By Mike Powe, Margaret O’Neal, Reina Murray, and Carson Hartmann
On Thursday, February 9, as part of Preservation Leadership Forum’s webinar series, the National Trust’s Preservation Green Lab hosted a webinar about the Atlas of ReUrbanism—what it is, how it can be used, and what extensions may be coming down the pike. You can listen to a recording of the webinar and download a PDF version of the slides.
Attendees participated in a moderated Q&A at the end of the webinar. This is a summary of the questions answered on air as well as a selection of those that we did not get to, edited for clarity.
What is the Atlas of ReUrbanism now, and where is it going?
The Atlas of ReUrbanism is a tool for urban leaders and advocates to better understand and leverage opportunities in American cities. This work builds on the Character Score methodology developed for our Older, Smaller, Better research study—which notes the value of everyday, smaller-scale development in creating stronger, more equitable neighborhoods and cities. Currently the Atlas consists of three pieces: a summary report with baseline information and comparative tables for 50 cities across the country, interactive maps for a handful of these cities, and “takeaway” factsheets with topline analysis for the cities with live maps. By summer, interactive maps and factsheets will be available for all 50 cities included in the summary report.
What data is the Atlas built on? Where did they come from? Are they free?
The Atlas combines data that characterize cities’ buildings, people, housing, and jobs. For each city, we match these data to a grid of 200-by-200-meter squares—the equivalent of about one-and-a-half city blocks. This grid allows us to readily analyze and easily map Atlas data at a human scale.
Data on a city’s buildings originate from local assessors and historic preservation offices. They include information on structures and parcels, such as the years of construction and historic designation information. They can be found through local data portals and are generally made available for free, though some municipalities charge for their use. Demographic, housing, and job data come from the different products of the U.S. Census (the Decennial Census, the American Community Survey, and Longitudinal Employer-Household Dynamics research)—except for women- and minority-owned business data, which come from Dun & Bradstreet. All Census data are available for free at www.census.gov, whereas Dun & Bradstreet’s data are available for purchase.
Have you found the data sources reliable? When are you planning on updating the information for each city?
This is a tricky question and depends on the data source. For example, we pull the information that makes up the Character Score (median year built, diversity of building age, and granularity) from tax assessor files, which are often kept at the city or county level. And we know that there is some uncertainty associated with the most critical piece of information we collect—the “year built” date. We’ve repeatedly heard that, when the date is unknown, an arbitrary year is often used instead.
While it’s fair to assume that not all “year built” information is 100 percent accurate, we believe that two key aspects of our methodology lessen error and reduce risk. First, our Character Score metric represents the character of blocks in relative terms—are the buildings on one block relatively older or newer than the buildings on blocks across the city? Presuming that the “year built” data listed aren’t radically different than the real date of construction, the relative trend should hold true. Second, aggregating data at the grid square level and looking only at the median year constructed lessens the impact of individual erroneous dates.
We relied on a variety of sources for cultural resource data. We’ve generally discovered that the accuracy of a data set correlates with lower levels of government, so we used data from a city or county’s geographic information system (GIS), preservation, or planning department whenever possible. If those weren’t available, we used data from a statewide source, such as a state historic preservation office or state GIS department. When more localized sources were not available, we relied on the National Park Service’s National Register of Historic Places spatial data.
For both tax assessor and cultural resource data, we are aware of varying levels of uncertainty in the data we’ve used. However, we don’t have a way to verify the extent of this uncertainty, save advocating for improved research and survey—which we encourage at the local level!
What additional layers of data would you like to see added in the future?
We would like to add layers that further emphasize the value of cities’ older fabric. For example, we could highlight the inherent walkability and transit accessibility of high–Character Score areas by adding bus stop and rail line layers, as well as walkability and bike-ability measures. We could give users a picture of quality of life by including layers that situate green spaces, schools, or amenities. For coastal cities we are already planning to include National Oceanic and Atmospheric Administration sea level rise data and Federal Emergency Management Agency flood zone data.
We’re also interested in adding layers that address local issues or events. This could include block party locations in Philadelphia, demolitions and building permits issued in Detroit, or areas of seismic risk in Seattle. We hope that engaging local partners and advocates will help us identify potential layers in future Atlas cities as well as improve on the maps we’ve already released.
How did you choose the 50 cities for the initial launch?
Our goal was to have at least one city representing each state, but we were constrained by the availability and quality of particular data. For example, to build the Character Score, we need to have “year built” data for all the structures. Unfortunately, some cities and counties either don’t have data that are readily analyzable, complete, and accurate; require payment for access to them; or don’t have such data at all.
The Atlas was initially conceived as a way to understand the fabric of cities—places with at least 100,000 residents—though we see real value and potential in mapping and analyzing smaller cities and towns in the future.
Can you explain “high” versus “low” Character Scores? What do you want us to take away from those comparisons?
The Character Score combines three equally weighted measures: the median age of buildings; the diversity of the age of buildings; and granularity—the size of buildings and parcels. Red squares show the highest Character Score areas—places with the oldest, smallest buildings mixed together with new development. Blue squares show areas with the newest, largest buildings of similar age.
We use Character Score to understand where a city’s older fabric exists and what impact it has—in particular, how areas of higher Character Score compare to lower-scoring ones in terms of people, jobs, and opportunities. Looking at the factsheet for Portland, Oregon, for example, we see that high–Character Score areas have greater population density, more jobs in small and new businesses, and a lower median resident age than low-scoring areas. In another example, New York City’s factsheet shows that high-scoring areas are more diverse and house nearly twice as many women- and minority-owned businesses. We hope that comparisons like these demonstrate the value of older buildings, blocks, and neighborhoods and that our factsheets can be used as supporting material when advocating for building reuse and preservation.
For further reading about the development of the Character Score and its relationship to the ideas and observations of Jane Jacobs, check out Older, Smaller, Better.
How can different audiences use the Atlas? I’m specifically thinking about real estate developers, community development corporations, or other urbanists.
Over the past few years, we’ve done some modeling with demographic and economic trend data to spotlight areas of cities where targeted attention from policymakers, real estate developers, community development organizations, and other urban professionals could have the greatest community impact. More information on this modeling can be found in our Partnership for Building Reuse reports about Philadelphia, Baltimore, Chicago, and Detroit. This experience led us to see how real estate professionals, preservationists, and others could leverage a data and mapping platform to see cities in a different way. With a few clicks on the interactive map, you can understand where historic character, economic vitality, and population diversity intersect. In future versions, we plan to add more ways to visualize character, density, diversity, and other measures.
We’re eager to hear what people like about the Atlas, what could work better, and what additional layers would be useful and interesting. We think the Atlas could be useful for journalists learning about the local context of neighborhoods, preservationists aiming to understand the historic characteristics of cities at a glance, and both for-profit and nonprofit developers aiming to reuse older buildings and reinforce older neighborhoods. Of course, if you find other uses, please let us know!
You mentioned themes of affordability, density, and diversity in the presentation. To what extent does the Atlas speak to these issues?
The three principal Atlas products directly speak to these issues in different ways. The summary report includes charts and graphics that show the statistical link between older, smaller buildings and affordable rental housing, population density, women- and minority-owned businesses, and jobs in new and small businesses. The factsheets draw out key statistical takeaways related to these themes in each individual city and provide a data summary of how high–Character Score areas compare to areas with newer, larger buildings. The interactive maps allow users to punch in their own addresses and check out local statistics.
Affordability, density, and diversity are contentious topics in many cities. We hope that the policymakers and people who live and work in these cities will turn to the Atlas of ReUrbanism for solid baseline information that can inform debates and support smart decisions about cities’ futures.
What are potential extensions of this work?
Right now, our intern, Mari Webb, is crunching the Atlas data with projected sea level rise information for Boston, Massachusetts. In the future we’d love to mash together Atlas data with information about solar power potential, beloved bars and restaurants, seismic activity, energy consumption, and historic tax credit projects, to name a few. More ideas keep coming all the time. For instance, one webinar attendee suggested that we explore the statistical relationship between the construction of U.S. Department of Housing and Urban Development–assisted housing units and the presence of vacant housing units in older buildings and blocks. We hadn’t considered that question before, but we love the idea and look forward to taking it on soon.
With the foundational work of collecting, crunching, and organizing this massive amount of data complete, we’re now looking forward to quickly spinning the Atlas database into several new studies in rapid succession, so stay tuned! There will be much more to come in the months ahead. We’re excited by the potential for new discoveries and new paths of exploration, and we hope you are too!
Mike Powe, Margaret O’Neal, and Carson Hartmann are the Preservation Green Lab’s director of research, senior manager of sustainable preservation, and research analyst, respectively. Reina Murray is the GIS analyst for the National Trust.
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