Measure Walkability With 2 Useful Tools For 2 Good Reasons

For someone wanting to create better human habitat, there are two reasons to measure walkability. The first is to assess the gap between rhetoric and reality concerning what local governments say and do. The second is to provide a municipality that is committed to building at the human scale with the ability to clarify where they’re at today and identify impactful investments in the public realm to improve a community’s quality of life.

The public realm is everything that we experience (or access) from the sidewalk. Impactful investments might be the addition of street trees, the construction of a mixed-use building, or a park. I think of the sidewalk as the public realm’s backbone.

Two tools of relevance measure (or quantify) walkability. To assess the gap between what’s said and done in a municipality, we’d use Walk Score. To identify impactful investments to positively impact walkability, we’d use State of Place.

Speaking of Walk Score and State of Place in the same breath suggests there are similarities between the two. But they’re quite different tools used for different purposes.

Measure Walkability and Assess the Rhetoric/Reality Gap with Walk Score

The auto-dependent nature of new housing that gets built within a municipality is frequently at odds with claims made by local governments regarding their intention to pursue walkable, mixed-use development. Examining the gap between what’s said and done can be a sobering exercise. Quantifying walkability with Walk Score brings the gap into focus, making it possible to assess the degree to which local governments pursue human-scale development.

Perhaps you’re already familiar with walk scores? Simply enter an address at walkscore.com and you’ll get back a walk score that ranges from 0 (entirely car-dependent) to 100 (eminently walkable).

Alternatively, you may visit one of many websites that connect to Walk Score via their API (application programming interface). If you go to Zillow, for example, and bring up a listing, you’ll find it displays that property’s walk score. Under the hood, Zillow is providing Walk Score with an address, and in return, Walk Score is sending Zillow back a score. 

Assessing the Gap

Assessing the Rhetoric/Reality gap is a straightforward four-step process available to anyone:

1. Review master planning documents for the municipality (available online). Note what’s said regarding sustainability, mix-use development, and walkability.
2. Within the plan area of interest, search for new homes on realtor.com. 
3. Step through a reasonably sized set of houses (e.g., 50 or more), copy/paste addresses into walkscore.com, and calculate an average and median score for your sample. (Excel is your friend here.)
4. As you work through the list, look up the address in Google Maps to get a sense of road patterns and proximity to other uses. Note whether there is anything that looks remotely like a walkable environment in the making, suggesting scores might later increase. (Google Street View is your friend here.)

With an average walk score in hand, and factoring in what you see happening in the municipality, you’ve got some sense of the gap between rhetoric and reality.

In early 2024, I ran through the steps above for the two municipalities where I worked as an urban planner in the U.S. and Canada. The average walk scores for recent development at the time were 18 and 8.5, respectively. Despite these two municipalities having much to say about sustainability and climate change, results on the ground show that both abide by the rule of perpetual urban sprawl. The Canadian municipality in particular serves as a cautionary tale regarding how rhetoric can bear little resemblance to facts on the ground.  

There are four achievements municipalities would need to attain to consistently build human-scale projects generating high walk scores. Quantifying the walkability of new construction with Walk Score can support more informed discussions and help move the needle in the right direction.

Walk Score’s Origins

The Walk Score algorithm is the brainchild of Jessie Kocher and Matt Lerner—two urban-oriented techies who studied computer science at Brown University in the 1990s. The algorithm produces a score for any address in the country by considering aspects of urban form, such as street connectivity, block length, and distances to amenities.

A key benefit of Walk Score is that its inputs draw from publicly available data sources such as the U.S. Census and Google and the coverage is vast. You can generate a walk score for any address in the United States, the UK, Canada, or Australia.

Wanting their algorithms to produce a score that is as accurate a measure of walkability as possible given the data sources available, Lerner and Kocher made the methodology and limitations known to others.

Walk Score’s Limitations

Walk Score’s limitations become apparent when comparing the score produced for the house in the neighborhood where my wife and I raised our children in Halifax, Nova Scotia, with two past home addresses in Philadelphia and Washington, DC. 

The Halifax home produces a score of 89 (very walkable). My former address in Philadelphia at 501 North 32nd Street generates a score of 86 (Very Walkable) and the address in Washington DC (2803 11 St. NW) produces a score of 94 (Walkers Paradise). 

These scores may suggest the Washington, DC address provided us with the best environment, but in reality, the Halifax address provides more convenience in terms of accessing a variety of places that meet monthly needs. The Halifax neighborhood was safer and more conducive to raising children for reasons I’ve described in a separate article.

All three scores suggest a similar lifestyle can be had in each location. Yet living at either the Philadelphia or Washington addresses comes with complications, shortcomings, and dangers that don’t exist in Halifax for reasons related to America’s unique domestic history that negatively affect many cities in the present day. Walk Score provides a starting point for discussion, but underlying calculations, such as block size and proximity to businesses, are only part of a broader set of factors affecting walkability.

The fact is, there is no nationwide data set that considers the myriad of conditions our mind consciously and unconsciously evaluates when deciding how comfortable we are using our feet rather than a car. That’s not Walk Score’s fault and the tool’s existence has done more to advance a broader discussion about walkability than all the academic research combined. But the limitations are there.

There is, however, another tool that accounts for a broader set of conditions that shape perceptions about the quality of a walk. Here I’m referring to the aforementioned State of Place.

Quantify Walkability and Identify Impactful Investments with State of Place

State of Place is something vastly different from Walk Score. Whereas Walk Score’s raison d’être is to produce a score for a specific address, State of Place is a decision support tool that helps organizations identify impactful investments that improve the walkability of the area under consideration. It’s a software solution that public and private organizations license with help from State of Place staff. For this article, let’s assume it’s in the hands of municipal government.

At its core, State of Place quantifies the degree to which the built environment that surrounds us influences our willingness, indeed our desire, to use our feet to get from point A to point B.

State of Place allows you to establish priorities relating to aesthetics, built form, traffic safety, and more. You can then run simulations to grasp the impact of potential choices made and leverage sophisticated forecasting models to see longer-term economic, social, and environmental impacts.

Below is a high-level outline of State of Place’s workflow. It leads a municipality to a set of recommendations regarding investments that produce a more walkable environment.

Stage 1: Assess

The first stage involves producing a quantitative score and accompanying analysis that State of Place staff uses to drive the rest of the process. Steps include:

1. Identify physical blocks to analyze to quantify walkability. This makes up a study area.
2. Assemble street-level photographs for each block.
3. Leverage AI to analyze street-level photographs in the study area, looking for the presence of 120+ physical features understood to enhance or degrade walkable environments.  
   • Features of interest include street trees, parking lots and their relation to buildings, building height, building type, presence of garage doors, building condition, presence of trash, broken windows, freeway overpasses, number of travel lanes, cul-de-sacs, marked street crossings, curb cuts, and more.
4. Generate a State of Place Index, which is an overall score for the study area based on an automated analysis of the features collected.
5. Generate a State of Place Profile, based on the same automated analysis, which is a set of 10 individual scores corresponding to 10 key “dimensions” of the physical environment known to influence the degree to which we’re willing to walk places. Below, I list these dimensions verbatim as defined by State of Place:
   • Density – Building compactness and height
   • Proximity – Access to non-residential destinations and diversity of land use
   • Pedestrian & Bike Amenities – Features that make it comfortable for pedestrians and bicyclists
   • Aesthetics – Urban design features that make places dynamic and inviting
   • Form – Streetscape continuity and enclosure
   • Public Space – Presence, quality, and access to hard and soft-scaped public places
   • Connectivity – Ease of access, lack of pedestrian barriers
   • Recreational Facilities – Presence of outdoor and indoor physical activity facilities
   • Traffic Safety – Quality and safety of the intersections and traffic calming measures
   • Personal Safety – Features that influence perception of personal safety.

The algorithm that produces the State of Place Index and State of Place Profile is an implementation of a data model developed by researchers at the University of California known as the Irvine Minnesota Inventory. The model provides an objective measure of the degree to which the presence or absence of distinct physical features influences our willingness or ability to walk.

Stage 2: Produce Customized Recommendations

With overall scores (i.e., State of Place Index) and analysis (i.e., State of Place Profile) in hand, staff works with a municipality to produce a set of goals and customized recommendations. Steps include:

1. Establish broad goals to increase walkability, rents, safety, and more.
2. Prioritize the 10 dimensions of the physical environment that comprise the State of Place Profile.
3. Set levels of feasibility for changing each of the 10 dimensions.
4. Generate automated recommendations based on goals, priorities, and feasibility. Recommendations are driven by proprietary forecasting models factoring in the AI-generated data and goals/priorities/feasibility.

Stage 3: Analyze Scenarios

With goals and recommendations defined, staff works with a municipality to produce Sim-City-like simulations (scenarios) to assess the effectiveness of various decisions shaping the public realm. Steps include:

1. Use a State of Place module similar to SimCity to make detailed changes to the public realm within the study area and see the impact of changes in real time.
2. Recalculate the State of Place Index and State of Place Profile based on changes.

Stage 4: Forecast Outcomes

Staff and the municipality run forecasting models to gain insight into some of the longer-term impacts of choices made. Steps include:

1. Run proprietary forecasting models to determine how proposed projects will impact a range of outcomes, such as pedestrian safety, health, real estate value, transit ridership, air quality, and more.
2. Models clarify ROI, justify investments, and lay the groundwork for constructive stakeholder engagement to get buy-in.

No Inherent Limitations with State of Place

As mentioned, Walk Score’s limitations relate to is use of publicly available, nationwide datasets (e.g., census data, Google Maps) that don’t contain information about features such as trees, parking lots, building height, or whether there’s trash on the ground and graffiti on the walls. Within a study area, State of Place knows about all of this and more. As such, there are no significant limitations concerning the level of analysis produced.

Initially, people had to manually gather and input the detailed information for State of Place’s analysis. They had to be trained to know what to collect as they walked the blocks and recorded their observations. Later, State of Place introduced an AI engine to analyze street-level images and auto-generate feature information. As of this writing, State of Place has analyzed over 50,000 blocks.

State of Place’s Origins.

Urban design academic and entrepreneur Mariela Alfonzo founded State of Place as a software company in 2017. This event was the culmination of over 15 years of research and collaboration, examining how the built environment shapes the quality of our lives from a social, economic, environmental, and health perspective.

In the years leading up to the State of Place’s launch, Alfonzo and her colleagues progressively refined data models, tools, and ideas with the backing and collaboration of organizations such as the Robert Wood Johnson Foundation, Virginia Tech, the Brookings Institution, NYU, and the National Science Foundation. Many people understood the value proposition of quantifying walkability to make better choices and create optimal outcomes.

Putting State of Place’s Impact in Context

State of Place summarizes its mission as:

Citymakers struggle to build trust, drive consensus, & optimize resources needed to create livable, equitable & sustainable places.

State of Place helps you harness the power of data to more quickly, efficiently, & effectively deliver places people deserve.

In the end, State of Place’s effectiveness depends upon municipal leaders committing to human-scale development. And it’s rare to find a municipality that can credibly state that it has rejected producing auto-dependent development in favor of building at the human scale.

But there are exceptions. Consider the case of Tigard, Oregon, which licensed State of Place after committing to breaking with auto-dependency and embracing walkable, mixed-use development in a 500-acre section of the municipality referred to as the Tigard Triangle. New regulations and results on the ground show that this jurisdiction has indeed broken with the status quo.

Tigard understands what human-scale design entails and they’ve created development regulations such as those shown below, which include key human-scale design principles such as limiting building height to 6 stories.

Evidence of putting these standards into practice appears in the form of new multi-family buildings like the one shown below which contribute to a pedestrian-friendly public realm.

Tigard’s use of State of Place was not solely responsible for their shift in development patterns. But the tool’s use played a role. It helped clarify where Tigard was in terms of providing residents with a walkable environment in which to live. And it helped shape a viable future with a level of clarity that they might not have had otherwise.