Share This Article:

Interpolating Socioeconomic Data for the Analysis of Deforestation: A Comparison of Methods

Full-Text HTML Download Download as PDF (Size:869KB) PP. 358-365
DOI: 10.4236/jgis.2012.44041    5,479 Downloads   8,212 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

M. Farfán, J. François Mas and L. Osorio, "Interpolating Socioeconomic Data for the Analysis of Deforestation: A Comparison of Methods," Journal of Geographic Information System, Vol. 4 No. 4, 2012, pp. 358-365. doi: 10.4236/jgis.2012.44041.

References

[1] P. M. Vitousek, “Beyond Global Warming: Ecology and Global Change,” Ecology, Vol. 75, No. 7, 1994, pp. 1861-1876. doi:10.2307/1941591
[2] C. Agarwal, G. M.Green, J. M. Grove, T. P. Evans and C. M. Schweik, “A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time, and Human Choice,” CIPEC Collaborative Report No.1, Department of Agriculture, Forest Service, Northeastern Research Station, Newton Square, 2002, p. 61. doi:10.1016/S1364-8152(03)00161-0
[3] J. F. Mas, H. Puig, J. L. Palacio and A. S. Lopez, “Modelling Deforestation Using GIS and Artificial Neural Networks,” Environment Modelling and Software, Vol. 19, No. 5, 2004, pp. 461-471.
[4] FAO, “Global Forest Resources Assessment 2005: Progress towards Sustainable Forest Management,” FAO Forestry Paper 147, Food and Agriculture Organization of the United Nations, Rome, 2006.
[5] J. C. Allen and D. F. Barnes, “The Causes of Deforestation in Developing Countries,” Annals of the Association of American Geographers, Vol. 75, No. 2, 1985, pp. 163-184. doi:10.1111/j.1467-8306.1985.tb00079.x
[6] A. Angelsen and D. Kaimowitz, “Rethinking the Causes of Deforestation: Lessons from Economic Models,” World Bank Research Observer, Vol. 14, No. 1, 1999, pp. 73-98. doi:10.1093/wbro/14.1.73
[7] H. J. Geist and E. F. Lambin, “What Drives Tropical Deforestation? A Meta-Analysis of Proximate and Underlying Causes of Deforestation Based on Subnational Scale Case Study Evidence,” LUCC Report Series, No. 4, University of Louvain, Louvainla-Neuve, 2001.
[8] E. F. Lambin, B. L. Turner, J. G. Helmut, et al., “The Causes of Land-Use and Land-Cover Change: Moving beyond the Myths,” Global Environmental Change, Vol. 11, No. 4, 2001, pp. 261-269. doi:10.1016/S0959-3780(01)00007-3
[9] M. Voltz and R. Webster, “A Comparison of Kriging, Cubic Splines and Classification for Predicting Soil Properties from Sample Information,” Journal of Soil Science, Vol. 41, No. 3, 1990, pp. 473-490. doi:10.1111/j.1365-2389.1990.tb00080.x
[10] P. I. Booker, “Modeling Spatial Variability Using Soil Profiles in the Riverland of South Australia,” Environment International, Vol. 27, No. 2, 2001, pp. 121-126. doi:10.1016/S0160-4120(01)00071-X
[11] I. A. Nalder and R. Wein, “Spatial Interpolation of Climatic Normals: Test of a New Method in the Canadian Boreal Forest,” Agricultural and Forest Meteorology, Vol. 91, No. 4, 1998, pp. 211-225. doi:10.1016/S0168-1923(98)00102-6
[12] J. Wallerman, S. Joyce, C. P. Vencatasawmy and H. Olsson, “Prediction of Forest Steam Volume Using Kriging Adapted to Detect Edges,” Canadian Journal Forest Research, Vol. 32, No. 3, 2002, pp. 509-518. doi:10.1139/x01-214
[13] J. L. Hernandez-Stefanoni and R. Ponce-Hernandez, “Mapping the Spatial Variability of Plant Diversity in a Tropical Forest: Comparison of Spatial Interpolation Methods,” Environmental Monitoring and Assessment, Vol. 1, No. 117, 2006, pp. 307-334. doi:10.1007/s10661-006-0885-z
[14] FAO, “Forest Resources Assesment 1990. Survey of Tropical Forest Cover and Study of Change Process,” No. 130, Food and Agriculture Organization of the United Nations, Rome, 1996.
[15] F. Achard, H. D. Eva, H. J. Stibin, P. Mayaux, J. Gallego, T. Richards and J. P. Malingreau, “Determination of Deforestation Rates of the World’s Humid Tropical Forests,” Science, Vol. 297, No. 5583, 2002, pp. 999-1002. doi:10.1126/science.1070656
[16] R Development Core Team, “R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing,” Vienna, 2011. http://www.R-project.org
[17] P. A. Burrough and R. A. MacDonell, “Principles of Geographical Information Systems (Spatial Information Systems and Geostatistics),” Oxford University Press, Oxford, 1998.
[18] J. Li and A. D. Heap, “A Review of Comparative Studies of Spatial Interpolation Methods in Environmental Sciences: Performance and Impact Factors,” Ecological Informatics, Vol. 6, No. 6, 2011, pp. 228-241. doi:10.1016/j.ecoinf.2010.12.003
[19] K. P. Burnham and D. R. Anderson, “Model Selection and Multimodel Inference,” Springer-Verlag, New York, 2002.
[20] R. Mac Nally, “Regression and Model Building in Conservation Biology, Biogeography and Ecology: The Distinction between and Reconciliation of ‘Predictive’ and ‘Explanatory’ Models,” Biodiversity and Conservation, Vol. 9, No. 5, 2000, pp. 655-671. doi:10.1023/A:1008985925162
[21] R. Mac Nally, “Hierarchical Partitioning as an Interpretative Tool in Multivariate Inference,” Australian Journal of Ecology, Vol. 21, No. 2, 1996, pp. 224-228. doi:10.1111/j.1442-9993.1996.tb00602.x
[22] A. Chevan and M. Sutherland, “Hierarchical Partitioning,” The American Statistician, Vol. 45, No. 2, 1991, pp. 90-96.
[23] R. Mac Nally and C. J. Walsh, “Hierarchical Partitioning Public-Domain Software,” Biodiversity and Conservation, Vol. 13, No. 3, 2004, pp. 659-660. doi:10.1023/B:BIOC.0000009515.11717.0b
[24] R. Mac Nally, “Multiple Regression and Inference in Ecology and Conservation Biology: Further Comments on Identifying Important Predictor Variables,” Biodiversity and Conservation, Vol. 11, No. 8, 2002, pp. 1397-1401. doi:10.1023/A:1016250716679
[25] S. T. Buckland, K. P. Burnham and N. H. Augustin, “Model Selection: An Integral Part of Inference,” Biometrics, Vol. 53, No. 2, 2002, pp. 603-618. doi:10.2307/2533961
[26] M. Baumann, T. Kuemmerle, M. Elbakidze, M. Ozdogan, V. C. Radeloff, N. S. Keuler, A. V. Prishchepov, I. Kruhlov and P. Hostert, “Patterns and Drivers of PostSocialist Farmland Abandonment in Western Ukraine,” Land Use Policy, Vol. 28, No. 3, 2011, pp. 552-562. doi:10.1016/j.landusepol.2010.11.003
[27] E. H Isaaks and R. M. Srivastava, “Applied Geostatistics,” Oxford University Press, New York, 1989.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.