Shekhar, ShashiMohan, PradeepOliver, DevZhou, Xun2020-09-022020-09-022012-05-10https://hdl.handle.net/11299/215893Crime pattern analysis (CPA) is the process of analytical reasoning facilitated by an understanding about the nature of an underlying spatial framework that generates crime. For example, law enforcement agencies may seek to identify regions of sudden increase in crime activity, namely, crime outbreaks. Many analytical tools facilitate this reasoning process by providing support for techniques such as hotspot analysis. However, in practice, police departments are desirous of scalable tools for existing techniques and new insights including, interaction between different crime types. Identifying new insights using scalable tools may help reduce the human effort that may be required in CPA. Formally, given a spatial crime dataset and other information familiar to law enforcement agencies, the CPA process identifies interesting, potentially useful and previously unknown crime patterns. For example, analysis of an urban crime dataset might reveal that downtown bars frequently lead to assaults just after bar closing. However, CPA is challenging due to: (a) the large size of crime datasets, and (b) a potentially large collection of interesting crime patterns. This chapter explores, spatial frequent pattern mining (SFPM), which is a spatial data driven approach for CPA and describes SFPM in the context of one type of CPA, outbreak detection. We present a case study to discover interesting, useful and non-trivial crime outbreaks in a dataset from Lincoln, NE. A review of emerging trends and new research needs in CPA methods for study to discover interesting, useful and non-trivial crime outbreaks in a dataset from outbreak detection is also presented.en-USReport