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Review
.2011 Jul 1:8:71.
doi: 10.1186/1479-5868-8-71.

Using Geographic Information Systems (GIS) to assess the role of the built environment in influencing obesity: a glossary

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Review

Using Geographic Information Systems (GIS) to assess the role of the built environment in influencing obesity: a glossary

Lukar E Thornton et al. Int J Behav Nutr Phys Act..

Abstract

Features of the built environment are increasingly being recognised as potentially important determinants of obesity. This has come about, in part, because of advances in methodological tools such as Geographic Information Systems (GIS). GIS has made the procurement of data related to the built environment easier and given researchers the flexibility to create a new generation of environmental exposure measures such as the travel time to the nearest supermarket or calculations of the amount of neighbourhood greenspace. Given the rapid advances in the availability of GIS data and the relative ease of use of GIS software, a glossary on the use of GIS to assess the built environment is timely. As a case study, we draw on aspects the food and physical activity environments as they might apply to obesity, to define key GIS terms related to data collection, concepts, and the measurement of environmental features.

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Figures

Figure 1
Figure 1
Examples of measures of accessibility. Terms: accessibility, activity space, buffer, centroid (geometric within an administrative unit), network distance. This figure demonstrates the different approaches to measuring boundaries of spatial units used foraccessibilitymeasures such asdensity. Firstly, the point from which the measures will be taken is defined; in this case a geometriccentroidof an administrative unit (census collector district, the smallest administrative spatial unit in Australia) is calculated. From this point, twobuffersare drawn; the first using Euclidean (straight-line) distance and the other usingnetwork distance. The third spatial unit relates toactivity space. This relates to an individual's travel patterns over a course of a day with the destinations visited and the travel routes mapped (both of which can be captured using aGlobal Position System (GPS)device). Abufferis also placed around these to capture exposures nearby to the visited locations and also nearby to their household (represented by the geometric centroid).
Figure 2
Figure 2
Comparison of environments with: a) a grid street pattern with high-connectivity; b) a poorly connected street network. Terms: accessibility, connectivity, walkability. Figure 2 demonstrates the differences between high street-networkconnectivity(Figure 2a) that would provide a more direct route between a origin and destination compared to low street-networkconnectivitywith many cul-de-sacs and dead-ends (Figure 2b) which reduces the directness travel routes.
Figure 3
Figure 3
An example of a map resulting from kernel density estimation. Terms: accessibility, kernel density estimation. Figure 3 demonstrates the output map resulting from kernel density estimation with the kernel size set at two kilometres. Darker areas indicate where resources are more densely located while lighter colouring relate to areas with reduced accessibility.
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References

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