idw_xy {ptools}

Inverse distance weighted sums

Description

Given a base X/Y dataset, calculates clipped inverse distance weighted sums of points from feature dataset

Usage

idw_xy(base, feat, clip = 1, weight = 1)

Arguments

base

base dataset (eg gridcells), needs to be SpatialPolygonsDataFrame

feat

feature dataset (eg another crime generator), needs to be SpatialPointsDataFrame

clip

scaler minimum value for weight, default 1 (so weights cannot be below 0)

weight

if 1 (default), does not use weights, else pass in string that is the variable name for weights in feat

Details

This generates a inverse distance weighted sum of features within specified distance of the base centroid. Weights are clipped to never be below clip value, which prevents division by 0 (or division by a very small distance number) Uses loops and calculates all pairwise distances, so can be slow for large base and feature datasets. Consider aggregating/weighting feature dataset if it is too slow. Useful for quantifying features nearby (Groff, 2014), or for egohoods (e.g. spatial smoothing of demographic info, Hipp & Boessen, 2013).

Value

A vector of IDW weighted sums

References

Groff, E. R. (2014). Quantifying the exposure of street segments to drinking places nearby. Journal of Quantitative Criminology, 30(3), 527-548.

Hipp, J. R., & Boessen, A. (2013). Egohoods as waves washing across the city: A new measure of “neighborhoods”. Criminology, 51(2), 287-327.

See Also

dist_xy() for calculating distance to nearest

count_xy() for counting points inside polygon

kern_xy() for estimating gaussian density of points for features at base polygon xy coords

bisq_xy() to estimate bi-square kernel weights of points for features at base polygon xy coords

idw_xy() to estimate inverse distance weights of points for features at base polygon xy coords

Examples

data(nyc_cafe); data(nyc_bor)
gr_nyc <- prep_grid(nyc_bor,15000)
gr_nyc$idwcafe <- idw_xy(gr_nyc,nyc_cafe)
head(gr_nyc@data)