They are concerned with what they call spatial issues, for example how rural versus urban influences their model [or the units that make up their model].

Noetsi,

the main issue in performing any "regular" statistical approach on spatial data is the so-called "spatial autocorrelation": locations that are close to one another tend to have more similar values relative to locations that are further apart. For example, think about ground elevation (above the sea level): if you select two points that are close to each other, they are likely to have similar elevation relative to a third point that lies miles away from those two.

The fact that spatial data tend to be autocorrelated (either positively [low values tend to go with low values, high values with high values] or negatively [high values go with low values]) hampers the use of traditional statistical approaches since the latter are based on the assumption of the independence of the observations. As a matter of fact, in spatial data, the value of a variable at a location can be correlated to the values of the nearby locations (due to spatial autocorrelation).

To circumvent that issue, spatial regression models have been devised: these models "modify" traditional modelling strategies incorporating the way in which observations are spatially correlated into the model.

There is a large amount of literature on this; e.g.:

F. Dormann, C., M. McPherson, J., B. Araújo, M., Bivand, R., Bolliger, J., Carl, G., G. Davies, R., Hirzel, A., Jetz, W., Daniel Kissling, W., Kühn, I., Ohlemüller, R., R. Peres-Neto, P., Reineking, B., Schröder, B., M. Schurr, F., Wilson, R., 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. Ecography (Cop.). 30, 609–628. doi:10.1111/j.2007.0906-7590.05171.x

Dormann, C.F., 2007. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Glob. Ecol. Biogeogr. 16, 129–138. doi:10.1111/j.1466-8238.2006.00279.x

de Frutos, Á., Olea, P.P., Vera, R., 2007. Analyzing and modelling spatial distribution of summering lesser kestrel: The role of spatial autocorrelation. Ecol. Modell. 200, 33–44. doi:10.1016/j.ecolmodel.2006.07.007

Augustin, N.H., Mugglestone, M.A., Buckland, S.T., 1996. An autologistic model for the spatial distribution of wildlife. J. Appl. Ecol. 33, 339–347. doi:10.2307/2404755

If you are interested in reading one (or all) of them, feel free to ask.

Best

Gm