Do spatial effects really matter in regression analysis

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Spatial residual analysis of six modeling techniques (2005, methodology): “the OLS assumptions, independence of observations, is often violated due to temporal orland spatial autocorrelations in data, which leads to a biased estimation of the standard errors of model parameters and, consequently, misleading significance tests ( Anselin and Griffith, 1988; Fox et al., 2001”
Testing for Spatial Autocorrelation among the Residuals of the Geographically Weighted Regression (2000, background): “…the standard assumption of constant variance of the disturbance terms may fail to hold in the presence of spatial autocorrelation (for example, see Anselin, 1988a; Anselin and Griffith, 1988; Cliff and Ord, 1973; 1981; Cordy and Griffith, 1993; Griffith, 1988; Kra« mer and Donninger, 1987).”
Thirty years of spatial econometrics (2010, background): “In: Bartels C, Ketellapper R (eds) Exploratory and explanatory statistical analysis of spatial data. Martinus Nijhoff, Boston, MA Holloway G, Shankar B, Rahman S (2002) Bayesian spatial probit estimation: A primer and an application to HYV rice adoption. Agricultural Economics 27: 383–402 Hordijk L ”
Economic Distance and Cross-Country Spillovers (2001, neutral): “For some nice examples of these models see, for example, Moulton (1990), Case (1991 ), Anselin and Griffith (1988), and Kelejian and Prucha (1999).”