An Analytical Framework for Integrating the Spatiotemporal Dynamics of Environmental Context and Individual Mobility in Exposure Assessment: A Study on the Relationship between Food Environment Exposures and Body Weight

Abstract

In past studies, individual environmental exposures were largely measured in a static manner. In this study, we develop and implement an analytical framework that dynamically represents environmental context (the environmental context cube) and effectively integrates individual daily movement (individual space-time tunnel) for accurately deriving individual environmental exposures (the environmental context exposure index). The framework is applied to examine the relationship between food environment exposures and the overweight status of 46 participants using data collected with global positioning systems (GPS) in Columbus, Ohio, and binary logistic regression models. The results indicate that the proposed framework generates more reliable measurements of individual food environment exposures when compared to other widely used methods. Taking into account the complex spatial and temporal dynamics of individual environmental exposures, the proposed framework also helps to mitigate the uncertain geographic context problem (UGCoP). It can be used in other environmental health studies concerning environmental influences on a wide range of health behaviors and outcomes.

Keywords: GIS; GPS; environmental context cube; environmental context exposure index; environmental health; food environment; the uncertain geographic context problem (UGCoP).

Figure A1

An example of the space-time cube (aquarium).

Figure 1

The proposed analytical framework.

Figure 2

The study area (Franklin County) and the location of BMI-unhealthy food outlets.

Figure 3

The food environment layers at different time of a day generated by the three different distance-decay methods (the food environment layers with spatial resolution of 100 m × 100 m are used in this figure to illustrate the three distance-decay methods).

Figure 4

The process of the temporal interpolation and the generation of the 3-D environmental context cube with food environment layers.

Figure 5

Implementation of the 3-D environmental context cube using a 3-D point cloud. (a): an environmental context cube, (b) voxels in the cube represented by points at the centroid of the original voxels, (c) the corresponding 3-D point cloud.

Figure 6

A GPS trajectory (a) and an individual space-time tunnel (b) projected into an environmental context cube.

Figure 7

The 3-D intersection of the point cloud and individual space-time tunnel.

Figure 8

Four widely used methods for delineating individual exposure space based on one subject’s GPS trajectory data.

Figure 9

Comparison of the exposure measures obtained with different methods for each participant. (ECC: environmental context cube; KD: kernel density estimation; ISDD: inverse-square distance decay function; NEDD: negative-exponent distance decay function; GTB: GPS trajectory buffers; MCP: minimum convex polygons; SDE1: standard deviation ellipses with one standard deviation; SDE2: standard deviation ellipses with two standard deviations.)