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Determinants of land use changes: a spatial multinomial probit approach

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  • Olivier Parent

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  • Raja Chakir

Abstract

Changes in land use patterns impact significantly environmental conditions as well as economic and social welfare. These changes are influenced by socio-economic as well as pedo-climatic factors. A good understanding of how these factors influence land use patterns would provide new dimensions to policy making and public policy evaluation. To this end, we propose a Spatial Multinomial Probit model to examine the determinants of land use change, at the parcel level, in the French D´epartement du Rhones from 1992 to 2003. It is based on an economic model that assumes that landowners have a choice between 4 land use categories for a given parcel at a given date: (1) agricultural, (2) forest, (3) urban and (4) no use. Each landowner compares costs and benefits associated with each parcel and each class and chooses the optimal land use to maximize his/her profit. We propose a Spatial Multinomial Probit model that allows for covariates and spatial dependence, and we use these features to explore the relative importance of factors that drive landowners to choose a specific land use category. Our findings suggest the presence of spatial patterns, implying that the decision to choose land use for a given parcel of land is strongly influenced by nonobserved factors in neighboring parcels. Moreover, results confirm that interdependencies among land use alternatives, accounted for in the spatial multinomial probit model by the correlation structure, are required for the estimation of land use decision.

Suggested Citation

  • Olivier Parent & Raja Chakir, 2008. "Determinants of land use changes: a spatial multinomial probit approach," University of Cincinnati, Economics Working Papers Series 2008-06, University of Cincinnati, Department of Economics.
  • Handle: RePEc:cin:ucecwp:2008-06
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    2. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
    3. Yiyi Wang & Kara Kockelman & Paul Damien, 2014. "A spatial autoregressive multinomial probit model for anticipating land-use change in Austin, Texas," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(1), pages 251-278, January.
    4. Chakir, Raja & Lungarska, Anna, 2015. "Agricultural land rents in land use models: a spatial econometric analysis," 150th Seminar, October 22-23, 2015, Edinburgh, Scotland 212641, European Association of Agricultural Economists.
    5. Manuel Ruiz & Fernando López & Antonio Páez, 2010. "Testing for spatial association of qualitative data using symbolic dynamics," Journal of Geographical Systems, Springer, vol. 12(3), pages 281-309, September.
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    8. Chakir, Raja & Le Gallo, Julie, 2013. "Predicting land use allocation in France: A spatial panel data analysis," Ecological Economics, Elsevier, vol. 92(C), pages 114-125.
    9. D. M. Lambert & C. N. Boyer & L. He, 2016. "Spatial-temporal heteroskedastic robust covariance estimation for Markov transition probabilities: an application examining land use change," Letters in Spatial and Resource Sciences, Springer, vol. 9(3), pages 353-362, October.
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    13. Chandra R. Bhat & Subodh K. Dubey & Mohammad Jobair Bin Alam & Waleed H. Khushefati, 2015. "A New Spatial Multiple Discrete-Continuous Modeling Approach To Land Use Change Analysis," Journal of Regional Science, Wiley Blackwell, vol. 55(5), pages 801-841, November.
    14. Páez, Antonio & López, Fernando A. & Ruiz, Manuel & Morency, Catherine, 2013. "Development of an indicator to assess the spatial fit of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 217-233.
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    20. Padeiro, Miguel, 2014. "The influence of transport infrastructures on land-use conversion decisions within municipal plans," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(1), pages 79-93.
    21. Nazneen Ferdous & Chandra Bhat, 2013. "A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns," Journal of Geographical Systems, Springer, vol. 15(1), pages 1-29, January.
    22. Raja Chakir & Thibault Laurent & Anne Ruiz-Gazen & Christine Thomas-Agnan & Céline Vignes, 2017. "Prédiction de l’usage des sols sur un zonage régulier à différentes résolutions et à partir de covariables facilement accessibles," Revue économique, Presses de Sciences-Po, vol. 68(3), pages 435-469.
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