1. CRIME PATTERN DETECTION

3. Pattern Analysis

4. Pattern Results

5. Advantages of CPD

6. Limitations of CPD

7. Conclusions

10. Theft

11. Motor vehicle theft

13. Gunrunning

14. Money laundering

15. Extortion

16. Murder for hire

17. Fraud

18. Human trafficking

20. Peer pressure Criminals have not been taught the difference between ‘right and wrong.’ Mental illness. A failure to rehabilitate ex-offenders back into society

21. The sociologist Zygmunt Bauman argues that “criminals steal status items in order to appear ‘normal’ within such a materialistic society”

22. The peak age of criminal activity is during the years 16-25. WHAT MAKES THEM COMMIT THEM?

23. Boys often have to ‘prove’ their masculinity which can, at times, result in criminal activity The likelihood of a young person belonging to a subculture is high, and some subcultures engage in criminal behavior Young people may have few legitimate means available of acquiring material goods Less responsibilities Teenage rebellion can lead to people breaking the law

24. negative impacts of crime upon an area

25. Depopulation, particularly in urban areas High levels of crime may damage community spirit and result in less neighborliness. High crime levels can contribute to environmental poverty Once a region with a high level of crime is labeling as a bad area, it might become a ghetto

26. Several causes of deviant behavior that you also need to be aware of

27. People may feel alienated from society. Deviant behavior may simply be the product of teenage rebellion In order to conform to the subculture of that group, people adopt the ways of the subculture.

28. STATISTICS

30. CRIME Pattern DETECTION

31. Questions investigators face Are there correlations between the crime type and the location of the incident? What are the distributions of crime types involving suspects of different ethnic origin? How can I quickly extract reports characterized by certain parameters of interest? For example: robberies performed by white teenagers involving the knife threat. Are there correlations between the type of crime, weapon employed, and the location of the incident? What is the most typical weapon in cases when high school students are charged with weapon possession?

32. Why crime pattern analysis? To implement a data analysis framework which works with the geospatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. To use semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy.

33. Steps involved in crime pattern analysis Determine geo-spatial plots of crime in a city Using proper clustering techniques to identify patterns Analyzing patterns and drawing conclusions

34. Step #1 DETERMINE GEO-SPATIAL PLOTS OF CRIME IN A CITY Collecting Information Police department records Electronic systems for crime reporting. (N.D.A) Narrative or description of the crime Modus Operandi Translate occurrences of crime into plots on a geographical map of a city

35. STEP #2 USING PROPER CLUSTERING TECHNIQUES TO IDENTIFY PATTERNS

36. CLUSTERING Crime terminology a cluster is a group of crimes in a geographical region or a hot spot of crime. Data mining terminology a cluster is group of similar data points (a possible crime pattern)

37. Cluster analysis or clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense.

38. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis, information retrieval, and bioinformatics.

39. Clustering Technique Task of identifying groups of records that are similar between themselves but different from the rest of the data and of finding the variables providing the best clustering Clusters will useful for identifying a crime spree committed by one or same group of suspects.

40. These clusters will then be presented to the detectives to drill down using their domain expertise. Automated detection of crime patterns, allows the detectives to focus on crime sprees first and solving one of these crimes results in solving the whole spree” groups of incidents suspected to be one spree, the complete evidence can be built from the different bits of information from each of the crime incidents.

41. Why Clustering? Crimes vary in nature widely Nature of crimes change over time Crime database often contains several unsolved crimes. Less predictive quality for solving future crimes

42. Why Clustering? In order to be able to detect newer and unknown patterns in future, clustering techniques work better. K-Means Clustering was used here.

43. K-Means Clustering The k-means algorithm assigns each point to the cluster whose centroid is nearest. The center is the average of all the points in the cluster Example: The data set has three dimensions and the cluster has two points: X = (x1,x2,x3) and Y = (y1,y2,y3). Then the centroid Z becomes Z = (z1,z2,z3) , where , and

44. K-Means Algorithm Choose the number of clusters, k. Randomly generate k clusters and determine the cluster centers, or directly generate k random points as cluster centers. Assign each point to the nearest cluster center, where "nearest" is defined with respect to one of the distance measures discussed above. Recompute the new cluster centers. Repeat the two previous steps until some convergence criterion is met (usually that the assignment hasn't changed).

45. STEP 2 STEP 1 STEP 3 STEP 4

46. WHY DATA MINING APPROACH? not be easy for a computer data analyst or detective to identify these patterns by simple querying 2. deal with enormous amounts of data and dealing with noisy or missing data about the crime incidents

47. STEPS INVOLVED IN CLUSTERING Sorting of records – first sort will be on the most important characteristic based on the detective’s experience.

48. 2. Use data mining to detect much more complex patterns since in real life there are many attributes or factors for crime and often there is partial information available about the crime. Identify the significant attributes for the clustering. Placing different weights on different attributes dynamically based on the crime types being clustered

49. 5. Cluster the dataset for crime patterns and then present the results to the detective or the domain expert along with the statistics of the important attributes. 6. The detective looks at the clusters, smallest clusters first and then gives the expert recommendations. unsolved crimes can be clustered based on the significant attributes and the result is given to detectives for inspection

50. STEP #3 ANALYZING PATTERNS AND DRAWING CONCLUSIONS

51. PATTERN RESULTS

53. ADVANTAGES OF Crime pattern DETECTION

54. Learn from historical crime patterns and enhance crime resolution rate. Preempt future incidents by putting in place preventive mechanisms based on observed patterns. Reduce the training time for officers assigned to a new location and having no prior knowledge of site-specific crime patterns. Increase operational efficiency by optimally redeploying limited resources (like personnel, equipment, etc.) to the right place at the right time.

55. LIMITATIONS OF CRIME PATTERN DETECTION

56. Crime pattern analysis can only help the detective, not replace them Data mining is sensitive to quality of input data that may be inaccurate, have missing information, be data entry error prone Mapping real data to data mining attributes is not always an easy task and often requires skilled data miner and crime data analyst with good domain knowledge