| proxistat-package | R Documentation |
This package has functions helping to calculate distances between points, such as the distances between all points, distances to all points within some maximum distance, distance to nearest single point, etc. It also can create a proximity score for each spatial unit like a Census block group, to quantify the distance-weighted count of nearby points.
This package has functions helping to calculate distances between geographic points, such as the distances between all points, distances to all points within some maximum distance, distance to nearest single point, etc. It also can create a proximity score for each spatial unit like a Census block group, to quantify the distance-weighted count of nearby points. This proximity score can be used in environmental justice (EJ) analysis, for example.
This package relies on the sp package for the actual calculation of distance.
A vector of points can be specified using a data.frame of two columns, "lat" and "lon"
which specify latitude and longitude in decimal degrees.
It returns the distances from one or more frompoints to one or more topoints.
Key functions include
get.nearest to find the one among topoints nearest each frompoints
get.distances to find distances quickly within an optional search radius
proxistat to create a proximity score that quantifies,
for each spatial unit like a Census block group,
how many topoints are nearby and how close they are
convert to convert units (miles, km)
info@ejanalysis.com<info@ejanalysis.com>
http://ejanalysis.github.io
http://www.ejanalysis.com/
sp package documentation for basic distance function.
Some discussion of this type of proximity indicator is available in the EJSCREEN mapping tool documentation:
U.S. Environmental Protection Agency (2015). EJSCREEN Technical Documentation.
http://www.epa.gov/ejscreen
http://en.wikipedia.org/wiki/Longitude and http://en.wikipedia.org/wiki/Decimal_degrees
sp, US block points dataset: http://ejanalysis.github.io/UScensus2010blocks/, deltalon.per.km, deltalon.per.km, meters.per.degree.lat, meters.per.degree.lon
test.from <- structure(list(fromlat = c(38.9567309094, 38.9507043428),
fromlon = c(-77.0896572305, -77.0896199948)),
.Names = c("lat","lon"), row.names = c("6054762", "6054764"), class = "data.frame")
test.to <- structure(list(tolat = c(38.9575019287, 38.9507043428, 38.9514152435),
tolon = c(-77.0892818598, -77.0896199948, -77.0972395245)),
.Names = c("lat","lon"), class = "data.frame", row.names = c("6054762", "6054763", "6054764"))
setseed(999)
t100=testpoints(100)
t10=testpoints(10)
t3=testpoints(3)
get.distances(
test.from[1:2,], test.to[1:3, ], radius=0.7, units='km', return.rownums=TRUE, return.latlons=TRUE
)
get.nearest(test.from, test.to)
get.distances( t3, t10, units='km', return.crosstab=TRUE)
get.distances( t3, t10, units='km')
get.distances( t3, t10, radius=300, units='km')
proxistat(t3, t10, radius = 300, units = "km", area = c(100001:100003))
proxistat( t3, t10, radius=300, units='km')
1/get.nearest( t3, t10, radius=300, units='km')
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