, Volume 61, Issue 4, pp 353–363 | Cite as

Temporal dynamics and spatial scales: Modeling deforestation in the southern Yucatán peninsular region

  • Jacqueline Geoghegan
  • Laura Schneider
  • Colin Vance


Two spatially explicit econometric land use change models are presented, focusing on tropical deforestation caused by agricultural expansion in the southern Yucatán peninsula, Mexico. The two models developed are both based on conceptually similar theoretical models of farmer behavior. However, there are different empirical specifications of this theoretical model according to the scale of the analysis as well as the availability of temporal data on the observation of deforestation. For both models, the unit of observation for the dependent variable of deforestation is the TM pixel from satellite data. However, the socio-economic explanatory variables are derived from different sources. The first econometric model links the satellite data for the entire study region with aggregate census data at the village level. This model is estimated using a discrete choice logit model over a single time period. The second econometric model uses individual household survey data for a small random sample of the region, linked to satellite data for the plots of each household over multiple time periods. This model is estimated using a dynamic hazard model that estimates the risk of a specific pixel converting from forest to agricultural use. Both estimated models are used to predict deforestation and the results of the two modeling approaches are compared.


econometric models spatially explicit land use change tropical deforestation 


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  1. Achard F., Eva H., Glinni A., Mayaux P., Richards T. and Stibig H.J. (eds), 1998: Identification of Deforestation Hot Spot Areas in the Humid Tropics. Trees Publ. Series B. Research Report No. 4. Space Application Institute, Global Vegetation Monitoring Unit. Joint Research Centre, European Commission. Brussels.Google Scholar
  2. Allison, P.D. 2000Survival Analysis Using the SAS System: A Practical GuideSAS Institute Inc.Carey, NCGoogle Scholar
  3. Chomitz, K.M., Gray, D.A. 1996Roads, land, markets and deforestation: a spatial model of land use in BelizeWorld Bank Economic Review10487512Google Scholar
  4. Cropper, M., Griffiths, C., Mani, M. 1999Roads, population pressures, and deforestation in ThailandLand Economics7558731976–1989Google Scholar
  5. Cropper, M., Puri, J., Griffiths, C. 2001Predicting the location of deforestationLand Economics77172186Google Scholar
  6. Deaton, A. 1997The Analysis of Household Surveys: A Microeconometric Approach to Development PolicyThe Johns Hopkins University PressBaltimoreGoogle Scholar
  7. Dvorak, K. 1992Resource management by west African farmers and the economics of shifting cultivationAmerican Journal of Agricultural Economics74809815Google Scholar
  8. Geoghegan J. and Bockstael N.E., 2000: Smart growth and the supply of sprawl. Paper presented at the Association of Environmental and Resource Economists Workshop on the Effectiveness of Resource and Environmental Regulation. La Jolla, CA.Google Scholar
  9. Geoghegan, J., Cortina Villar, S., Klepeis, P., Macario Mendoza, P., Ogneva-Himmelberger, Y., Roy Chowdhury, R., Turner, B.L.,II, Vance, C. 2001Modeling tropical deforestation in the southern Yucatán peninsular region: comparing survey and satellite dataAgriculture, Ecosystems, and Environment852546Google Scholar
  10. Geoghegan, J., Pritchard, L.,Jr., Ogneva-Himmelberger, Y., Roy Chowdhury, R., Sanderson, S., Turner, B.L.,II 1998‘Socializing the pixel’ and ‘pixelizing the social’ in land-use and land-cover changeLiverman, D.Moran, E.Rindfuss, R.Stern, P. eds. People and Pixels: Linking Remote Sensing and Social ScienceNational Academy of Science PressWashington, DC5169Committee on the Human Dimensions of Global Environmental Change, National Research CouncilGoogle Scholar
  11. Irwin E.G. and Bockstael N.E., 2001: Interacting agents, spatial externalities, and the endogenous evolution of residential land use pattern. Journal of Economic Geography forthcoming.Google Scholar
  12. Isaacks, E.H., Srivastava, R.M. 1989Applied GeostatisticsOxford University PressOxfordGoogle Scholar
  13. Klepeis, P., Turner, B.L.,II 2001Integrated land history and global change science: the example of the southern Yucatán peninsular region projectLand Use Policy1827239Google Scholar
  14. Nelson, G., Harris, V., Stone, S. 2001Deforestation, land use, and property rightsLand Economics77187205Google Scholar
  15. Nelson, G.A., Hellerstein, D. 1997Do roads cause deforestation? Using satellite images in econometric analysis of land useAmerican Journal of Agricultural Economics798088Google Scholar
  16. Pfaff, A. 1999What drives deforestation in the Brazilian amazon? Evidence from satellite and socioeconomic dataJournal of Environmental Economics and Management372643Google Scholar
  17. Vance, C., Geoghegan, J. 2004Modeling the determinants of semi-subsistent and commercial land-uses in an agricultural frontier of southern Mexico: a switching regression approachInternational Regional Science Review27326347Google Scholar
  18. Veldkamp, A., Lambin, E.F. 2001Predicting land-use changeAgriculture, Ecosystems & Environment8516Google Scholar
  19. Warwick, D.P., Lininger, C.A. 1975The Sample Survey: Theory and PracticeMcGraw Hill, Inc.New YorkGoogle Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Jacqueline Geoghegan
    • 1
  • Laura Schneider
    • 2
  • Colin Vance
    • 3
  1. 1.Department of Economics and George Perkins Marsh InstituteClark UniversityWorcesterUSA
  2. 2.Department of GeographyRutgers UniversityNew JerseyUSA
  3. 3.German Aerospace CenterInstitute of Transport ResearchBerlinGermany

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