State of Place is a Predictive Urban Data Analytics Platform that
Makes the Case for Great Places.
Here’s How We Do That:
Quantifying What People Love about Cities
The Data: Why 290+?
We collect data on over 290 built environment features – like street trees, sidewalks, benches, curbcuts, etc. – related to walkability at the street level. Here's a handy guide with all of the features we collect, grouped into various urban design categories. These "micro-scale" built environment features are what impact the "touch, see, and feel" of walkability - the things that actually impact our choice to walk, and the experience of that walk. We often get asked how we differ from other known walkability measures, like Walk Score. The fact that we measure these nitty-gritty features is one of the key differences between a robust measure of walkability and place quality, like the State of Place Index, and a "proxy" for walkability that's based primarily on density (and to a certain extent, quality) of destinations, like Walk Score. Consider that walkability is more than just access to and quality of destinations - it's about the safety, comfort, and pleasurability of the walk as well. The micro-scale nature of the data that we collect allows us to offer a more comprehensive measure of walkability that is more suitable for diagnostic purposes, which is partly what helps position State of Place as an effective data-driven, evidence-based planning, policy, and investment tool.
THE DATA: WHY these 290+?
State of Place's foundation was set by an objective audit tool known as the Irvine Minnesota Inventory (IMI). The IMI was developed between 2003-2005 by a team of researchers at the University of California, Irvine: Kris Day, Marlon Boarnet, and yes, Mariela Alfonzo (our very own Founder and CEO) and then tested for reliability with help from researchers at the University of Minnesota (hence the name). Funded by the Robert Wood Johnson Foundation, through a decade-long partnership with Active Living Research, the goal of the IMI was to create an objective measure of the built environment features that (were then hypothesized to) impact physical activity, including both purposeful walking (walking to destinations) and recreational walking (walking for leisure or exercise).
At the time, empirical connections between the built environment and health (via the mechanism of physical activity and walking) were scant, partly because researchers lacked tools to quantify the urban design features that might impact this relationship. The IMI aimed to fill this gap. Accordingly, its developers aimed to include an extensively comprehensive list of built environment characteristics that might impact people's decisions to walk. After scouring nearly 60 different neighborhoods in California, ranging from 1) low, middle, high-income; 2) urban, suburban, exurban and rural; 3) and residential, mixed-use, and commercial, an initial list of over 240 features was developed; it was streamlined to 162 after reliability testing was conducted in Irvine and Minneapolis.
The tool was used widely in academia and even in practice for several years. Then, in 2011, Alfonzo and Day began to set their (research) sights on China, having grown incredibly interested by its rapid pace of (mostly auto-oriented) development coupled with its rising obesity rates. Seeking to empirically establish the links between the built environment and health in China that had been well-acknowledged in the Western context by over a decade's' worth of research findings, they knew the first step in achieving that aim was to update and adapt the IMI. After canvassing over 15 neighborhoods throughout Beijing and Shanghai and carefully noting all of the needed modifications illuminated by years of applying the tool, the IMI was revised, adding a number of items, most notably features that would impede pedestrian movement, characteristics of the streetscape, and a number of more detailed land use types. Turns out a lot of the new items inspired by direct observation in the streets of China would impact walkability no matter what. So, and then there were...290!
State of Place Index
Our proprietary algorithm aggregates the 290 data points into an index from 0-100, that indicates how walkable – convenient, safe, comfortable, and pleasurable – a block, group of blocks, or neighborhood is. The highest score is determined by the highest observed score in our database, as you don't need to have all 290 features to be considered walkable or to have good quality of place. How do we know that the highest observed score in our database is actually walkable? Well, I'm glad you asked!
You see, when we first applied the algorithm, we carefully constructed a sample of neighborhoods - as part of a Brookings Institution study our Founder co-authored with Chris Leinberger - that varied from low to high walkability and everything in between. We started with the "universe" of neighborhoods in the Washington D.C. metropolitan region - 201 neighborhoods to be exact. We placed these neighborhoods along a "continuum" of walkability based on their Walk Scores and divided them into 5 levels of walkability according to the mean (average score) and standard deviation (on average how "far" was each neighborhood away from the average score) and then sampled a random, representative number of neighborhoods from each level (this sampling strategy is called random representative stratified sampling). In total, there were 66 neighborhoods in the sample, that served a "microcosm" of the various types of neighborhoods found in the region (and across typical U.S Metros) and included a mix of urban, suburban, exurban and rural communities with a variety of land-use mixes, densities, and of course, walkability. Since collecting data for 1500+ blocks across the original sample of 66 neighborhoods, we have quadrupled our database - and the original scores have pretty much held constant. While the neighborhood I used to call home in NYC, the East Village, did "bump" off the original highest scoring neighborhood, that's exactly what you'd expect and shows that our original meticulousness in carefully constructing our sample paid off!
What this means in plain speak is that we did not "a priori" (sorry, I know that's not plain speak, but I love using this term - it means beforehand, essentially) decide what a neighborhood should and should not have to be considered a great place. We simply let the numbers do the talking. In that same vein, we do NOT assign weights to any specific features over others. More on that below...
State of Place Profile
The State of Place Index is composed of a set of ten urban design dimensions – the State of Place Profile – empirically known to impact people’s decisions to walk, based on a meta-analysis (study of studies) of the relationship between the built environment and walking/physical activity. The State of Place Profile serves as a walkability diagnostic tool, highlighting an area’s built environment “assets and needs,” or why a community is or is not walkable. As with the State of Place Index, the lowest and highest scores for each dimension in the Profile is based on the lowest and highest naturally occurring blocks and neighborhoods in our (growing) database! Additionally, we do not assign weights to any of the ten dimensions - and no, that doesn't mean that something that seemingly doesn't matter as much (like recreational facilities) ends up counting more - because the Index is NOT an average of the ten dimensions. Remember, the Index is scored relative to the Index scores for all blocks and neighborhoods. This is done by design. If we weigh stuff, communities won't know - objectively - what features are present or absent (what's working and what's not). This is also why the State of Place Index (and Profile) are perfect "independent" variables (academics, we're talking to you!). The "weights" come in after...as we explain below.
Making the Case for Great Places
Based on the Brookings Institution study, mentioned above, the State of Place Index is linked to economic value – in other words, as the State of Place Index increases, so do real estate premiums, significantly. In addition to collecting built environment data for that carefully constructed sample of 66 neighborhoods reference above, we also collected data on their average office rents, retail rents and revenues, and residential for-sale and rental rates. Holding household income constant, we found that for each additional 20 points on the State of Place Index, we saw increases of about $9/sq.ft. for office rents, $7/sq.ft, for retail rents, an 80% increase in retail revenues, a $300 increase in residential rents, and an $81/sq.ft. increase in for-sale residential values.
Each of the ten urban design dimensions that make up the State of Place Profile have a different “magnitude” of impact on different real estate premiums. For example, pedestrian amenities may add more value for retail vs. office tenants; traffic safety may add more value than aesthetics for residential homebuyers. Using a technique known as "multi-criterion analysis," our analytics platform sets evidence-based urban design priorities based on what customers goals actually want to achieve (some communities may want to just focus on built environment changes or development that actually increase pedestrian volumes while others may want to implement projects most likely to boost retail revenues). Additionally, some of the ten urban design dimensions are harder to change the others (urban form, connectivity, and density are hard to change once they are in place while aspects such as aesthetics and personal safety might be lower hanging fruit in terms of implementing changes - sometimes a coat of paint can go a long way!). Accordingly, customers can adjust the feasibility of making some changes over others (to reflect their specific context - for example, if you're building a community from scratch or already have plans to raze the existing street grid, all of a sudden, changing form, connectivity and density is relatively easy). The Prioritization analysis then takes into account the existing conditions of the area (State of Place Index and Profile, the goals customers indicated they want to maximize, and the feasibility of making changes and arranges the ten dimensions in order or priority and indicates how much more important it is to focus on improving certain dimensions over others. In other words, the platform identifies which types of built environment changes will lead to the biggest bang for the buck!
Our urban data analytics platform not only quantifies the walkability and quality of place of existing places, but it can also project out the walkability of proposed development projects, conceptual schemes, and masterplans. In other words, customers can essentially play "SimCity" - adding benches, trees, widening sidewalks, adding mixed-use development - to a block, set of blocks, neighborhood, etc. - and see how that would impact the State of Place Index and Profile - in real time (ok, you have to sing the Jeopardy theme song for a few seconds while our servers crunch the numbers, but basically!). In other words, cities can use this to test how their plans would actually improve quality of place - and even use it to objectively evaluate responses to their RFPs! And developers can use it to see how their would plans "measure" up to the State of Place Index of competing properties - and add amenities if their original visions fall short! That can like save, lots of money and heartache! Yeah, we know... :)
Ok, so remember how we told you about the Brookings Institution study findings? Yeah, those premiums tied to the State of Place Index are impressive - but kinda still academic. Pointing to these kinds of studies is certainly helpful (yeah, that's why our Founder spent four years of her life conducting it!), but can they get you to turn your naysayers from nay to yay? We thought so. That's why we created a forecasting model to help translate what the increase in State of Place Index that our scenario analysis quantifies into what this means to the BOTTOM LINE - because money talks. So based on the Brookings findings, we created a forecasting model to predict how changes in the State of Place Index may impact economic value in other parts of the U.S. Specifically, we calculate how projected increases to the State of Place Index (based on your proposed plans, projects, and developments - or what you conjured by while essentially playing SimCity) would impact real estate premiums, and based the amount of existing and/or proposed square footage of office, retail, and/or residential space, and what your preferred return period is, we calculate the value capture of the project. And then based on the estimated project cost, we calculate the ROI of the project! This allows customers to not only identify which projects would lead to the highest ROI, but also, yes, Make the (Data-Driven) Case for Great Places!! That's why we love us some DATA!! Now, stop geeking out and go create places people love!!!