Measuring changes in neighborhood disorder using Google Street View longitudinal imagery: a feasibility study.

Abstract

Few studies have used longitudinal imagery of Google Street View (GSV) despite its potential for measuring changes in urban streetscapes characteristics relevant to health, such as neighborhood disorder. Neighborhood disorder has been previously associated with health outcomes. We conducted a feasibility study exploring image availability over time in the Philadelphia metropolitan region and describing changes in neighborhood disorder in this region between 2009, 2014, and 2019. Our team audited Street View images from 192 street segments in the Philadelphia Metropolitan Region. On each segment, we measured the number of images available through time, and for locations where imagery from more than one time point was available, we collected 8 neighborhood disorder indicators at 3 different times (up to 2009, up to 2014, and up to 2019). More than 70% of streets segments had at least one image. Neighborhood disorder increased between 2009 and 2019. Future studies should study the determinants of change of neighborhood disorder using longitudinal GSV imagery.