Place Matters: Mapping Community Loss As a New Social Indicator
Urban researchers recognize that “place matters,” e.g., differences in neighborhood conditions can powerfully affect local residents’ well-being. The Community Loss Index (CLI) measures a neighborhood condition comprised of six losses closely associated with stress: incarceration, foster care placement, sudden death, long-term hospitalization, job loss and foreclosure. Each was selected based on an extensive literature review of its community impact, Hobfoll’s resource loss/stress relationship theory, and data available at the appropriate geographic scale.
These losses regularly occur in poor neighborhoods where large numbers of people living in close proximity routinely suffer multiple losses. Yet they are rarely recognized or measuredcollectively as a place-based stressor that has a community-wide impact. The CLI which corrects for this, is part of larger study that “unpacks poverty” by exploring the hypothesis that stress operates as a pathway between a variety of neighborhood conditions (including community loss) and the clustering of health and social problems in some but not other neighborhoods.
Method
Data were collected from US Census and NYC administrative sources. The City’s varied district and administrative boundaries were reconciled using GIS technology to convert data into zip codes. Different measurement scales available for each loss were standardized by converting them into 10 “loss ranks” (deciles) using the Jenks natural breaks method and calculating the rank of each loss in every zip code. Based on the sum of the six loses, the overall CLI score indicates the accumulated loss for each zip code. The descriptive analysis of loss citywide was visualized with maps and the neighborhood analysis (clusters of zip codes) was visualized with bar charts indicating the average rank for each loss and “whisker diagrams” marking their maximum and minimum observed values.
Results
Citywide the CLI shows NYC as sharply and uniformly divided into high-loss areas (all 6 losses ranked at or above the city wide average) and low-loss areas (all ranked below). It also reveals variations among neighborhoods within each type of area. For example, among the high-loss-area neighborhoods, the highest ranked losses were foster-care placement in Staten Island and the Bronx, unemployment in Harlem and foreclosures in Jamaica. Among the low-loss-area neighborhoods, the highest ranked losses were sudden death in Manhattan and long-term hospitalization in Flushing. CLI also documents the distribution of race, age, immigrant, and poverty groups in both the high-and low-loss areas. Generally, those most likely to live in the high-loss area ranked low in the low-loss area. However, the demographics also varied by neighborhood. For example, blacks were concentrated in the high-loss area of NYC but within it they were more likely to live in Brooklyn and Jamaica than in Staten Island.
Conclusions
CLI effectively captures a previously unrecognized component of poverty and disrupts the view of poverty as a uniform experience. Potentially adaptable to other cites, CLI can help communities and public officials to unpack poverty in the context of place, to understand how community loss varies by geography and demographics and to work together to fine-tune interventions based on actual community needs.