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Volume lll, Number 3&4, 1997
Issue Topic: Community-Based Initiatives
Promising Practices
Neighborhood Indicators and Community Initiatives
G. Thomas Kingsley, Director of the Center for Public Finance and Housing at the Urban Institute, discusses the use of neighborhood indicators and their relevance to community initiatives.
Evaluators have long desired better data on the neighborhood contexts in which the programs they are assessing operate. The trouble is that assembling adequate neighborhood data has normally been prohibitively expensive. In assessing a drug prevention program, for example, one cannot rely only on data on the changing pattern of neighborhood drug use. One needs to know about a host of other neighborhood level changes—social, economic, and even physical—that may have interacted to influence that pattern. Additionally, since one wants to know how the neighborhood changes along all specific dimensions, year by year, as the program intervention is underway, data from the last census are seldom good enough.
In the past few years, however, the prospects for obtaining more substantial neighborhood information have improved markedly. Most local administrative agencies have now automated their record-keeping, and with the advent of computer-based address-matching, it is no longer an arduous task to add up totals for neighborhoods, defined in many ways. Depending on the files assembled, one can create and frequently update neighborhood indicators from data on jobs, births, deaths, crimes, incidences of illness, student school performance, openings and closings of public assistance cases, housing-code violations, building construction and demolition, changes in property values and taxes, toxic emissions, and a number of other topics.
Local institutions in at least six cities now collect data on a regular basis from a number of different agencies and integrate them into a neighborhood-level information system (the Atlanta Project, the Boston Foundation's Boston Persistent Poverty Project, the Center for Urban Poverty and Social Change at Case Western Reserve University in Cleveland, the Piton Foundation in Denver, the Urban Strategies Council in Oakland, and the Providence Plan). Because they maintain such systems, these institutions can provide one-stop shopping which offers enormous efficiency for a variety of local users. They are non-governmental entities, not seen as being aligned to any short-term political interests, and they emphasize careful data cleaning, maintenance of confidentiality, and responsible data use. Accordingly, they are positioned to maintain the trust of data providers and users over the long term. Because of much reduced costs due to automation and the ability to raise funds through a mix of fee income and general support from local businesses and foundations, these organizations are—or have definite potential to become—locally self-sustaining.
Perhaps more noteworthy, however, is that these organizations do not see themselves only as data suppliers or traditional research institutions. Rather, their primary aim is democratizing information—getting information directly to neighborhood groups and other local stakeholders, and helping them use it themselves, rather than having the analysis done for them by outside professionals.
In 1995, these six organizations formed partnership with the Urban Institute to establish the National Neighborhood Indicators Project (NNIP). Funded by the Annie E. Casey and Rockefeller Foundations, NNIP activities currently include:
- Drawing on mutual experiences (and new field testing) to develop prototype approaches and tools for using information more effectively in community capacity building
- Finding innovative ways to use their data to support better local policymaking—for example, in designing local strategies to respond to welfare reform
- Assembling data from across their systems, and comparing patterns of change, to provide better insights as to what is actually happening to conditions in inner-city neighborhoods nationally in the 1990s
- Helping institutions in other cities develop similar systems and capacities for use
The NNIP partners generally feel that the state of the art in using automated data in these ways is still in its infancy. Nonetheless, concrete applications in their cities have convinced them these approaches warrant further development.
The Atlanta Project, for example, has been working with several neighborhood groups over the past few years to prepare maps and tables showing parcel-level data on tax delinquency, code enforcement violations, and other property conditions. Just looking over the maps, community residents saw opportunities for action that they had not seen before. Their analyses became the basis for several initiatives: targeting assistance to elderly homeowners in jeopardy of losing their homes due to outstanding tax liens; selectively reinvesting in key community properties found to be ripe for redevelopment; working with city agencies to shift code enforcement strategies to crack down more effectively on absentee property owners with decaying and abandoned properties; and motivating the state legislature to pass new laws expediting foreclosure processes when communities are prepared to redevelop sites with nonprofit housing.
At the metropolitan level, another example is gaining national prominence. NNIP's partner in Cleveland (the Center for Urban Poverty and Social Change) began by working with automated data on welfare cases. Staff members examined the characteristics of different cohorts of county AFDC recipients and were able to estimate and map by census tract, those recipients that would be imminently vulnerable to losing benefits under welfare-reform time limits. They also used geographic data on employment to analyze and map spatial patterns of recent entry-level job openings in the area. They found that the residential locations of vulnerable AFDC recipients were tightly concentrated in space, mostly in a few inner-Cleveland neighborhoods, while the entry-level employment opportunities likely to be relevant for these prospective job-seekers were largely in metropolitan fringe areas. They went further to estimate tract level income losses likely to occur under welfare reform and to calculate commute times that would be required for AFDC recipients to access various shares of new entry-level jobs.
These basic findings were not surprising, but the contrasts were striking, and the fact that the analysts had been able actually to quantify and map this “spatial mismatch” made a critical difference. The maps they produced (with associated hard numbers by neighborhood) cast powerfully memorable images. They captured the attention of the local media and, then, of policymakers. In response, the state has since allocated substantial funding for transportation assistance in Cleveland's welfare-to-work efforts, and local transportation planners are working with the analysis team to test out alternative strategies for getting vulnerable recipients to jobs more rapidly. The team has since begun assembling related neighborhood data (e.g., on the locations and capacities of day-care centers and job-linkage services, and the pattern of rental housing affordability).
Again, preliminary indications are that the production of solid data that can serve as a basis for sensible response strategies may well prove to be a critical step in motivating local actors to develop such strategies.
A full description of the project is provided in Democratizing information: First year report of the national neighborhood indicators project (1996). Washington, DC: The Urban Institute. 2100 M Street, N.W., Washington, DC 20037. Tel: 202-857-8687.
G. Thomas Kingsley
Director of the Center for Public
Finance and Housing
The Urban Institute