Skip to main content
SpringerLink
Log in

Spatial Regression Analysis vs. Kriging Methods for Spatial Estimation

  • Published:
International Advances in Economic Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Representative examples are most of the writings of Cliff and Ord (1973, 1981), Barlett (1975), Ripley (1981), Wackernagel (2003), Finkenstädt et al. (2006).

  2. Illustrative examples are Hordijk and Paelinck (1976), Anselin (1988), Rey and Dev (2006), Brunsdon et al. (2007), Li et al (2007), Le Gallo and Chasco (2008).

  3. Results have been obtained by using GeoDa

  4. Results have been obtained by using Isatis software

References

  • Anselin, L. (1988). Spatial econometrics: Methods and models. Boston: Kluwer.

    Google Scholar 

  • Anselin, L. (2001). Spatial Externalities, Spatial Multipliers and Spatial Econometrics. Discussion paper of Regional Economics Applications Laboratory, REAL, 01-T-11. August.

  • Baller, R., Anselin, L., Messner, S., & Hawkins, D. (2001). Structural covariates of U.S. county homicide rates: incorporating spatial effect. Criminology, 39(3), 561–590 August.

    Article  Google Scholar 

  • Barlett, M. (1975). The statistical analysis of spatial pattern. London: Chapman & Hall.

    Google Scholar 

  • Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussions). Journal of the Royal Statistical Society, Series B, 36, 192–236.

    Google Scholar 

  • Bodson, , & Peeters, (1975). Estimation of the coefficients of a linear regression in the presence of spatial autocorrelation: an application to a Belgian labour demand function. Enviroment and Planning, A7, 455–472.

    Article  Google Scholar 

  • Brunsdon, C., Fotherigham, S., & Charlton, M. (2007). Geographically weighted discriminant analysis. Geographical Analysis, 39, 376–396 October.

    Article  Google Scholar 

  • Case, A., Rosen, H., & Hines, J. (1993). Budget spillovers and fiscal policy interdependence: evidence from the states. Journal of Public Economics, 52, 285–307 October.

    Article  Google Scholar 

  • Chasco. C. (2003). Econometría Espacial Aplicada a la Predicción—Extrapolación de Datos Microterritoriales. Tesis Doctoral. Universidad Autónoma de Madrid. Consejería de Economía e Innovación Tecnológica.

  • Cliff, A., & Ord, J. (1973). Spatial autocorrelation. London: Pion.

    Google Scholar 

  • Cliff, A., & Ord, J. (1981). Spatial processes: Models and applications. London: Pion.

    Google Scholar 

  • Dacey, M. (1968). A review of measures of contiguity for two and K-Color Maps. In spatial analysis: a reader in statistical geography (pp. 479–495). B. Barry. February.

  • Emery, X. (2000). Geoestadística lineal. Departamento de Ingeniería de Minas. Facultad de Ciencias Físicas y Matemáticas. Universidad de Chile.

  • Finkenstädt, B. F., Held, L., & Isham, V. (2006). Statistical Methods for Spatio-temporal systems. CRC/Chapman and Hall.

  • Florax, R., & Folmer, H. (1992). Specification and estimation of spatial linear regression models: Monte Carlo evaluation of pre-test estimators. Regional Science and Urban Economics, 22, 405–432 April.

    Article  Google Scholar 

  • Haining, R. (1986). Spatial models and regional science: a comment on Anselin’s paper and research directions. Journal of Regional Science, 26, 793–798 November.

    Article  Google Scholar 

  • Haining, R. (1995). Data problems in spatial econometric modelling. In L. Anselin, & R. Florax (Eds.), New directions in spatial econometrics (pp. 156–171). Berlín: Springer August.

    Google Scholar 

  • Hordijk, L., & Paelinck, J. (1976). Some principles and results in spatial econometrics. Recherches Economiques de Louvain, 42, 175–197.

    Google Scholar 

  • Le Gallo, J., & Chasco, C. (2008). Spatial analysis of urban growth in Spain, 1900–2001. Empirical Economics, 34, 1, 59–80. February.

    Google Scholar 

  • Li, H., Calder, C., & Cressie, N. (2007). Beyond Moran’s I: testing for spatial dependence based on the spatial autorregressive model. Geographical Analysis, 39, 357–375.

    Article  Google Scholar 

  • Ma, J., Haining, R., & Wise, S (1997). SAGE User’s Guide. Sheffield Center for Geographic Information and Spatial Analysis. University of Sheffield. January.

  • Matheron, G. (1965). Les Variables Régionalisées et leur Estimation. Une Application de la Théorie des Functions Aléatories aux Sciences de la Nature. Masson & Cie, Eds.

  • Molho, I. (1995). Spatial autocorrelation in British unemployment. Journal of Regional Science, 35(4), 641–658 November.

    Article  Google Scholar 

  • Montero Lorenzo, J. M. (2004). El precio medio del metro cuadrado de la vivienda: Una aproximación desde la perspectiva de la Geoestadística. Estudios de Economía Aplicada, 22(3), 675–693 December.

    Google Scholar 

  • Montero Lorenzo, J. M., Larraz Iribas, B. (2006). Estimación espacial del precio de la vivienda mediante métodos de krigeado. Estadística Española, 48(162), 201–240 May.

    Google Scholar 

  • Paelinck, J. H. P., & Klaasen, L. H. (1979). Spatial econometrics. Farnborough: Saxon.

    Google Scholar 

  • Rey, S., & Dev, B. (2006). σ-Convergence in the presence of spatial effects. Papers in Regional Science, 85(2), 217–234 June.

    Article  Google Scholar 

  • Rey, S., & Montouri, B. (1999). US regional income convergence: a spatial econometric perspective. Regional Studies, 33(2), 143–156 February.

    Article  Google Scholar 

  • Ripley, B. (1981). Spatial statistics. New York: Academic.

    Google Scholar 

  • Toral, A. (2001). El factor especial en la convergencia de las regiones de la UE: 1980–1996. Tesis doctoral, ICADE. Universidad de Comillas. Madrid.

  • Van der Kruk, R. (2001). Economic Impacts of wetland amenities: A spatial econometric analysis of the Ductch housing market. 41st Congress of ERSA, Zagreb. CD-ROM. August.

  • Wackernagel, H. (2003). Multivariate geostatistics. An introduction with applications (3rd ed.). Berlín: Springer.

    Google Scholar 

Download references

Acknowledgement

I would like to express my gratitude to all those who gave me the opportunity to complete this paper. I would like to give special thanks to Prof. Dr. J.M. Montero Lorenzo from the University of Castilla—La Mancha, whose stimulating suggestions and encouragement were of great help.

Author information

Authors and Affiliations

  1. Facultad de Ciencias Jurídicas y Sociales de Toledo, University of Castilla—La Mancha, Cobertizo de San Pedro Mártir, S/N. 45.071, Toledo, Spain

    Gema Fernández-Avilés Calderón

Authors
  1. Gema Fernández-Avilés Calderón
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gema Fernández-Avilés Calderón.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fernández-Avilés Calderón, G. Spatial Regression Analysis vs. Kriging Methods for Spatial Estimation. Int Adv Econ Res 15, 44–58 (2009). https://doi.org/10.1007/s11294-008-9189-0

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11294-008-9189-0

Keywords

JEL

Search

Navigation