Visualizing Neighborhood Change in Detroit

Five years after the city’s massive bankruptcy, neighborhoods are changing and data is offering a detailed look at the process.

by Daniel Fisher, Data-Smart City Solutions / August 28, 2018
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This story was originally published by Data-Smart City Solutions.

Detroit is changing. Nearly five years have passed since Detroit filed the largest municipal bankruptcy in the history of the United States — an event which marked the nadir of a decades-long decline. The city has recovered since then, quickly in some areas and more slowly in others. Foreclosures are down, and downtown real estate price are up. A few months ago, the state financial review commission entered a “dormancy period” after the city posted three consecutive budget surpluses, from 2015 to 2017. If Detroit can avoid running a deficit for the next ten years, the commission will dissolve entirely, and the Motor City will be in complete control of its finances once again.

Change, in this case, appears to be a good thing, but it’s important not to assume that the revitalization of a city translates to benefits for all the people who live there. Many of the signs that traditionally indicate the improved health of a city — for instance, higher real estate prices — also signal the disruption of communities, as the rising cost of housing and the cultural shifts that come with it can drive some residents away.

Turning the Corner (TTC), a five-city initiative guided by the Urban Institute’s National Neighborhood Indicators Partnership (NNIP) and the Funders’ Network’s Federal Reserve-Philanthropy Initiative, aims to begin conversations about displacement and equitable revitalization before that happens. Launched in January 2016, the project pilots a research model that monitors neighborhood change, drives informed government action, and supports displacement prevention and inclusive revitalization. Local teams in Buffalo, Detroit, Milwaukee, Phoenix, and Twin Cities conducted independent research to understand neighborhood change and displacement risk in their communities. The Urban Institute, funded by the Kresge Foundation, will synthesize lessons from the five cities.

As one of the TTC participants, Data Driven Detroit (D3), NNIP’s local partner, developed metrics to identify areas at risk of “transformational neighborhood change:” investment-catalyzed economic changes to neighborhoods that can alter the composition or culture of their communities.

One of the results of that research is an interactive map that compiles dozens of data points for 9,444 of the city’s 15,933 census blocks and color codes them according to the likelihood of neighborhood change. (Thousands of blocks were screened out because of their low residential population or because they had already experienced residential change.)

The interactive index comes at the end of a story map detailing the research process. To develop the index, D3 conducted a number of focus groups and one-on-one interviews in Southwest Detroit and the North End — two neighborhoods locals believed were already experiencing transformational change — to glean information about recent changes in business, the built environment, and the community. Quantitative research was directed at administrative data on foreclosures and building permits, as well as at surveys conducted by the city of Detroit and the Census Bureau. Individual variables were then consolidated into distinct indices which captured different aspects of neighborhood change: Housing Stability, Crime, Social Advantage (density of residential vacancies), Business, and Protective Activities (e.g., density of calls made by fire alarms).

On its own, each index gives a different view of the city. Compare, for instance, the Housing Stability Index, which reflects variables measuring the number of blight violations and speculator-owned properties, to the Crime Index, which reflects statistics both on property and violent crimes.

Detroit Map No 2

 

It’s interesting to note the disparities between the two. For example, despite the fact that Census Block 261635007004016 has the “lowest stability” for housing, which D3 regards as being highly conducive to neighborhood change, its reported crime rate is relatively high; that, in addition to its low Business and Social Advantage score, push it down to the bottom quintile of the Neighborhood Index.

Detroit Map No 5

On its own, each index also provides insight relevant for future policy. The Housing Index, for instance, may be helpful in drafting policy to reduce resident turnover, and the Crime Index is useful for similar policy purposes, if not also to reveal inconsistencies between resident perception and what is reported and recorded by police.

In their final Analytical Report (PDF), the researchers make a particular note of the difference between the two. In their interviews with long-time residents of changing neighborhoods, they often encountered the sentiment that the neighborhood no longer felt safe as it used to, despite the fact that all statistics suggest otherwise.

“Even though safety is getting better, the last 2-3 years I’ve felt unsafe walking out this front door and I never felt like that before,” said one community member (pp 16 of the report).

Some pin the blame on a breakdown in neighborly communication: They say newcomers simply aren’t as invested in forming communities as older residents, partly because the original motivation is gone. “When [you have] no resources… you’re kind of protected. You’re forced to talk to your neighbor,” said another community member (ibid).

Yet another community member stressed the idea that displacement extends beyond its physical component. “It feels strange, like I’m not displaced but I am somehow” (11).

The comment captures well the frustrating difficulty not only of assessing the extent of a problem, but also of defining it in the first place. Detroit is changing — this much we know — but it is a type of change which occasionally escapes words and numbers. D3’s map, though, might very well lead the way to a fuller understanding.