Collaborative Research: Using Computer Vision to Measure Neighborhood Variables Affecting Health

点击翻译按钮获取中文摘要

This project will develop an automated method, using advances in computer science, to observe and record systematically the physical conditions of neighborhood environments at a large-scale. Neighborhood environments play a significant role in shaping the health of individuals and communities, consequently contributing to inequality in the U.S. Past research suggests that the presence of physical disorder, poorly maintained properties, and vacant lots in neighborhoods can negatively affect physical and mental health, attract more crime and disorder, and lead to neighborhood disinvestment. The measures resulting from this study will facilitate examination of these processes and provide a powerful resource for the scientific research community. They will be more broadly beneficial as well by allowing policymakers, practitioners, and the public to track neighborhood progress and target improvements.The project takes advantage of Google Street View imagery--the largest publicly available longitudinal dataset of visual appearance of street blocks--and will use Amazon's Mechanical Turk--a crowdsourcing platform--and existing field survey data to identify indicators of physical disorder and maintenance, such as trash and blighted buildings, on a sample of street segments across three distinct cities: Boston, Detroit, and Los Angeles. These data will be used to train an algorithm that draws on recent advances in machine learning and computer vision. Reliability and validity of the method for identifying characteristics and measures will be tested throughout each step of the process. The resulting longitudinal measures of the physical conditions of neighborhoods will be linked to longitudinal health surveys conducted in each of the three cities to analyze the relationship between physical neighborhood conditions and health. In addition, the new measures will be released as a publicly available database of longitudinal measures of physical neighborhood conditions across multiple cities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.