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Predictive Crime Analytics

Analyzing CMPD crime data using predictive models to predict case closure time, number of crimes, and case status.

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Predictive Crime Analytics

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  1. Predictive Crime Analytics

  2. Project Objective The project aims to analyze the crime data provided by CMPD and design predictive models implementing classification and neural networks to predict: • In how many days a case can be closed • Number of crimes to occur • Case status of an Incident Our goal is to build predictive models based on different inputs, evaluate them and choose the best one.

  3. CMPD DataRetrieval • Time period- 2011-2016 • 42 tables (6 years X 7 tables) • Linked by Complain_No • Imported into an MS SQL Server

  4. Data Enrichment Database CMPD Data Weather Unemployment Twitter Special Events

  5. Joining Data Charlotte Zipcode table was used to filter out all the records other than Charlotte Area Different tables within CMPD database are joined using Unique Complaint_No For Joining other Data Sources we used • Twitter + CMPD using Date • Special_Events + CMPD using Date

  6. Unemployment + CMPD using (Year-Month) Unemployment: Incident:

  7. Weather + CMPD using Date Weather Incident:

  8. Data Processing • The data format was specified • Outliers were replaced with the mean value of the field. Outlier cut off value was set to 3 standard deviations. • Dates & Times cannot be used directly by most algorithms, so we estimated the duration period. • All the missing data entries were replaced with: • Continuous fields: replace with mean • Nominal fields: replace with mode • Ordinal fields: replace with median

  9. Missing Values Treatment • The features with > 50% missing values were excluded. • The rows with > 50% missing values were excluded. • The fields with too many unique categories were excluded (> 100 categories). • The categorical fields with >90% values in a single category were excluded. • Sparse categories were merged to maximize association with target. • Input fields that have only one category after supervised merging are excluded.

  10. Analysis Type of incident distribution

  11. The trend of the number of Incidents over the Incident hour and the case status

  12. Number of Incidents compared by year and day of the week

  13. Day of week over vehicle theft

  14. Analysis of the vehicle body type that have been stolen most frequently per zip code

  15. Feature engineering Feature engineering is fundamental to the application of machine learning. In order to improve our initial results we used Microsoft SQL Server Management Studio (SSMS) to create the following features: • We created Day of week and time of day features • Time period for a case to be closed was calculated from the reported and clearance date

  16. Predictive Modeling (1) Classification : Decision Tree Tool : SPSS Target : Case_Status Tree Depth : 5 Input : WeekDay : feature extracted from Incident_From_Date (MM/DD/YYYY HHMM).Month : feature extracted from Incident_From_Date (MM/DD/YYYY HHMM).TimeFrame : feature extracted from Incident_From_Date (MM/DD/YYYY HHMM).Place1: General place type (e.g., Residential, Retail, Open Area, etc.)Reporting_Agency : Agency that took the report - Airport Police, Charlotte Mecklenburg PoliceLocation_Type : General location type - Indoors, Outdoors, Parking Lot, Parking Deck, OtherTemp_Range : Discretized feature from the mean temperature Events& Unemployment_Rate : Taken from augmented Weather and Unemployment dataset

  17. Incident_From_Date ( MM/DD/YYYY HHMM) → Discretization → Time Frame Mean Temperature F → Interval Scaling → Temperature_Range

  18. Performance Analysis :

  19. Predictive Modeling (2) Method : Neural Network - Multilayer perceptron Tool : SPSS Target : CLearance TimeFrame Important Predictors : Year MonthIncident_Hour Numeber_Of_Tweets IncidentsMeanTemperature F WeekDay MeanSealevelPressure Day MeanVisibilityMiles

  20. Evaluation: “Day” level

  21. Predictive Modeling (3) Method : Neural Network - Multilayer perceptron Tool : SPSS Target : Number of Incidents Important Predictors : Year MonthIncident_Hour Numeber_Of_Tweets WeekDayMeanTemperature F Unemployement ClearanceTimeFrame Day MeanVisibilityMiles MeanWindSpeedMPH Employment

  22. Evaluation: “Month” level

  23. Evaluation

  24. Accuracy: • Sample size • Ratio between the sample size and the number of features used • The relationship between features • Initial weights and biases • Target variable • Ratio of training set: test set : validation set

  25. Future Work • Improve the individual Model Performance • Add more datasets • —Application of Deep-Learning methods • Implement Unsupervised Learning

  26. Credits: Mansi Dubey Madlen Ivanova Preneesh Jayaraj

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