@inproceedings{853f372c801443a4a1ee6264b0c4dbb0,
title = "Discovery of Burglary Hotspots and Extraction of Their Features",
abstract = "Investigation of the underline contributing factors to burglaries within a geographical area, is essential in finding solutions. Such investigation for a geographical area in the southeast of Georgia is the subject of this paper. Our goal is bifold: (a) discovering the burglary hotspots for the area and (b) extracting the features associated with each given hotspot. Such features are the underlying contributing factors for each hotspot, which differ from one hotspot to the next. Data sources used for meeting the goal are cleansed city Police reports, United States Census, Google maps, and the City Ordinances. The discovery of the hotspots is accomplished by using the Optics clustering technique and feature extraction is performed by an association analysis.",
keywords = "Association Analysis, Burglary Hotspots, Data Mining, Graphical User Interface, Optic Clustering",
author = "Andrew Little and Hashemi, {Ray R.} and Young, {Jeffrey A.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 ; Conference date: 16-12-2020 Through 18-12-2020",
year = "2020",
month = dec,
doi = "10.1109/CSCI51800.2020.00077",
language = "English",
series = "Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "412--417",
booktitle = "Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020",
address = "United States",
}