 17 |
| 2025-10-01 | apartment | 26 |
| 2025-10-01 | office | 15 |
| 2025-10-01 | house | 14 |
| 2026-01-01 | apartment | 24 |
| 2026-01-01 | office | 21 |
| 2026-01-01 | house | 10 |
| 2026-04-01 | apartment | 20 |
| 2026-04-01 | office | 14 |
| 2026-04-01 | house | 15 |
| 2026-07-01 | apartment | 21 |
| 2026-07-01 | office | 10 |
| 2026-07-01 | house | 20 |

### Figure 30. Summarize within: Parcels [82]

| bin_start | bin_end | count |
| --- | --- | --- |
| 752 | 1,843 | 112 |
| 1,843 | 2,934 | 213 |
| 2,934 | 4,024 | 188 |
| 4,024 | 5,115 | 183 |
| 5,115 | 6,206 | 125 |
| 6,206 | 7,296 | 99 |
| 7,296 | 8,387 | 76 |
| 8,387 | 9,478 | 50 |
| 9,478 | 1.057e+04 | 32 |
| 1.057e+04 | 1.166e+04 | 12 |
| 1.166e+04 | 1.275e+04 | 7 |
| 1.275e+04 | 1.384e+04 | 3 |

### Figure 31. Summarize within: Parcels [83]

| bin_start | bin_end | count |
| --- | --- | --- |
| 752 | 1,843 | 112 |
| 1,843 | 2,934 | 213 |
| 2,934 | 4,024 | 188 |
| 4,024 | 5,115 | 183 |
| 5,115 | 6,206 | 125 |
| 6,206 | 7,296 | 99 |
| 7,296 | 8,387 | 76 |
| 8,387 | 9,478 | 50 |
| 9,478 | 1.057e+04 | 32 |
| 1.057e+04 | 1.166e+04 | 12 |
| 1.166e+04 | 1.275e+04 | 7 |
| 1.275e+04 | 1.384e+04 | 3 |

### Figure 32. Summarize within: Parcels [84]

| bin_start | bin_end | count |
| --- | --- | --- |
| 752 | 1,843 | 112 |
| 1,843 | 2,934 | 213 |
| 2,934 | 4,024 | 188 |
| 4,024 | 5,115 | 183 |
| 5,115 | 6,206 | 125 |
| 6,206 | 7,296 | 99 |
| 7,296 | 8,387 | 76 |
| 8,387 | 9,478 | 50 |
| 9,478 | 1.057e+04 | 32 |
| 1.057e+04 | 1.166e+04 | 12 |
| 1.166e+04 | 1.275e+04 | 7 |
| 1.275e+04 | 1.384e+04 | 3 |

### Figure 33. Hot spot analysis: Property sales [85]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -2 | -1.5 | not_significant | Not significant | 25 |
| -1.5 | -1 | not_significant | Not significant | 8 |
| -1 | -0.5 | not_significant | Not significant | 21 |
| -0.5 | 0 | not_significant | Not significant | 63 |
| 0 | 0.5 | not_significant | Not significant | 27 |
| 0.5 | 1 | not_significant | Not significant | 37 |
| 1 | 1.5 | not_significant | Not significant | 12 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 2 |
| 1.5 | 2 | not_significant | Not significant | 12 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 2 |
| 2.5 | 3 | hot_95 | Hot spot, 95% confidence | 2 |
| 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 2 |

### Figure 34. Hot spot analysis: Building permits [86]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -2.5 | -2 | cold_95 | Cold spot, 95% confidence | 1 |
| -2 | -1.5 | cold_90 | Cold spot, 90% confidence | 11 |
| -2 | -1.5 | not_significant | Not significant | 7 |
| -1.5 | -1 | not_significant | Not significant | 29 |
| -1 | -0.5 | not_significant | Not significant | 81 |
| -0.5 | 0 | not_significant | Not significant | 72 |
| 0 | 0.5 | not_significant | Not significant | 93 |
| 0.5 | 1 | not_significant | Not significant | 38 |
| 1 | 1.5 | not_significant | Not significant | 25 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 9 |
| 1.5 | 2 | not_significant | Not significant | 4 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 9 |
| 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 35. Calculate density: Property sales [87]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 2.182 | 89 |
| 2.182 | 4.364 | 81 |
| 4.364 | 6.545 | 81 |
| 6.545 | 8.727 | 82 |
| 8.727 | 10.91 | 80 |
| 10.91 | 13.09 | 122 |
| 13.09 | 15.27 | 81 |
| 15.27 | 17.45 | 82 |
| 17.45 | 19.64 | 76 |
| 19.64 | 21.82 | 57 |
| 21.82 | 24 | 36 |

### Figure 36. Calculate density: Building permits [88]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 2.5 | 68 |
| 2.5 | 5 | 87 |
| 5 | 7.5 | 139 |
| 7.5 | 10 | 94 |
| 10 | 12.5 | 135 |
| 12.5 | 15 | 92 |
| 15 | 17.5 | 139 |
| 17.5 | 20 | 94 |
| 20 | 22.5 | 138 |
| 22.5 | 25 | 89 |
| 25 | 27.5 | 102 |
| 27.5 | 30 | 34 |

### Figure 37. Find point clusters: Transit stations [89]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 12 | 1 | 100.0% |

### Figure 38. Find point clusters: cluster hulls [90]

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

### Figure 39. Find point clusters: Property sales [91]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 950 | 1 | 100.0% |

### Figure 40. Find point clusters: Building permits [92]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 126 | 0.3316 | 33.2% |
| 5 | 37 | 0.09737 | 9.7% |
| 6 | 33 | 0.08684 | 8.7% |
| 0 | 29 | 0.07632 | 7.6% |
| 10 | 20 | 0.05263 | 5.3% |
| 4 | 19 | 0.05 | 5.0% |
| 2 | 18 | 0.04737 | 4.7% |
| 8 | 17 | 0.04474 | 4.5% |
| 1 | 16 | 0.0421 | 4.2% |
| 9 | 15 | 0.03947 | 3.9% |
| 3 | 13 | 0.03421 | 3.4% |
| 11 | 10 | 0.02632 | 2.6% |
| 12 | 10 | 0.02632 | 2.6% |
| 13 | 10 | 0.02632 | 2.6% |
| 7 | 7 | 0.01842 | 1.8% |

### Figure 41. Find point clusters: cluster hulls [93]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.07143 | 7.1% |
| 1 | 1 | 0.07143 | 7.1% |
| 10 | 1 | 0.07143 | 7.1% |
| 11 | 1 | 0.07143 | 7.1% |
| 12 | 1 | 0.07143 | 7.1% |
| 13 | 1 | 0.07143 | 7.1% |
| 2 | 1 | 0.07143 | 7.1% |
| 3 | 1 | 0.07143 | 7.1% |
| 4 | 1 | 0.07143 | 7.1% |
| 5 | 1 | 0.07143 | 7.1% |
| 6 | 1 | 0.07143 | 7.1% |
| 7 | 1 | 0.07143 | 7.1% |
| 8 | 1 | 0.07143 | 7.1% |
| 9 | 1 | 0.07143 | 7.1% |

### Figure 42. Find outliers: Parcels [94]

| value | lag | type |
| --- | --- | --- |
| -1.428 | -0.8778 | NS |
| -0.8984 | -0.9939 | NS |
| -0.615 | -1.065 | NS |
| -1.12 | -0.8688 | NS |
| -0.2331 | -0.615 | NS |
| -0.615 | -0.2331 | NS |
| -1.133 | -0.6335 | NS |
| -0.7752 | -0.9292 | NS |
| -0.4918 | -0.9764 | NS |
| -0.27 | -0.8203 | NS |
| -1.527 | -0.4015 | NS |
| -0.2208 | -0.4548 | NS |
| -0.923 | -0.1037 | NS |
| 0.0133 | -0.5719 | NS |
| -0.7998 | -0.7259 | NS |
| -0.5288 | -0.8614 | NS |
| -0.923 | -0.6643 | NS |
| -1.675 | -1.12 | NS |
| -0.5657 | -1.675 | NS |
| -1.675 | -1.03 | NS |
| -0.3316 | -1.157 | NS |
| -1.317 | -0.6314 | NS |
| -0.9969 | -0.7382 | NS |
| -0.5657 | -1.371 | NS |
| -0.3809 | -1.034 | NS |
| -1.034 | -0.6889 | NS |
| -1.219 | -0.96 | NS |
| -0.96 | -1.219 | NS |
| -0.8121 | -1.004 | NS |
| -0.5904 | -1.003 | NS |
| -1.798 | -0.5616 | NS |
| -0.2824 | -1.018 | NS |
| 0.0996 | -0.3316 | NS |
| -0.0852 | -0.2392 | NS |
| -0.578 | -0.2413 | NS |
| -0.7259 | -1.083 | NS |
| -1.391 | -0.7259 | NS |
| -0.4425 | -0.9846 | NS |
| -1.675 | -0.4918 | NS |
| -0.4672 | -0.8943 | NS |
| -0.5657 | -1.071 | NS |
| -0.8737 | -1.268 | NS |
| -1.675 | -0.7957 | NS |
| -0.8614 | -1.067 | NS |
| -0.652 | -1.05 | NS |
| 1.381 | 0.6417 | NS |
| 0.5554 | 1.054 | NS |
| 0.7279 | 0.884 | NS |
| 1.036 | 1.412 | NS |
| 0.7156 | 1.165 | NS |
| 2.108 | 0.847 | NS |
| 0.7895 | 1.229 | NS |
| 0.4938 | 0.2105 | NS |
| -0.2577 | 0.6868 | NS |
| 0.6786 | 0.3747 | NS |
| 0.9374 | 0.3829 | NS |
| 0.3829 | 0.9497 | NS |
| -1.28 | -0.0206 | NS |
| -0.578 | -0.1961 | NS |
| 0.3706 | -0.4333 | NS |
| 0.6293 | 0.001 | NS |
| -0.5041 | -0.4179 | NS |
| -0.7136 | -0.7197 | NS |
| -0.9353 | -0.7875 | NS |
| -1.022 | -0.9271 | NS |
| -0.9723 | -0.9415 | NS |
| -0.96 | -0.9446 | NS |
| -0.8491 | -0.8214 | NS |
| -1.157 | -1.133 | NS |
| -1.305 | -1.083 | NS |
| -0.9477 | -1.114 | NS |
| -1.145 | -1.065 | NS |
| -0.8491 | -0.9641 | NS |
| -0.7752 | -0.6458 | NS |
| -0.4425 | -1.005 | NS |
| -1.798 | -0.6479 | NS |
| -0.3563 | -1.41 | NS |
| -1.022 | -1.018 | NS |
| -0.9969 | -0.8429 | NS |
| -0.652 | -1.009 | NS |
| -1.022 | -0.7752 | NS |
| -0.8614 | -0.1715 | NS |
| -0.1715 | -1.015 | NS |
| -0.6643 | -0.652 | NS |
| -1.022 | -0.6643 | NS |
| -0.7382 | -0.5626 | NS |
| -0.5534 | -0.716 | NS |
| -0.578 | -0.7423 | NS |
| -0.5411 | -0.6427 | NS |
| -0.7013 | -0.5411 | NS |
| -1.108 | -0.7937 | NS |
| -0.8861 | -0.9045 | NS |
| -0.7013 | -0.9969 | NS |
| -0.615 | -0.1407 | NS |
| 0.3706 | -0.3994 | NS |
| -0.1838 | 0.3706 | NS |
| -0.4672 | -0.2639 | NS |
| 0.038 | -0.5575 | NS |
| -0.5657 | -0.3563 | NS |
| -0.6397 | -0.2639 | NS |
| 0.7279 | 1.122 | NS |
| 1.122 | 0.7279 | NS |
| 1.085 | -0.7998 | NS |
| -1.798 | 0.7238 | NS |
| 0.1981 | 0.0585 | NS |
| 0.8881 | -0.4015 | NS |
| 0.1242 | 0.9312 | NS |
| 0.5924 | 0.654 | NS |
| 1.27 | 0.4281 | NS |
| 0.962 | 0.5801 | NS |
| 0.8018 | 0.6601 | NS |
| 0.3583 | 0.8819 | NS |
| 0.8634 | 0.8203 | NS |
| 0.8511 | 1.116 | NS |
| 0.8881 | 0.3337 | NS |
| 0.5801 | 0.8881 | NS |
| 0.962 | 0.8922 | NS |
| 1.097 | 0.9045 | NS |
| 1.196 | 0.8716 | NS |
| 0.5554 | 0.9086 | NS |
| 1.997 | 1.362 | NS |
| 2.095 | 0.0996 | NS |
| -1.798 | 1.073 | NS |
| -1.145 | -0.9969 | NS |
| -0.9969 | -1.145 | NS |
| -0.3316 | -0.7813 | NS |
| -0.7136 | -0.4672 | NS |
| -0.8491 | -0.6971 | NS |
| 0.001 | -0.5842 | NS |
| -1.391 | -1.058 | NS |
| -1.675 | -0.7136 | NS |
| -0.8984 | -1.04 | NS |
| -1.058 | -0.8984 | NS |
| -0.8984 | -1.071 | NS |
| -0.8121 | -1.075 | NS |
| -1.379 | -0.8861 | NS |
| -0.9477 | -1.079 | NS |
| -1.169 | -0.9846 | NS |
| -1.798 | -0.578 | NS |
| -1.798 | -0.7629 | NS |
| -0.7629 | -1.798 | NS |
| -0.6889 | -0.9477 | NS |
| -1.034 | -0.8787 | NS |
| -0.8984 | -0.9939 | NS |
| -1.675 | -0.5565 | NS |
| -1.342 | -0.2947 | NS |
| -0.1838 | -0.8409 | NS |
| -0.4056 | -1.025 | NS |
| -0.7752 | -0.229 | NS |
| -0.0976 | -0.6397 | NS |
| -0.5041 | -0.3193 | NS |
| -0.5411 | -1.151 | NS |
| -1.798 | -0.2392 | NS |
| -0.8614 | -0.1653 | NS |
| 0.4199 | -0.5329 | NS |
| -0.7505 | -0.1427 | NS |
| 0.0133 | -0.4015 | NS |
| -0.3563 | -0.0976 | NS |
| -0.0976 | -0.153 | NS |
| -0.2331 | -0.5103 | NS |
| -0.8861 | -0.27 | NS |
| -0.1345 | -0.2454 | NS |
| -0.2577 | -0.0483 | NS |
| 0.038 | -0.7834 | NS |
| -0.2947 | -0.1386 | NS |
| -0.3193 | -0.2146 | NS |
| -0.1345 | -0.2824 | NS |
| -0.2331 | -0.424 | NS |
| -0.7136 | -0.2331 | NS |
| -0.2084 | 0.1797 | NS |
| -0.1715 | 0.1612 | NS |
| 0.5308 | -0.076 | NS |
| -0.5657 | 0.5862 | NS |
| 0.6417 | 0.0215 | NS |
| -1.798 | -1.798 | NS |
| -1.798 | 0.079 | NS |
| 1.196 | -0.4795 | NS |
| 0.8388 | 0.1735 | NS |
| 0.3953 | 1.19 | NS |
| 1.849 | 0.8059 | NS |
| 1.356 | 0.9702 | NS |
| 0.6663 | 1.162 | NS |
| 0.5677 | 1.192 | NS |
| 1.714 | 0.5677 | NS |
| 1.097 | 0.7033 | NS |
| 0.7033 | 0.7217 | NS |
| 1.369 | 0.4445 | NS |
| 0.038 | 0.8758 | NS |
| 0.3829 | 0.038 | NS |
| 1.763 | 0.8634 | NS |
| 0.8511 | 1.167 | NS |
| 0.962 | 1.13 | NS |
| 0.7772 | 1.192 | NS |
| 0.4322 | 0.9928 | NS |
| 1.43 | 0.7977 | NS |
| 0.0503 | -0.2084 | NS |
| 0.1489 | 0.2885 | NS |
| 0.3829 | 0.2269 | NS |
| 0.4322 | -0.2762 | NS |
| -0.2454 | -0.3132 | NS |
| -1.058 | 0.0934 | NS |
| -0.6027 | -0.4343 | NS |
| 0.5677 | -0.8799 | NS |
| -1.157 | -0.3316 | NS |
| -0.3193 | -0.4713 | NS |
| -0.6397 | -0.6643 | NS |
| -0.7752 | -0.6191 | NS |
| -0.8984 | -0.7074 | NS |
| -0.036 | -1.601 | NS |
| -1.527 | -0.9169 | NS |
| -1.798 | -1.071 | NS |
| -0.307 | -1.675 | NS |
| -1.675 | -0.2885 | NS |
| -1.046 | -0.8861 | NS |
| -0.8245 | -0.3932 | NS |
| -0.8861 | -0.3727 | NS |
| 0.7526 | -1.071 | NS |
| 0.0133 | -1.077 | NS |
| -0.3563 | 0.0133 | NS |
| -1.317 | 0.309 | NS |
| 0.309 | -1.317 | NS |
| 0.3953 | -1.582 | NS |
| -1.49 | -0.424 | NS |
| -1.243 | -1.354 | NS |
| -1.798 | -0.5842 | NS |
| -0.7629 | -1.262 | NS |
| -0.7259 | -0.7629 | NS |
| -0.3563 | -0.6643 | NS |
| -1.403 | -0.3152 | NS |
| -0.4425 | -0.6355 | NS |
| -0.1468 | -0.7341 | NS |
| 0.0626 | -0.7136 | NS |
| -0.1222 | -0.652 | NS |
| -0.2208 | -0.6735 | NS |
| -1.798 | -0.3932 | NS |
| -0.1961 | -0.0791 | NS |
| -0.4548 | 0.0503 | NS |
| 0.2967 | -0.5863 | NS |
| -0.2331 | -0.0852 | NS |
| -0.0852 | -0.2331 | NS |
| -0.8737 | -0.1438 | NS |
| -0.923 | -0.1797 | NS |
| 0.7649 | -0.6427 | NS |
| -0.4302 | -0.344 | NS |
| 0.0503 | -0.1037 | NS |
| -0.1099 | -0.4302 | NS |
| -0.9107 | -0.2885 | NS |
| -0.307 | -0.4179 | NS |
| 0.0503 | -0.2516 | NS |
| -0.1961 | -0.8737 | NS |
| -1.798 | 0.0215 | NS |
| 0.0133 | -0.5945 | NS |
| 0.0133 | -0.5945 | NS |
| 0.001 | 0.0133 | NS |
| 0.2228 | -0.8121 | NS |
| -0.8121 | 0.0873 | NS |
| -0.0483 | -0.8121 | NS |
| -0.3316 | -0.5164 | NS |
| -0.5164 | -0.4918 | NS |
| 0.1242 | -0.153 | NS |
| 0.001 | -0.4056 | NS |
| -0.307 | -0.3029 | NS |
| 0.0996 | 0.2782 | NS |
| -0.0852 | -0.2146 | NS |
| -0.27 | -0.1838 | NS |
| -0.1838 | -0.27 | NS |
| 1.122 | 1.042 | NS |
| 1.245 | 1.171 | NS |
| 1.221 | 1.332 | NS |
| 1.048 | 0.6725 | NS |
| 0.6786 | 0.8634 | NS |
| 0.6786 | 0.2474 | NS |
| 0.3583 | -0.2577 | NS |
| -0.2577 | 0.3829 | NS |
| 0.346 | 0.6663 | NS |
| 0.6293 | 0.9497 | NS |
| 1.553 | 0.6293 | NS |
| 0.038 | 0.5924 | NS |
| 0.8018 | 0.6088 | NS |
| 0.5924 | 1.073 | NS |
| 1.073 | 0.5924 | NS |
| 1.295 | 1.024 | NS |
| 0.6663 | 1.233 | NS |
| 0.9743 | 0.9805 | NS |
| 0.4815 | 0.8758 | NS |
| 1.578 | 0.5739 | NS |
| 0.6663 | 0.8634 | NS |
| 0.9866 | 0.962 | NS |
| 1.467 | 0.6951 | NS |
| 0.4569 | 1.032 | NS |
| 0.6417 | 0.962 | NS |
| -0.96 | -0.9785 | NS |
| -0.7998 | -0.96 | NS |
| -1.798 | -0.1468 | NS |
| -0.1468 | -1.798 | NS |
| -0.615 | -1.51 | NS |
| -1.058 | -1.145 | NS |
| -1.675 | -0.9189 | NS |
| -0.27 | -0.6602 | NS |
| -0.344 | -0.116 | NS |
| 0.038 | -0.4548 | NS |
| -1.502 | -0.5534 | NS |
| -0.7382 | -1.471 | NS |
| -1.675 | -0.8676 | NS |
| -0.2331 | -1.206 | NS |
| -0.7382 | -0.7505 | NS |
| -0.8861 | -0.9353 | NS |
| -1.133 | -0.8121 | NS |
| -1.219 | -1.163 | NS |
| -1.083 | -1.219 | NS |
| -0.7629 | -0.6807 | NS |
| 0.0873 | -0.7752 | NS |
| -1.403 | -0.5863 | NS |
| -0.4795 | -0.96 | NS |
| -0.5164 | -0.9723 | NS |
| -0.8368 | -1.163 | NS |
| -1.034 | -0.9538 | NS |
| -1.601 | -0.9107 | NS |
| -0.8614 | -1.428 | NS |
| -1.798 | -0.8368 | NS |
| -0.8368 | -1.798 | NS |
| -1.108 | -0.0606 | NS |
| -0.4179 | -0.9785 | NS |
| -0.5041 | 0.1119 | NS |
| 0.1119 | -0.5041 | NS |
| -0.344 | -0.0575 | NS |
| -0.652 | 0.2803 | NS |
| 0.0873 | 0.0339 | NS |
| 1.097 | -0.2824 | NS |
| -0.4672 | -0.3481 | NS |
| -0.0852 | -0.2577 | NS |
| -0.0483 | -0.0647 | NS |
| -1.798 | -0.4672 | NS |
| -0.7382 | -0.8245 | NS |
| -0.1222 | 0.0256 | NS |
| 0.0256 | -0.96 | NS |
| 0.1612 | -0.5288 | NS |
| -0.5288 | -0.1407 | NS |
| -0.2331 | -0.385 | NS |
| -0.9107 | -0.1622 | NS |
| 0.2721 | -0.4384 | NS |
| -0.1715 | -0.2618 | NS |
| -1.034 | -0.0976 | NS |
| 0.0133 | -1.034 | NS |
| -0.5288 | -0.9415 | NS |
| -1.798 | -0.5288 | NS |
| -0.0236 | -0.9969 | NS |
| -0.9969 | 0.1981 | NS |
| 0.4199 | -0.8429 | NS |
| 1.418 | 1.171 | NS |
| 0.6663 | 1.523 | NS |
| 1.627 | 1.11 | NS |
| -0.1838 | 0.9004 | NS |
| 1.319 | 0.2597 | NS |
| 0.7033 | 0.5677 | NS |
| 0.4076 | 0.2228 | NS |
| 0.7033 | 0.5102 | NS |
| 0.2228 | 0.4528 | NS |
| 0.2474 | 0.6232 | NS |
| 1.024 | 0.6786 | NS |
| 0.6786 | 0.9558 | NS |
| 1.11 | 0.8727 | NS |
| 0.9866 | 0.7772 | NS |
| 1.024 | 0.654 | NS |
| 0.654 | 1.042 | NS |
| 0.999 | 0.7526 | NS |
| 0.7526 | 1.122 | NS |
| 0.5308 | 0.9558 | NS |
| 1.245 | 0.6417 | NS |
| 0.5185 | 0.4322 | NS |
| 0.4322 | -0.6397 | NS |
| -0.0236 | -0.0113 | NS |
| -0.0113 | 0.0318 | NS |
| -0.9723 | -0.1345 | NS |
| 0.309 | -0.7752 | NS |
| -0.578 | -0.5 | NS |
| -1.083 | -1.256 | NS |
| -0.8368 | -0.9436 | NS |
| -1.169 | -0.8368 | NS |
| -0.7505 | -0.7197 | NS |
| -1.477 | -0.6397 | NS |
| -1.44 | -0.4487 | NS |
| -0.1592 | -1.379 | NS |
| -1.317 | -0.1653 | NS |
| -0.8245 | -0.9477 | NS |
| -0.9477 | -0.7813 | NS |
| -0.652 | -0.4302 | NS |
| -0.4302 | -0.8553 | NS |
| -1.798 | -1.133 | NS |
| -1.133 | -1.379 | NS |
| -0.7875 | -0.8902 | NS |
| -0.96 | -1.025 | NS |
| -1.798 | -0.7095 | NS |
| -0.96 | -1.477 | NS |
| -1.477 | -1.028 | NS |
| -1.095 | -1.477 | NS |
| -1.65 | -0.9538 | NS |
| -1.046 | -1.65 | NS |
| -0.2947 | -0.96 | NS |
| -0.96 | -0.2947 | NS |
| -0.8491 | -0.2824 | NS |
| -0.1468 | -0.5041 | NS |
| -1.798 | -0.1838 | NS |
| 0.1365 | -1.252 | NS |
| -0.5041 | -0.7875 | NS |
| -1.453 | -0.1345 | NS |
| 0.5431 | -0.7998 | NS |
| -0.7998 | -0.0236 | NS |
| 0.3583 | -0.4425 | NS |
| -0.8368 | -0.7197 | NS |
| -1.798 | -0.8429 | NS |
| 0.1489 | -0.3255 | NS |
| 0.0873 | 0.1612 | NS |
| -1.798 | 0.1858 | NS |
| 0.346 | -1.798 | NS |
| -0.4425 | -1.173 | NS |
| -1.194 | -1.12 | NS |
| -1.798 | -0.6355 | NS |
| -0.1468 | -0.5472 | NS |
| -0.923 | -0.036 | NS |
| -1.798 | -0.1386 | NS |
| -0.307 | -0.6355 | NS |
| 0.1119 | -0.7752 | NS |
| -0.2208 | -0.6643 | NS |
| 0.0133 | -0.1345 | NS |
| 0.4692 | -0.1222 | NS |
| -0.7382 | 0.2803 | NS |
| -0.6889 | -0.1879 | NS |
| -0.1838 | -0.7444 | NS |
| -0.7998 | -0.3152 | NS |
| 1.245 | 0.962 | NS |
| 1.393 | 0.6909 | NS |
| 0.1489 | 1.11 | NS |
| 0.6786 | 0.9774 | NS |
| 0.7649 | 1.005 | NS |
| 0.8511 | 1.011 | NS |
| 1.159 | 0.9086 | NS |
| 1.11 | 0.2936 | NS |
| 0.9004 | 0.8326 | NS |
| 0.962 | 0.0708 | NS |
| -1.798 | 0.9784 | NS |
| 1.147 | -0.3686 | NS |
| -0.3686 | 1.085 | NS |
| 0.4445 | 0.8142 | NS |
| 0.6417 | 0.3152 | NS |
| 0.1858 | 0.5862 | NS |
| 1.061 | 0.7279 | NS |
| 0.8018 | 1.061 | NS |
| 1.553 | -0.1222 | NS |
| -0.1222 | 1.171 | NS |
| 0.7895 | 0.6725 | NS |
| 1.147 | 1.073 | NS |
| 1.073 | 0.8018 | NS |
| 0.9743 | 0.6786 | NS |
| -1.798 | 0.6376 | NS |
| 0.5554 | -0.4364 | NS |
| 0.925 | -0.3111 | NS |
| 0.0873 | 0.1181 | NS |
| 0.2474 | 0.0873 | NS |
| -1.145 | -0.4179 | NS |
| -0.4179 | -0.7813 | NS |
| -0.4179 | -0.8614 | NS |
| -0.8121 | -0.5103 | NS |
| -0.5534 | -0.6397 | NS |
| -0.4672 | -0.6314 | NS |
| -0.5288 | -0.6355 | NS |
| 0.038 | -0.4856 | NS |
| -0.1715 | -0.6828 | NS |
| -0.5041 | -0.3604 | NS |
| -0.6273 | -0.3193 | NS |
| -0.2824 | -0.4343 | NS |
| -0.7382 | -1.052 | NS |
| -1.157 | -0.9107 | NS |
| -1.058 | -0.3604 | NS |
| -0.3193 | -0.7546 | NS |
| -0.3316 | -0.8429 | NS |
| -1.157 | -0.5842 | NS |
| -0.8368 | -0.7444 | NS |
| -0.3809 | -1.554 | NS |
| -1.662 | -0.8737 | NS |
| -1.798 | -0.5934 | NS |
| -0.4425 | -1.116 | NS |
| -1.675 | -0.4548 | NS |
| -0.4548 | -1.588 | NS |
| 0.1981 | -0.7711 | NS |
| -0.2208 | -0.6314 | NS |
| -0.4179 | -0.5657 | NS |
| -1.675 | -0.193 | NS |
| -0.1715 | -0.1838 | NS |
| -0.1838 | -0.3809 | NS |
| -0.1592 | -0.2084 | NS |
| -0.27 | -0.1592 | NS |
| -0.036 | -0.845 | NS |
| -0.578 | -0.036 | NS |
| -0.5904 | -0.5 | NS |
| -0.1961 | -0.5472 | NS |
| -0.5041 | -0.1509 | NS |
| -0.8491 | -0.6766 | NS |
| -0.4795 | -0.0688 | NS |
| 0.2474 | -0.3111 | NS |

600 more rows in the chart's JSON (chart-22928ec9-70d1-47be-8138-15ef9ace47fc.json in the bundle).

### Figure 43. Find outliers: Green spaces [95]

| value | lag | type |
| --- | --- | --- |
| 1.243 | 0.3681 | NS |
| -1.382 | 0.1622 | NS |
| -0.9962 | 0.2394 | NS |
| -0.8417 | -0.1004 | NS |
| -1.305 | 0.0592 | NS |
| 0.78 | -0.121 | NS |
| 1.398 | -0.5586 | NS |
| 0.2394 | -0.0695 | NS |
| 0.1622 | -0.1158 | NS |
| 0.7027 | 0.7542 | NS |

### Figure 44. Find outliers: Zoning districts [96]

| value | lag | type |
| --- | --- | --- |
| -0.6299 | 0.8237 | NS |
| -0.6299 | 1.429 | NS |
| -0.6299 | 0.8237 | NS |
| -0.6299 | 0.0969 | NS |
| 0.8237 | 0.0162 | NS |
| 2.035 | 0.0969 | NS |
| -1.357 | 0.8237 | NS |
| 0.8237 | -0.6299 | NS |
| -0.6299 | 0.0969 | NS |
| 0.8237 | -0.6299 | NS |

### Figure 45. Find point clusters: Building permits over time [97]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | extension | 1 |
| 2023-01-01 | new build | 1 |
| 2023-01-01 | change of use | 2 |
| 2023-01-01 | demolition | 0 |
| 2023-02-01 | extension | 4 |
| 2023-02-01 | new build | 1 |
| 2023-02-01 | change of use | 0 |
| 2023-02-01 | demolition | 0 |
| 2023-03-01 | extension | 1 |
| 2023-03-01 | new build | 3 |
| 2023-03-01 | change of use | 1 |
| 2023-03-01 | demolition | 0 |
| 2023-04-01 | extension | 2 |
| 2023-04-01 | new build | 2 |
| 2023-04-01 | change of use | 0 |
| 2023-04-01 | demolition | 3 |
| 2023-05-01 | extension | 5 |
| 2023-05-01 | new build | 3 |
| 2023-05-01 | change of use | 2 |
| 2023-05-01 | demolition | 0 |
| 2023-06-01 | extension | 3 |
| 2023-06-01 | new build | 3 |
| 2023-06-01 | change of use | 1 |
| 2023-06-01 | demolition | 0 |
| 2023-07-01 | extension | 1 |
| 2023-07-01 | new build | 2 |
| 2023-07-01 | change of use | 0 |
| 2023-07-01 | demolition | 1 |
| 2023-08-01 | extension | 2 |
| 2023-08-01 | new build | 5 |
| 2023-08-01 | change of use | 1 |
| 2023-08-01 | demolition | 2 |
| 2023-09-01 | extension | 2 |
| 2023-09-01 | new build | 2 |
| 2023-09-01 | change of use | 0 |
| 2023-09-01 | demolition | 1 |
| 2023-10-01 | extension | 4 |
| 2023-10-01 | new build | 2 |
| 2023-10-01 | change of use | 1 |
| 2023-10-01 | demolition | 1 |
| 2023-11-01 | extension | 2 |
| 2023-11-01 | new build | 1 |
| 2023-11-01 | change of use | 2 |
| 2023-11-01 | demolition | 0 |
| 2023-12-01 | extension | 5 |
| 2023-12-01 | new build | 3 |
| 2023-12-01 | change of use | 2 |
| 2023-12-01 | demolition | 1 |
| 2024-01-01 | extension | 0 |
| 2024-01-01 | new build | 1 |
| 2024-01-01 | change of use | 3 |
| 2024-01-01 | demolition | 1 |
| 2024-02-01 | extension | 3 |
| 2024-02-01 | new build | 1 |
| 2024-02-01 | change of use | 1 |
| 2024-02-01 | demolition | 1 |
| 2024-03-01 | extension | 1 |
| 2024-03-01 | new build | 0 |
| 2024-03-01 | change of use | 0 |
| 2024-03-01 | demolition | 0 |
| 2024-04-01 | extension | 2 |
| 2024-04-01 | new build | 0 |
| 2024-04-01 | change of use | 3 |
| 2024-04-01 | demolition | 0 |
| 2024-05-01 | extension | 5 |
| 2024-05-01 | new build | 6 |
| 2024-05-01 | change of use | 1 |
| 2024-05-01 | demolition | 1 |
| 2024-06-01 | extension | 2 |
| 2024-06-01 | new build | 0 |
| 2024-06-01 | change of use | 3 |
| 2024-06-01 | demolition | 0 |
| 2024-07-01 | extension | 3 |
| 2024-07-01 | new build | 4 |
| 2024-07-01 | change of use | 2 |
| 2024-07-01 | demolition | 1 |
| 2024-08-01 | extension | 0 |
| 2024-08-01 | new build | 5 |
| 2024-08-01 | change of use | 4 |
| 2024-08-01 | demolition | 1 |
| 2024-09-01 | extension | 0 |
| 2024-09-01 | new build | 4 |
| 2024-09-01 | change of use | 4 |
| 2024-09-01 | demolition | 2 |
| 2024-10-01 | extension | 2 |
| 2024-10-01 | new build | 4 |
| 2024-10-01 | change of use | 1 |
| 2024-10-01 | demolition | 2 |
| 2024-11-01 | extension | 5 |
| 2024-11-01 | new build | 5 |
| 2024-11-01 | change of use | 1 |
| 2024-11-01 | demolition | 0 |
| 2024-12-01 | extension | 2 |
| 2024-12-01 | new build | 3 |
| 2024-12-01 | change of use | 1 |
| 2024-12-01 | demolition | 3 |
| 2025-01-01 | extension | 2 |
| 2025-01-01 | new build | 4 |
| 2025-01-01 | change of use | 0 |
| 2025-01-01 | demolition | 0 |
| 2025-02-01 | extension | 1 |
| 2025-02-01 | new build | 1 |
| 2025-02-01 | change of use | 2 |
| 2025-02-01 | demolition | 0 |
| 2025-03-01 | extension | 0 |
| 2025-03-01 | new build | 2 |
| 2025-03-01 | change of use | 2 |
| 2025-03-01 | demolition | 0 |
| 2025-04-01 | extension | 3 |
| 2025-04-01 | new build | 1 |
| 2025-04-01 | change of use | 3 |
| 2025-04-01 | demolition | 0 |
| 2025-05-01 | extension | 4 |
| 2025-05-01 | new build | 4 |
| 2025-05-01 | change of use | 3 |
| 2025-05-01 | demolition | 1 |
| 2025-06-01 | extension | 3 |
| 2025-06-01 | new build | 2 |
| 2025-06-01 | change of use | 3 |
| 2025-06-01 | demolition | 1 |
| 2025-07-01 | extension | 2 |
| 2025-07-01 | new build | 2 |
| 2025-07-01 | change of use | 2 |
| 2025-07-01 | demolition | 0 |
| 2025-08-01 | extension | 4 |
| 2025-08-01 | new build | 3 |
| 2025-08-01 | change of use | 2 |
| 2025-08-01 | demolition | 1 |
| 2025-09-01 | extension | 5 |
| 2025-09-01 | new build | 5 |
| 2025-09-01 | change of use | 3 |
| 2025-09-01 | demolition | 2 |
| 2025-10-01 | extension | 4 |
| 2025-10-01 | new build | 3 |
| 2025-10-01 | change of use | 4 |
| 2025-10-01 | demolition | 1 |
| 2025-11-01 | extension | 4 |
| 2025-11-01 | new build | 4 |
| 2025-11-01 | change of use | 3 |
| 2025-11-01 | demolition | 1 |
| 2025-12-01 | extension | 4 |
| 2025-12-01 | new build | 3 |
| 2025-12-01 | change of use | 3 |
| 2025-12-01 | demolition | 0 |
| 2026-01-01 | extension | 4 |
| 2026-01-01 | new build | 4 |
| 2026-01-01 | change of use | 3 |
| 2026-01-01 | demolition | 0 |
| 2026-02-01 | extension | 4 |
| 2026-02-01 | new build | 2 |
| 2026-02-01 | change of use | 2 |
| 2026-02-01 | demolition | 0 |
| 2026-03-01 | extension | 5 |
| 2026-03-01 | new build | 2 |
| 2026-03-01 | change of use | 6 |
| 2026-03-01 | demolition | 2 |
| 2026-04-01 | extension | 1 |
| 2026-04-01 | new build | 7 |
| 2026-04-01 | change of use | 0 |
| 2026-04-01 | demolition | 1 |
| 2026-05-01 | extension | 2 |
| 2026-05-01 | new build | 2 |
| 2026-05-01 | change of use | 1 |
| 2026-05-01 | demolition | 0 |
| 2026-06-01 | extension | 4 |
| 2026-06-01 | new build | 6 |
| 2026-06-01 | change of use | 4 |
| 2026-06-01 | demolition | 0 |
| 2026-07-01 | extension | 7 |
| 2026-07-01 | new build | 4 |
| 2026-07-01 | change of use | 1 |
| 2026-07-01 | demolition | 1 |
| 2026-08-01 | extension | 3 |
| 2026-08-01 | new build | 3 |
| 2026-08-01 | change of use | 3 |
| 2026-08-01 | demolition | 3 |
| 2026-09-01 | extension | 7 |
| 2026-09-01 | new build | 2 |
| 2026-09-01 | change of use | 2 |
| 2026-09-01 | demolition | 0 |

### Figure 46. Find point clusters: Property sales over time [98]

| period | series | value |
| --- | --- | --- |
| 2022-01-01 | apartment | 21 |
| 2022-01-01 | office | 15 |
| 2022-01-01 | house | 10 |
| 2022-04-01 | apartment | 24 |
| 2022-04-01 | office | 10 |
| 2022-04-01 | house | 12 |
| 2022-07-01 | apartment | 19 |
| 2022-07-01 | office | 13 |
| 2022-07-01 | house | 10 |
| 2022-10-01 | apartment | 33 |
| 2022-10-01 | office | 12 |
| 2022-10-01 | house | 13 |
| 2023-01-01 | apartment | 14 |
| 2023-01-01 | office | 14 |
| 2023-01-01 | house | 12 |
| 2023-04-01 | apartment | 26 |
| 2023-04-01 | office | 12 |
| 2023-04-01 | house | 11 |
| 2023-07-01 | apartment | 29 |
| 2023-07-01 | office | 15 |
| 2023-07-01 | house | 17 |
| 2023-10-01 | apartment | 19 |
| 2023-10-01 | office | 19 |
| 2023-10-01 | house | 10 |
| 2024-01-01 | apartment | 22 |
| 2024-01-01 | office | 14 |
| 2024-01-01 | house | 14 |
| 2024-04-01 | apartment | 25 |
| 2024-04-01 | office | 15 |
| 2024-04-01 | house | 16 |
| 2024-07-01 | apartment | 22 |
| 2024-07-01 | office | 12 |
| 2024-07-01 | house | 12 |
| 2024-10-01 | apartment | 15 |
| 2024-10-01 | office | 13 |
| 2024-10-01 | house | 12 |
| 2025-01-01 | apartment | 23 |
| 2025-01-01 | office | 13 |
| 2025-01-01 | house | 11 |
| 2025-04-01 | apartment | 27 |
| 2025-04-01 | office | 18 |
| 2025-04-01 | house | 15 |
| 2025-07-01 | apartment | 21 |
| 2025-07-01 | office | 13 |
| 2025-07-01 | house | 17 |
| 2025-10-01 | apartment | 26 |
| 2025-10-01 | office | 15 |
| 2025-10-01 | house | 14 |
| 2026-01-01 | apartment | 24 |
| 2026-01-01 | office | 21 |
| 2026-01-01 | house | 10 |
| 2026-04-01 | apartment | 20 |
| 2026-04-01 | office | 14 |
| 2026-04-01 | house | 15 |
| 2026-07-01 | apartment | 21 |
| 2026-07-01 | office | 10 |
| 2026-07-01 | house | 20 |

### Figure 47. Hot spot analysis: Building permits over time [99]

| period | series | value |
| --- | --- | --- |
| 2023-01-01 | extension | 1 |
| 2023-01-01 | new build | 1 |
| 2023-01-01 | change of use | 2 |
| 2023-01-01 | demolition | 0 |
| 2023-02-01 | extension | 4 |
| 2023-02-01 | new build | 1 |
| 2023-02-01 | change of use | 0 |
| 2023-02-01 | demolition | 0 |
| 2023-03-01 | extension | 1 |
| 2023-03-01 | new build | 3 |
| 2023-03-01 | change of use | 1 |
| 2023-03-01 | demolition | 0 |
| 2023-04-01 | extension | 2 |
| 2023-04-01 | new build | 2 |
| 2023-04-01 | change of use | 0 |
| 2023-04-01 | demolition | 3 |
| 2023-05-01 | extension | 5 |
| 2023-05-01 | new build | 3 |
| 2023-05-01 | change of use | 2 |
| 2023-05-01 | demolition | 0 |
| 2023-06-01 | extension | 3 |
| 2023-06-01 | new build | 3 |
| 2023-06-01 | change of use | 1 |
| 2023-06-01 | demolition | 0 |
| 2023-07-01 | extension | 1 |
| 2023-07-01 | new build | 2 |
| 2023-07-01 | change of use | 0 |
| 2023-07-01 | demolition | 1 |
| 2023-08-01 | extension | 2 |
| 2023-08-01 | new build | 5 |
| 2023-08-01 | change of use | 1 |
| 2023-08-01 | demolition | 2 |
| 2023-09-01 | extension | 2 |
| 2023-09-01 | new build | 2 |
| 2023-09-01 | change of use | 0 |
| 2023-09-01 | demolition | 1 |
| 2023-10-01 | extension | 4 |
| 2023-10-01 | new build | 2 |
| 2023-10-01 | change of use | 1 |
| 2023-10-01 | demolition | 1 |
| 2023-11-01 | extension | 2 |
| 2023-11-01 | new build | 1 |
| 2023-11-01 | change of use | 2 |
| 2023-11-01 | demolition | 0 |
| 2023-12-01 | extension | 5 |
| 2023-12-01 | new build | 3 |
| 2023-12-01 | change of use | 2 |
| 2023-12-01 | demolition | 1 |
| 2024-01-01 | extension | 0 |
| 2024-01-01 | new build | 1 |
| 2024-01-01 | change of use | 3 |
| 2024-01-01 | demolition | 1 |
| 2024-02-01 | extension | 3 |
| 2024-02-01 | new build | 1 |
| 2024-02-01 | change of use | 1 |
| 2024-02-01 | demolition | 1 |
| 2024-03-01 | extension | 1 |
| 2024-03-01 | new build | 0 |
| 2024-03-01 | change of use | 0 |
| 2024-03-01 | demolition | 0 |
| 2024-04-01 | extension | 2 |
| 2024-04-01 | new build | 0 |
| 2024-04-01 | change of use | 3 |
| 2024-04-01 | demolition | 0 |
| 2024-05-01 | extension | 5 |
| 2024-05-01 | new build | 6 |
| 2024-05-01 | change of use | 1 |
| 2024-05-01 | demolition | 1 |
| 2024-06-01 | extension | 2 |
| 2024-06-01 | new build | 0 |
| 2024-06-01 | change of use | 3 |
| 2024-06-01 | demolition | 0 |
| 2024-07-01 | extension | 3 |
| 2024-07-01 | new build | 4 |
| 2024-07-01 | change of use | 2 |
| 2024-07-01 | demolition | 1 |
| 2024-08-01 | extension | 0 |
| 2024-08-01 | new build | 5 |
| 2024-08-01 | change of use | 4 |
| 2024-08-01 | demolition | 1 |
| 2024-09-01 | extension | 0 |
| 2024-09-01 | new build | 4 |
| 2024-09-01 | change of use | 4 |
| 2024-09-01 | demolition | 2 |
| 2024-10-01 | extension | 2 |
| 2024-10-01 | new build | 4 |
| 2024-10-01 | change of use | 1 |
| 2024-10-01 | demolition | 2 |
| 2024-11-01 | extension | 5 |
| 2024-11-01 | new build | 5 |
| 2024-11-01 | change of use | 1 |
| 2024-11-01 | demolition | 0 |
| 2024-12-01 | extension | 2 |
| 2024-12-01 | new build | 3 |
| 2024-12-01 | change of use | 1 |
| 2024-12-01 | demolition | 3 |
| 2025-01-01 | extension | 2 |
| 2025-01-01 | new build | 4 |
| 2025-01-01 | change of use | 0 |
| 2025-01-01 | demolition | 0 |
| 2025-02-01 | extension | 1 |
| 2025-02-01 | new build | 1 |
| 2025-02-01 | change of use | 2 |
| 2025-02-01 | demolition | 0 |
| 2025-03-01 | extension | 0 |
| 2025-03-01 | new build | 2 |
| 2025-03-01 | change of use | 2 |
| 2025-03-01 | demolition | 0 |
| 2025-04-01 | extension | 3 |
| 2025-04-01 | new build | 1 |
| 2025-04-01 | change of use | 3 |
| 2025-04-01 | demolition | 0 |
| 2025-05-01 | extension | 4 |
| 2025-05-01 | new build | 4 |
| 2025-05-01 | change of use | 3 |
| 2025-05-01 | demolition | 1 |
| 2025-06-01 | extension | 3 |
| 2025-06-01 | new build | 2 |
| 2025-06-01 | change of use | 3 |
| 2025-06-01 | demolition | 1 |
| 2025-07-01 | extension | 2 |
| 2025-07-01 | new build | 2 |
| 2025-07-01 | change of use | 2 |
| 2025-07-01 | demolition | 0 |
| 2025-08-01 | extension | 4 |
| 2025-08-01 | new build | 3 |
| 2025-08-01 | change of use | 2 |
| 2025-08-01 | demolition | 1 |
| 2025-09-01 | extension | 5 |
| 2025-09-01 | new build | 5 |
| 2025-09-01 | change of use | 3 |
| 2025-09-01 | demolition | 2 |
| 2025-10-01 | extension | 4 |
| 2025-10-01 | new build | 3 |
| 2025-10-01 | change of use | 4 |
| 2025-10-01 | demolition | 1 |
| 2025-11-01 | extension | 4 |
| 2025-11-01 | new build | 4 |
| 2025-11-01 | change of use | 3 |
| 2025-11-01 | demolition | 1 |
| 2025-12-01 | extension | 4 |
| 2025-12-01 | new build | 3 |
| 2025-12-01 | change of use | 3 |
| 2025-12-01 | demolition | 0 |
| 2026-01-01 | extension | 4 |
| 2026-01-01 | new build | 4 |
| 2026-01-01 | change of use | 3 |
| 2026-01-01 | demolition | 0 |
| 2026-02-01 | extension | 4 |
| 2026-02-01 | new build | 2 |
| 2026-02-01 | change of use | 2 |
| 2026-02-01 | demolition | 0 |
| 2026-03-01 | extension | 5 |
| 2026-03-01 | new build | 2 |
| 2026-03-01 | change of use | 6 |
| 2026-03-01 | demolition | 2 |
| 2026-04-01 | extension | 1 |
| 2026-04-01 | new build | 7 |
| 2026-04-01 | change of use | 0 |
| 2026-04-01 | demolition | 1 |
| 2026-05-01 | extension | 2 |
| 2026-05-01 | new build | 2 |
| 2026-05-01 | change of use | 1 |
| 2026-05-01 | demolition | 0 |
| 2026-06-01 | extension | 4 |
| 2026-06-01 | new build | 6 |
| 2026-06-01 | change of use | 4 |
| 2026-06-01 | demolition | 0 |
| 2026-07-01 | extension | 7 |
| 2026-07-01 | new build | 4 |
| 2026-07-01 | change of use | 1 |
| 2026-07-01 | demolition | 1 |
| 2026-08-01 | extension | 3 |
| 2026-08-01 | new build | 3 |
| 2026-08-01 | change of use | 3 |
| 2026-08-01 | demolition | 3 |
| 2026-09-01 | extension | 7 |
| 2026-09-01 | new build | 2 |
| 2026-09-01 | change of use | 2 |
| 2026-09-01 | demolition | 0 |

### Figure 48. Repeat and near repeat classification: Property sales over time [100]

| period | series | value |
| --- | --- | --- |
| 2022-01-01 | apartment | 21 |
| 2022-01-01 | office | 15 |
| 2022-01-01 | house | 10 |
| 2022-04-01 | apartment | 24 |
| 2022-04-01 | office | 10 |
| 2022-04-01 | house | 12 |
| 2022-07-01 | apartment | 19 |
| 2022-07-01 | office | 13 |
| 2022-07-01 | house | 10 |
| 2022-10-01 | apartment | 33 |
| 2022-10-01 | office | 12 |
| 2022-10-01 | house | 13 |
| 2023-01-01 | apartment | 14 |
| 2023-01-01 | office | 14 |
| 2023-01-01 | house | 12 |
| 2023-04-01 | apartment | 26 |
| 2023-04-01 | office | 12 |
| 2023-04-01 | house | 11 |
| 2023-07-01 | apartment | 29 |
| 2023-07-01 | office | 15 |
| 2023-07-01 | house | 17 |
| 2023-10-01 | apartment | 19 |
| 2023-10-01 | office | 19 |
| 2023-10-01 | house | 10 |
| 2024-01-01 | apartment | 22 |
| 2024-01-01 | office | 14 |
| 2024-01-01 | house | 14 |
| 2024-04-01 | apartment | 25 |
| 2024-04-01 | office | 15 |
| 2024-04-01 | house | 16 |
| 2024-07-01 | apartment | 22 |
| 2024-07-01 | office | 12 |
| 2024-07-01 | house | 12 |
| 2024-10-01 | apartment | 15 |
| 2024-10-01 | office | 13 |
| 2024-10-01 | house | 12 |
| 2025-01-01 | apartment | 23 |
| 2025-01-01 | office | 13 |
| 2025-01-01 | house | 11 |
| 2025-04-01 | apartment | 27 |
| 2025-04-01 | office | 18 |
| 2025-04-01 | house | 15 |
| 2025-07-01 | apartment | 21 |
| 2025-07-01 | office | 13 |
| 2025-07-01 | house | 17 |
| 2025-10-01 | apartment | 26 |
| 2025-10-01 | office | 15 |
| 2025-10-01 | house | 14 |
| 2026-01-01 | apartment | 24 |
| 2026-01-01 | office | 21 |
| 2026-01-01 | house | 10 |
| 2026-04-01 | apartment | 20 |
| 2026-04-01 | office | 14 |
| 2026-04-01 | house | 15 |
| 2026-07-01 | apartment | 21 |
| 2026-07-01 | office | 10 |
| 2026-07-01 | house | 20 |


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