The modifiable area unit problem, empirical analysis by statistical simulation

grantor: University of TorontoThe Modifiable Area Unit Problem (MAUP) has been discussed in the spatial analysis literature since the 1930's, but it is the recent surge in the availability of desktop computing power and Geographical Information Systems software that have caused both a resurgence of interest in the problem and a greater need to learn more about it. Many spatial datasets are collected on a fine resolution (i.e. a large number of small spatial units) but, for the sake of privacy and/or size concerns, are released only after being spatially aggregated to a coarser resolution (i.e. a smaller number of larger spatial units). The chief example of this process is census data which are collected from every household, but released only at the Enumeration Area or Census Tract level of spatial resolution. When values are averaged over the process of aggregation, variability in the dataset is lost and values of statistics computed at the different resolutions will be different; this change is called the ' scale effect'. One also gets different values of statistics depending on how the spatial aggregation occurs; this variability is called the ' zoning effect'. The purpose of studying the MAUP is to try to estimate the true values of the statistics at the original level of spatial resolution. Knowing these would allow researchers to attempt to make estimates of the data values using either synthetic spatial data generators like the one described in this thesis or by other techniques. Many studies of the MAUP have been made using specific datasets and examining various statistics, such as correlations. Although interesting properties have been documented, this approach is ultimately unsatisfactory because researchers have had no control over the various properties of the datasets, all of which could potentially affect the MAUP. This research has focused on the creation of a synthetic spatial dataset generator that can systematically vary means, variances, correlations, spatial autocorrelations and spatial connectivity matrices of variables in order to study their effects on univariate, bivariate, and multivariate statistics. Even though the MAUP has traditionally been written off as an intractable problem, results from the various experiments described in this thesis indicate that there is a degree of regularity in the behaviour of aggregated statistics that depends on the spatial autocorrelation and configuration of the variable values. If the MAUP can be solved, however, it is clear that it will likely be a complex procedure.Ph.D