title: Assessing patterns of spatial behavior in health studies: their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study).
abstract: Prior epidemiological studies have mainly focused on local residential neighborhoods to assess environmental exposures. However, individual spatial behavior may modify residential neighborhood influences, with weaker health effects expected for mobile populations. By examining individual patterns of daily mobility and associated socio-demographic profiles and transportation modes, this article seeks to develop innovative methods to account for daily mobility in health studies. We used data from the RECORD Cohort Study collected in 2011-2012 in the Paris metropolitan area, France. A sample of 2062 individuals was investigated. Participants' perceived residential neighborhood boundaries and regular activity locations were geocoded using the VERITAS application. Twenty-four indicators were created to qualify individual space-time patterns, using spatial analysis methods and a geographic information system. Three domains of indicators were considered: lifestyle indicators, indicators related to the geometry of the activity space, and indicators related to the importance of the residential neighborhood in the overall activity space. Principal component analysis was used to identify main dimensions of spatial behavior. Multilevel linear regression was used to determine which individual characteristics were associated with each spatial behavior dimension. The factor analysis generated five dimensions of spatial behavior: importance of the residential neighborhood in the activity space, volume of activities, and size, eccentricity, and specialization of the activity space. Age, socioeconomic status, and location of the household in the region were the main predictors of daily mobility patterns. Activity spaces of small sizes centered on the residential neighborhood and implying a large volume of activities were associated with walking and/or biking as a transportation mode. Examination of patterns of spatial behavior by individual socio-demographic characteristics and in relation to transportation modes is useful to identify populations with specific mobility/accessibility needs and has implications for investigating transportation-related physical activity and assessing environmental exposures and their effects on health.
authors.affiliation: Department of Social and Preventive Medicine, Université de Montréal, Canada; Montreal University Hospital Research Center (CRCHUM), Canada; Sorbonne Universités, UPMC Université, Paris 06, France; INSERM, UMR_S 1136, France. Electronic address: camille.perchoux@umontreal.ca.
authors.forename: Camille
authors.initials: C
authors.lastname: Perchoux
authors.affiliation: Department of Social and Preventive Medicine, Université de Montréal, Canada; Montreal University Hospital Research Center (CRCHUM), Canada.
authors.forename: Yan
authors.initials: Y
authors.lastname: Kestens
authors.affiliation: Centre d'Investigations Préventives et Cliniques, France.
authors.forename: Frédérique
authors.initials: F
authors.lastname: Thomas
authors.affiliation: Department of Social and Preventive Medicine, Université de Montréal, Canada; Centre de recherche du CHU Sainte Justine, Canada.
authors.forename: Andraea
authors.initials: A
authors.lastname: Van Hulst
authors.affiliation: Montreal University Hospital Research Center (CRCHUM), Canada.
authors.forename: Benoit
authors.initials: B
authors.lastname: Thierry
authors.affiliation: Sorbonne Universités, UPMC Université, Paris 06, France; INSERM, UMR_S 1136, France.
authors.forename: Basile
authors.initials: B
authors.lastname: Chaix
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001642:Bicycling / Q000706:statistics & numerical data; D005260:Female; D005602:France; D006801:Humans; D008297:Male; D008875:Middle Aged; D012111:Residence Characteristics / Q000706:statistics & numerical data*; D012737:Sex Factors; D012959:Socioeconomic Factors; D062206:Spatial Analysis; D013037:Spatial Behavior; D014186:Transportation / Q000379:methods*; D016138:Walking / Q000706:statistics & numerical data
keywords: Mobility; Paris (France); Principal component analysis; Socioeconomic status; Spatial analysis; Spatial behavior