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Taming Near Repeat Calculation for Crime Analysis via Cohesive Subgraph Computing

Authors:
Zhaoming Yin
Xuan Shi

Keywords: Near-Repeat; Graph Analysis.

Abstract:
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 would generate two event pairs if two events happened within a given space and time limit. When the number of events is significant, however, NR calculation is time consuming and how these pairs are organized has not yet been 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; this way, a graph is formed. Cohesive subgraph approaches are applied to identify the event chains. k-clique, k-truss, k-core plus Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithms are implemented in sequence to their varied range of abilities to find cohesive subgraphs. Real-world crime data in Chicago, New York, and Washington DC are utilized to conduct experiments. The experiments confirmed that near repeat has a substantial effect on real big crime data by conducting Map-reduce empowered Knox tests. The performances of 4 different algorithms are validated, with the quality gauged by the distribution of the number of cohesive subgraphs and their clustering coefficients. The proposed framework is the first to process the real crime data of million records and is the first to detect NR events with a size of more than 2.

Pages: 1 to 8

Copyright: Copyright (c) IARIA, 2020

Publication date: March 22, 2020

Published in: conference

ISSN: 2308-393X

ISBN: 978-1-61208-762-7

Location: Valencia, Spain

Dates: from November 21, 2020 to November 25, 2020