GeoBitLarge Spatial Model

Real estate and urban planning

Parcels, zoning, prices and buildability

Lisbon, Portugalsynthetic data, real placeMaps, charts and report
The question

Where are sale prices rising fastest over time, how do parcels compare across zoning, and where is the terrain too steep (slope) to build?

A planning department uploads parcels, zoning districts, property sales, building permits, transit stations, green spaces, a terrain model, planning applications and land values by year, and a committee brief. It wants to know where prices rise fastest, which parcels are underused and where terrain limits building.

38.7369, -9.1427data covers about 5 by 4 km
11datasets uploaded
18output layers
21maps made
27charts made
44findings
469 sStart run time

Every map and chart this run made is here. 21 map images, 27 charts and the cited report (PDF, Word, Markdown), as exported on 2026-10-22.

Findings (first few)

  • Evidenced: 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 12345.
  • Evidenced: By dataset: Property sales 950; Building permits 380; Planning applications 160; Land values by zone and year 90 1.
  • Evidenced: 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.
  • Evidenced: 12 of the 35 quarters had no events 1.
  • Evidenced: The busiest quarters were Q2 2025 (133), Q4 2025 (125), Q1 2025 (122) 1.

What Start ran

Describe mode, the local model only (no paid model); run status succeeded, 8 analyst agents.

gp_calculate_densitygp_find_point_clustersgp_hot_spotsgp_near_repeatgp_outliersgp_space_time_hot_spotsgp_summarize_withinrs_zonal_statistics

Maps

Every map image this run exported, with legend, north arrow and scale bar. Select one to view it full size.

Emerging hot spots: Property sales
Emerging hot spots: Property salesFig. 2
Emerging hot spots: Building permits
Emerging hot spots: Building permitsFig. 3
Repeat and near repeat classification: Property sales
Repeat and near repeat classification: Property salesFig. 4
Summarize within: Parcels
Summarize within: ParcelsFig. 5
Summarize within: Parcels
Summarize within: ParcelsFig. 6
Summarize within: Parcels
Summarize within: ParcelsFig. 7
Parcels by built_far
Parcels by built_farFig. 8
Green spaces by area_ha
Green spaces by area_haFig. 9
Zoning districts by max_far
Zoning districts by max_farFig. 10
Hot spot analysis: Property sales
Hot spot analysis: Property salesFig. 11
Hot spot analysis: Building permits
Hot spot analysis: Building permitsFig. 12
Calculate density: Property sales
Calculate density: Property salesFig. 13
Calculate density: Building permits
Calculate density: Building permitsFig. 14
Find point clusters: Transit stations
Find point clusters: Transit stationsFig. 15
Find point clusters: cluster hulls
Find point clusters: cluster hullsFig. 16
Find point clusters: Property sales
Find point clusters: Property salesFig. 17
Find point clusters: Building permits
Find point clusters: Building permitsFig. 18
Find point clusters: cluster hulls
Find point clusters: cluster hullsFig. 19
Find outliers: Parcels
Find outliers: ParcelsFig. 20
Find outliers: Green spaces
Find outliers: Green spacesFig. 21
Find outliers: Zoning districts
Find outliers: Zoning districtsFig. 22

Charts

Every chart image this run exported.

Parcels over time (year_built)
Parcels over time (year_built)Fig. 23
Property sales over time (date)
Property sales over time (date)Fig. 24
Building permits over time (decision_month)
Building permits over time (decision_month)Fig. 25
Land values by zone and year over time (year)
Land values by zone and year over time (year)Fig. 26
Emerging hot spots: Property sales
Emerging hot spots: Property salesFig. 27
Emerging hot spots: Building permits
Emerging hot spots: Building permitsFig. 28
Repeat and near repeat classification: Property sales
Repeat and near repeat classification: Property salesFig. 29
Summarize within: Parcels
Summarize within: ParcelsFig. 30
Summarize within: Parcels
Summarize within: ParcelsFig. 31
Summarize within: Parcels
Summarize within: ParcelsFig. 32
Hot spot analysis: Property sales
Hot spot analysis: Property salesFig. 33
Hot spot analysis: Building permits
Hot spot analysis: Building permitsFig. 34
Calculate density: Property sales
Calculate density: Property salesFig. 35
Calculate density: Building permits
Calculate density: Building permitsFig. 36
Find point clusters: Transit stations
Find point clusters: Transit stationsFig. 37
Find point clusters: cluster hulls
Find point clusters: cluster hullsFig. 38
Find point clusters: Property sales
Find point clusters: Property salesFig. 39
Find point clusters: Building permits
Find point clusters: Building permitsFig. 40
Find point clusters: cluster hulls
Find point clusters: cluster hullsFig. 41
Find outliers: Parcels
Find outliers: ParcelsFig. 42
Find outliers: Green spaces
Find outliers: Green spacesFig. 43
Find outliers: Zoning districts
Find outliers: Zoning districtsFig. 44
Find point clusters: Building permits over time
Find point clusters: Building permits over timeFig. 45
Find point clusters: Property sales over time
Find point clusters: Property sales over timeFig. 46
Hot spot analysis: Building permits over time
Hot spot analysis: Building permits over timeFig. 47
Repeat and near repeat classification: Property sales over time
Repeat and near repeat classification: Property sales over timeFig. 48
Timeline: events per quarter by dataset
Timeline: events per quarter by datasetFig. 1

The cited report

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

DataKindRowsTime spanSummarySource
Zoning districtsvector, Polygon1010 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
Parcelsvector, Polygon1,1001,100 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 salesvector, Point9502022-01-01 to 2026-09-30950 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 permitsvector, Point3802023-01-01 to 2026-09-01380 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 stationsvector, Point1212 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 spacesvector, Polygon1010 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
Streetsvector, LineString151151 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)raster1-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 yeartable902018-01-01 to 2026-01-01Table 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 applicationstable1602025-01-01 to 2025-12-28Table 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 briefdocument1-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, Lisbonrecord108
Emerging hot spots: Property saleslayer, Polygon81109
Emerging hot spots: Building permitslayer, Polygon90110
Repeat and near repeat classification: Property saleslayer, Point950111
Summarize within: Parcelslayer, Polygon1,100112
Summarize within: Parcelslayer, Polygon1,100113
Summarize within: Parcelslayer, Polygon1,100114
Hot spot analysis: Property saleslayer, Polygon213115
Hot spot analysis: Building permitslayer, Point380116
Calculate density: Property saleslayer, Polygon867117
Calculate density: Building permitslayer, Polygon1,211118
Find point clusters: Transit stationslayer, Point12119
Find point clusters: cluster hullslayer, Polygon1120
Find point clusters: Property saleslayer, Point950121
Find point clusters: Building permitslayer, Point380122
Find point clusters: cluster hullslayer, Polygon14123
Find outliers: Parcelslayer, Polygon1,100124
Find outliers: Green spaceslayer, Polygon10125
Find outliers: Zoning districtslayer, Polygon10126

Timeline

Timeline: events per quarter by dataset
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 12345. 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.

ResultValue
result, count81
result, elapsed ms21
result, resolution9
result, features used950
result, time neighbors0
billing, cost0.1475
billing, list price0.1475
billing, upstream cost0

Findings

  • Evidenced: Space time hot spots on Property sales: Emerging hot spots: 1 layer saved: Emerging hot spots: Property sales (81 features) 89.
  • 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.

ResultValue
result, count90
result, elapsed ms19
result, resolution9
result, features used380
result, time neighbors0
billing, cost0.119
billing, list price0.119
billing, upstream cost0

Findings

  • Evidenced: Space time hot spots on Building permits: Emerging hot spots: 1 layer saved: Emerging hot spots: Building permits (90 features) 1112.
  • 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) 915. It reported result, days 30; result, count 950; result, chains 43; result, distance m 100; result, elapsed ms 23; result, pairs linked 51 15.

ResultValue
result, days30
result, count950
result, chains43
result, distance m100
result, elapsed ms23
result, pairs linked51
billing, cost0.0785
billing, list price0.0785
billing, upstream cost0

Findings

  • Evidenced: Near repeat on Property sales: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Property sales (950 features) 915.
  • 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) 181920. It reported result, count 1,100; result, elapsed ms 43; billing, cost 0.03226; billing, list price 0.03226; billing, upstream cost 0 18.

ResultValue
result, count1,100
result, elapsed ms43
billing, cost0.03226
billing, list price0.03226
billing, upstream cost0

Findings

  • Evidenced: Summarize within on Transit stations, Parcels: Summarize within: 1 layer saved: Summarize within: Parcels (1100 features) 181920.
  • 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) 92021. It reported result, count 1,100; result, elapsed ms 471; billing, cost 0.05102; billing, list price 0.05102; billing, upstream cost 0 21.

ResultValue
result, count1,100
result, elapsed ms471
billing, cost0.05102
billing, list price0.05102
billing, upstream cost0

Findings

  • Evidenced: Summarize within on Property sales, Parcels: Summarize within: 1 layer saved: Summarize within: Parcels (1100 features) 92021.
  • 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) 122022. It reported result, count 1,100; result, elapsed ms 49; billing, cost 0.03962; billing, list price 0.03962; billing, upstream cost 0 22.

ResultValue
result, count1,100
result, elapsed ms49
billing, cost0.03962
billing, list price0.03962
billing, upstream cost0

Findings

  • Evidenced: Summarize within on Building permits, Parcels: Summarize within: 1 layer saved: Summarize within: Parcels (1100 features) 122022.
  • 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) 930. 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.

ResultValue
result, bins213
result, count213
result, elapsed ms37
result, cell size m200
result, incidents used950
result, distance band m200
billing, cost0.069
billing, list price0.069
billing, upstream cost0

Findings

  • Evidenced: Hot spots on Property sales: Hot spot analysis: 1 layer saved: Hot spot analysis: Property sales (213 features) 930.
  • 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) 1232. 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.

ResultValue
result, count380
result, elapsed ms10
result, excluded null0
result, distance band m500
billing, cost0.0576
billing, list price0.0576
billing, upstream cost0

Findings

  • Evidenced: Hot spots on Building permits: Hot spot analysis: 1 layer saved: Hot spot analysis: Building permits (380 features) 1232.
  • 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

ResultValue
result, count867
result, elapsed ms31
result, bandwidth m200
result, cell size m100
result, excluded null0
result, max density per km2364
billing, cost0.069
billing, list price0.069
billing, upstream cost0

Findings

  • Evidenced: Calculate density on Property sales: Calculate density: 1 layer saved: Calculate density: Property sales (867 features) 935.
  • 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.

ResultValue
result, count1,211
result, elapsed ms41
result, bandwidth m500
result, cell size m100
result, excluded null0
result, max density per km22,113
billing, cost0.0576
billing, list price0.0576
billing, upstream cost0

Findings

  • Evidenced: Calculate density on Building permits: Calculate density: 1 layer saved: Calculate density: Building permits (1211 features) 1237.
  • 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.

ResultValue
result, count12
result, noise0
result, distance m2,000
result, elapsed ms5
result, min points3
billing, cost0.05024
billing, list price0.05024
billing, upstream cost0

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) 1940.
  • 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) 942. 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.

ResultValue
result, count950
result, noise950
result, distance m100
result, elapsed ms12
result, min points20
billing, cost0.069
billing, list price0.069
billing, upstream cost0

Findings

  • Evidenced: Find point clusters on Property sales: Find point clusters: 1 layer saved: Find point clusters: Property sales (950 features) 942.
  • 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.

ResultValue
result, count380
result, noise126
result, distance m200
result, elapsed ms11
result, min points10
billing, cost0.0576
billing, list price0.0576
billing, upstream cost0

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) 1244.
  • 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.

ResultValue
result, count1,100
result, elapsed ms74
result, permutations199
result, excluded null0
result, distance band m86.92
billing, cost0.083
billing, list price0.083
billing, upstream cost0

Findings

  • Evidenced: Outliers on Parcels: Find outliers: 1 layer saved: Find outliers: Parcels (1100 features) 2047.
  • 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) 4950. 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.

ResultValue
result, count10
result, elapsed ms3
result, permutations199
result, excluded null0
result, distance band m1,747
billing, cost0.0503
billing, list price0.0503
billing, upstream cost0

Findings

  • Evidenced: Outliers on Green spaces: Find outliers: 1 layer saved: Find outliers: Green spaces (10 features) 4950.
  • 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) 5253. 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.

ResultValue
result, count10
result, elapsed ms2
result, permutations199
result, excluded null0
result, distance band m1,435
billing, cost0.0503
billing, list price0.0503
billing, upstream cost0

Findings

  • Evidenced: Outliers on Zoning districts: Find outliers: 1 layer saved: Find outliers: Zoning districts (10 features) 5253.
  • 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) 545556575859606162636465666768697071727374 and 26 chart(s) 75767778798081828384858687888990919293949596979899100.

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

Figure 1. Timeline: events per quarter by dataset 1

periodseriesvalue
2018-01-01Property sales0
2018-01-01Building permits0
2018-01-01Planning applications0
2018-01-01Land values by zone and year10
2018-04-01Property sales0
2018-04-01Building permits0
2018-04-01Planning applications0
2018-04-01Land values by zone and year0
2018-07-01Property sales0
2018-07-01Building permits0
2018-07-01Planning applications0
2018-07-01Land values by zone and year0
2018-10-01Property sales0
2018-10-01Building permits0
2018-10-01Planning applications0
2018-10-01Land values by zone and year0
2019-01-01Property sales0
2019-01-01Building permits0
2019-01-01Planning applications0
2019-01-01Land values by zone and year10
2019-04-01Property sales0
2019-04-01Building permits0
2019-04-01Planning applications0
2019-04-01Land values by zone and year0
2019-07-01Property sales0
2019-07-01Building permits0
2019-07-01Planning applications0
2019-07-01Land values by zone and year0
2019-10-01Property sales0
2019-10-01Building permits0
2019-10-01Planning applications0
2019-10-01Land values by zone and year0
2020-01-01Property sales0
2020-01-01Building permits0
2020-01-01Planning applications0
2020-01-01Land values by zone and year10
2020-04-01Property sales0
2020-04-01Building permits0
2020-04-01Planning applications0
2020-04-01Land values by zone and year0
2020-07-01Property sales0
2020-07-01Building permits0
2020-07-01Planning applications0
2020-07-01Land values by zone and year0
2020-10-01Property sales0
2020-10-01Building permits0
2020-10-01Planning applications0
2020-10-01Land values by zone and year0
2021-01-01Property sales0
2021-01-01Building permits0
2021-01-01Planning applications0
2021-01-01Land values by zone and year10
2021-04-01Property sales0
2021-04-01Building permits0
2021-04-01Planning applications0
2021-04-01Land values by zone and year0
2021-07-01Property sales0
2021-07-01Building permits0
2021-07-01Planning applications0
2021-07-01Land values by zone and year0
2021-10-01Property sales0
2021-10-01Building permits0
2021-10-01Planning applications0
2021-10-01Land values by zone and year0
2022-01-01Property sales46
2022-01-01Building permits0
2022-01-01Planning applications0
2022-01-01Land values by zone and year10
2022-04-01Property sales46
2022-04-01Building permits0
2022-04-01Planning applications0
2022-04-01Land values by zone and year0
2022-07-01Property sales42
2022-07-01Building permits0
2022-07-01Planning applications0
2022-07-01Land values by zone and year0
2022-10-01Property sales58
2022-10-01Building permits0
2022-10-01Planning applications0
2022-10-01Land values by zone and year0
2023-01-01Property sales40
2023-01-01Building permits14
2023-01-01Planning applications0
2023-01-01Land values by zone and year10
2023-04-01Property sales49
2023-04-01Building permits24
2023-04-01Planning applications0
2023-04-01Land values by zone and year0
2023-07-01Property sales61
2023-07-01Building permits19
2023-07-01Planning applications0
2023-07-01Land values by zone and year0
2023-10-01Property sales48
2023-10-01Building permits24
2023-10-01Planning applications0
2023-10-01Land values by zone and year0
2024-01-01Property sales50
2024-01-01Building permits12
2024-01-01Planning applications0
2024-01-01Land values by zone and year10
2024-04-01Property sales56
2024-04-01Building permits23
2024-04-01Planning applications0
2024-04-01Land values by zone and year0
2024-07-01Property sales46
2024-07-01Building permits30
2024-07-01Planning applications0
2024-07-01Land values by zone and year0
2024-10-01Property sales40
2024-10-01Building permits29
2024-10-01Planning applications0
2024-10-01Land values by zone and year0
2025-01-01Property sales47
2025-01-01Building permits14
2025-01-01Planning applications51
2025-01-01Land values by zone and year10
2025-04-01Property sales60
2025-04-01Building permits28
2025-04-01Planning applications45
2025-04-01Land values by zone and year0
2025-07-01Property sales51
2025-07-01Building permits31
2025-07-01Planning applications28
2025-07-01Land values by zone and year0
2025-10-01Property sales55
2025-10-01Building permits34
2025-10-01Planning applications36
2025-10-01Land values by zone and year0
2026-01-01Property sales55
2026-01-01Building permits34
2026-01-01Planning applications0
2026-01-01Land values by zone and year10
2026-04-01Property sales49
2026-04-01Building permits28
2026-04-01Planning applications0
2026-04-01Land values by zone and year0
2026-07-01Property sales51
2026-07-01Building permits36
2026-07-01Planning applications0
2026-07-01Land values by zone and year0

Figure 23. Parcels over time (year_built) 75

bin_startbin_endcount
00.322582
0.32250.64563
0.6450.9675166
0.96751.29200
1.291.613194
1.6131.935103
1.9352.25792
2.2572.5871
2.582.90262
2.9023.22545
3.2253.54718
3.5473.874

Figure 24. Property sales over time (date) 76

periodseriesvalue
2022-01-01apartment21
2022-01-01office15
2022-01-01house10
2022-04-01apartment24
2022-04-01office10
2022-04-01house12
2022-07-01apartment19
2022-07-01office13
2022-07-01house10
2022-10-01apartment33
2022-10-01office12
2022-10-01house13
2023-01-01apartment14
2023-01-01office14
2023-01-01house12
2023-04-01apartment26
2023-04-01office12
2023-04-01house11
2023-07-01apartment29
2023-07-01office15
2023-07-01house17
2023-10-01apartment19
2023-10-01office19
2023-10-01house10
2024-01-01apartment22
2024-01-01office14
2024-01-01house14
2024-04-01apartment25
2024-04-01office15
2024-04-01house16
2024-07-01apartment22
2024-07-01office12
2024-07-01house12
2024-10-01apartment15
2024-10-01office13
2024-10-01house12
2025-01-01apartment23
2025-01-01office13
2025-01-01house11
2025-04-01apartment27
2025-04-01office18
2025-04-01house15
2025-07-01apartment21
2025-07-01office13
2025-07-01house17
2025-10-01apartment26
2025-10-01office15
2025-10-01house14
2026-01-01apartment24
2026-01-01office21
2026-01-01house10
2026-04-01apartment20
2026-04-01office14
2026-04-01house15
2026-07-01apartment21
2026-07-01office10
2026-07-01house20

Figure 25. Building permits over time (decision_month) 77

periodseriesvalue
2023-01-01extension31
2023-01-01new build14
2023-01-01change of use22
2023-01-01demolition0
2023-02-01extension54
2023-02-01new build28
2023-02-01change of use0
2023-02-01demolition0
2023-03-01extension34
2023-03-01new build47
2023-03-01change of use28
2023-03-01demolition0
2023-04-01extension65
2023-04-01new build13
2023-04-01change of use0
2023-04-01demolition41
2023-05-01extension82
2023-05-01new build53
2023-05-01change of use45
2023-05-01demolition0
2023-06-01extension26
2023-06-01new build84
2023-06-01change of use3
2023-06-01demolition0
2023-07-01extension15
2023-07-01new build43
2023-07-01change of use0
2023-07-01demolition8
2023-08-01extension32
2023-08-01new build102
2023-08-01change of use8
2023-08-01demolition24
2023-09-01extension57
2023-09-01new build57
2023-09-01change of use0
2023-09-01demolition31
2023-10-01extension70
2023-10-01new build3
2023-10-01change of use37
2023-10-01demolition17
2023-11-01extension42
2023-11-01new build39
2023-11-01change of use36
2023-11-01demolition0
2023-12-01extension94
2023-12-01new build67
2023-12-01change of use65
2023-12-01demolition2
2024-01-01extension0
2024-01-01new build19
2024-01-01change of use34
2024-01-01demolition30
2024-02-01extension99
2024-02-01new build21
2024-02-01change of use32
2024-02-01demolition39
2024-03-01extension23
2024-03-01new build0
2024-03-01change of use0
2024-03-01demolition0
2024-04-01extension12
2024-04-01new build0
2024-04-01change of use45
2024-04-01demolition0
2024-05-01extension105
2024-05-01new build110
2024-05-01change of use7
2024-05-01demolition6
2024-06-01extension46
2024-06-01new build0
2024-06-01change of use58
2024-06-01demolition0
2024-07-01extension69
2024-07-01new build28
2024-07-01change of use71
2024-07-01demolition32
2024-08-01extension0
2024-08-01new build81
2024-08-01change of use89
2024-08-01demolition8
2024-09-01extension0
2024-09-01new build96
2024-09-01change of use98
2024-09-01demolition39
2024-10-01extension43
2024-10-01new build77
2024-10-01change of use32
2024-10-01demolition56
2024-11-01extension175
2024-11-01new build176
2024-11-01change of use26
2024-11-01demolition0
2024-12-01extension49
2024-12-01new build80
2024-12-01change of use15
2024-12-01demolition41
2025-01-01extension41
2025-01-01new build64
2025-01-01change of use0
2025-01-01demolition0
2025-02-01extension24
2025-02-01new build28
2025-02-01change of use53
2025-02-01demolition0
2025-03-01extension0
2025-03-01new build32
2025-03-01change of use28
2025-03-01demolition0
2025-04-01extension41
2025-04-01new build33
2025-04-01change of use55
2025-04-01demolition0
2025-05-01extension67
2025-05-01new build76
2025-05-01change of use57
2025-05-01demolition27
2025-06-01extension87
2025-06-01new build47
2025-06-01change of use73
2025-06-01demolition4
2025-07-01extension16
2025-07-01new build47
2025-07-01change of use30
2025-07-01demolition0
2025-08-01extension62
2025-08-01new build69
2025-08-01change of use43
2025-08-01demolition26
2025-09-01extension116
2025-09-01new build90
2025-09-01change of use63
2025-09-01demolition65
2025-10-01extension70
2025-10-01new build54
2025-10-01change of use119
2025-10-01demolition18
2025-11-01extension55
2025-11-01new build64
2025-11-01change of use80
2025-11-01demolition10
2025-12-01extension108
2025-12-01new build23
2025-12-01change of use23
2025-12-01demolition0
2026-01-01extension130
2026-01-01new build117
2026-01-01change of use39
2026-01-01demolition0
2026-02-01extension108
2026-02-01new build22
2026-02-01change of use28
2026-02-01demolition0
2026-03-01extension80
2026-03-01new build35
2026-03-01change of use85
2026-03-01demolition3
2026-04-01extension21
2026-04-01new build140
2026-04-01change of use0
2026-04-01demolition32
2026-05-01extension55
2026-05-01new build65
2026-05-01change of use17
2026-05-01demolition0
2026-06-01extension63
2026-06-01new build112
2026-06-01change of use93
2026-06-01demolition0
2026-07-01extension167
2026-07-01new build112
2026-07-01change of use9
2026-07-01demolition14
2026-08-01extension66
2026-08-01new build65
2026-08-01change of use67
2026-08-01demolition104
2026-09-01extension130
2026-09-01new build34
2026-09-01change of use66
2026-09-01demolition0

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

periodseriesvalue
2018-01-01C061
2018-01-01I071
2018-01-01M051
2018-01-01M081
2018-01-01M101
2018-01-01Other5
2018-04-01C060
2018-04-01I070
2018-04-01M050
2018-04-01M080
2018-04-01M100
2018-04-01Other0
2018-07-01C060
2018-07-01I070
2018-07-01M050
2018-07-01M080
2018-07-01M100
2018-07-01Other0
2018-10-01C060
2018-10-01I070
2018-10-01M050
2018-10-01M080
2018-10-01M100
2018-10-01Other0
2019-01-01C061
2019-01-01I071
2019-01-01M051
2019-01-01M081
2019-01-01M101
2019-01-01Other5
2019-04-01C060
2019-04-01I070
2019-04-01M050
2019-04-01M080
2019-04-01M100
2019-04-01Other0
2019-07-01C060
2019-07-01I070
2019-07-01M050
2019-07-01M080
2019-07-01M100
2019-07-01Other0
2019-10-01C060
2019-10-01I070
2019-10-01M050
2019-10-01M080
2019-10-01M100
2019-10-01Other0
2020-01-01C061
2020-01-01I071
2020-01-01M051
2020-01-01M081
2020-01-01M101
2020-01-01Other5
2020-04-01C060
2020-04-01I070
2020-04-01M050
2020-04-01M080
2020-04-01M100
2020-04-01Other0
2020-07-01C060
2020-07-01I070
2020-07-01M050
2020-07-01M080
2020-07-01M100
2020-07-01Other0
2020-10-01C060
2020-10-01I070
2020-10-01M050
2020-10-01M080
2020-10-01M100
2020-10-01Other0
2021-01-01C061
2021-01-01I071
2021-01-01M051
2021-01-01M081
2021-01-01M101
2021-01-01Other5
2021-04-01C060
2021-04-01I070
2021-04-01M050
2021-04-01M080
2021-04-01M100
2021-04-01Other0
2021-07-01C060
2021-07-01I070
2021-07-01M050
2021-07-01M080
2021-07-01M100
2021-07-01Other0
2021-10-01C060
2021-10-01I070
2021-10-01M050
2021-10-01M080
2021-10-01M100
2021-10-01Other0
2022-01-01C061
2022-01-01I071
2022-01-01M051
2022-01-01M081
2022-01-01M101
2022-01-01Other5
2022-04-01C060
2022-04-01I070
2022-04-01M050
2022-04-01M080
2022-04-01M100
2022-04-01Other0
2022-07-01C060
2022-07-01I070
2022-07-01M050
2022-07-01M080
2022-07-01M100
2022-07-01Other0
2022-10-01C060
2022-10-01I070
2022-10-01M050
2022-10-01M080
2022-10-01M100
2022-10-01Other0
2023-01-01C061
2023-01-01I071
2023-01-01M051
2023-01-01M081
2023-01-01M101
2023-01-01Other5
2023-04-01C060
2023-04-01I070
2023-04-01M050
2023-04-01M080
2023-04-01M100
2023-04-01Other0
2023-07-01C060
2023-07-01I070
2023-07-01M050
2023-07-01M080
2023-07-01M100
2023-07-01Other0
2023-10-01C060
2023-10-01I070
2023-10-01M050
2023-10-01M080
2023-10-01M100
2023-10-01Other0
2024-01-01C061
2024-01-01I071
2024-01-01M051
2024-01-01M081
2024-01-01M101
2024-01-01Other5
2024-04-01C060
2024-04-01I070
2024-04-01M050
2024-04-01M080
2024-04-01M100
2024-04-01Other0
2024-07-01C060
2024-07-01I070
2024-07-01M050
2024-07-01M080
2024-07-01M100
2024-07-01Other0
2024-10-01C060
2024-10-01I070
2024-10-01M050
2024-10-01M080
2024-10-01M100
2024-10-01Other0
2025-01-01C061
2025-01-01I071
2025-01-01M051
2025-01-01M081
2025-01-01M101
2025-01-01Other5
2025-04-01C060
2025-04-01I070
2025-04-01M050
2025-04-01M080
2025-04-01M100
2025-04-01Other0
2025-07-01C060
2025-07-01I070
2025-07-01M050
2025-07-01M080
2025-07-01M100
2025-07-01Other0
2025-10-01C060
2025-10-01I070
2025-10-01M050
2025-10-01M080
2025-10-01M100
2025-10-01Other0
2026-01-01C061
2026-01-01I071
2026-01-01M051
2026-01-01M081
2026-01-01M101
2026-01-01Other5

Figure 27. Emerging hot spots: Property sales 79

bin_startbin_endcount
-0.963-0.146623
-0.14660.669717
0.66971.48614
1.4862.30313
2.3033.1194
3.1193.9356
3.9354.7523
4.7525.5681

Figure 28. Emerging hot spots: Building permits 80

bin_startbin_endcount
-0.6090.0861242
0.086120.781221
0.78121.4769
1.4762.1719
2.1712.8675
2.8673.5623
3.5624.2570
4.2574.9521

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

periodseriesvalue
2022-01-01apartment21
2022-01-01office15
2022-01-01house10
2022-04-01apartment24
2022-04-01office10
2022-04-01house12
2022-07-01apartment19
2022-07-01office13
2022-07-01house10
2022-10-01apartment33
2022-10-01office12
2022-10-01house13
2023-01-01apartment14
2023-01-01office14
2023-01-01house12
2023-04-01apartment26
2023-04-01office12
2023-04-01house11
2023-07-01apartment29
2023-07-01office15
2023-07-01house17
2023-10-01apartment19
2023-10-01office19
2023-10-01house10
2024-01-01apartment22
2024-01-01office14
2024-01-01house14
2024-04-01apartment25
2024-04-01office15
2024-04-01house16
2024-07-01apartment22
2024-07-01office12
2024-07-01house12
2024-10-01apartment15
2024-10-01office13
2024-10-01house12
2025-01-01apartment23
2025-01-01office13
2025-01-01house11
2025-04-01apartment27
2025-04-01office18
2025-04-01house15
2025-07-01apartment21
2025-07-01office13
2025-07-01house17
2025-10-01apartment26
2025-10-01office15
2025-10-01house14
2026-01-01apartment24
2026-01-01office21
2026-01-01house10
2026-04-01apartment20
2026-04-01office14
2026-04-01house15
2026-07-01apartment21
2026-07-01office10
2026-07-01house20

Figure 30. Summarize within: Parcels 82

bin_startbin_endcount
7521,843112
1,8432,934213
2,9344,024188
4,0245,115183
5,1156,206125
6,2067,29699
7,2968,38776
8,3879,47850
9,4781.057e+0432
1.057e+041.166e+0412
1.166e+041.275e+047
1.275e+041.384e+043

Figure 31. Summarize within: Parcels 83

bin_startbin_endcount
7521,843112
1,8432,934213
2,9344,024188
4,0245,115183
5,1156,206125
6,2067,29699
7,2968,38776
8,3879,47850
9,4781.057e+0432
1.057e+041.166e+0412
1.166e+041.275e+047
1.275e+041.384e+043

Figure 32. Summarize within: Parcels 84

bin_startbin_endcount
7521,843112
1,8432,934213
2,9344,024188
4,0245,115183
5,1156,206125
6,2067,29699
7,2968,38776
8,3879,47850
9,4781.057e+0432
1.057e+041.166e+0412
1.166e+041.275e+047
1.275e+041.384e+043

Figure 33. Hot spot analysis: Property sales 85

bin_startbin_endbandband_labelcount
-2-1.5not_significantNot significant25
-1.5-1not_significantNot significant8
-1-0.5not_significantNot significant21
-0.50not_significantNot significant63
00.5not_significantNot significant27
0.51not_significantNot significant37
11.5not_significantNot significant12
1.52hot_90Hot spot, 90% confidence2
1.52not_significantNot significant12
22.5hot_95Hot spot, 95% confidence2
2.53hot_95Hot spot, 95% confidence2
3.54hot_99Hot spot, 99% confidence2

Figure 34. Hot spot analysis: Building permits 86

bin_startbin_endbandband_labelcount
-2.5-2cold_95Cold spot, 95% confidence1
-2-1.5cold_90Cold spot, 90% confidence11
-2-1.5not_significantNot significant7
-1.5-1not_significantNot significant29
-1-0.5not_significantNot significant81
-0.50not_significantNot significant72
00.5not_significantNot significant93
0.51not_significantNot significant38
11.5not_significantNot significant25
1.52hot_90Hot spot, 90% confidence9
1.52not_significantNot significant4
22.5hot_95Hot spot, 95% confidence9
2.53hot_99Hot spot, 99% confidence1

Figure 35. Calculate density: Property sales 87

bin_startbin_endcount
02.18289
2.1824.36481
4.3646.54581
6.5458.72782
8.72710.9180
10.9113.09122
13.0915.2781
15.2717.4582
17.4519.6476
19.6421.8257
21.822436

Figure 36. Calculate density: Building permits 88

bin_startbin_endcount
02.568
2.5587
57.5139
7.51094
1012.5135
12.51592
1517.5139
17.52094
2022.5138
22.52589
2527.5102
27.53034

Figure 37. Find point clusters: Transit stations 89

clusterfeaturesfractionshare
0121100.0%

Figure 38. Find point clusters: cluster hulls 90

clusterfeaturesfractionshare
011100.0%

Figure 39. Find point clusters: Property sales 91

clusterfeaturesfractionshare
Noise9501100.0%

Figure 40. Find point clusters: Building permits 92

clusterfeaturesfractionshare
Noise1260.331633.2%
5370.097379.7%
6330.086848.7%
0290.076327.6%
10200.052635.3%
4190.055.0%
2180.047374.7%
8170.044744.5%
1160.04214.2%
9150.039473.9%
3130.034213.4%
11100.026322.6%
12100.026322.6%
13100.026322.6%
770.018421.8%

Figure 41. Find point clusters: cluster hulls 93

clusterfeaturesfractionshare
010.071437.1%
110.071437.1%
1010.071437.1%
1110.071437.1%
1210.071437.1%
1310.071437.1%
210.071437.1%
310.071437.1%
410.071437.1%
510.071437.1%
610.071437.1%
710.071437.1%
810.071437.1%
910.071437.1%

Figure 42. Find outliers: Parcels 94

valuelagtype
-1.428-0.8778NS
-0.8984-0.9939NS
-0.615-1.065NS
-1.12-0.8688NS
-0.2331-0.615NS
-0.615-0.2331NS
-1.133-0.6335NS
-0.7752-0.9292NS
-0.4918-0.9764NS
-0.27-0.8203NS
-1.527-0.4015NS
-0.2208-0.4548NS
-0.923-0.1037NS
0.0133-0.5719NS
-0.7998-0.7259NS
-0.5288-0.8614NS
-0.923-0.6643NS
-1.675-1.12NS
-0.5657-1.675NS
-1.675-1.03NS
-0.3316-1.157NS
-1.317-0.6314NS
-0.9969-0.7382NS
-0.5657-1.371NS
-0.3809-1.034NS
-1.034-0.6889NS
-1.219-0.96NS
-0.96-1.219NS
-0.8121-1.004NS
-0.5904-1.003NS
-1.798-0.5616NS
-0.2824-1.018NS
0.0996-0.3316NS
-0.0852-0.2392NS
-0.578-0.2413NS
-0.7259-1.083NS
-1.391-0.7259NS
-0.4425-0.9846NS
-1.675-0.4918NS
-0.4672-0.8943NS
-0.5657-1.071NS
-0.8737-1.268NS
-1.675-0.7957NS
-0.8614-1.067NS
-0.652-1.05NS
1.3810.6417NS
0.55541.054NS
0.72790.884NS
1.0361.412NS
0.71561.165NS
2.1080.847NS
0.78951.229NS
0.49380.2105NS
-0.25770.6868NS
0.67860.3747NS
0.93740.3829NS
0.38290.9497NS
-1.28-0.0206NS
-0.578-0.1961NS
0.3706-0.4333NS
0.62930.001NS
-0.5041-0.4179NS
-0.7136-0.7197NS
-0.9353-0.7875NS
-1.022-0.9271NS
-0.9723-0.9415NS
-0.96-0.9446NS
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-1.157-1.133NS
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-0.3563-1.41NS
-1.022-1.018NS
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-0.578-0.7423NS
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-1.108-0.7937NS
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0.3706-0.3994NS
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0.038-0.5575NS
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0.72791.122NS
1.1220.7279NS
1.085-0.7998NS
-1.7980.7238NS
0.19810.0585NS
0.8881-0.4015NS
0.12420.9312NS
0.59240.654NS
1.270.4281NS
0.9620.5801NS
0.80180.6601NS
0.35830.8819NS
0.86340.8203NS
0.85111.116NS
0.88810.3337NS
0.58010.8881NS
0.9620.8922NS
1.0970.9045NS
1.1960.8716NS
0.55540.9086NS
1.9971.362NS
2.0950.0996NS
-1.7981.073NS
-1.145-0.9969NS
-0.9969-1.145NS
-0.3316-0.7813NS
-0.7136-0.4672NS
-0.8491-0.6971NS
0.001-0.5842NS
-1.391-1.058NS
-1.675-0.7136NS
-0.8984-1.04NS
-1.058-0.8984NS
-0.8984-1.071NS
-0.8121-1.075NS
-1.379-0.8861NS
-0.9477-1.079NS
-1.169-0.9846NS
-1.798-0.578NS
-1.798-0.7629NS
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-0.6889-0.9477NS
-1.034-0.8787NS
-0.8984-0.9939NS
-1.675-0.5565NS
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0.4199-0.5329NS
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0.0133-0.4015NS
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0.038-0.7834NS
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-0.3193-0.2146NS
-0.1345-0.2824NS
-0.2331-0.424NS
-0.7136-0.2331NS
-0.20840.1797NS
-0.17150.1612NS
0.5308-0.076NS
-0.56570.5862NS
0.64170.0215NS
-1.798-1.798NS
-1.7980.079NS
1.196-0.4795NS
0.83880.1735NS
0.39531.19NS
1.8490.8059NS
1.3560.9702NS
0.66631.162NS
0.56771.192NS
1.7140.5677NS
1.0970.7033NS
0.70330.7217NS
1.3690.4445NS
0.0380.8758NS
0.38290.038NS
1.7630.8634NS
0.85111.167NS
0.9621.13NS
0.77721.192NS
0.43220.9928NS
1.430.7977NS
0.0503-0.2084NS
0.14890.2885NS
0.38290.2269NS
0.4322-0.2762NS
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0.5677-0.8799NS
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0.7526-1.071NS
0.0133-1.077NS
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0.309-1.317NS
0.3953-1.582NS
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0.0626-0.7136NS
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0.2967-0.5863NS
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0.7649-0.6427NS
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0.0503-0.1037NS
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0.0503-0.2516NS
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0.0133-0.5945NS
0.0133-0.5945NS
0.0010.0133NS
0.2228-0.8121NS
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0.1242-0.153NS
0.001-0.4056NS
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0.09960.2782NS
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1.1221.042NS
1.2451.171NS
1.2211.332NS
1.0480.6725NS
0.67860.8634NS
0.67860.2474NS
0.3583-0.2577NS
-0.25770.3829NS
0.3460.6663NS
0.62930.9497NS
1.5530.6293NS
0.0380.5924NS
0.80180.6088NS
0.59241.073NS
1.0730.5924NS
1.2951.024NS
0.66631.233NS
0.97430.9805NS
0.48150.8758NS
1.5780.5739NS
0.66630.8634NS
0.98660.962NS
1.4670.6951NS
0.45691.032NS
0.64170.962NS
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0.038-0.4548NS
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0.0873-0.7752NS
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0.1119-0.5041NS
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0.08730.0339NS
1.097-0.2824NS
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0.0256-0.96NS
0.1612-0.5288NS
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0.0133-1.034NS
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0.4199-0.8429NS
1.4181.171NS
0.66631.523NS
1.6271.11NS
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1.3190.2597NS
0.70330.5677NS
0.40760.2228NS
0.70330.5102NS
0.22280.4528NS
0.24740.6232NS
1.0240.6786NS
0.67860.9558NS
1.110.8727NS
0.98660.7772NS
1.0240.654NS
0.6541.042NS
0.9990.7526NS
0.75261.122NS
0.53080.9558NS
1.2450.6417NS
0.51850.4322NS
0.4322-0.6397NS
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0.309-0.7752NS
-0.578-0.5NS
-1.083-1.256NS
-0.8368-0.9436NS
-1.169-0.8368NS
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-1.477-0.6397NS
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-0.1592-1.379NS
-1.317-0.1653NS
-0.8245-0.9477NS
-0.9477-0.7813NS
-0.652-0.4302NS
-0.4302-0.8553NS
-1.798-1.133NS
-1.133-1.379NS
-0.7875-0.8902NS
-0.96-1.025NS
-1.798-0.7095NS
-0.96-1.477NS
-1.477-1.028NS
-1.095-1.477NS
-1.65-0.9538NS
-1.046-1.65NS
-0.2947-0.96NS
-0.96-0.2947NS
-0.8491-0.2824NS
-0.1468-0.5041NS
-1.798-0.1838NS
0.1365-1.252NS
-0.5041-0.7875NS
-1.453-0.1345NS
0.5431-0.7998NS
-0.7998-0.0236NS
0.3583-0.4425NS
-0.8368-0.7197NS
-1.798-0.8429NS
0.1489-0.3255NS
0.08730.1612NS
-1.7980.1858NS
0.346-1.798NS
-0.4425-1.173NS
-1.194-1.12NS
-1.798-0.6355NS
-0.1468-0.5472NS
-0.923-0.036NS
-1.798-0.1386NS
-0.307-0.6355NS
0.1119-0.7752NS
-0.2208-0.6643NS
0.0133-0.1345NS
0.4692-0.1222NS
-0.73820.2803NS
-0.6889-0.1879NS
-0.1838-0.7444NS
-0.7998-0.3152NS
1.2450.962NS
1.3930.6909NS
0.14891.11NS
0.67860.9774NS
0.76491.005NS
0.85111.011NS
1.1590.9086NS
1.110.2936NS
0.90040.8326NS
0.9620.0708NS
-1.7980.9784NS
1.147-0.3686NS
-0.36861.085NS
0.44450.8142NS
0.64170.3152NS
0.18580.5862NS
1.0610.7279NS
0.80181.061NS
1.553-0.1222NS
-0.12221.171NS
0.78950.6725NS
1.1471.073NS
1.0730.8018NS
0.97430.6786NS
-1.7980.6376NS
0.5554-0.4364NS
0.925-0.3111NS
0.08730.1181NS
0.24740.0873NS
-1.145-0.4179NS
-0.4179-0.7813NS
-0.4179-0.8614NS
-0.8121-0.5103NS
-0.5534-0.6397NS
-0.4672-0.6314NS
-0.5288-0.6355NS
0.038-0.4856NS
-0.1715-0.6828NS
-0.5041-0.3604NS
-0.6273-0.3193NS
-0.2824-0.4343NS
-0.7382-1.052NS
-1.157-0.9107NS
-1.058-0.3604NS
-0.3193-0.7546NS
-0.3316-0.8429NS
-1.157-0.5842NS
-0.8368-0.7444NS
-0.3809-1.554NS
-1.662-0.8737NS
-1.798-0.5934NS
-0.4425-1.116NS
-1.675-0.4548NS
-0.4548-1.588NS
0.1981-0.7711NS
-0.2208-0.6314NS
-0.4179-0.5657NS
-1.675-0.193NS
-0.1715-0.1838NS
-0.1838-0.3809NS
-0.1592-0.2084NS
-0.27-0.1592NS
-0.036-0.845NS
-0.578-0.036NS
-0.5904-0.5NS
-0.1961-0.5472NS
-0.5041-0.1509NS
-0.8491-0.6766NS
-0.4795-0.0688NS
0.2474-0.3111NS

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

valuelagtype
1.2430.3681NS
-1.3820.1622NS
-0.99620.2394NS
-0.8417-0.1004NS
-1.3050.0592NS
0.78-0.121NS
1.398-0.5586NS
0.2394-0.0695NS
0.1622-0.1158NS
0.70270.7542NS

Figure 44. Find outliers: Zoning districts 96

valuelagtype
-0.62990.8237NS
-0.62991.429NS
-0.62990.8237NS
-0.62990.0969NS
0.82370.0162NS
2.0350.0969NS
-1.3570.8237NS
0.8237-0.6299NS
-0.62990.0969NS
0.8237-0.6299NS

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

periodseriesvalue
2023-01-01extension1
2023-01-01new build1
2023-01-01change of use2
2023-01-01demolition0
2023-02-01extension4
2023-02-01new build1
2023-02-01change of use0
2023-02-01demolition0
2023-03-01extension1
2023-03-01new build3
2023-03-01change of use1
2023-03-01demolition0
2023-04-01extension2
2023-04-01new build2
2023-04-01change of use0
2023-04-01demolition3
2023-05-01extension5
2023-05-01new build3
2023-05-01change of use2
2023-05-01demolition0
2023-06-01extension3
2023-06-01new build3
2023-06-01change of use1
2023-06-01demolition0
2023-07-01extension1
2023-07-01new build2
2023-07-01change of use0
2023-07-01demolition1
2023-08-01extension2
2023-08-01new build5
2023-08-01change of use1
2023-08-01demolition2
2023-09-01extension2
2023-09-01new build2
2023-09-01change of use0
2023-09-01demolition1
2023-10-01extension4
2023-10-01new build2
2023-10-01change of use1
2023-10-01demolition1
2023-11-01extension2
2023-11-01new build1
2023-11-01change of use2
2023-11-01demolition0
2023-12-01extension5
2023-12-01new build3
2023-12-01change of use2
2023-12-01demolition1
2024-01-01extension0
2024-01-01new build1
2024-01-01change of use3
2024-01-01demolition1
2024-02-01extension3
2024-02-01new build1
2024-02-01change of use1
2024-02-01demolition1
2024-03-01extension1
2024-03-01new build0
2024-03-01change of use0
2024-03-01demolition0
2024-04-01extension2
2024-04-01new build0
2024-04-01change of use3
2024-04-01demolition0
2024-05-01extension5
2024-05-01new build6
2024-05-01change of use1
2024-05-01demolition1
2024-06-01extension2
2024-06-01new build0
2024-06-01change of use3
2024-06-01demolition0
2024-07-01extension3
2024-07-01new build4
2024-07-01change of use2
2024-07-01demolition1
2024-08-01extension0
2024-08-01new build5
2024-08-01change of use4
2024-08-01demolition1
2024-09-01extension0
2024-09-01new build4
2024-09-01change of use4
2024-09-01demolition2
2024-10-01extension2
2024-10-01new build4
2024-10-01change of use1
2024-10-01demolition2
2024-11-01extension5
2024-11-01new build5
2024-11-01change of use1
2024-11-01demolition0
2024-12-01extension2
2024-12-01new build3
2024-12-01change of use1
2024-12-01demolition3
2025-01-01extension2
2025-01-01new build4
2025-01-01change of use0
2025-01-01demolition0
2025-02-01extension1
2025-02-01new build1
2025-02-01change of use2
2025-02-01demolition0
2025-03-01extension0
2025-03-01new build2
2025-03-01change of use2
2025-03-01demolition0
2025-04-01extension3
2025-04-01new build1
2025-04-01change of use3
2025-04-01demolition0
2025-05-01extension4
2025-05-01new build4
2025-05-01change of use3
2025-05-01demolition1
2025-06-01extension3
2025-06-01new build2
2025-06-01change of use3
2025-06-01demolition1
2025-07-01extension2
2025-07-01new build2
2025-07-01change of use2
2025-07-01demolition0
2025-08-01extension4
2025-08-01new build3
2025-08-01change of use2
2025-08-01demolition1
2025-09-01extension5
2025-09-01new build5
2025-09-01change of use3
2025-09-01demolition2
2025-10-01extension4
2025-10-01new build3
2025-10-01change of use4
2025-10-01demolition1
2025-11-01extension4
2025-11-01new build4
2025-11-01change of use3
2025-11-01demolition1
2025-12-01extension4
2025-12-01new build3
2025-12-01change of use3
2025-12-01demolition0
2026-01-01extension4
2026-01-01new build4
2026-01-01change of use3
2026-01-01demolition0
2026-02-01extension4
2026-02-01new build2
2026-02-01change of use2
2026-02-01demolition0
2026-03-01extension5
2026-03-01new build2
2026-03-01change of use6
2026-03-01demolition2
2026-04-01extension1
2026-04-01new build7
2026-04-01change of use0
2026-04-01demolition1
2026-05-01extension2
2026-05-01new build2
2026-05-01change of use1
2026-05-01demolition0
2026-06-01extension4
2026-06-01new build6
2026-06-01change of use4
2026-06-01demolition0
2026-07-01extension7
2026-07-01new build4
2026-07-01change of use1
2026-07-01demolition1
2026-08-01extension3
2026-08-01new build3
2026-08-01change of use3
2026-08-01demolition3
2026-09-01extension7
2026-09-01new build2
2026-09-01change of use2
2026-09-01demolition0

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

periodseriesvalue
2022-01-01apartment21
2022-01-01office15
2022-01-01house10
2022-04-01apartment24
2022-04-01office10
2022-04-01house12
2022-07-01apartment19
2022-07-01office13
2022-07-01house10
2022-10-01apartment33
2022-10-01office12
2022-10-01house13
2023-01-01apartment14
2023-01-01office14
2023-01-01house12
2023-04-01apartment26
2023-04-01office12
2023-04-01house11
2023-07-01apartment29
2023-07-01office15
2023-07-01house17
2023-10-01apartment19
2023-10-01office19
2023-10-01house10
2024-01-01apartment22
2024-01-01office14
2024-01-01house14
2024-04-01apartment25
2024-04-01office15
2024-04-01house16
2024-07-01apartment22
2024-07-01office12
2024-07-01house12
2024-10-01apartment15
2024-10-01office13
2024-10-01house12
2025-01-01apartment23
2025-01-01office13
2025-01-01house11
2025-04-01apartment27
2025-04-01office18
2025-04-01house15
2025-07-01apartment21
2025-07-01office13
2025-07-01house17
2025-10-01apartment26
2025-10-01office15
2025-10-01house14
2026-01-01apartment24
2026-01-01office21
2026-01-01house10
2026-04-01apartment20
2026-04-01office14
2026-04-01house15
2026-07-01apartment21
2026-07-01office10
2026-07-01house20

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

periodseriesvalue
2023-01-01extension1
2023-01-01new build1
2023-01-01change of use2
2023-01-01demolition0
2023-02-01extension4
2023-02-01new build1
2023-02-01change of use0
2023-02-01demolition0
2023-03-01extension1
2023-03-01new build3
2023-03-01change of use1
2023-03-01demolition0
2023-04-01extension2
2023-04-01new build2
2023-04-01change of use0
2023-04-01demolition3
2023-05-01extension5
2023-05-01new build3
2023-05-01change of use2
2023-05-01demolition0
2023-06-01extension3
2023-06-01new build3
2023-06-01change of use1
2023-06-01demolition0
2023-07-01extension1
2023-07-01new build2
2023-07-01change of use0
2023-07-01demolition1
2023-08-01extension2
2023-08-01new build5
2023-08-01change of use1
2023-08-01demolition2
2023-09-01extension2
2023-09-01new build2
2023-09-01change of use0
2023-09-01demolition1
2023-10-01extension4
2023-10-01new build2
2023-10-01change of use1
2023-10-01demolition1
2023-11-01extension2
2023-11-01new build1
2023-11-01change of use2
2023-11-01demolition0
2023-12-01extension5
2023-12-01new build3
2023-12-01change of use2
2023-12-01demolition1
2024-01-01extension0
2024-01-01new build1
2024-01-01change of use3
2024-01-01demolition1
2024-02-01extension3
2024-02-01new build1
2024-02-01change of use1
2024-02-01demolition1
2024-03-01extension1
2024-03-01new build0
2024-03-01change of use0
2024-03-01demolition0
2024-04-01extension2
2024-04-01new build0
2024-04-01change of use3
2024-04-01demolition0
2024-05-01extension5
2024-05-01new build6
2024-05-01change of use1
2024-05-01demolition1
2024-06-01extension2
2024-06-01new build0
2024-06-01change of use3
2024-06-01demolition0
2024-07-01extension3
2024-07-01new build4
2024-07-01change of use2
2024-07-01demolition1
2024-08-01extension0
2024-08-01new build5
2024-08-01change of use4
2024-08-01demolition1
2024-09-01extension0
2024-09-01new build4
2024-09-01change of use4
2024-09-01demolition2
2024-10-01extension2
2024-10-01new build4
2024-10-01change of use1
2024-10-01demolition2
2024-11-01extension5
2024-11-01new build5
2024-11-01change of use1
2024-11-01demolition0
2024-12-01extension2
2024-12-01new build3
2024-12-01change of use1
2024-12-01demolition3
2025-01-01extension2
2025-01-01new build4
2025-01-01change of use0
2025-01-01demolition0
2025-02-01extension1
2025-02-01new build1
2025-02-01change of use2
2025-02-01demolition0
2025-03-01extension0
2025-03-01new build2
2025-03-01change of use2
2025-03-01demolition0
2025-04-01extension3
2025-04-01new build1
2025-04-01change of use3
2025-04-01demolition0
2025-05-01extension4
2025-05-01new build4
2025-05-01change of use3
2025-05-01demolition1
2025-06-01extension3
2025-06-01new build2
2025-06-01change of use3
2025-06-01demolition1
2025-07-01extension2
2025-07-01new build2
2025-07-01change of use2
2025-07-01demolition0
2025-08-01extension4
2025-08-01new build3
2025-08-01change of use2
2025-08-01demolition1
2025-09-01extension5
2025-09-01new build5
2025-09-01change of use3
2025-09-01demolition2
2025-10-01extension4
2025-10-01new build3
2025-10-01change of use4
2025-10-01demolition1
2025-11-01extension4
2025-11-01new build4
2025-11-01change of use3
2025-11-01demolition1
2025-12-01extension4
2025-12-01new build3
2025-12-01change of use3
2025-12-01demolition0
2026-01-01extension4
2026-01-01new build4
2026-01-01change of use3
2026-01-01demolition0
2026-02-01extension4
2026-02-01new build2
2026-02-01change of use2
2026-02-01demolition0
2026-03-01extension5
2026-03-01new build2
2026-03-01change of use6
2026-03-01demolition2
2026-04-01extension1
2026-04-01new build7
2026-04-01change of use0
2026-04-01demolition1
2026-05-01extension2
2026-05-01new build2
2026-05-01change of use1
2026-05-01demolition0
2026-06-01extension4
2026-06-01new build6
2026-06-01change of use4
2026-06-01demolition0
2026-07-01extension7
2026-07-01new build4
2026-07-01change of use1
2026-07-01demolition1
2026-08-01extension3
2026-08-01new build3
2026-08-01change of use3
2026-08-01demolition3
2026-09-01extension7
2026-09-01new build2
2026-09-01change of use2
2026-09-01demolition0

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

periodseriesvalue
2022-01-01apartment21
2022-01-01office15
2022-01-01house10
2022-04-01apartment24
2022-04-01office10
2022-04-01house12
2022-07-01apartment19
2022-07-01office13
2022-07-01house10
2022-10-01apartment33
2022-10-01office12
2022-10-01house13
2023-01-01apartment14
2023-01-01office14
2023-01-01house12
2023-04-01apartment26
2023-04-01office12
2023-04-01house11
2023-07-01apartment29
2023-07-01office15
2023-07-01house17
2023-10-01apartment19
2023-10-01office19
2023-10-01house10
2024-01-01apartment22
2024-01-01office14
2024-01-01house14
2024-04-01apartment25
2024-04-01office15
2024-04-01house16
2024-07-01apartment22
2024-07-01office12
2024-07-01house12
2024-10-01apartment15
2024-10-01office13
2024-10-01house12
2025-01-01apartment23
2025-01-01office13
2025-01-01house11
2025-04-01apartment27
2025-04-01office18
2025-04-01house15
2025-07-01apartment21
2025-07-01office13
2025-07-01house17
2025-10-01apartment26
2025-10-01office15
2025-10-01house14
2026-01-01apartment24
2026-01-01office21
2026-01-01house10
2026-04-01apartment20
2026-04-01office14
2026-04-01house15
2026-07-01apartment21
2026-07-01office10
2026-07-01house20

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

The data

The files this sample uploads: synthetic data generated at a real place. Download any of them and add them to a project of your own.

DatasetKindRowsTime spanFile
Zoning districts
Zoning districts with height and floor area limits.		vector, Polygon10zoning.geojson
Parcels
Land parcels with built floor area ratio, slope and vacancy.		vector, Polygon1,100parcels.geojson
Property sales
Recorded property sales by day. Dated by date (day).		vector, Point9502022-01-01
to 2026-09-30		property_sales.geojson
Building permits
Building permits by decision month. Dated by decision_month (month).		vector, Point3802023-01-01
to 2026-09-01		building_permits.geojson
Transit stations
Metro stations with daily entries.		vector, Point12transit_stations.geojson
Green spaces
Public gardens, parks and squares.		vector, Polygon10green_spaces.geojson
Streetsvector, LineString151streets.geojson (not in the current sample data files)
City terrain model (DEM)
A 15 m terrain model of the city's hills (synthetic).		rastercity_terrain_dem.tif
Land values by zone and year
Assessed land value per zoning district and year (joins to Zoning districts on zone_code). Dated by year (year).		table902018-01-01
to 2026-01-01		land_values_by_year.csv
Planning applications
Planning applications per parcel (joins to Parcels on parcel_id).		table1602025-01-01
to 2025-12-28		planning_applications.csv
Planning committee brief
A brief naming zones and the regeneration area.		documentcommittee_brief.md, committee_brief.pdf

What the data was built to show

  • Prices rise fastest in the riverside regeneration area near 38.72320, -9.13807 (Jardim dos Pinheiros).
  • Vacant parcels concentrate in the same area; permits grow over time.
  • The terrain model shows four hills where slope limits building.

Expected analysis families: space_time, map, aggregation, terrain, hot_spots.

How the product read each dataset

The profile written when the data was added, before Start; no model call is needed for it.

  • Zoning districts: 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.
  • Parcels: 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.
  • Property sales: 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.
  • Building permits: 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.
  • Transit stations: 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.
  • Green spaces: 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.
  • Streets: 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.
  • City terrain model (DEM): 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.
  • Land values by zone and year: 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).
  • Planning applications: 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).
  • Planning committee brief: 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).

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