Our research explored the use of city-administrative data to inform the context of neighborhoods. The first step resolved a meaningful structure drawn from contemporary Philadelphia archival data originating with many diverse municipal departments, including public health, welfare, police, fire, education, housing, and licensing. We were able to effectively distinguish multiple-marker dimensions focusing on the degree of social stress experienced by people within neighborhoods, the relative state of structural decline for the neighborhood environs, and the measure of felony crime that affected each neighborhood. The resultant dimensions avoided the limitations of measures that convert frequency data to percentages or rates, and the dimensions were weighted such that the differential contributions of the various markers were reflected in summative scores. Each of the three neighborhood dimensions was demonstrated to convey a substantial amount of reliable variability that was uniquely independent of the other dimensions.
Because the neighborhood dimensions might find application in many types of studies that concentrate on certain geographic or demographic subgroups, it was deemed imperative to assess the structural integrity of the dimensions as they are applied exclusively for subsets of neighborhoods. Thus, Philadelphia’s 1,816 census-block neighborhoods were partitioned into sets that manifested incremental population density, neighborhoods whose residents were highly concentrated poverty vs. neighborhoods with lower poverty concentration, and sets of neighborhoods whose residents are distinguished by incremental levels of racial isolation. Massey and Denton39
noted these sociological distinctions as being crucial elements of the economic, political, and behavioral dynamics of urban populations. It was demonstrated that the dimensional structures unique to the various levels of the population density, poverty, and racial isolation constructs were closely or reasonably represented by the overall dimensional structure resolved for Philadelphia. This evidence lends support for the extension of the city dimensions to investigations focused more narrowly on specific types of neighborhoods.
Because the literature has presented over recent years a variety of studies that apply available data from the US Census to describe neighborhoods, we thought it important to test empirically the uniqueness and redundancy of census- and city-based dimensional structures. To this end, we scored Philadelphia’s neighborhoods according to the factor-analytic scheme derived by Sampson et al.12
for the Chicago metropolitan area. A comparison of the city and census dimensions is further facilitated in that the city dimensional structure deliberately avoided use of alternatively available census information. Bimultivariate comparisons of Philadelphia’s neighborhoods, as depicted by the city and census dimensions, revealed some overlap. Both data sources appear to share a common link in that concentrated disadvantage, as extracted from the census, was positively related to all of the city dimensions. Moreover, census-based phenomena describing the stability of residency in neighborhoods was inversely related to neighborhood crime—a finding that makes great sense whether one tends to view instability as a cause of crime or the reverse. Also interesting was the evidence that, notwithstanding some significant overlap in common variance, more than half of the information conveyed by city vs. census dimensions was nonredundant. This invited the prospect that neighborhood characteristics as informed jointly by the data sources could substantially enrich the overall understanding of census-block neighborhoods.
Meaningful tests of the independent and joint utility of city and census dimensions would have to rest on their capacity to inform phenomena that were not by definition tautologically identified with the markers that comprise the neighborhood dimensions. We decided to explore the ability of the neighborhood dimensions to explain the unique neighborhood variability in resident students’ academic achievement. We emphasize neighborhood variability because, whereas abundant evidence demonstrates that individual students’ school performance is related to personal or familial status, poverty, and such, the pertinent interest here is with that aspect of student achievement that is clearly peculiar to the neighborhood rather than the person. Hence, multilevel modeling was used to isolate the achievement phenomena that distantly differentiated neighborhoods. Once accomplished, the investigation puts the census and then the city dimensions to work, attempting explanation of neighborhood differences in achievement (viz., the neighborhood effects).
The measures used were the same applied by Pennsylvania for all of its high-stakes assessments under federal law. It was learned that most of the neighborhood reading achievement effects were explained by either census or city dimensions and that most or almost all of the neighborhood effects for mathematics were also explained. Although evident that some dimensions (census concentrated disadvantage, city crime, and structural decline) carried most of the weight, all of the dimensions played a significant role in some respect. In all of these tests, the census-based dimensions showed some noticeable advantage in accounting for neighborhood differences. This, we suggest, follows from the fact that the census dimensions cover a somewhat broader scope of sociologic information, whereas the city-based dimensions focus more particularly on local variation related to municipal services.
When the city and census dimensions were permitted to reciprocally augment one another through combined models, their explanatory power was increased, and the models more precisely fitted the data. It is not inconsequential to explain 60% to 70% of the distinctions between neighborhoods, as accomplished here. We noted also that the explanatory power of the combined neighborhood dimensions tended to increase from fifth to eight grade (~10% from the cross-sectional perspective). This might strengthen the argument that student school performance is incrementally affected, as students reside and interact over longer periods in urban neighborhoods. There are indeed plentiful examples of cascading effects of macro environment on school performance.40
Two important findings concern the social stress dimension. First, the dimension points to the bundling of social problems (low birth weights, infant mortality, teen pregnancy, child abuse, and neglect) at the neighborhood level. Second, there is high correlation between the social stress and concentrated disadvantage dimensions. When operating in the same equation, the concentrated disadvantage dimension mediates (partially reduces the unique impact of) social stress. Investigations of other cities have shown similar clustering of social problems and correlations with concentrated poverty.2,41
While useful as a summary predictor, concentrated disadvantage does not lend insight into the processes that lead to and result from economic disadvantage, the processes that distinguish neighborhoods, including neighborhoods that may suffer relative economic impoverishment, but for different reasons and with different consequences. Taken together, the social stress dimension sheds light on the social problems that accompany concentrated disadvantage. Thus, unlike census data, city-administrative data can inform the specific levels of social stress experienced by residents.
The story imparted by a neighborhood’s level of structural decline affords another set of perspectives on the processes that distinguish neighborhoods. It speaks to the relative state of the physical surrounds and to the stability of that state. Neighborhoods with elevated scores on this dimension are typically riddled with abandoned and already razed buildings, and those not uninhabitable are often in arrears for tax or utility payments. Structural decline signals many processes that affect neighborhoods. First, there is the psychological impact upon residents surrounded by the constant reminders of deterioration and blight. Second, the abundance of property liens and termination of even basic utilities are symptoms of broader disinvestment in the community. At still another level, we observed in our efforts to produce sound and nonredundant dimensions of structural decline the role of indicators such as water shut offs and demolitions were often just the more visible tips of icebergs. As have many researchers learned, we saw substantial colinearity among the variables that depict neighborhoods. But the extraordinarily high intercorrellations actually depict many closely linked chains of events as recorded in city data. To illustrate, property disinvestment may manifest itself in delinquent taxes, leading to discontinued water, electric, and gas service, abandonment, fire, condemnation, and demolition. The numerous variables that constitute these chains are thus sequential or coincidental and understandably highly correlated. Whereas sound measurement precludes the use of multiple indicators that are so linked, certain indicators will tend to rise to the top as more stable or viable proxies for many other indicators. Thus, variables such as liens, water shut offs, and demolitions serve to represent much broader stories of neighborhood decline.
While difficult to study a causal relationship between structural decline and poor health and well-being outcomes, research has shown associations. Studies have found associations between features of the built environment and self-esteem42
and conduct and oppositional defiant disorders.43
Cohen et al.44
found that boarded-up housing remained a predictor of gonorrhea rates, all-cause premature mortality, and premature mortality due to malignant neoplasms, diabetes, homicide, and suicide after control for sociodemographic factors.
City-archival data also highlight the unique public safety profiles that differentiate neighborhoods. Felony crime, like good schools, is one of those considerations that translate into neighborhood popularity and growth, or lack thereof. Crime is both a cause and an effect of neighborhood differences, and its presence is perhaps the characteristic attracting the media and repelling investment—but not reflected in census data. High values on all three neighborhood dimensions derived from city data comport with the processes hypothesized to negatively influence health and well-being.
Hill et al.45
emphasize that the very presence of neighborhood crime and other distressing signs of disorder and decay (abandoned buildings, vandalism, drug use) results in chronic psychophysiological distress, which in turn leads to poor health outcome, and Furstenberg46
notes that elevated crime levels for already high-risk neighborhoods functions to undermine parents’ ability to navigate successful child-rearing strategies.
Research has shown that exposure to neighborhood crime and violence increases the likelihood of depression, anxiety,47
and posttraumatic stress disorder,49
and poor school performance.50
Reliance on city-archival data does present limitations, both known and unknown. Municipal administration data ordinarily are gathered by numerous organizations, and, depending on the government, conventions may be substantially disparate for any given city. Notwithstanding the more socioeconomic foci of national census data and the sampling errors that census data entail,51
there is at least a general strategy and order to the way that data are collected and prepared for analysis. Municipal data, partly because they originate from so many different arms of government and partly because the rules on quality assurance are less universal and, perhaps, enforced, are more vulnerable to error. Also, the integrity of the collection and preparation of municipal data are probably more dependent on inconstant abilities to fund the process and to avoid deliberate falsification in reporting. It is often difficult to reconcile missing data and, to the extent that many departments collect data, it can be very difficult and expensive to penetrate the bureaucratic boundaries that resist sharing information and to afford the time and expertise to accurately merge data that were never intended to occupy a single dataset. Given the limitation, we believe that the analysis is informative, especially as it contrasts city-archival and census data under the same limitation.
Our study benefited from the foresight and investment of many Philadelphia agencies determined to pool their data in the hope that the whole would exceed the sum of its parts. Nonetheless, our work was impeded as times by inconstant data linking information, the tendency of solitary data sources to change data definitions over time without clear documentation, and the politics of asking much of many. We are indebted to the City of Philadelphia and the University of Pennsylvania for their close cooperation in creating the KIDS, NIS, and other CML data networks. Our study is perhaps also limited by the use of explanatory data (as represented by the city neighborhood dimensional structure and outcome data (school achievement) having been generated by the same city-administrative system. This is offset somewhat by the use of the US Census data, but it does encourage a search for multiple independent sources of information. Finally, we are limited by the fact that most city-archival data were never intended for research purposes. Researchers will always be looking for data that might better answer the questions really cared about, rather than the questions evoked by the available data.
Our research has allowed census-block groups to define the geographic boundaries of neighborhoods. This is defensible from the standpoint that block groups are large enough to provide reasonable within-neighborhood variability and small enough to avert the risk of insensitivity to important variations in socioeconomic, ethnic, and structural distinctions that vary widely across larger boundary limits (e.g., census tracts). Block groups also tend to comport frequently with local service precinct and ward concepts. There exists, nonetheless, a wide variety of conventions that might alternatively be adopted to define neighborhoods (parishes, schools, recreation facilities, shopping), although their boundaries may tend to be more indefinite, and pertinent data are less available, but block-group boundaries may not adequately represent the locations of the phenomena that most influence human lives. To illustrate, consider those block-group residents living near the boundaries. Are not the adjacent block groups as or more relevant than the assigned block group? And what of the many city block groups that would partition neighborhoods by the midline of streets? To some degree, this problem may be mitigated through spatial weighting techniques, which would account for spatial autocorrelation that might exist for specific variables. Thus, to the extent that much of the data pertaining to block groups originate at the individual person level and thereafter is aggregated over many people, it is possible to weigh environmental influences by their proximity to the individual people presumably influenced. Statistical point pattern analysis52
returns focus to the individual’s personal location and builds the neighborhood construct around that location. Such methods may prove more sensitive than aggregated data within fixed boundaries for neighborhood definition. Spatial techniques might also be integrated with temporal measures that assess the duration of exposure associated with particular locations.