  Near repeat (NR) is a well known phenomenon in crime analysis assuming that
crime events exhibit correlations within a given time and space frame.
Traditional NR calculation generates 2 event pairs if 2 events happened within
a given space and time limit. When the number of events is large, however, NR
calculation is time consuming and how these pairs are organized are not yet
explored. In this paper, we designed a new approach to calculate clusters of NR
events efficiently. To begin with, R-tree is utilized to index crime events, a
single event is represented by a vertex whereas edges are constructed by range
querying the vertex in R-tree, and a graph is formed. Cohesive subgraph
approaches are applied to identify the event chains. k-clique, k-truss, k-core
plus DBSCAN algorithms are implemented in sequence with respect to their varied
range of ability to find cohesive subgraphs. Real world crime data in Chicago,
New York and Washington DC are utilized to conduct experiments. The experiment
confirmed that near repeat is a solid effect in real big crime data by
conducting Mapreduce empowered knox tests. The performance of 4 different
algorithms are validated, while the quality of the algorithms are gauged by the
distribution of number of cohesive subgraphs and their clustering coefficients.
The proposed framework is the first to process the real crime data of million
record scale, and is the first to detect NR events with size of more than 2.
