crimeClust_hier: Agglomerative Hierarchical Crime Series Clustering
In crimelinkage: Statistical Methods for Crime Series Linkage

Description Usage Arguments Details Value References See Also Examples

Run hierarchical clustering on a set of crimes using the log Bayes Factor as the similarity metric.

1 2	crimeClust_hier(crimedata, varlist, estimateBF, linkage = c("average", "single", "complete"), ...)

`crimedata`	data.frame of crime incidents. Must contain a column named `crimeID`.
`varlist`	a list of the variable names (columns of `crimedata`) used to create evidence variables with `compareCrimes`.
`estimateBF`	function to estimate the log bayes factor from evidence variables
`linkage`	the type of linkage for hierarchical clustering “average” uses the average bayes factor “single” uses the largest bayes factor (most similar) “complete” uses the smallest bayes factor (least similar)
`...`	other arguments passed to `compareCrimes`

This function first compares all crime pairs using compareCrimes, then uses estimateBF to estimate the log Bayes factor for every pair. Next, it passes this information into hclust to carry out the agglomerative hierarchical clustering. Because hclust requires a dissimilarity, this uses the negative log Bayes factor.

The input varlist is a list with elements named: crimeID, spatial, temporal, categorical, and numerical. Each element should be a vector of the column names of crimedata corresponding to that feature. See compareCrimes for more details.

An object of class hclust (from hclust).

Porter, M. D. (2014). A Statistical Approach to Crime Linkage. arXiv preprint arXiv:1410.2285.. http://arxiv.org/abs/1410.2285

clusterPath, plot_hcc

data(crimes)
 #- cluster the first 10 crime incidents
 crimedata = crimes[1:10,]
 varlist = list(spatial = c("X", "Y"), temporal = c("DT.FROM","DT.TO"),
     categorical = c("MO1",  "MO2", "MO3"))
 estimateBF <- function(X) rnorm(NROW(X))   # random estimation of log Bayes Factor
 HC = crimeClust_hier(crimedata,varlist,estimateBF)
 plot_hcc(HC,yticks=-2:2)

 # See vignette: "Crime Series Identification and Clustering" for more examples.