Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity

Considerable research has been conducted to advance our understanding of how environmental factors influence people’s health behaviors (e.g., leisure-time physical inactivity) at the neighborhood level. However, different environmental factors may operate differently at different geographic location...

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Main Authors: Jue Wang, Kangjae Lee, Mei-Po Kwan
Format: Article
Language:English
Published: MDPI AG 2018-04-01
Series:ISPRS International Journal of Geo-Information
Subjects:
GIS
Online Access:http://www.mdpi.com/2220-9964/7/4/143
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spelling doaj-d83378443bbe42fe9deb286aa0f79a622020-11-24T20:58:59ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-04-017414310.3390/ijgi7040143ijgi7040143Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-StationarityJue Wang0Kangjae Lee1Mei-Po Kwan2Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Natural History Building, MC-150, 1301 W Green Street, Urbana, IL 61801, USAIllinois Informatics Institute, University of Illinois at Urbana-Champaign, National Center for Supercomputing Applications, MC-257, 1205 W Clark St., Urbana, IL 61801, USADepartment of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Natural History Building, MC-150, 1301 W Green Street, Urbana, IL 61801, USAConsiderable research has been conducted to advance our understanding of how environmental factors influence people’s health behaviors (e.g., leisure-time physical inactivity) at the neighborhood level. However, different environmental factors may operate differently at different geographic locations. This study explores the inconsistent findings regarding the associations between environmental exposures and physical inactivity. To address spatial autocorrelation and explore the impact of spatial non-stationarity on research results which may lead to biased estimators, this study uses spatial regression models to examine the associations between leisure-time physical inactivity and different social and physical environmental factors for all counties in the conterminous U.S. By comparing the results with the conventional ordinary least squares regression and spatial lag model, the geographically weighted regression model adequately addresses the problem of spatial autocorrelation (Moran’s I of the residual = 0.0293) and highlights the spatial non-stationarity of the associations. The existence of spatial non-stationarity that leads to biased estimators, which were often ignored in past research, may be another reason for the inconsistent findings in previous studies besides the modifiable areal unit problem and the uncertain geographic context problem. Also, the observed associations between environmental variables and leisure-time physical inactivity are helpful for developing location-based policies and interventions to encourage people to undertake more physical activity.http://www.mdpi.com/2220-9964/7/4/143physical activityspatial regressionspatial autocorrelationspatial non-stationarityenvironmental healthGIS
collection DOAJ
language English
format Article
sources DOAJ
author Jue Wang
Kangjae Lee
Mei-Po Kwan
spellingShingle Jue Wang
Kangjae Lee
Mei-Po Kwan
Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity
ISPRS International Journal of Geo-Information
physical activity
spatial regression
spatial autocorrelation
spatial non-stationarity
environmental health
GIS
author_facet Jue Wang
Kangjae Lee
Mei-Po Kwan
author_sort Jue Wang
title Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity
title_short Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity
title_full Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity
title_fullStr Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity
title_full_unstemmed Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity
title_sort environmental influences on leisure-time physical inactivity in the u.s.: an exploration of spatial non-stationarity
publisher MDPI AG
series ISPRS International Journal of Geo-Information
issn 2220-9964
publishDate 2018-04-01
description Considerable research has been conducted to advance our understanding of how environmental factors influence people’s health behaviors (e.g., leisure-time physical inactivity) at the neighborhood level. However, different environmental factors may operate differently at different geographic locations. This study explores the inconsistent findings regarding the associations between environmental exposures and physical inactivity. To address spatial autocorrelation and explore the impact of spatial non-stationarity on research results which may lead to biased estimators, this study uses spatial regression models to examine the associations between leisure-time physical inactivity and different social and physical environmental factors for all counties in the conterminous U.S. By comparing the results with the conventional ordinary least squares regression and spatial lag model, the geographically weighted regression model adequately addresses the problem of spatial autocorrelation (Moran’s I of the residual = 0.0293) and highlights the spatial non-stationarity of the associations. The existence of spatial non-stationarity that leads to biased estimators, which were often ignored in past research, may be another reason for the inconsistent findings in previous studies besides the modifiable areal unit problem and the uncertain geographic context problem. Also, the observed associations between environmental variables and leisure-time physical inactivity are helpful for developing location-based policies and interventions to encourage people to undertake more physical activity.
topic physical activity
spatial regression
spatial autocorrelation
spatial non-stationarity
environmental health
GIS
url http://www.mdpi.com/2220-9964/7/4/143
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