
Jeff Sloan Playground
Urban Plaza, in the top tier overall (score 46, rank ~89th percentile). Strongest: enclosure; weakest: natural comfort.
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Jeff Sloan Playground scores 46.3 / 100. Strongest dimensions: enclosure / eyes on park and connectivity. Weakest: amenity diversity (11.9). Border-vacuum risk is low. This score is a transparent reading of Jane Jacobs-style vitality factors, not a definitive judgment.
Area · 0.17 ha
What's here
Getting around
No walking paths mapped in OpenStreetMap.
No seating mapped in OpenStreetMap.
Nearest parking 143 m away. None of the 3 nearby lots records whether it has accessible spaces.
Path, seating and parking data comes from OpenStreetMap and reflects what volunteers have mapped, not a survey. Absence here means nobody has recorded it. This section does not assess wheelchair access: the relevant OpenStreetMap tag is present on well under 1% of Toronto paths.
Weighted across six dimensions · confidence 66%
Data confidence: moderate
Scores are not bell-curved. Percentiles and expected scores provide context without changing the underlying model.
Loading map…
The parks map is loading.Boundary: OpenStreetMap contributors (ODbL).
Explain this score
Where did the 46 come from? Each weighted contribution against a neutral 50 baseline. Green = pushed up; red = pulled down.
Sum of contributions = the headline score. A negative bar means that dimension dragged the park below the city-wide neutral baseline.
Why this park works
Jeff Sloan Playground works because its enclosure score (87) is in the top tier and its edge activation (33) is also top quartile (13 mid-rise buildings frame the edge with passive surveillance).
What limits this park
Jeff Sloan Playground doesn't have a clear weakness. Every measured dimension is at or above the middle of the pack.
Most distinctive characteristic
Most distinctive feature: exceptionally high enclosure (87, top decile).
Jacobs reading
Jeff Sloan Playground sits between an urban social park and an ecological retreat: moderately useful for both, exceptionally suited to neither.
Tradeoffs
- Strong physical conditions (score 46) but weak observed activity signals (7). The model says this should work, but events, mentions, and counters say it isn't being used at the level the urban form would predict.
Performance in context
- A modest overperformer for its urban plaza typology (+9 vs the median in pocket Urban Plaza).
Typology classification
Classified as Urban Plaza: 1728 m², paved (0% canopy), 49.1 buildings/100 m
Edge Activation
Within 100 m of the park edge: 3 active uses (transit_stop) and 0 dead/hostile uses (none). Active edges keep "eyes on the park" through the day; parking lots, blank institutional walls, rail and highway frontages drain street life.
Source: OSM POIs (amenity/shop) + Toronto Building Footprints + land use
Connectivity
Connectivity blends paths, intersections, transit, entrances, and edge density. This park has 0 mapped paths/walkways and 9 sidewalk segments within 50 m; 10 street intersections within 100 m; 22 transit stops within a 400 m walk; 0 estimated access points across ~167 m of perimeter. edge density is healthy, no superblock penalty. Source coverage: centreline, pedestrian_network, transit_osm.
Source: Toronto Centreline V2 + Pedestrian Network + OSM transit stops
Amenity Diversity
1 distinct amenity types in the park (playground). Diversity, not raw count, drives the score so a park with many distinct activity types can outrank a larger park that repeats the same use.
Source: Toronto Parks & Recreation Facilities + OSM amenity tags
Natural Comfort
Natural-comfort components for this park: ~6.3% effective canopy (0.0% from contiguous tree polygons + scattered tree density); nearest waterbody ~669 m; 9 city-mapped trees inside the polygon (9.0/ha). Reading: exposed. Source coverage: waterbodies, street_trees. Impervious surface is approximated (Toronto's authoritative layer ships only as a raster GeoTIFF).
Source: Toronto Treed Area + Ravine + Waterbodies + Street Tree Inventory
Enclosure / Eyes on Park
82 buildings within 25 m of the park edge (13 mid-rise, 69 low-rise, 0 tower); avg edge height 7.7 m (~3 floors); 49.1 buildings per 100 m of 167 m perimeter (strong frontage density); edges are low-rise (mostly 2 to 3 floors); no towers immediately adjacent. "Eyes on the park" come strongest from the 13 mid-rise edge buildings.
Source: Toronto 3D Massing (building footprints + heights)
Border Vacuum Risk
Park edges face the city. No significant border vacuum detected.
Source: Toronto Street Centreline (highways) + rail layer + OSM landuse + building footprints
Equity Context
Equity Context requires inputs not yet loaded for this park (Toronto Neighbourhood Profiles). Score is held at a neutral 50 with low confidence. Read with caution.
Source: Toronto Neighbourhood Profiles
Amenities (1 types · 2 records)
- playground
Nearby active-edge features (9)
- transit stop: Dundas Street East82 m
- transit stop: Dundas Street East86 m
- transit stop: Columbine Avenue97 m
- transit stop: Dixon Avenue114 m
- transit stop: Dixon Avenue115 m
- retail: Toby's Food Market140 m
- parking lot143 m
- parking lot170 m
- parking lot189 m
Park profile
Five-axis radar across the structural dimensions.
Citywide percentile ranks
Across all Toronto parks in the dataset.
- Overall vitality89th
- Edge activation89th
- Connectivity66th
- Amenity diversity58th
- Natural comfort38th
- Enclosure93th
Most similar parks
Closest in metric space across the five structural dimensions.
- Bennett ParkUrban Plaza44
- Mount Pleasant ParketteUrban Plaza45
- Monsignor Fraser College ParkUrban Plaza47
- Brownfield SiteNeighbourhood Park50
- Santa Chiara ParketteUrban Plaza41
Most opposite parks
Furthest in metric space. Useful for recognising what kind of park this isn’t.
- Trca Lands ( 26)Ravine / Naturalized Park27
- Toronto Islands - Muggs Island ParkRavine / Naturalized Park25
- Rouge ParkRavine / Naturalized Park28
- Rouge ParkWaterfront Park25
- Rouge ParkRavine / Naturalized Park26
Human activity signals
Programming, social attention, temporal rhythm, and nearby pedestrian / cycling flow. An experimental aggregate layer that complements the spatial scores. Partial coverage, partial confidence.
Activity reading: no inputs available. The strongest signal is consistent rhythm across the day. Source coverage: google-places.
Does this score feel accurate?
Your read of Jeff Sloan Playground matters. We’re testing whether the model lines up with how people actually use the park. Submissions are stored locally; no account needed.
Tell us how this park feels
We measure structure (canopy, edges, connectivity). You measure feeling. Both matter, and disagreement is itself useful civic data.
What would improve this park?
Generated from the weakest measured dimensions: a starting point, not a prescription.
- Activate the edges: encourage cafés, retail or community uses on the streets that face the park; replace blank or parking-lot edges where possible.
- Diversify what people can do in the park (playground, washroom, water, shade, performance, sport, garden): even small additions raise this score.
- Increase canopy and reduce paved area. Shade and water features extend usable hours and seasons.
Data sources
- City of Toronto Open Data: Parks (Green Space)Polygon boundaries, official names, types. Boundaries are reconciled against OpenStreetMap (ODbL) for new and updated parks.
- Parks & Recreation FacilitiesInventory of in-park amenities (washrooms, fields, rinks…).
- Toronto Pedestrian NetworkSidewalk segments around and through parks; estimated park entrances.
- Toronto Centreline V2Street segments + intersection nodes near park edges; trails and walkways.
- Toronto 3D MassingBuilding footprints + heights for edge-building counts, frontage density, and tower-in-the-park risk.
- Toronto Treed AreaTree canopy share inside park polygons via stratified-grid sampling.
- Toronto Waterbodies & RiversWater surface inside parks + nearest-water distance for cooling.
- Ravine & Natural Feature ProtectionRavine overlap as a cooling / natural-comfort signal.
- Toronto Street Tree InventoryTree count + density inside park polygons.
- Neighbourhood Profiles(Pending) Equity context proxy.
- OpenStreetMap (Overpass API)Cafés, restaurants, retail, transit stops, parking, highways, rail.