Pxx | R Documentation |
Mean proximity, Pxx, computes the mean distance between the members of a group. The distance matrix can be expressed as a linear or as an inverse exponential function of the distance between spatial unit centroids.The function can be used in two ways: to provide a distance matrix or a external geographic information source (spatial object or shape file).
Pxx(x, d = NULL, fdist = 'e', distin = 'm', distout = 'm', diagval = '0',
beta = 1, spatobj = NULL, folder = NULL, shape = NULL)
x |
an object of class matrix (or which can be coerced to that class), where each column represents the distribution of a group within spatial units. The number of columns should be greater than 1 (at least 2 groups are required). You should not include a column with total population, because this will be interpreted as a group. |
d |
a matrix of the distances between spatial unit centroids |
fdist |
the method used for distance interaction matrix: e' for inverse exponential function (by default) and 'l' for linear. |
distin |
input metric conversion, based on bink package and includes conversions from 'm', 'km', 'inch', 'ft', 'yd', 'mi', 'naut_mi', etc. |
distout |
output metric conversion, based on bink package and includes conversions to 'm', 'km', 'inch', 'ft', 'yd', 'mi', 'naut_mi', etc. |
diagval |
when providing a spatial object or a shape file, the user has the choice of the spatial matrix diagonal definition: diagval = '0' (by default) for an null diagonal and diagval = 'a' to compute the diagonal as 0.6 * square root (spatial/organizational unitsarea) (White, 1983) |
beta |
distance decay parameter |
spatobj |
a spatial object (SpatialPolygonsDataFrame) with geographic information |
folder |
a character vector with the folder (directory) name indicating where the shapefile is located on the drive |
shape |
a character vector with the name of the shapefile (without the .shp extension). |
A numeric vector containing the Pxx index values for each group
White M. J. (1983) The Measurement of Spatial Segregation. American Journal of Sociology, 88, p. 1008-1019
Proximity measures:
Pxy
, Poo
, SP
Clustering Indices:
ACL
, RCL
x <- segdata@data[ ,1:2]
ar<-area(segdata)
dist <- distance(segdata)
diag(dist)<-sqrt(ar) * 0.6
foldername <- system.file('extdata', package = 'OasisR')
shapename <- 'segdata'
Pxx(x, spatobj = segdata)
Pxx(x, folder = foldername, shape = shapename, fdist = 'l')
Pxx(x, spatobj = segdata, diagval ='a')
Pxx(x, d = dist, fdist = 'e')
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