By Reid Ewing
Scenario planning in the U.S. has only continued to grow in acceptance and impact since I first wrote about it in this column over a decade ago ("Regional Scenario Plans and Meta-Analysis," March 2007). But I have never written about the software innovations that have helped fuel this growth. UrbanFootprint, Envision Tomorrow, CommunityViz, and INDEX PlanBuilder are just four of the leading software packages out there and ready to be put to use.
This column focuses on the first of these, UrbanFootprint, which arguably is on the fastest growth curve with users and customers from Kimley Horn and Cambridge Systematics, to HDR and AECOM, Envision Utah, City of San Diego, University of Michigan, Cornell University, and dozens of other public and private institutions. But the other three are certainly worthy of your consideration, particularly Envision Tomorrow, since it is open source. (See links to reviews of these tools in the author bio.)
UrbanFootprint is a cloud-based software platform developed by Joe DiStefano and Peter Calthorpe. As principals of Calthorpe Associates, they led major regional and large-scale planning projects for nearly 25 years prior to spinning off UrbanFootprint as a dedicated software company.
From 2008 until 2012, the firm worked with the state of California to develop scenarios and models to inform major legislation in support of the state's greenhouse gas reduction goals. The first generation of UrbanFootprint emerged from this process and was used by metropolitan planning organizations to develop and model regional plans and policies.
Today, UrbanFootprint's broad collection of features empowers planners, designers, analysts, and advocates by dramatically reducing the time it takes to collect the data. With access to hundreds of cleaned and curated datasets, including national coverage of parcel-level land-use data, it is more than a scenario planning platform. It provides users with the data they need to understand existing conditions in a matter of minutes anywhere in the U.S., enabling users to develop and test future land-use and policy scenarios in a fraction of the time such endeavors traditionally take. Furthermore, it allows users to predict environmental, health, social, transportation, and fiscal impacts of different scenarios using built-in state-of-the-art mathematical models.
Practicing professionals are thereby freed up to do what they do best, using UrbanFootprint to refine plans and designs, analyze impacts throughout a process, and ultimately advocate for the best plan or design. Other tools can help a planner do some or all of this work, but UrbanFootprint does it all in the cloud, and comes preloaded with parcel-level data, hundreds of additional datasets, and a state-of-the art transportation module.
UrbanFootprint's cloud platform, commercially released in 2018, is unique among scenario planning tools. Because it is web-based, the end user does not need access to high-performance computing machines. It lets multiple planners work on the same project at the same time, from any location with internet access. Planners and designers in cities, private sector firms, and universities are already collaborating on hundreds of neighborhood, citywide, corridor, and regional projects, analyzing thousands of scenarios with the system's built in models and reports. Major organizations like ICLEI, The Nature Conservancy, the state of California, and StreetLight Data are on board, too.
A planning application
California senate bill SB 827 proposed a dramatic shift in zoning policy throughout California. Largely spurred by the state's severe housing shortage, this bill — introduced in April 2018 — generated controversy from all sides of the housing debate in its aim to substantially upzone residential parcels within a half-mile of major transit stops statewide.
A common thread throughout such debates was the question of how this would alter existing conditions throughout urban areas in California.
Data-driven scenario planning was an ideal tool for understanding such impacts and was used to inform communities and inform policy makers with better data as they refined the legislation. UrbanFootprint's planners and designers used the tool to quickly analyze how the bill could impact housing capacity at sites in the San Francisco Bay Area, arguably the housing crisis epicenter.
The transportation module in action
At the risk of going on and on about one of my new favorite tools, the UrbanFootprint transportation module really stands out. It is a high-level travel model that quickly produces estimates of vehicle miles traveled and travel mode shares for land-use and transportation scenarios with sensitivities to the effects of the built environment on travel behavior.
In particular, it intersects with the Mixed-Use Development method introduced in "Mixed-Use Development Trip Generation Model," Transportation Research Record, 2344(1), 98–106, something I worked on. The MXD module consists of statistical models based on research of observed relationships between built environmental characteristics known as "D" variables and travel behavior in cities and regions across the U.S. The Ds are development density, land-use diversity, urban street design, destination accessibility, distance to transit, and demographics. Literally hundreds of studies have found that as the Ds increase (except distance to transit, which has the opposite relationship), vehicle miles traveled decrease and walking and transit use increase. For more on the Ds, see the earlier column "Making Sense of Different Results: Is Meta-Regression Necessarily Best?" from January 2017.
Within UrbanFootprint, the MXD model is applied as an adjustment to a traditional four-step travel demand forecasting model. UrbanFootprint involves the following three steps:
1. TRIP GENERATION, where total trips associated with land uses in the project area are estimated using standard Institute of Transportation Engineers trip generation rates.
2. TRIP DISTRIBUTION, where trips are allocated to likely origins or destinations using a gravity model.
3. MODE CHOICE, where trips are assigned to travel modes according to MXD statistical models, which estimate the degree to which vehicle trips will be reduced due to internal capture of trips within MXDs, external walking, external biking, and external transit use.
The model works at the individual parcel scale to produce results sensitive to local household D variables. Parcel-scale results readily reveal variations and patterns in vehicle miles traveled and mode shares.
In turn, the VMT estimates are used to calculate transportation-related costs of development, such as greenhouse gas emissions and pollutant emissions and household auto costs. The original UrbanFootprint was based on trip-level data for six regions and 239 MXDs. This is currently being updated to 31 regions and 622 MXDs.
I sat through a demonstration of UrbanFootprint a couple weeks ago, and was impressed. If you need a speedy turnaround on a scenario planning project, it is one of the tools you will want to consider.
Reid Ewing is a distinguished professor of city and metropolitan planning at the University of Utah, an associate editor of the Journal of the American Planning Association and Cities, and an editorial board member of the Journal of Planning Education and Research and Landscape and Urban Planning. Sixty-nine past Research You Can Use columns on a wide range of topics are available. For reviews of scenario planning tools and their capabilities, see two publications from the Lincoln Institute of Land Policy: Urban Planning Tools for Climate Change Mitigation, 2009 and Opening Access to Scenario Planning Tools, 2012.