Local and global approaches to spatial data analysis in ecology

Geographic analyses in ecology may be separated into those that attempt generalizations to achieve ‘global’ insights, and those that attempt to explore and document local variation. Ecological studies at the broad scale usually set out to test specific hypotheses (such as the effect of energy on species richness) and focus on establishing global relationships before examining local residual variation. However, geographical pattern in model residuals (Jetz & Rahbek, 2002; Fig. 1c) can also lead to important insights. In a recent issue of Global Ecology and Biogeography , a study by Foody (2004) illustrates how a method for estimating local variation in model parameters, geographically weighted regression (GWR, Fotheringham et al ., 2002), may enhance data exploration. Standard global methods, such as linear or logistic multiple regression, estimate a single parameter for each explanatory variable. In contrast, GWR allows parameter values to vary continuously in geographical space, and local parameter values are estimated by assigning higher weights to nearby observations than more distant ones. The user varies the ‘bandwidth’ in GWR, which determines the rate at which weights decrease with distance.