spatialsegregation-segregationFun {spatialsegregation} | R Documentation |
Compute the spatial exposure (segregation vs. mingling) features from a given multitype point pattern. Usage of
shortcuts minglingF
, isarF
, shannonF
and simpsonF
highly recommended.
segregationFun(X, fun="isar", r=NULL, ntype="geometric", funpars=NULL, toroidal=FALSE, minusRange=0, included=NULL, dbg=FALSE, doDists=FALSE, prepRange=0.0, prepGraph=NULL, prepGraphIsTarget=FALSE)
X |
Multitype point pattern of class ppp (see package 'spatstat') |
fun |
Default "isar". Takes "isar","mingling","shannon" and "simpson", see below. |
r |
Vector for the neighbourhood defining graph, e.g. "geometric" graph with different ranges. See below. |
ntype |
Default "geometric". Type of the neighbourhood graph. Accepts: "knn", "geometric", "delauney", "gabriel". |
funpars |
Default NULL. Parameter(s) for the measure. Mingling: c(i,j), where i= only for type i (0 for all), j=1 -> ratio version. ISAR: i, i=type (integer). Shannon: 0 or 1, see v2 in shannonF . Simpson: none. |
toroidal |
Default FALSE. If TRUE, use a toroidal correction in distance calculation. Works at the moment only for rectangular windows and "geometric" or "knn" graph. |
minusRange |
If given, included -vector is created with points with distance atleast minusRange from the border. |
included |
boolean-vector of length |pp|. included[i]==TRUE => pp[i] included in calculations. |
dbg |
Default FALSE. Print additional runtime texts. |
doDists |
Default TRUE. Precalculate distances for speed. Be aware of memory requirements, n*(n-1)! |
prepRange |
Default 0. If >0, shrink the search space for neighbourhoods by searching only points within distance R i.e. precalculates a geometric graph. |
prepGraph |
Precalculated graph for the point pattern. If given, The prepRange , dodists and toroidal are ignored and calculations are carried
using the prepGraph as a starting point. Useful for huge datasets. |
prepGraphIsTarget |
If TRUE, precalculated graph prepGraph is used to calculate a single function value directly, all other neighbourhood parameters are ignored. |
This is the general function for computing the spatial exposure (segregation/mingling) features. Used by minglingF, shannonF, simpsonF and isarF, which should be preferred for better (and nicer) outcome.
Possible neighbourhood relations for the spatial version include geometric, k-nearest neighbours, Delauney, and Gabriel.
Delauney and Gabriel are parameter free, so given r
has no meaning. In geometric graph, r
is a vector
of distances (sizes of the surrounding 'disc') and for k-nn r
is the vector of neighbourhood abundances for each point
to consider in the calculation of the spatial exposure measures. The basic type of spatial summary uses range, or 'geometric'
graph connections with varying neighbourhood parameter.
For geometric
and knn
, the calculations are done by shrinking the graph given by the largest value of r
. If dealing with large datasets,
it is advisable to give preprocessing range, prepRange
. The algorithm first calculates a geometric graph with parameter
prepRange
, and uses this as basis for finding the needed neighbourhoods. Speeds up calculations. prepGraph
, if given, works as the
preprocessed geometric graph. But make sure prepRange
is large enough (e.g. in geometric
, prepRange
>max(r
)).
The doDists
option speeds up calculations by precomputing the pairwise distances but takes n*(n-1) memory!
For border correction, use minusRange
for reduced border correction (for rectangular windows only). If using geometric
or knn
neighbourhoods,
the option toroidal
for toroidal correction is also available. The vector included
can be given for more specific minus
-correction,
only those points with TRUE (1) value are used in calculation. However, the neighbourhoods are calculated with all points.
Date: | 2009-09-06 |
License: | GPL v2 or later |
Tuomas Rajala University of Jyvaskyla, Finland tuomas.a.rajala@jyu.fi