TY - JOUR
T1 - Quantifying spatial misclassification in exposure to noise complaints among low-income housing residents across New York City neighborhoods
T2 - a Global Positioning System (GPS) study
AU - Duncan, Dustin T.
AU - Tamura, Kosuke
AU - Regan, Seann D.
AU - Athens, Jessica
AU - Elbel, Brian
AU - Meline, Julie
AU - Al-Ajlouni, Yazan A.
AU - Chaix, Basile
N1 - Publisher Copyright:
© 2016 Elsevier Inc.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Purpose To 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. Methods Data 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. Results There 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. Conclusions These 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.
AB - Purpose To 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. Methods Data 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. Results There 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. Conclusions These 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.
KW - Geographic information systems
KW - Global positioning systems
KW - Low-income housing residents
KW - Neighborhoods
KW - Noise complaint exposure
KW - Spatial epidemiology
KW - Spatial misclassification
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U2 - 10.1016/j.annepidem.2016.09.017
DO - 10.1016/j.annepidem.2016.09.017
M3 - Article
C2 - 28063754
AN - SCOPUS:85008516499
SN - 1047-2797
VL - 27
SP - 67
EP - 75
JO - Annals of Epidemiology
JF - Annals of Epidemiology
IS - 1
ER -