Abstract
This study compares local-level socioeconomic variables interpolated with three different methods: 1) Thiessen polygons, 2) Inverse distance weighting, and 3) Areas of influence based on cost of distance. The main objective was to determine the interpolation technique capable of generating the most efficient variable to explain the distribution of deforestation through two statistical approaches: generalized linear models and hierarchical partition. The study was conducted in two regions of western Mexico: Coyuquilla River watershed, and the Sierra de Manantlan Biosphere Reserve (SMBR). For SMBR it was found that the Thiessen polygons and areas of influence were the techniques that interpolated variables with greatest explanatory power for the deforestation process, in Coyuquilla it was inverse distance weighting. These differences are related to the distribution and the spatial correlation of the values of the variables.
Highlights
Land-use/land-cover changes (LULCC) have become a central question to be addressed in recent years
This study compares local-level socioeconomic variables interpolated with three different methods: 1) Thiessen polygons, 2) Inverse distance weighting, and 3) Areas of influence based on cost of distance
The selected variables were interpolated through Thiessen polygons, inverse distance weighting (IDW) and areas of influence (Figures 3 and 4), and used in the generalized linear models (GLM) and hierarchical partitioning (HP)
Summary
Land-use/land-cover changes (LULCC) have become a central question to be addressed in recent years. The study of the factors that drive deforestation processes involves biophysical and socioeconomic ones. Geographical information systems (GIS) provide tools to fulfill such task by estimating the values of an environmental variable at unsampled sites using point data from observations within the same region. These methods have been widely used in other environmental matters like soil mapping [9,10] and climatic data [11]. They have been applied to ecological studies such as the prediction of forest volume [12] and the characterization of the spatial structure of vegetation communities [13]
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