id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-348269-6z0kiapa	Nguyen, Quynh C.	Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases	2020-09-01		.txt	text/plain	5833	304	47	We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). In examining associations between built environment characteristics and COVID cases, we controlled for demographic compositional characteristics of areas and population density, which has previously been utilized in econometric studies as a proxy for air pollution and other factors found with greater prevalence in urban areas [15, 16] . Additionally, previous studies found that physical disorder in the neighborhood environments is significantly associated with higher prevalence of chronic diseases [19] and poor self-rated health [20] , which also increases the chances of contracting COVID-19 [21, 22] . From GSV images, we created indicators of urban development (non-single family home, single lane roads), walkability (crosswalks, sidewalks), and physical disorder (dilapidated building, visible utility wires).	./cache/cord-348269-6z0kiapa.txt	./txt/cord-348269-6z0kiapa.txt