Modelling Area Data

Manfred M. Fischer*, Jinfeng Wang

*Corresponding author for this work

Publication: Chapter in book/Conference proceedingChapter in edited volume

Abstract

Exploratory spatial data analysis is often a preliminary step to more formal modelling approaches that seek to establish relationships between the observations of a variable and the observations of other variables, recorded for each areal unit. The focus in this chapter is on spatial regression models in a simple cross-sectional setting, leaving out of consideration the analysis of panel data. We, moreover, assume that the data concerned can be taken to be approximately normally distributed. This assumption is—to varying degrees—involved in most of the spatial regression techniques that we will consider. Note that the assumption of normality is not tenable if the variable of interest is a count or a proportion. In these cases we would expect models for such data to involve probability distributions such as the Poisson or binomial. The chapter consists of five sections, starting with a treatment of the specification of spatial dependence in a regression model. Next, specification tests are considered to detect the presence of spatial dependence. This is followed by a review of the spatial Durbin model (SDM) that nests many of the models widely used in the literature, and by a discussion of spatial regression model estimation based on the maximum likelihood (ML) principle. The chapter closes with some remarks on model parameter interpretation, an issue that had been largely neglected so far. Readers interested in implementing the models, methods and techniques discussed in this chapter find useful MATLAB code which is publicly available at spatial-econometrics.com, LeSage’s spatial econometrics toolbox (downloadable from http://www.spatial-econometrics.com/ ), see Liu and LeSage (2010) Journal of Geographical Systems 12(1):69–87 for a brief description. Another useful open software is the spdep package of the R project (downloadable from http://cran.r-project.org ).

Original languageEnglish
Title of host publicationSpatial Data Analysis
Subtitle of host publicationModels, Methods and Techniques
EditorsManfred M. Fischer, Jinfeng Wang
Place of PublicationBerlin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages31-44
Number of pages14
Edition1
ISBN (Electronic)978-3-642-21720-3
ISBN (Print)978-3-642-21719-7
DOIs
Publication statusPublished - 2011

Publication series

SeriesSpringerBriefs in Regional Science
ISSN2192-0427

Bibliographical note

Publisher Copyright:
© 2011, Manfred M. Fischer.

Keywords

  • Area data
  • Higher order models
  • Maximum likelihood estimation
  • Model parameter interpretation
  • Spatial Durbin model
  • Spatial error models
  • Spatial lag models
  • Spatial regression models
  • Specification search
  • Tests for spatial dependence

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