Beyond Space (As We Knew It): Toward Temporally Integrated Geographies of Segregation, Health, and Accessibility - Space-Time Integration in Geography and GIScience - page 46

Moving beyond the conventional focus on static residential spaces and toward
temporally integrated perspectives, however, poses many challenges. While it is
now possible to collect high resolution space-time data on people's daily activities
and trips using location-aware devices like global positioning systems (GPS) and
mobile phones (Ahas et al. 2010 ;Shovaletal. 2011 ; Almanza et al. 2012 ;
Rodríguez et al. 2012 ; Richardson et al. 2013 ; Wiehe et al. 2013 ), high quality
data are still costly and time consuming to collect. Further, reliably linking the
space-time data of people's movement to other relevant attributes (e.g., activity type,
real-time sociogeographic context) is fraught with difficulties. There are few widely
available methods for analyzing the complex relationships among human space-time
trajectories, racial segregation, environmental exposure, and accessibility. Taking
into account certain facets of time (e.g., people's subjective experiences of time)
remains difficult. Modeling human movements and incorporating time also faces
complex issues of uncertainty. Recent studies, however, have begun to address some
of these difficulties. For instance, recent studies have attempted to model human
mobility and travel in probabilistic terms (e.g., Ettema and Timmermans 2007 ;
González et al. 2008 ; Kuijpers and Othman 2009 ). Qualitative approaches also
provide promising alternatives for grappling with people's complex spatiotemporal
experiences (e.g., Kwan and Ding 2008 ; Valentine and Sadgrove 2012 ). To fully
address the challenges of temporally integrated geographies, much remains to be
done in future research.
Acknowledgements I thank Martin Dijst and three anonymous reviewers for their helpful
comments on earlier drafts of this article. I am also grateful to Malia Jones, Stephen Matthews and
John Palmer for kindly providing me copies of their unpublished or forthcoming papers, which
have helped improved this article considerably. I was supported by the following grants while
writing this article: NSF BCS-1244691, NIH R01DA032371-01, NIDA R01DA028692, NIDA
R01CA129771, and NSFC 41228001.
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