Racial, ethnic, and economic characteristics of neighborhoods have long been viewed by sociologists as determinants of socioeconomic attainment and social outcomes (most notably, crime and delinquency). More recently, interest has shifted to the role of neighborhood environments in shaping health and health disparities, following mounting evidence that poor health outcomes tend to cluster spatially. While social attributes of neighborhoods, such as social cohesion, collective efficacy, and demographic composition, can be measured using survey questions or existing censuses, measuring physical aspects of neighborhoods, including physical disorder and the built environment, usually require some form of systematic field observation. The high costs and logistical constraints of sending field observers to neighborhoods have greatly limited the inclusion of measures of neighborhood physical characteristics in analyses of health, with most such studies focusing on single cities and often on selected neighborhoods within cities. The proposed research will assess the feasibility, reliability, and validity of using new technology, freely available online via Google, to measure neighborhood physical disorder and built environment at a fraction of the cost of field studies. Specifically, we will develop procedures for using Google Earth and Google Street View to measure neighborhood physical disorder and the built environment, and evaluate the validity, reliability, cost- effectiveness, and psychometric properties of these procedures. Furthermore, we will assess the efficiency of alternative neighborhood spatial scales and block face sampling strategies within the Google Street View and Earth frameworks. The methodology we propose to develop has the potential to transform the way neighborhood data are collected, allowing the linkage of contextual data to local and national studies very inexpensively and creating unparalleled public resources for studying contextual influences on health and well-being. It will be particularly useful for large-sample, multi-site studies such as the new National Children's Study.
The proposed research will assess the feasibility, reliability, and validity of using new technology, freely available online via Google, to measure neighborhood physical disorder and built environment at a fraction of the cost of field studies. The methodology we propose to develop has the potential to transform the way neighborhood data are collected, allowing the linkage of contextual data to local and national studies very inexpensively and creating unparalleled public resources for studying contextual influences on health and well- being.
| Mooney, Stephen J; Bader, Michael D M; Lovasi, Gina S et al. (2017) Street Audits to Measure Neighborhood Disorder: Virtual or In-Person? Am J Epidemiol 186:265-273 |
| Quinn, James W; Mooney, Stephen J; Sheehan, Daniel M et al. (2016) Neighborhood Physical Disorder in New York City. J Maps 12:53-60 |
| Mooney, Stephen J; DiMaggio, Charles J; Lovasi, Gina S et al. (2016) Use of Google Street View to Assess Environmental Contributions to Pedestrian Injury. Am J Public Health 106:462-9 |
| Bader, Michael D M; Mooney, Stephen J; Lee, Yeon Jin et al. (2015) Development and deployment of the Computer Assisted Neighborhood Visual Assessment System (CANVAS) to measure health-related neighborhood conditions. Health Place 31:163-72 |
| Mooney, Stephen J; Bader, Michael D M; Lovasi, Gina S et al. (2014) Validity of an ecometric neighborhood physical disorder measure constructed by virtual street audit. Am J Epidemiol 180:626-35 |
| Rundle, Andrew G; Bader, Michael D M; Richards, Catherine A et al. (2011) Using Google Street View to audit neighborhood environments. Am J Prev Med 40:94-100 |
