Geospatial data is available at increasingly finer spatial resolutions. However, such data can also be problematic because it may result in increased financial, resource and time cost. It might also be unnecessary if the phenomenon of interest does not vary at this scale or if the scientific application does not require it. This paper explores the scale effects associated with increased spatial resolution using the entropy-based local indicator of spatial association (LISA-ELSA). Results are illustrated for two datasets a digital elevation model (30 m ASTER GDEM) and for a raster PM2.5 air pollution map (1000 m). It is shown that increasing the size of the local window for the LISA can be used to explore the effect of reducing the spatial resolution. It is possible to identify areas where the spatial association is invariant with increasing window size and which could be represented with coarser pixels.