Quantifying Urban Sprawl using Land Use Data

Digital land use data, generally derived by remote sensing and organized in grid form, have become widely and cheaply available for even the most remote areas of the globe. Here we investigate how to use land use data to measure three of the most characteristic aspects of urban sprawl: low density, low continuity of land use type (scatteredness), and low compactness of the shape of the city. For each of these categories we present possible urban sprawl indicators. Some of these indicators have been used in the literature before, others we developed ourselves. We illustrate how simple changes to common density indices can improve their meaningfulness. With respect to scatteredness we show that entropy indices might not be the most suitable type of index as their interpretation is ambiguous. A variant on Moran's I index performs this task better. When it comes to measuring compactness the grid structure of land use data can inflate the boundary of the measured area. We introduce compactness indices that correct for this problem. To illustrate the discussed indices we apply them to the city of Graz, the second largest town in Austria, using data from the CORINE Land Cover (CLC) Project (2006).