dist.buf {REAT}

Counting points in a buffer

Description

Counting points within a buffer of a given distance with points with given coordinates

Usage

dist.buf(startpoints, sp_id, lat_start, lon_start, endpoints, ep_id, lat_end, lon_end, 
ep_sum = NULL, bufdist = 500, extract_local = TRUE, unit = "m")

Arguments

startpoints

A data frame containing the start points

sp_id

Column containing the IDs of the startpoints in the data frame startpoints

lat_start

Column containing the latitudes of the start points in the data frame startpoints

lon_start

Column containing the longitudes of the start points in the data frame startpoints

endpoints

A data frame containing the points to count

ep_id

Column containing the IDs of the points to count in the data frame endpoints

lat_end

Column containing the latitudes of the points to count in the data frame endpoints

lon_end

Column containing the longitudes of the points to count in the data frame endpoints

ep_sum

Column of an additional variable in the data frame endpoints to sum

bufdist

The buffer distance

extract_local

Logical argument that indicates if the start points should be included or not (default: TRUE)

unit

Unit of the buffer distance: unit="m" for meters, unit="km" for kilometers or unit="miles" for miles

Details

The function is based on the idea of a buffer analysis in GIS (Geographic Information System), e.g. to count the points of interest within a given buffer distance.

Value

The function returns a list containing:

count_table

A data.frame containing two columns: The start point IDs (from) and the number of counted points in the given buffer distance (count_location)

distmat

A data.frame containing the corresponding distance matrix wiht I x J rows

Author(s)

Thomas Wieland

References

de Lange, N. (2013): “Geoinformatik in Theorie und Praxis”. 3rd edition. Berlin : Springer Spektrum.

Krider, R. E./Putler, R. S. (2013): “Which Birds of a Feather Flock Together? Clustering and Avoidance Patterns of Similar Retail Outlets”. In: Geographical Analysis, 45, 2, p. 123-149

See Also

dist, dist.mat

Examples

citynames <- c("Goettingen", "Karlsruhe", "Freiburg")
lat <- c(51.556307, 49.009603, 47.9874)
lon <- c(9.947375, 8.417004, 7.8945)
citynames <- c("Goettingen", "Karlsruhe", "Freiburg")
cities <- data.frame(citynames, lat, lon)
dist.mat (cities, "citynames", "lat", "lon", cities, "citynames", "lat", "lon")
# Euclidean distance matrix (3 x 3 cities = 9 distances)
dist.buf (cities, "citynames", "lat", "lon", cities, "citynames", "lat", "lon", bufdist = 300000)
# Cities within 300 km