Spatial Analysis Essay ⋆ Political Science Essay Examples ⋆ EssayEmpire

Spatial autocor relation is the clustering of similar or dissimilar behaviors, processes, and events among neighboring observations and is predicted by many theories in the social sciences. Spatial dependence can produce biased and inconsistent parameter estimates or biased standard errors. In contrast to the serial dependence of time series, where the past influences the future but not vice versa, spatial dependence is multidimensional—neighbors influence each other. As a consequence, spatial autocorrelation is modeled via maximum likelihood estimation or instrumental variables models. Geostatistical data and lattice data are two of the principal forms of spatial data. In the case of geostatistical data, the observed data are sample data from a continuous underlying surface and the researcher’s principal interest is inferring information for unsampled locations based on the sample data. In the case of lattice data, the continuous underlying surface is partitioned into a finite number of areal units, or polygons, such as countries, states, or localities.

Two principal spatial models are generally used for lattice data. The spatial lag model incorporates spatial autocorrelation via a spatially lagged dependent variable. This model corresponds to a diffusion process in which neighbors influence each other. A spatial error model, in contrast, incorporates spatial dependence via the error term. This model is appropriate if the spatial dependence is produced by variables omitted from the model. Emerging frontiers in spatial analysis include spatial panel data models, spatial probit models, and spatial survival models.

Bibliography:

Anselin, Luc. Spatial Econometrics: Methods and Models. Studies in operational regional science, 4. Dordrecht: Kluwer Academic Publishers, 1988.
Beck, N., K. S. Gleditsch, and K. Beardsley. “Space Is More than Geography: Using Spatial Econometrics in the Study of Political Economy.” International Studies Quarterly 50, no. 1 (2006): 27-44.
Franzese, R. J., and J. C. Hays. “Interdependence in Comparative Politics: Substance, Theory, Empirics, Substance.” Comparative Political Studies 41, no. 4 (2008): 742-780.
Darmofal, David. Spatial Analysis for the Social Sciences. Analytical Methods for Social Research Series. Cambridge University Press, 2011 (forthcoming).

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