Person:

O'Brien, Daniel

Objectives: “Broken windows” theory is an influential model of neighborhood change, but there is disagreement over whether public disorder leads to more serious crime. This article distinguishes between public and private disorder, arguing that large-scale administrative data provide new opportunities to examine broken windows theory and alternative models of neighborhood change. Method: We apply an ecometric methodology to two databases from Boston: 1,000,000+ 911 dispatches and indicators of physical disorder from 200,000+ requests for nonemergency services. Both distinguish between disorder in public and private spaces. A cross-lag longitudinal analysis was conducted using two full years of data (2011–2012). Results: The two databases provided six dimensions of physical and social disorder and crime. The cross-lag model revealed eight pathways by which one form of disorder or crime in 2011 predicted a significant increase in another in 2012. Although traditional interpretations of broken windows emphasize the role of public disorder, private conflict most strongly predicted future crime. Conclusions: Our results describe a social escalation model where future disorder and crime emerge not from public cues but from private disorder within the community, demonstrating how “big data” from administrative records, when properly measured and interpreted, represent a growing resource for studying neighborhood change.

The collection of large-scale administrative records in electronic form by many cities provides a new opportunity for the measurement and longitudinal tracking of neighborhood characteristics, but one that requires novel methodologies that convert such data into research-relevant measures. The authors illustrate these challenges by developing measures of “broken windows” from Boston’s constituent relationship management (CRM) system (aka 311 hotline). A 16-month archive of the CRM database contains more than 300,000 address-based requests for city services, many of which reference physical incivilities (e.g., graffiti removal). The authors carry out three ecometric analyses, each building on the previous one. Analysis 1 examines the content of the measure, identifying 28 items that constitute two independent constructs, private neglect and public denigration. Analysis 2 assesses the validity of the measure by using investigator-initiated neighborhood audits to examine the “civic response rate” across neighborhoods. Indicators of civic response were then extracted from the CRM database so that measurement adjustments could be automated. These adjustments were calibrated against measures of litter from the objective audits. Analysis 3 examines the reliability of the composite measure of physical disorder at different spatiotemporal windows, finding that census tracts can be measured at two-month intervals and census block groups at six-month intervals. The final measures are highly detailed, can be tracked longitudinally, and are virtually costless. This framework thus provides an example of how new forms of large-scale administrative data can yield ecometric measurement for urban science while illustrating the methodological challenges that must be addressed.