Walk Score. Drive less, live more.
Walk Score Blog

2014 City and Neighborhood Ranking Methodology

Our 2014 ranking is the first time we’ve calculated city and neighborhood scores using our Street Smart Walk Score algorithm.  To calculate the rankings, we scored over 10 million locations and computed over 2 billion walking routes for 2,500 U.S. cities and more than 10,000 neighborhoods.

We’ve made a number of significant improvements to the Walk Score algorithm:

  • Walking routes: Billions of walking routes are quickly calculated using our Travel Time API.
  • Depth of choice: To capture what makes a place truly walkable, we’re analyzing hundreds of nearby amenities for each location to measure depth of choice (see ChoiceMaps for more info).
  • Pedestrian friendliness: We’ve improved our analysis of pedestrian friendliness with better road metrics such as intersection density and average block length.
  • Mixed use: We’re using population data to determine whether a neighborhood is mixed use (residential and commercial) or single use.
  • Improved local data: We’ve continued to improve our local data sources, including over 35,000 additions and removals of places from Walk Score users.
Walk Score Point Grid

Walk Score Point Grid

Ranking Methodology

To rank cities and neighborhoods, we calculate the Walk Score of approximately every city block (technically a grid of latitude and longitude points spaced roughly 500 feet apart).

Each point is weighted by population density so that the rankings reflect where people live and so that neighborhoods and cities do not have lower scores because of parks, bodies of water, etc.

Roosevelt Island: Before and After

Since this is the first city and neighborhood ranking we’ve done with our Street Smart algorithm, here’s a fun island example.  You can see the score decreasing for Roosevelt Island since water barriers prevent residents from accessing nearby Manhattan.

The Walk Score for Roosevelt Island decreases due to water barriers.

The Walk Score for Roosevelt Island decreases due to water barriers.

How Scores Are Changing

Almost all of our city and neighborhood scores have changed — some improved and some declined. The trend line in the graph below shows that neighborhoods with low scores decreased the most. This is likely due to longer routed distances, poor road metrics like intersection density and block length, and a lack of mixed use development.

Neighborhoods with high scores tended to improve a little because our Street Smart algorithm records high depth of choice in categories like restaurants and shopping.

Some neighborhoods improved, some declined.

Some neighborhoods improved, some declined.

A More Complete Picture of Location

With these updates to the Walk Score algorithm, our expansion to over 300 cities with Transit Score and 100+ cities with Bike Score, we’re able to provide a more complete picture of what’s outside the four walls of a home or apartment.

Thanks for your support!

Leave a Reply