AccessScore: Regions API #

The AccessScore: Regions API returns an accessibility score in the range (0, 1) for each region (neighborhood) — higher is more accessible. A region's score is the street-length-weighted mean of the AccessScores of its audited streets, so long streets count proportionally more than short ones.

AccessScore extends the method introduced in Li et al., A Pilot Study of Sidewalk Equity in Seattle (Urban Access 2022). It is experimental and the weights are subject to change. See the AccessScore: Streets API for how individual street scores are computed.

AccessScore: Regions Preview #

Below is a live preview of region AccessScores in Hackensack, New Jersey, retrieved directly from the API. Each region is colored from red (low accessibility) to green (high); use the Color by dropdown to switch between the AccessScore and audit coverage. Hover or click a region to see its score and coverage.

One method, not the method. There is no single correct way to measure street- and neighborhood-level accessibility—researchers and practitioners use many different approaches. AccessScore implements just one such algorithm, and it is experimental: the weighting is deliberately simple and subject to change. Treat these scores as one lens rather than a definitive measure, and compute your own index from the Label Clusters API if a different method suits your needs better.

Endpoint#

Returns a region boundary and its AccessScore for each region in the queried area, optionally filtered by the Query Parameters below.

GET /v3/api/accessScoreRegions

How the score is computed#

Each of the region's audited streets is scored as described in the AccessScore: Streets API. The region's score is the mean of those street scores weighted by street length: Σ(score × length) / Σ(length). Unaudited streets are excluded. When a region has no audited streets, its score is null.

coverage reports the fraction of the region's streets that have been audited, so you can judge how complete a region's score is.

Quick Download #

Download region AccessScore data directly in your preferred format:

Note: This downloads scores for all regions. For filtered data, use the API Query Parameters described below.

Query Parameters#

All parameters are optional.

Note: When multiple location filters are provided (bbox, regionId, and regionName), bbox takes precedence over region filters, and regionId takes precedence over regionName.

Responses#

Success Response (200 OK)#

On success, the API returns 200 OK and the requested data in the specified filetype format.

GeoJSON Format (Default) #

Returns a GeoJSON FeatureCollection where each feature is a region (MultiPolygon, WGS84 / EPSG:4326).

{
    "type": "FeatureCollection",
    "features": [
        {
            "type": "Feature",
            "geometry": { "type": "MultiPolygon", "coordinates": [...] },
            "properties": {
                "region_id": 8,
                "name": "Terhune Park",
                "score": 0.5623,
                "coverage": 1.0,
                "audited_street_count": 52,
                "total_street_count": 52,
                "avg_cluster_counts": {
                    "CurbRamp": 2.1, "NoCurbRamp": 0.3, "Obstacle": 0.4, "SurfaceProblem": 0.6,
                    "NoSidewalk": 0.5, "Crosswalk": 0.9, "Signal": 0.1
                }
            }
        },
        ...
    ]
}

GeoJSON Field Descriptions #

CSV Format #

If filetype=csv, the first row is the header. The per-type avg_cluster_counts are flattened into one avg_n_* column per label type, and the geometry is simplified to the region centroid.

region_id,name,score,coverage,audited_street_count,total_street_count,avg_n_curb_ramp,avg_n_no_curb_ramp,avg_n_obstacle,avg_n_surface_problem,avg_n_no_sidewalk,avg_n_crosswalk,avg_n_signal,center_point
8,Terhune Park,0.5623,1.0,52,52,2.1,0.3,0.4,0.6,0.5,0.9,0.1,"-74.012,40.895"
...

Shapefile Format #

If filetype=shapefile, the response is a ZIP archive of Shapefile components (.shp, .shx, .dbf, .prj). Because the DBF format truncates column names at 10 characters, the per-type columns use short codes (e.g. aCRamp); the GeoJSON, CSV, and GeoPackage formats keep the full names.

GeoPackage Format #

If filetype=geopackage, the response is a GeoPackage (.gpkg) file with full geometry and attributes (full snake_case column names).

Error Responses#

• 400 Bad Request: Invalid parameter values (e.g., malformed bounding box, non-positive or unknown region id).
• 500 Internal Server Error: An unexpected error occurred on the server.

{
    "status": 400,
    "code": "INVALID_PARAMETER",
    "message": "No region found with regionId 99999.",
    "parameter": "regionId"
}

Best Practices#

• Read coverage alongside score: a high score over low coverage reflects only the audited fraction of the region.
• Treat the score as relative, not absolute: the weights are experimental. Use scores to compare regions, not as a calibrated index.
• Drill down: use the AccessScore: Streets API to see which streets drive a region's score.

Contribute#

Project Sidewalk is an open-source project created by the Makeability Lab and hosted on GitHub. We welcome your contributions! If you found a bug or have a feature request, please open an issue on GitHub.

You can also email us at sidewalk@cs.uw.edu

Project Sidewalk in Your City!#

If you are interested in bringing Project Sidewalk to your city, please read our Wiki page.