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Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Crime Forecasting Using Data Mining Techniques.

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Presentation on theme: "Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Crime Forecasting Using Data Mining Techniques."— Presentation transcript:

1 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Crime Forecasting Using Data Mining Techniques Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Department of Computer Science, Department of Sociology University of Massachusetts Boston The 4th Workshop on Data Mining Case Studies and Practice Prize, Vancouver, Canada, December, 2011 Present by: Chung-Hsien Yu

2 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Architected a data structure which contains aggregated counts of crime-related events from original crime records. Harvested additional spatial and temporal features from the data. Employed an ensemble classification to perform the crime forecasting. Proposed the best forecasting approach to achieve the most stable outcomes Build a model that takes advantage of implicit and explicit spatial and temporal data to make reliable crime predictions. CONTRIBUTIONS 2

3 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Original Data 3 Residential Burglary 911 Calls Arrest Foreclosure Street Robbery

4 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Aggregated Data 4 3 1 1 1

5 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Aggregated Data 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 2 6 1 0 5 6 6 2 7 5 3 3 1 3 4 4 3 1 4 0 4 3 3 2 8 9 4 0 6 4 5 1 2 3 2 3 0 3 0 2 0 1 2 5 0 0 0 0 5

6 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Predicting Crime Hotspot: residential burglary count > 0 Heating-up: residential burglary increased 6

7 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Broken Windows Theory Focus on Offenders – Street Robbery – Motor Vehicle Larceny – Commercial Burglary Focus on Places – Foreclosure – Arrest – Residential Burglary 7

8 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Feature Selection VariablesTime serials Total records Average (Number/year) Total Attributes Commercial Robbery6 years, 2004-20094006726 Street Robbery6 years, 2004-200918,3213,05426 Residential Burglary6 years, 2004-200912,0202,00328 Commercial Burglary5 years, 2005-20094,43874075 Moto Vehicle Larceny4 years, 2006-200929,6857,42124 Arrest2004-Nov.2010254,30942,98259 911 Calls6 years, 2004-20092,527,162421,19436 Mayor's Hotline15 Mon., Oct.2008-200912,2399,79119 Construction Permit6 years, 2004-200930,7735,12932 Foreclosure6 years, 2004-200911,6711,94534 Commercial Robbery Person6 years, 2004-20091,0051688 Street Robbery Person6 years, 2004-200932,0645,3448 8

9 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding 3 5 0 0 2 6 0 3 3 1 0 0 0 0 4 8 6 2 4 3 2 3 1 2 3 0 0 0 0 t-Month-Based Approach 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 3 1 1 0 5 0 0 2 6 0 3 3 1 0 0 0 0 0 1 0 4 3 3 2 8 9 4 0 6 4 5 1 2 2 2 5 4 3 0 2 3 1 2 3 0 0 0 0 0 4 3 1 1 0 1 2 5 0 3 3 1 3 0 0 4 3 1 4 0 0 3 2 7 1 4 0 0 5 3 5 2 3 4 1 4 3 0 2 3 1 2 3 0 0 0 0 t months data predict (t+1) month data 9

10 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Experimenting different data mining techniques: 1NN, SVM, Decision Tree (J48), Neural Network, and Naïve Bayes. Different grid size: 24 x 20 (one-half mile square), 41 x 40 (one-quarter mile square). Hotspot vs. Heating-up. EXPERIMENTS 10

11 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding 1NN with/without constrained 11

12 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Compare Classification Methods 12

13 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Compare Classification Methods 13

14 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Voting Effect 14

15 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Grid Sizes 15

16 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding 1.Crime is strongly related to the location: 1NN with location constrained performs better. Naive Bayes: what has happened in a particular place in the past is likely to recur. Grid size matters because the larger grid cell exhibiting a broader spatial knowledge. 2.Predicting crime increase is harder but will be more useful. SUMMARY 16

17 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding How can we help the law enforcement? What are the obstacles? DEPLOYMENT 17

18 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Data Warehouse 18

19 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Visualized Reports 19

20 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Crime Forecasting 20

21 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding ACKNOWLEDGMENTS 21 Funded by the National Institute of Justice, 2009-DE-BX-K219. Funded by University of Massachusetts President's 2010 Science & Technology (S&T) Initiatives, 2011-2012

22 Crime Forecasting Using Data Mining Techniques: Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding Q & A THANK YOU!! 22

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