Quantifying spatial misclassification in exposure to noise complaints among low-income housing residents across New York City neighborhoods: a Global Positioning System (GPS) study

Abstract

PurposeTo examine if there was spatial misclassification in exposure to neighborhood noise complaints among a sample of low-income housing residents in New York City, comparing home-based spatial buffers and Global Positioning System (GPS) daily path buffers. MethodsData came from the community-based NYC Low-Income Housing, Neighborhoods and Health Study, where GPS tracking of the sample was conducted for a week (analytic n = 102). We created a GPS daily path buffer (a buffering zone drawn around GPS tracks) of 200 m and 400 m. We also used home-based buffers of 200 m and 400 m. Using these “neighborhoods” (or exposure areas), we calculated neighborhood exposure to noisy events from 311 complaints data (analytic n = 143,967). Friedman tests (to compare overall differences in neighborhood definitions) were applied. ResultsThere were differences in neighborhood noise complaints according to the selected neighborhood definitions (P < .05). For example, the mean neighborhood noise complaint count was 1196 per square kilometer for the 400-m home-based and 812 per square kilometer for the 400-m activity space buffer, illustrating how neighborhood definition influences the estimates of exposure to neighborhood noise complaints. ConclusionsThese analyses suggest that, whenever appropriate, GPS neighborhood definitions can be used in spatial epidemiology research in spatially mobile populations to understand people's lived experience.