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Identifying Local Deforestation Patterns Using Geographically Weighted Regression Models

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Geographical Information Systems Theory, Applications and Management (GISTAM 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 582))

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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.

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References

  1. Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Petrov, B., Csaki, F. (eds.) 2nd Symposium on Information Theory, pp. 267–281. Akademiai Kiado, Budapest (1973)

    Google Scholar 

  2. Alix-García, J., de Janvry, A., Sadoulet, E.: A tale of two communities: explaining deforestation in Mexico. World Dev. 33(2), 219–235 (2005)

    Article  Google Scholar 

  3. Belsley, D., Kuh, E., Welsch, R.: Regression Diagnostics: Identifying Inuential Data and Sources of Collinearity. Wiley, New York (1980)

    Book  Google Scholar 

  4. Bivand, R., Yu, D.: Package spgwr, Geographically weighted regression. http://cran.open-source-solution.org/web/packages/spgwr/spgwr.pdf

  5. Bonilla-Moheno, M., Redo, D.J., Mitchell Aide, T., Clark, M.L., Grau, H.R.: Vegetation change and land tenure in Mexico: a country-wide analysis. Land Use Policy 30(1), 355–364 (2013)

    Article  Google Scholar 

  6. Bray, D.B., Duran, E., Ramos, V.H., Mas, J.F., Velázquez, A., McNab, R.B., Barry, D., Radachowsky, J.: Tropical deforestation, community forests, and protected areas in the Maya Forest. Ecol. Soc. 13(2), 56 (2008)

    Google Scholar 

  7. Bezaury Creel, J.E., Torres, J.F., Ochoa-Ochoa, L., Castro Campos, M., Moreno Díaz, N.G.: Bases de datos georeferenciadas de áreas naturales protegidas y otros espacios dedicados y destinados a la conservación y uso sustentable de la biodiversisad en México. The Nature Conservancy (2011). Database on CD. Mexico

    Google Scholar 

  8. CONAPO: Índices de marginaci por localidad. http://www.conapo.gob.mx/es/CONAPO/Indice_de_Marginacion_por_Localidad_2010

  9. FAO: Global resources assessment. Forestry paper, 140 (2001)

    Google Scholar 

  10. Figueroa, F., Sánchez-Cordero, V., Meave, J.A., Trejo, I.: Socioeconomic context of land use and land cover change in Mexican biosphere reserves. Environ. Conserv. 36(3), 180–191 (2009)

    Article  Google Scholar 

  11. Fotheringham, S.A., Brunsdon, C., Charlton, M.: Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, Chichester (2002)

    Google Scholar 

  12. García-Barrios, L., Galván-Miyoshi, Y.M., Valdivieso Pérez, I.A., Masera, O.R., Bocco, G., Vandermeer, J.: Neotropical forest conservation, agricultural intensification and rural outmigration: the Mexican experience. BioScience 59(10), 863–873 (2009)

    Article  Google Scholar 

  13. Gollini, I., Lu, B., Charlton, M., Brunsdon, C., Harris, P.: GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models. J. Stat. Softw. 63(17), 1–50 (2015). http://www.jstatsoft.org/v63/i17/

    Article  Google Scholar 

  14. Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G.: High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013)

    Article  Google Scholar 

  15. INEGI: Carta topográfica escala 1: 250000. INEGI, México (2004)

    Google Scholar 

  16. INEGI: Conteo de población y vivienda 2005. Indicadores del censo de Población y vivienda. INEGI, México (2005)

    Google Scholar 

  17. INEGI: Censo de población y vivienda 2010. INEGI, México (2010)

    Google Scholar 

  18. Klooster, D.: Beyond deforestation: the social context of forest change in two indigenous communities in highland Mexico. J. Lat. Am. Geogr. 26, 47–59 (2000)

    Google Scholar 

  19. Lu, B., Harris, P., Charlton, M., Brunsdon, C.: The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models. Geospatial Inf. Sci. 17(2), 85–101 (2014). http://www.tandfonline.com//abs/10.1080/10095020.2014.917453

    Google Scholar 

  20. Mas, J.F., Pérez, V.A., Andablo, R.A., Castillo Santiago, M.A., Flamenco, S.A.: Assessing modifiable areal unit problem in the analysis of deforestation drivers using remote sensing and census data. Int. Arch. Photogrammetry Remote Sens. Spat. Inf. Sci. (ISPRS Archives) XL-3/W3, 77–80 (2015)

    Google Scholar 

  21. Mas, J.F., Velázquez, A., Díaz-Gallegos, J.R., Mayorga-Saucedo, R., Alcántara, C., Bocco, G., Castro, R., Fernández, T., Pérez-Vega, A.: Assessing land/use cover changes: a nationwide multidate spatial database for Mexico. Int. J. Appl. Earth Obs. Geoinformatics 5, 249–261 (2004)

    Article  Google Scholar 

  22. Mennis, J.L.: Mapping the results of geographically weighted regression. Cartographic J. 43(2), 171–179 (2006)

    Article  Google Scholar 

  23. Openshaw, S.: Ecological fallacies and the analysis of areal census data. Environ. plann. A 16, 17–31 (1984)

    Article  Google Scholar 

  24. Pineda-Jaimes, N.B., Bosque Sendra, J., Gómez Delgado, M., Franco Plata, R.: Exploring the driving forces behind deforestation in the state of Mexico (Mexico) using geographically weighted regression. Appl. Geogr. 30, 576–591 (2010)

    Article  Google Scholar 

  25. QGIS Development Team: QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org

  26. Core, R., Team, R.: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2014). http://www.R-project.org/

  27. Rudel, T.A., Horowitz, B.: Tropical Deforestation: Small Farmers and Local Clearing in the Ecuadorian Amazon. Columbia University Press, New York (2013)

    Google Scholar 

  28. SAGARPA: Listas de beneficiarios de PROCAMPO y PROGAN (2008–2011)

    Google Scholar 

Download references

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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Authors and Affiliations

  1. Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México, Antigua carretera a Pátzcuaro 8701, Col. Ex Hacienda de San José de la Huerta, 58190, Morelia, Michoacán, Mexico

    Jean-François Mas & Gabriela Cuevas

Authors
  1. Jean-François Mas
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  2. Gabriela Cuevas
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Correspondence to Jean-François Mas .

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Editors and Affiliations

  1. Escola Superior de Tecnologia de Setúbal, Setúbal, Portugal

    Cédric Grueau

  2. Universidade do Minho, Braga, Portugal

    Jorge Gustavo Rocha

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Mas, JF., Cuevas, G. (2016). Identifying Local Deforestation Patterns Using Geographically Weighted Regression Models. In: Grueau, C., Gustavo Rocha, J. (eds) Geographical Information Systems Theory, Applications and Management. GISTAM 2015. Communications in Computer and Information Science, vol 582. Springer, Cham. https://doi.org/10.1007/978-3-319-29589-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-29589-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29588-6

  • Online ISBN: 978-3-319-29589-3

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