Photo courtesy of Tony Fischer, Carpe Diem Photography, via Flickr.com

It is clear that we Americans face many challenges today. The prospect of global climate change has many of us look for ways to reduce our carbon footprint. Record high energy prices earlier this year have made us all aware of how vulnerable we are to such price spikes. Such challenges are daunting, but many people have turned to an unlikely solution: walkability.

The core principle of walkability is quite simple: give people the option to live their lives without having to get in a car. Less need for a car instantly produces a number of positive individual benefits including 1) paying less for fuel, maintenance, parking, and insurance, 2) less exposure to energy price spikes, 3) reduced greenhouse gas emissions, and 4) a healthier, more active lifestyle. And these benefits become more pronounced if you are able shed a car altogether: no more car payment!

This is all well and good, but how can I find a walkable community? I’m glad you asked.

While living in Seattle, I became intrigued by Alan Durning of the Sightline Institute and his concept of a “walkshed” that scored a location based on the quantity and diversity of amenities within a one-mile radius. A year later, Walk Score, which drew heavily from Durning’s walkshed concept, went live as the first application in the world to map walkability. While Walk Score is a fantastic application with a clever methodology, it has a number of acknowledged limitations. Using Philadelphia as a prototype and as part of Azavea’s 10% research project program, I am currently researching ways to overcome some of these limitations to more accurately calculate and map walkability.

Map showing the walking distance from points in Philadelphia to the closest train, subway, or trolley stop.

The first requirement of this new methodology is the ability measure the walkability of a location by determining the actual walking distance to a variety of assets. In many cases, “as-the-crow-flies” distances are accurate enough, but that accuracy can degrade quickly with the presence of barriers (rivers, highways, etc), disjointed street networks, or extreme topography. In other words, I need to be able to programmatically detect the actual walking distance to my favorite restaurant on the other side of the Schuylkill River. It may only be a quarter mile as the crow flies, but being bound to the street grid could significantly increase that distance.

The most time consuming step was the development a friction layer for the entire city. This layer had to accurately represent the “friction” a person would encounter walking around the city. For example, a city street or park would represent low walking friction while navigating across a river or highway would be quite high. By taking streets, trails, parks, rivers, highways, and railroad tracks into account, I was able to calculate the walking friction of every point in Philadelphia. This friction layer now allows me to calculate the walking distance to any defined point in the city. Above, you see a sample screenshot that represents the walking distance from every point in Philadelphia to the closest train, subway, or trolley stop.

I find this research incredibly fascinating, but the best part is that this project is just getting started! I have several new walking distance layers in the queue for amenities like bus stops, car-share locations, parks, grocery stores, farmers markets, cultural venues, and more. After these data sets are complete, I have plans to roll out a publicly available web application built on Azavea’s DecisionTree product. This application will not only mimic most of Walk Score’s functionality, but will allow each user give personalized weights to each walkability indicator.