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

Thornton LE, Pearce JR, Kavanagh AM - Int J Behav Nutr Phys Act (2011)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood, Victoria, 3125, Australia. lukar.thornton@deakin.edu.au

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.

Show MeSH

Related in: MedlinePlus

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3141619&req=5

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.

Mentions: Kernel density estimation is a technique for transforming point data to a continuous density surface map whereby the density of a feature can be estimated for any point on the surface (Figure 3). To create Kernel density estimates, the entire study region is partitioned into grid cells of a predetermined size. The kernels (which are usually circular in shape with the radius defined by the user) are then placed around the centroid of each cell (or alternatively the crossing point of the grid cells). For each feature within the kernel, weights are assigned as a defined function of distance from the geometric centroid of the kernel. This results in a density value being assigned to each cell so that density values can be calculated across the whole study region. In studies of the built environment and health, the technique has previously been used to calculate robust measures of exposure to one or more environmental features (e.g. access to food outlets or recreational facilities) across a study area [58-62]. This approach is advantageous compared to traditional density measures because a resource that is located closer to the grid cell is assigned more weight than resources that are located further away, with the weight approaching zero at the boundary of the kernel. Thus, the transition to the boundary represents a fuzzy rather than sharp boundary [53] and can be utilised as a gravity-based measure of accessibility [36].


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

Thornton LE, Pearce JR, Kavanagh AM - Int J Behav Nutr Phys Act (2011)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3141619&req=5

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.
Mentions: Kernel density estimation is a technique for transforming point data to a continuous density surface map whereby the density of a feature can be estimated for any point on the surface (Figure 3). To create Kernel density estimates, the entire study region is partitioned into grid cells of a predetermined size. The kernels (which are usually circular in shape with the radius defined by the user) are then placed around the centroid of each cell (or alternatively the crossing point of the grid cells). For each feature within the kernel, weights are assigned as a defined function of distance from the geometric centroid of the kernel. This results in a density value being assigned to each cell so that density values can be calculated across the whole study region. In studies of the built environment and health, the technique has previously been used to calculate robust measures of exposure to one or more environmental features (e.g. access to food outlets or recreational facilities) across a study area [58-62]. This approach is advantageous compared to traditional density measures because a resource that is located closer to the grid cell is assigned more weight than resources that are located further away, with the weight approaching zero at the boundary of the kernel. Thus, the transition to the boundary represents a fuzzy rather than sharp boundary [53] and can be utilised as a gravity-based measure of accessibility [36].

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood, Victoria, 3125, Australia. lukar.thornton@deakin.edu.au

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.

Show MeSH
Related in: MedlinePlus