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Spatial Regression Analysis vs. Kriging Methods for Spatial Estimation

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  • Gema Fernández-Avilés Calderón

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Abstract

Due to the rapid development of Geographic Information Systems (GIS) in recent years, spatial data analysis has received considerable attention and played an important role in social science. Although many standard statistical techniques are attractive in traditional data analysis, they cannot be implemented uncritically for spatial data. Generally, most of the studies in spatial data analysis can be divided into two branches: the model-driven approach and the data-driven approach. The main aim of this paper is the comparison of both approaches. To carry out such a task, crime rate data in Columbus (Ohio), coming from a well-known database, have been used. The main aim of this paper is to illustrate how spatial effects can be viewed as spatial econometric models, which assess the limitations of standard techniques in a spatial context, suggesting alternative methods to deal with this problem. An application to the crime rate in Columbus (Ohio) has been carried out. Copyright International Atlantic Economic Society 2009

Suggested Citation

  • Gema Fernández-Avilés Calderón, 2009. "Spatial Regression Analysis vs. Kriging Methods for Spatial Estimation," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 15(1), pages 44-58, February.
  • Handle: RePEc:kap:iaecre:v:15:y:2009:i:1:p:44-58:10.1007/s11294-008-9189-0
    DOI: 10.1007/s11294-008-9189-0
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    References listed on IDEAS

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    1. Florax, Raymond & Folmer, Henk, 1992. "Specification and estimation of spatial linear regression models : Monte Carlo evaluation of pre-test estimators," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 405-432, September.
    2. P Bodson & D Peeters, 1975. "Estimation of the Coefficients of a Linear Regression in the Presence of Spatial Autocorrelation. An Application to a Belgian Labour-Demand Function," Environment and Planning A, , vol. 7(4), pages 455-472, June.
    3. Julie Gallo & Coro Chasco, 2008. "Spatial analysis of urban growth in Spain, 1900–2001," Empirical Economics, Springer, vol. 34(1), pages 59-80, February.
    4. René Van der Kruk, 2001. "Economic Impacts of Wetland Amenities A Spatial Econometric Analysis of the Housing Market," ERSA conference papers ersa01p76, European Regional Science Association.
    5. Montero Lorenzo, José María, 2004. "El precio medio del metro cuadrado de la vivienda libre: Una aproximación metodológica desde la perspectiva de la Geoestadística," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 22, pages 1-18, Diciembre.
    6. Sergio Rey & Brett Montouri, 1999. "US Regional Income Convergence: A Spatial Econometric Perspective," Regional Studies, Taylor & Francis Journals, vol. 33(2), pages 143-156.
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    Cited by:

    1. Mohamed Amara & Mohamed Ayadi, 2011. "Local Employment Growth in the Coastal Area of Tunisia: A Dynamic Spatial Panel Approach," Working Papers 650, Economic Research Forum, revised 12 Jan 2011.

    More about this item

    Keywords

    Weight matrix; Spatial correlation; Spatial econometrics; Econometric models; Autocorrelation; Kriging estimator; C10; C21; C40; E00;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • E00 - Macroeconomics and Monetary Economics - - General - - - General

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