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Measuring changes in neighborhood disorder using Google Street View longitudinal imagery: a feasibility study
Journal article   Open access   Peer reviewed

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

Pedro Gullon, Dustin Fry, Jesse J. Plascak, Stephen J. Mooney and Gina S. Lovasi
Cities & health, v ahead-of-print(ahead-of-print), pp 1-7
18 May 2023
DOI: https://doi.org/10.1080/23748834.2023.2207931
url
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578651View
Accepted (AM)Open Access (License Unspecified),  Open

Abstract

Geographic factors longitudinal studies observation pedestrians residence characteristics
Few studies have used longitudinal imagery of Google Street View (GSV) despite its potential for measuring changes in urban streetscape 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 eight neighborhood disorder indicators at three different times (up to 2009, up to 2014, and up to 2019). More than 70% of the 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.

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