Are Toronto’s station environments gaining more - and more varied - amenities?
Greater Toronto region · 5 figures · ≈ 8 min core read · 2026
A spatial comparison of points-of-interest (POI) abundance and functional richness around rail-based transit stations and stops across the Greater Toronto region.
For developers and retailers, the question is not simply where amenities are concentrated, but where the local urban ecosystem appears to be changing.
In 30 seconds
Mapped POI volume increased, but the annual geography barely moved.
After filtering and classification, the number of retained OpenStreetMap POI records was approximately 19% higher in July 2026 than in July 2023. This reflects change in the mapped record, including both real-world evolution and OSM coverage or editing - not a verified 19% increase in businesses or amenities.
The direct change map reveals the useful signal.
It identifies localized intensification, functional diversification and combined growth. Several clusters appear along rail-based transit corridors and around selected station environments, although the wider proximity zone is not uniformly more dynamic.
This is a screening tool, not an opportunity map.
The patterns can narrow a search area for retail, development or planning research. They do not establish causality or replace demographic, mobility, competition, real estate and planning evidence.
1. WHY DO RAW AMENITY LOCATIONS BECOME UNREADABLE AT METROPOLITAN SCALE?
Raw locations show what exists, but they bury the patterns we want to compare.
Points of interest make urban functions visible: shops, restaurants, schools, health services, cultural destinations, community facilities and workplaces. Together, they offer a broad picture of what exists across a territory.
But tens of thousands of points do not automatically produce insight. Dense central areas dominate, isolated points disappear, and comparison between dates becomes nearly impossible.
The raw map is still useful for checking coverage and obvious gaps. Its failure is analytical: it cannot show clearly where abundance and functional variety are changing.
The question becomes: can the same records be reorganized to reveal both concentration and change?
Figure 01 · Why the raw point map fails at metropolitan scale.
Retained POIs from the 2023 and 2026 OpenStreetMap snapshots are shown with Toronto’s rail-based transit network. Volume and coverage are visible; individual changes are not.
2. DO AREAS AROUND RAIL TRANSIT SHOW STRONGER AMENITY GROWTH?
Transit proximity is a hypothesis to test, not an assumed advantage.
Figure 02 · Defining the transit-proximity hypothesis.
Rail-based transit routes, stations and 1,750-metre Euclidean buffers within the rectangular study frame.
The analysis uses a rectangular frame of approximately 70 by 70 kilometres rather than municipal limits. Lake Ontario and the principal water network establish the physical base; rail-based public transit provides the territorial structure.
Regional rail, subway, light-rail and streetcar routes - along with their stations and stops - were extracted from OpenStreetMap through BBBike. Access points matter more than lines alone: proximity to a track does not necessarily provide access to the network.
A 1,750-metre Euclidean buffer was created around selected stations and stops. At 5 kilometres per hour, that equals about 21 minutes in a straight line. It is not a 21-minute pedestrian catchment: actual routes are shaped by streets, barriers, entrances and crossings.
Hexagons that intersect with the buffers define the highlighted station-proximity area. Territory beyond it will remain visible but muted, preserving a regional comparison without presenting the buffer as an official TOD boundary.
Hypothesis: if rail-based transit supports or attracts more complete urban environments, some station areas should show stronger growth in both the number and variety of mapped POIs.
3. HOW CAN AMENITY CHANGE BE MEASURED CONSISTENTLY ACROSS A METROPOLITAN REGION?
We compare two dimensions: abundance and functional richness.
Historical OpenStreetMap states for July 1, 2023, and July 1, 2026, were extracted through the ohsome API. Features carrying relevant information tags were represented as points (centroids) and classified.
Eight functional categories were retained: health care; education and childcare; food retail; food and hospitality; civic and community uses; culture and recreation; retail and services; and employment and production.
Public-realm features and heterogeneous uncategorized records were excluded because they did not contribute consistently to the question.
The retained POIs were aggregated into a common 500-metre hexagonal grid. Each cell received two measures: POI abundance, the total retained count; and functional richness, the number of represented categories from zero to eight.
The distinction prevents a false equivalence. Eight POIs spanning all eight categories are broad but shallow; eighty POIs across the same categories combine breadth with much greater intensity. A high count can also describe a narrowly specialized place. The two measures were therefore mapped together rather than collapsed into one score.
4. WHY DON’T THE 2023 AND 2026 MAPS SHOW THE CHANGE CLEARLY?
A shared baseline makes the years comparable, but local shifts can remain hidden.
The 2023 and 2026 maps use the same grid, categories, class boundaries, colours and visual hierarchy. Functional richness controls one colour dimension (blue); abundance controls the other (yellow).
Both maps reproduce a persistent metropolitan structure. Central Toronto dominates, secondary clusters follow established centres and corridors, and many peripheral areas remain sparse. The mapped total rises, but meaningful local changes can remain inside the same class and disappear at regional scale.
Figure 03 · Comparable annual baselines.
Before/after map POI abundance and functional richness in 2023 and 2026 using identical classes and symbology.
The annual maps answer what the geography looked like at each date. They do not answer clearly where it changed.
5. WHERE DID TORONTO’S AMENITY GEOGRAPHY ACTUALLY CHANGE?
A direct change map separates decline, stability, intensification and diversification.
A direct comparison classifies the direction of change in POI abundance and functional richness for every hexagon.
The first view isolates decline and mixed trajectories. Some cells lose POIs, some lose functional variety, and others gain on one dimension while declining on the other.
Figure 04A · Decline and trade-off signals.
Hexagons showing substantial contraction, loss of functional richness, combined decline or opposing movements between the two measures.
The complete map brings all nine trajectories together. It provides the most comprehensive representation, but its complexity also demonstrates why different decision questions require different views of the same evidence.
Figure 04B · The complete geography of change.
Map combining decrease, relative stability and increase in POI abundance and functional richness.
Focus on growth signals
For development and location screening, the growth-positive trajectories provide a more focused starting point.
- Intensification identifies substantial POI growth without a change in category richness.
- Diversification identifies a broader functional mix without substantial change in POI volume.
- Combined growth identifies cells where both measures increase.
Figure 04C · Where growth signals warrant a closer look.
Intensification, diversification and combined growth are isolated to support second-stage screening. Station-proximity areas remain highlighted, while the wider territory is retained as context.
These locations could be relevant for development or retail, depending on the decision at stake :
- for retailers, growth may signal expanding demand and increasing competition;
- for developers, an evolving amenity ecosystem—but not land availability, planning feasibility, housing demand or financial returns.
The map identifies where a second-stage investigation may be worthwhile. It narrows the search; it does not make the decision.
6. HOW DO WE MOVE FROM A REGIONAL SIGNAL TO A LOCAL INVESTIGATION?
A growth cluster is a lead to investigate, not an opportunity in itself.
To illustrate how the regional screen could support a next analytical step, one cluster of growth-positive hexagons is examined more closely :
BRAMPTON INNOVATION DISTRICT STATION
The selected area is not presented as Toronto’s strongest station environment, an investment recommendation or a ranking result. It is an illustrative case chosen to demonstrate how a metropolitan signal can be converted into a more focused territorial question.
Within the selected station area, four additional hexagons showed combined growth, nine gained functional richness, and seven recorded higher POI abundance.
The next question is not whether the map has identified an opportunity. It is what may explain the mapped change.
Figure 05 · From regional signal to local investigation — Brampton Innovation District Station.
A second-stage investigation could examine development applications, building permits, population and housing change, business openings and closures, rents, competition, pedestrian access and station service levels. Aerial imagery can provide additional physical context where construction, redevelopment or changes in land use are visible.
This local examination remains illustrative. Its purpose is to show how a regional change map can identify where more detailed evidence should be assembled—not to validate the POI signal on its own.
7. WHAT CAN AMENITY CHANGE TELL DEVELOPERS, RETAILERS AND PLANNERS?
The map narrows where to look next — and what evidence is still needed before a decision.
The strategic value lies in comparing station environments and corridor sequences rather than treating the entire buffer as one opportunity zone.
Retail and service networks
Intensification may indicate growing commercial activity - or rising competition. Diversification may identify neighbourhoods acquiring a broader mix of functions. Either can help reduce a metropolitan search to locations requiring demand, competition, footfall, income and rent analysis.
Real estate development
Amenity momentum can direct attention toward evolving urban ecosystems. It does not prove housing growth, demand or land availability; it indicates where development applications, zoning, ownership, infrastructure and market absorption deserve closer examination.
Transit and planning
Comparing stations can reveal where functional diversification accompanies the network and where station areas remain relatively unchanged. This can structure questions about access, land use, station integration and service distribution.
The method is transferable. Any geolocated objects observed at multiple dates can be aggregated into a stable grid and examined through abundance, diversity and change. Spatial data becomes strategically useful when it is organized around a decision question rather than merely displayed.
Interpret with restraint
Method in brief
Territory
Approximately 70 × 70 kilometres in a projected metric coordinate system, using Ontario Hydro Network waterbodies and watercourses as physical context rather than municipal limits.
Transit
Rail-based routes, stations and stops from OpenStreetMap via BBBike. A 1,750-metre Euclidean buffer was calculated around selected access points; hexagons were associated on intersect.
Points of interest
Historical OSM states from the ohsome API for July 1, 2023, and July 1, 2026. Matching features were converted to centroids and classified into eight retained functional categories.
GIS processing
Spatial processing, classification, aggregation and cartography were completed in QGIS, using native processing tools wherever they provided a clear and reproducible workflow.
Aggregation
Spatial join to a common 500-metre hexagonal grid, producing annual POI abundance and functional-richness counts.
Temporal comparison
Identical annual classes. Symmetric percentage change for abundance, with a ±25% threshold and minimum absolute difference of 3 POIs. Any category gain or loss changes the richness direction.
Limits to interpretation
1. OSM completeness and editing intensity vary across space and time; mapped changes are not verified openings or closures.
2. Category definitions depend on OSM tags and the classification rules used.
3. The 1,750-metre buffer represents straight-line proximity, not pedestrian travel time, and different transit modes and service levels share one framework.
4. Hexagonal aggregation can conceal property-level conditions and create edge effects.
5. The ±25% threshold and 3-POI floor are exploratory choices, not universal benchmarks; relative change can still magnify low-base effects.
6. The analysis excludes population, housing production, transactions, rents, zoning, footfall, development applications and commercial competition. It is not financial, planning or acquisition advice.
Sources
Data and map credits
- Analysis and cartography © NEXT SpaceStrategy, 2026.
- POI and transit data © OpenStreetMap contributors — ODbL. Historical extraction: ohsome API / HeiGIT.
- Transit extraction: BBBike.
- Hydrography: Ontario Hydro Network. Contains information licensed under the Open Government Licence – Ontario.
About Nicolas DELFORGE
Founder, NextSpaceStrategy
Nicolas Delforge is an independent real estate, GIS and IWMS consultant with more than 20 years of experience connecting spatial evidence, development decisions and real estate operations.
