Local Deforestation Patterns in Mexico - An Approach using Geographiccally Weighted Regression

Abstract

This study identifies drivers of deforestation in Mexico by applying Geographically Weighted Regression (GWR) models to cartographic and statistical data. A wall-to-wall multitemporal GIS database was constructed incorporating digital data from Global Forest Change (2000-2012); along with ancillary data (road network, settlements, topography, socio-economical parameters and government policies). The database analysis allowed assessing the spatial distribution of deforestation at the municipal level. The statistical analysis of deforestation drivers presented here was focused on the rate of deforestation during the period 2007-2011 as dependent variable. In comparison with the global model, the use of GWR increased the goodness-of-fit (adjusted R2) from 0.46 (global model) to 0.58 (average R2 of GWR local models), with individual GWR models ranging from 0.52 to 0.64. The GWR model highlighted the spatial variation of the relationship between the rate of deforestation and its drivers. Factors identified as having a major impact on deforestation were related to topography (slope), accessibility (road and settlement density) and marginalization. Results indicate that some of the drivers explaining deforestation vary over space, and that the same driver can exhibit opposite effects depending on the region.