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 Data Retrieval 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
Database

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)
Period
labor force
employment
unemployment
unemployment rate
2011 01
Jan
130927
11.5 02
Feb
126619
11.1 03
Mar
122085
10.6 04
Apr
119310
10.4 05
May
121838
Incident:
Complaint_No
Year
Month
Block_No
Direction
Street_Name
Street_Type
Suffix
City
State
Zip
2011 01
4425
EDDLEMAN RD
CHARLOTTE NC
28208
2228
BEATTIES FORD
28216
2300 N
TRYON ST
4027
QUAIL GLENN CT K
28226

7. Weather + CMPD using Date
MeanDewPoint F
MinDewPoint F
MaxHumidity
MeanHumidity
MinHUmidity
01/01/2011 51 37
100 89 78
02/01/2011
05/02/2011 50
06/02/2011
07/02/2011 35
Incident:
Complaint_No
Incident_From_Date
Block_No
Direction
Street_Name
Street_Type
Suffix
City
State
Zip
01/01/2011
4425
EDDLEMAN RD
CHARLOTTE NC
28208
2228
BEATTIES FORD
28216
2300 N
TRYON ST
4027
QUAIL GLENN CT K
28226

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 Police Location_Type : General location type - Indoors, Outdoors, Parking Lot, Parking Deck, Other Temp_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
Time Frame
00: :59
Midnight
04: :59
Early Morning
08: :59
Morning
12: :59
Afternoon
16: :59
Evening
20: :59
Night
Temperature_Range
0 - 30

19. Performance Analysis :

21. Predictive Modeling (2)
Method : Neural Network - Multilayer perceptron
Tool : SPSS
Target : CLearance TimeFrame
Important Predictors : Year Month Incident_Hour Numeber_Of_Tweets
Incidents MeanTemperature F WeekDay MeanSealevelPressure
Day MeanVisibilityMiles

23. Evaluation: “Day” level

24. Predictive Modeling (3)
Method : Neural Network - Multilayer perceptron
Tool : SPSS
Target : Number of Incidents
Important Predictors : Year Month Incident_Hour Numeber_Of_Tweets
WeekDay MeanTemperature F Unemployement ClearanceTimeFrame
Day MeanVisibilityMiles MeanWindSpeedMPH Employment

26. Evaluation: “Month” level

28. Evaluation

29. 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

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

31. Credits:
Mansi Dubey
Madlen Ivanova
Preneesh Jayaraj