ASSESSING MODIFIABLE AREAL UNIT PROBLEM IN THE ANALYSIS OF DEFORESTATION DRIVERS USING REMOTE SENSING AND CENSUS DATA

Abstract

In order to identify drivers of land use / land cover change (LUCC), the rate of change is often compared with environmental and socio-economic variables such as slope, soil suitability or population density. Socio-economic information is obtained from census data which are collected for individual households but are commonly presented in aggregate on the basis of geographical units as municipalities. However, a common problem, known as the modifiable areal unit problem (MAUP), is that the results of statistical analysis are not independent of the scale and the spatial configuration of the units used to aggregate the information. In this article, we evaluate how strong MAUP effects are for a study on the deforestation drivers in Mexico at municipality level. This was done by taking socio-economic variables from the 2010 Census of Mexico along with environmental variables and the rate of deforestation. As population census is given for each human settlement and environmental variables are obtained from high resolution spatial database, it was possible to aggregate the information using spatial units (”pseudo municipalities”) with different sizes in order to observe the effect of scale and aggregation on the values of bivariate correlations (Pearsons r) between pairs of variables. We found that MAUP produces variations in the results, and we observed some variable pairs and some configurations of the spatial units where the effect was substantial.