dist.buf: Counting points in a buffer
In REAT: Regional Economic Analysis Toolbox

Description Usage Arguments Details Value Author(s) References See Also Examples

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

1 2	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")

`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

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.

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

Thomas Wieland

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

dist, dist.mat

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

        from         to               from_to distance
1 Goettingen Goettingen Goettingen-Goettingen   0.0000
2  Karlsruhe Goettingen  Karlsruhe-Goettingen 303.6554
3   Freiburg Goettingen   Freiburg-Goettingen 423.7633
4 Goettingen  Karlsruhe  Goettingen-Karlsruhe 303.6554
5  Karlsruhe  Karlsruhe   Karlsruhe-Karlsruhe   0.0000
6   Freiburg  Karlsruhe    Freiburg-Karlsruhe 120.1379
7 Goettingen   Freiburg   Goettingen-Freiburg 423.7633
8  Karlsruhe   Freiburg    Karlsruhe-Freiburg 120.1379
9   Freiburg   Freiburg     Freiburg-Freiburg   0.0000
$count_table
        from count_citynames
1   Freiburg               1
2 Goettingen               0
3  Karlsruhe               1

$distmat
        from         to              from_to distance count
2  Karlsruhe Goettingen Karlsruhe-Goettingen 303655.4     0
3   Freiburg Goettingen  Freiburg-Goettingen 423763.3     0
4 Goettingen  Karlsruhe Goettingen-Karlsruhe 303655.4     0
6   Freiburg  Karlsruhe   Freiburg-Karlsruhe 120137.9     1
7 Goettingen   Freiburg  Goettingen-Freiburg 423763.3     0
8  Karlsruhe   Freiburg   Karlsruhe-Freiburg 120137.9     1