How do I calculate the distance decay function for some points (representing 30 study sites) in relation to a series of underlying grid layers (representing landscape features such as roads; dog density, human density, habitat etc)?
I want to calculate the weighted mean of each variable 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.
Background
My aim is to model koala distribution in relation to habitat and threats in the surrounding matrix.
Traditionally, we would use point buffers at different distances (say 1km, 3km & 5km) to quantify the variables (eg calculate the road density (vector length) within 1km, 3km & 5km of the study sites) and then use statistical modelling to determine which buffer distance is significant.
However, a distance decay function would be a better approach given that would give more weight to the landscape features closer to the study sites than the features further away. I need to calculate the weighted mean of each variable (grid layer) around each study site. 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). How do I perform this spatial analysis in ArcGIS??
I want to calculate the weighted mean of each variable 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.
Background
My aim is to model koala distribution in relation to habitat and threats in the surrounding matrix.
Traditionally, we would use point buffers at different distances (say 1km, 3km & 5km) to quantify the variables (eg calculate the road density (vector length) within 1km, 3km & 5km of the study sites) and then use statistical modelling to determine which buffer distance is significant.
However, a distance decay function would be a better approach given that would give more weight to the landscape features closer to the study sites than the features further away. I need to calculate the weighted mean of each variable (grid layer) around each study site. 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). How do I perform this spatial analysis in ArcGIS??