Walk Score Rankings Methodology
We sampled the Walk Score of 10,017,714 locations to rank 2,500 U.S. cities and 10,017 neighborhoods on walkability for 2011.
Planners and Researchers: Learn about using Walk Score data in your research.
Data Sources
| Walk Score algorithm | Measures the walkability of individual addresses based on proximity to nearby amenities. |
| City boundaries | Provided by the U.S. Census 2010 Incorporated Places. Note: this is the city boundary not the entire metro area. |
| Neighborhood boundaries | Provided by Zillow.com. |
| Population data | Used to weight Walk Score by population density, data from the 2010 U.S. Census. |
The Walk Score Point Grid and Population Density Weighted Walk Score
The point grid.
Within a city or neighborhood, we sample the Walk Score of approximately each city block. To do this, we create a grid of points spaced roughly 500 feet apart (.0015 decimal degrees; exact distance will vary with latitude).
We weight the Walk Score of each point by population density so that the walkability rankings reflect where people live and so that neighborhoods/cities do not have lower scores because of parks, bodies of water, etc.
We use the following algorithm to calculate a population density-weighted Walk Score:
For each point in the grid:
- Expand each point by .00075 decimal degrees to create a grid cell
- Intersect the grid cell with the census blocks it intersects and for each census block:
- Calculate % of the census block the grid cell intersects
- Multiply that % by the total population of that census block
- Sum these partial populations to get the grid cell population
- Add the grid cell population to a variable called total_population
- Calculate the Walk Score at the center of the grid cell and multiply it by the grid cell population to get the weighted Walk Score
- Add the weighted Walk Score of this grid cell to a variable called weighted_walk_score
To calculate the Walk Score for an entire neighborhood/city, divide weighted_walk_score by total_population for the points within the boundary of the neighborhood/city.
The population total we display for neighborhoods/cities is the total_population variable mentioned above.
To calculate the Walk Score for a city, we include only points within the city boundary. This may exclude points that are inside a Zillow neighborhood boundary but outside the city boundary.
Walkability Heat Map and Neighborhood Rollovers
Before/after smoothing.
Heat maps: To generate the walkability heat map, we create a very small grid and assign each grid cell a color based on the Walk Score of the surrounding points. A spectrum of red to green is used to represent the range of Walk Score from 0 to 100.
Neighborhood rollovers: We show the Zillow neighborhood boundaries on the map when a user hovers over them with their mouse. To do this quickly, we simplify the Zillow neighborhood polygons using the Douglas-Peucker algorithm before transmitting them to the browser. In the browser, we use JavaScript to index all of the polygons using horizontal and vertical slices. This allows us to quickly determine which polygons might be intersected by a given mouse location before doing more detailed boundary checking.
Walk Score Data for Planning and Research
We provide walkability, public transit, and road connectivity data to researchers and planners. Learn more about using Walk Score data in your research.