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Crime Analytics: Analysis of crimes through news paper articles

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Crime analysis is one of the most important
activities of the majority of the intelligent and law enforcement
organizations all over the world. Generally they collect domestic
and foreign crime related data (intelligence) to prevent future
attacks and utilize a limited number of law enforcement
resources in an optimum manner. A major challenge faced by
most of the law enforcement and intelligence organizations is
efficiently and accurately analyzing the growing volumes of crime
related data. The vast geographical diversity and the complexity
of crime patterns have made the analyzing and recording of
crime data more difficult. Data mining is a powerful tool that can
be used effectively for analyzing large databases and deriving
important analytical results. This paper presents an intelligent
crime analysis system which is designed to overcome the above
mentioned problems. The proposed system is a web-based system
which comprises of crime analysis techniques such as hotspot
detection, crime comparison and crime pattern visualization. The
proposed system consists of a rich and simplified environment
that can be used effectively for processes of crime analysis.

Published in: Data & Analytics

Crime Analytics: Analysis of crimes through news paper articles

  1. 1. Crime Analytics: Analysis of Crimes Through Newspaper Articles Indika Perera Isuru Jayaweera Chamath Sajeewa Sampath Liyanage Tharindu Wijewardane Department of Computer Science and Engineering Faculty of Engineering University of Moratuwa Sri Lanka Adeesha Wijayasiri Department of Computer and Information Science and Engineering University of Florida United States
  2. 2. Introduction
  3. 3. Why Crime Analysis? • To identify general and specific crime trends, patterns and series in an ongoing, timely manner. • To utilize limited law enforcement resources. • To be proactive in detecting and preventing crimes. • To meet the law enforcement needs of changing society. A major challenge faced by most of the law enforcement and intelligence organizations is efficiently and accurately analyzing the growing volumes of crime related data. The vast geographical diversity and the complexity of crime patterns have made the analyzing and recording of crime data more difficult. Data mining is a powerful tool that can be used effectively for analyzing large databases and deriving important analytical results.
  4. 4. Crime Analysis Approaches Using in SriLanka • Police department uses manual crime recording and analysis system. • There is no free and open accessible crime analysis system in Sri Lanka.  Grave Crime Records  Minor Crime Records
  5. 5. Usefulness of Crime Analysis System ● Police Department can use the system when they create security plans. ● Police department can evaluate their existing plans. ● Investors can use the system when they want to find suitable areas for investments. ● Tourists and tourist agents can use the system when they are planning their tours.
  6. 6. Related Work
  7. 7. Crime Data Mining Techniques • Entity extraction • Association rule mining • Deviation detection • Classification Existing systems that use data mining techniques for crime investigation • Regional crime analysis program • Link analysis concepts • Data mining framework for crime pattern identification • Narcotics network in Tucson police department • Clustering techniques • Sequential pattern mining • String comparison
  8. 8. Crime Analysis Steps • Hotspot Detection • Crime Pattern Visualization • Nearest Police Station Detection A framework has been proposed which includes relationships between the crime data mining techniques and crime type characteristics. Framework has been developed by using Tucson Police Department crime classification database. Using this framework, investigator can determine the most suitable data mining technique for his/her task. As given by the proposed framework, investigators can use neural network techniques in crime entity extraction/ prediction, clustering techniques are effective in crime association/ prediction, and social network analysis can facilitate crime association/pattern visualization. • Crime Comparison • Crime Clock • Outbreaks Detection
  9. 9. • Web Crawling – multi threaded crawlers, preferential crawlers, focused crawlers • Document Classification – stop words removal, lemmatization/stemming, tf-idf, syntactic and semantic arrangement of words, support vector machines, word net/ ontology, different error costs, sampling techniques, cross validation • Entity Extraction – sentence splitting, tokenizing, POS tagging, supervised/ semi- supervised/ unsupervised entity extraction • Duplicate Detection – near duplicate detection, finger prints (shingles, simhash), hamming distance Preliminaries
  10. 10. The Proposed System
  11. 11.  This paper presents a web based crime analysis system.  Sri Lankan English newspapers (Daily Mirror, The Island, and Ceylon Today) are used as the source for details of crime incidents.  Newspaper articles are crawled using a focused crawler and then they are classified. The Proposed System
  12. 12.  Required entities are extracted from classified crime articles and duplicate detection is performed.  By using these preprocessed data, crime analysis operations are performed and results are displayed using a web based GUI.  Unlike most systems, this system is open to anyone who is interested in crime analysis. The Proposed System (cont.)
  13. 13. Web Crawling Crime Analysis and Prediction Document Classification Entity Extraction Duplicate Detection Our Solution
  14. 14. • Crawler – crawler4j, Jsoup, cookie handler • Document Classifier – Weka, LibSVM, SMOTE, Different Error Costs • Entity Extractor – GATE, ANNIE, Stanford NLP, Google Map API • Duplicate Detector – 64 bit simhash calculator, murmur hash calculator, hamming distances • Web Interface – HighCharts, Java scripts, AJAX Implementation Details
  15. 15. Results
  16. 16. Crime Hot Spot Analysis
  17. 17. Crime Comparison
  18. 18. Crime Pattern Visualization
  19. 19. Conclusion
  20. 20. • The proposed system performs crime analysis operations such as hotspot detection, crime comparison and crime pattern visualization. • Graphical user interface of the system uses graphs and diagrams to display the results which make crime analysis a very simple task. • Therefore law enforcement officers and other interested users will be able to use this system effectively and efficiently for crime analysis processes. • Also this is a publicly accessible system, so that anyone who is interested in this area will be able to use this system freely.
  21. 21. Future Work
  22. 22. • Crime prediction is expected to be implemented in future to enhance the functionality of the system. • Comprehensiveness of the news article collection can be further improved by extending the news article crawler to crawl more news websites. • Linguistic knowledge (WordNet, Ontology, etc.) can be incorporated with the document classifier module in order to improve the accuracy of the classification process. • Entity extraction module can be improved by incorporating more rules which will improve accuracy and comprehensiveness of the entity extraction process.
  23. 23. Questions?
  24. 24. Thank You!

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