Spatial statistics and spatial econometrics fill an impressive body of literature, clearly demonstrating the importance of the corresponding methods. Though the two fields are being referred to in the same breath, they differ substantially as they pursue quite different objectives and hence exhibit different interpretations. In this article we shed some light on the nature and relevance of those to demonstrate how the models can be employed with three data examples in a regional context. The discussion covers models for continuous responses and count data, real data examples and Monte Carlo simulations.
BesagJ (1974) Spatial interaction and the statistical analysis of lattice systems (with discussions). Journal of the Royal Statistical Society, Series B, 36, 192–236.
7.
BesagJKooperbergC (1995) On conditional and intrinsic autoregressions. Biometrika, 82, 733–46.
8.
BesagJYorkJMolliéA (1991) Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43, 1–59.
9.
BestNRichardsonSThomsonA (2005) A comparison of bayesian spatial models for disease mapping. Statistical Methods in Medical Research, 14, 35–59.
10.
BhatiA (2008) A generalized cross-entropy approach for modelling spatially correlated counts. Econometric Reviews, 27, 574–95.
11.
BivandRPebesmaEGómez-RubioV (2008) Applied spatial data analysis with R. New York: Springer Verlag.
12.
BreslowNEClaytonDG (1993) Approximate inference in generalized linear mixed model. Journal of the American Statistcal Association, 88, 9–25.
13.
ClaeskensGHjortNL (2008) Model selection and model averaging. Cambridge: Cambridge University Press.
14.
ClaytonDKaldorJ (1987) Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43, 671–81.
15.
CressieNA (1993) Statistics for spatial data. New York: Wiley.
16.
DigglePJ (2010) Historical introduction. In GelfandAEDigglePJFuentesMGuttorpgermanP (eds), Handbook of spatial statistics, pp. 3–16. Boca Raton: Chapman & Hall/CRC.
17.
ElhorstJP (2010) Applied spatial econometrics: raising the bar. Spatial Economic Analysis, 5, 9–28.
18.
FahrmeirLKneibTLangS (2004) Penalized sturctured additive regression for space-time data: a Bayesian perspective. Statistica Sinica, 14, 715–45.
19.
FischerMMScherngellTJansenbergerE (2005, August) The geography of knowledge spillovers between high-technology firms in Europe—evidence from a spatial interaction modelling perspective. ERSA conference papers ersa05p5, European Regional Science Association.
20.
FongYHgermanRueWakefieldJ (2010) Bayesian inference for generalized linear mixed models. Biostatistics, 11, 397–412.
21.
GaetanCGuyonX (2010) Spatial statistics and modelling. Berlin: Springer.
22.
GriffithDAPaelinckJH (2007) An equation by any other name is still the same: on spatial econometrics and spatial statistics. The Annals of Regional Science, 41, 209–27.
23.
HodgesJReichB (2010) Adding spatially-correlated errors can mess up the fixed effect you love. The American Statistician, 64, 335–44.
24.
KaiserMCressieN (1997) Modelling Poissson variables with positive spatial dependence. Statistics and Probability Letters, 35, 423–32.
25.
KrigeD (1951) A statistical approach to some mine valuation and allied problems on the Witwatersrand. Master's thesis, University of Witwatersrand.
26.
LambertDBrownJFloraxR (2010) A two-step estimator for a spatial lag model of counts: theory, small sample performance and an application. Regional Science and Urban Economics, 40, 241–52.
27.
LawsonABiggeriABöhningDLesaffreEVielJ-FBertolliniRE (1999) Disease mapping and risk assessment for public health. Chichester, UK: Wiley.
28.
LawsonABWilliamsFLR (2001) An introductory guide to disease mapping. New York: Wiley Medical Sciences.
29.
LeSageJFischerMScherngellT (2007) Knowledge spillovers across europe: evidence from a Poisson spatial interaction model with spatial effects. Papers in Regional Science, Blackwell Publishing, 86, 393–421.
30.
LeSageJKelley PaceRLamNCampanellaRLiuX (2011) New orleans business recovery in the aftermath of hurricane katrina. Journal of the Royal Statistical Society, Series A, 174, 1007–27.
31.
LeSageJPaceK (2009) Introduction to spatial econometrics. London: Taylor and Francis.
32.
MacNabY (2003) Hierarchical bayesian spatial modelling of small-area rates of non-rare disease. Statistics in Medicine, 22, 1761–73.
33.
MankiwNRomerDWeilD (1992) A contribution to the empirics of economic growth. Quarterly Journal of Economics, 107, 407–37.
34.
PaceRKLeSageJ (2010) Spatial econometrics. In GelfandAEDigglePJFuentesMGuttorpgermanP (eds), Handbook of spatial statistics, pp. 245–62. Boca Raton: Chapman & Hall/CRC.
PascuttoCWakefieldJBestNRichardsonSBernardinelliLStainesAElliottP (2000) Statistical issues in the analysis of disease mapping data. Statistics in Medicine, 19, 2493–519.
PinheiroJBatesD (2000) Mixed-Effects Models in S and Splus. New York: Springer Verlag.
39.
RipleyB (2004) Spatial statistics. New York: John Wiley & Sons.
40.
RueHHeldL (2005) Gaussian markov random fields: theory and applications. Boca Raton: Chapman & Hall/CRC.
41.
RueHHeldL (2010) Discrete spatial variation. In GelfandAEDigglePJFuentesMGuttorpgermanP (eds), Handbook of spatial statistics, pp. 171–200. Boca Raton: Chapman & Hall/CRC.
42.
RueHMartinoSChopinN (2009) Approximate Bayesian inference for latent gaussian models using integrated nested Laplace approximations. Journal of the Royal Statistical Society, Series B, 71, 319–92.
43.
SchrödleBHeldL (2010) A primer on disease mapping and ecological regression using INLA. Computational Statistics, 26, 241–58.
44.
SpiegelhalterDJBestNGCarlinBPvan der LindeA (2002) Bayesian measure of model complexity and fit. Journal of the Royal Statistical Society, Series B, 64, 583–639.
45.
VaidaFBlanchardS (2005) Conditional Akaike information for mixed-effects models. Biometrica, 92, 351–70.
46.
WagerCVaidaFKauermannG (2007) Model selection for P-spline smoothing using Akaike information criteria. Australian and New Zealand Journal of Statistics, 49, 173–90.
47.
WakefieldJ (2007) Disease mapping and spatial regression with count data. Biostatistics, 8, 158–83.
48.
WhittleP (1954) On stationary processes in the plane. Biometrica, 41, 434–49.