… regression models with spatially-varying coefficients is then multiple. Firstly, we can argue that
models that only consider spatial autocorrelation … to non-observable spatial heterogeneity. …

… by fitting models that ignored both broadscale spatial trend and fine-scale autocorrelation.
We … For each species, we fit OLS multiple regression models to habitat variables (hereafter, “…

… or spatial heterogeneity separately. In this study, we attempted to fit spatial regression
models (ie, spatial lag and spatial error models), the linear mixed-effects model, and GWR to the …

… For instance, in the context of explaining spatial patterns in ecology using regression models,
we might be interested in the extent to which intrinsic processes (eg dispersal, population …

… (positive or negative) but also the degree of spatial autocorrelation If the variable of
interest is the error term in a regression model, the question of model misspecification can be …

… regression coefficients, representing compelling degrees of spatial autocorrelation, and the
spatial autocorrelation … Poisson regression modeling, where the normalizing factors for the …

… spatial autocorrelation is ignored we simply do not know if we can trust the [regression model…
Therefore, … the presence of residual spatial autocorrelation should always be tested for … …

… 2 that spatial autocorrelation is … spatial autocorrelation is a nuisance against which empirical
grographers must be prepared. Instead, we should use a fmding of spatial autocorrelation to …

… to describe spatial autocorrelation for a data set of Ecuador. Positive spatial autocorrelation
was … (ii) To evaluate a spatial regression model and its scale dependency for this case study. …

… Note that the taxonomy used for describing the spatial autocorrelation in our data is similar
to the formulation of the SAR model types, although the underlying regression models are not …