Background

 

Growth and development are limited by the potential for land conversion and development. The developability of lands in a place or region is determined by a variety of factors, including geophysical characteristics, the extent of built-up lands, cultural and aesthetical resources, and legal constraints. When studying these factors’ collective effects on growth and development, two approaches have been used. The first is to use these factors as explanatory variables in statistical analysis. However, doing so could double (or triple) count some land covers. For example, one piece of land could fall into a national park (public land) and be covered by water (not suitable for development); therefore, that piece of land would be double counted in the two variables. The other approach is to aggregate them into one or more indices by factor analysis or other statistics-based weighted aggregation methods. However, such generated indices provide only a relative rank of land developability across geographic units and cannot provide an estimate of the amount of lands available for development.

 

A more accurate estimate of the total lands available for development (called “land developability” in this project) can be estimated using a simple spatial model (e.g. spatial overlay methods in ArcGIS). Environmental analysts often employ spatial overlay methods to study the interactions between environment, population, land use, and legal constraints by overlaying these layers at fine pixel sizes.

 

Although social scientists may be interested in borrowing spatial overlay methods to study the relationship between land use and development and a social phenomenon of interest, the fact that social data are aggregated at rather coarse scales (mostly political or geographical areas) imposes difficulties in taking into account land use and development variables that generally can be studied usefully only at very fine data resolution. For example, geophysical factors such as elevation and slope likely influence housing development, but they can be measured meaningfully only at the pixel level of analysis.

 

This project proposes an approach for linking land use and development to social data by developing a spatial model. The model aggregates the layers of land use and development into a single raster layer representing developable lands. The layer of developable lands is then aggregated to political or geographical areas from which a developability index representing the proportion of lands available for development can be derived. The index can then be linked to social data for social science research.