ptools (version 2.0.0)

idw_xy: 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)

Value

A vector of IDW weighted sums

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).

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

# \donttest{
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)
# }