An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago

Int J Environ Res Public Health. 2018 Apr 9;15(4):703. doi: 10.3390/ijerph15040703.

Abstract

Scholars in the fields of health geography, urban planning, and transportation studies have long attempted to understand the relationships among human movement, environmental context, and accessibility. One fundamental question for this research area is how to measure individual activity space, which is an indicator of where and how people have contact with their social and physical environments. Conventionally, standard deviational ellipses, road network buffers, minimum convex polygons, and kernel density surfaces have been used to represent people's activity space, but they all have shortcomings. Inconsistent findings of the effects of environmental exposures on health behaviors/outcomes suggest that the reliability of existing studies may be affected by the uncertain geographic context problem (UGCoP). This paper proposes the context-based crystal-growth activity space as an innovative method for generating individual activity space based on both GPS trajectories and the environmental context. This method not only considers people's actual daily activity patterns based on GPS tracks but also takes into account the environmental context which either constrains or encourages people's daily activity. Using GPS trajectory data collected in Chicago, the results indicate that the proposed new method generates more reasonable activity space when compared to other existing methods. This can help mitigate the UGCoP in environmental health studies.

Keywords: GIS; GPS; activity space; environmental exposure; the uncertain geographic context problem.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Activities of Daily Living*
  • Adolescent
  • Adult
  • Aged
  • Chicago
  • Environmental Health
  • Environmental Monitoring / methods*
  • Female
  • Geographic Information Systems
  • Humans
  • Male
  • Middle Aged
  • Reproducibility of Results
  • Young Adult