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Disaggregating Statistical Data at Field Level: An Entropy Approach

Author

Listed:
  • António Xavier

    (Universidade do Algarve e MeditBio)

  • Maria Belem Freitas
  • Maria do Socorro Rosário

    (Direção de Serviços de Estatística, GPP (Gabinete de Planeamento e Políticas))

  • Rui Fragoso

    (Universidade de Évora e CEFAGE-UE)

Abstract

The objective of this paper is to present an entropy approach in order to disaggregate agricultural data (temporary and permanent crop’s areas) at a local level. It comprises several steps of application. In a first one, a HJ-Biplot methodology and a cluster analysis is implemented. In a second step an interactive procedure is developed to establish relations between the land use cartography and statistics, and then coefficients of data allocation for each variable are created. Finally, in a third step, in order to manage information inputs and guaranty consistency a minimum cross entropy process is implemented. The model was applied to the region of Algarve and showed satisfactory results since the estimated values revealed a good approximation to the true values.

Suggested Citation

  • António Xavier & Maria Belem Freitas & Maria do Socorro Rosário & Rui Fragoso, 2016. "Disaggregating Statistical Data at Field Level: An Entropy Approach," CEFAGE-UE Working Papers 2016_06, University of Evora, CEFAGE-UE (Portugal).
    • Handle: RePEc:cfe:wpcefa:2016_06
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    References listed on IDEAS

    as
    1. Raja Chakir, 2009. "Spatial Downscaling of Agricultural Land-Use Data: An Econometric Approach Using Cross Entropy," Land Economics, University of Wisconsin Press, vol. 85(2), pages 238-251.
    2. You, Liangzhi & Wood, Stanley, 2006. "An entropy approach to spatial disaggregation of agricultural production," Agricultural Systems, Elsevier, vol. 90(1-3), pages 329-347, October.
    3. Louhichi, Kamel & Jacquet, Florence & Butault, Jean Pierre, 2012. "Estimating input allocation from heterogeneous data sources: A comparison of alternative estimation approaches," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 13(2), pages 1-20.
    4. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    5. Martins, Maria de Belem Costa Freitas & Xavier, Antonio Manuel de Sousa & Fragoso, Rui Manuel de Sousa, 2012. "Redistributing Agricultural Data by a Dasymetric Mapping Methodology," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 41(3), pages 1-16, December.
    6. Castela, Eugénia & Galindo, Purificación, 2010. "Ecological Inference for the characterization of electoral turnout: The Portuguese Case," Spatial and Organizational Dynamics Discussion Papers 2010-1, CIEO-Research Centre for Spatial and Organizational Dynamics, University of Algarve.
    7. Richard Howitt & Arnaud Reynaud, 2003. "Spatial disaggregation of agricultural production data using maximum entropy," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 30(3), pages 359-387, September.
    8. You, Liangzhi & Wood, Stanley & Wood-Sichra, Ulrike, 2009. "Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach," Agricultural Systems, Elsevier, vol. 99(2-3), pages 126-140, February.
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    Keywords

    Data disaggregation; Minimum Cross Entropy; Land Uses; Algarve.;
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