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Identifying Local Deforestation Patterns Using Geographically Weighted Regression Models

  • Jean-François MasEmail author
  • Gabriela Cuevas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 582)

Abstract

This study aimed at identifying drivers and patterns of deforestation in Mexico by applying Geographically Weighted Regression (GWR) models to cartographic and statistical data. We constucted a nation-wide multidate GIS database incorporating digital data about deforestation from the Global Forest Change database (2000–2013); along with ancillary data (topography, road network, settlements and population disribution, socio-economical indices and government policies). We computed the rate of deforestation during the period 2008–2011 at the municipal level. Local linear models were fitted using the rate of deforestation as dependent variable. In comparison with the global model, the use of GWR increased the goodness-of-fit (adjusted R2) from 0.20 (global model) to 0.63. The mapping of GWR models’ parameters and its significance, anables us to highlight 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, accessibility, cattle ranching and marginalization. Results indicate that the effect of these drivers varies over space, and that the same driver can even exhibit opposite effects depending on the region.

Keywords

Deforestation Drivers Geographically weighted regression Mexico 

Notes

Acknowledgements

This research has been funded by the Consejo Nacional de Ciencia y Tecnología (CONACyT) and the Secretaría de Educación Pública (grant CONACYT-SEP CB-2012-01-178816) and CONAFOR project: Construcción de las bases para la propuesta de un nivel nacional de referencia de las emisiones forestales y análisis de políticas públicas. The authors would like to thank the four reviewers for their careful review of our manuscript and providing us with their comments and suggestion to improve the quality of the manuscript.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Centro de Investigaciones en Geografía AmbientalUniversidad Nacional Autónoma de MéxicoMoreliaMexico

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