Most local search applications are atomic in nature - they return either individual things (e.g. a restaurant) or collections of things (e.g. coffee shops near a specific location). WalkScore is a site that provides aggregate search results - a computed value for the walkability of a location.
These values - your walk score - can be visualized as a heat map. Below, for example, is the distribution of walk scores for Seattle:
Note how the more walkable areas are in the centre where housing is denser and the richer neighbourhoods (with water frontage) are less walkable - thank goodness for those expensive cars!
Selecting a specific neighbourhood by mousing over it will also provide a graphical distribution of walkscores:
In addition to the walk score, for some cities, a transit score and heat map are available:
The site has a lot of information about how they compute walk and transit scores:
Walk Score measures how easy it is to live a car-lite lifestyle—not how pretty the area is for walking.
Walk Score uses a patent-pending system to measure the walkability of an address. The Walk Score algorithm awards points based on the distance to amenities in each category. If an amenity is within .25 miles (or .4 km), we assign the maximum number of points. The number of points declines as the distance approaches 1 mile (or 1.6 km)—no points are awarded for amenities further than 1 mile. The points are summed and normalized to yield a score from 0—100. The number of nearby amenities is the leading predictor of whether people walk.
More information can be found starting here.
I find this type of application interesting when considering the competition around local search. One area to innovate is in the UI and one possible area of innovation is to get away from applications of local data which are in the atomic paradigm.
WalkScore is based in Seattle.
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