I have some points (representing 30 study sites) and I want to calculate the weighted mean of several variables (landscape layers) using a negative exponential decay (weights function) to give more weight to the landscape features closer to the points (study sites) than the features (grid cells) further away. (The series of underlying grid layers represent landscape features such as roads; habitat etc.)
Also, how can I use spatial statistics to find the optimal distance (bandwidth) for my distance-decay function? In the literature everyone resorts to creating buffers (vector) at different distances (say 1km, 3km & 5km) to quantify the variables and then uses statistics to determine which buffer distance is significant. Other ecologists (Rhodes et al. 2006) have used the “negative exponential distance weighted density” of each variable and provided a scale parameter (L) which controls how rapidly the influence (ie weighting) of the variable declines with distance, eg L = EXP(-0.002*Distance)
I’m using ArcGIS spatial statistics; grid (raster) spatial analysis tools; and investigating GWmodel for R-stats from an earlier post.