Crime prediction-using-data-mining

1. Crime Prediction Using Data Mining
Alia ALHamwi
Joelle Shannakian
Khaled Ismaeel
Mohammad AlBash

2. Contents
• Introduction
• DataSet
• Data Processing
• Models Building
• Key Results

3. Introduction
• find spatial and temporal criminal
hotspots using a set of real-world
datasets of crimes.
• predict what type of crime might
occur next in a specific location
within a particular time.

4. Goal
• police forces & resources
• stay away
• improve living place choices

5. Data Sets
• Main Fields
• Crime Category
• DateTime
• Neighborhoods of the city (location)
• Is it Crime or accident ?

6. DENVER

7. Los Angeles

8. Data Processing
• Data Cleaning
• Data Reduction
– among the available 19 attributes in
Denver crimes dataset, we just selected four.
– crime vs accidents .
• Data Integration
– Crime_Type,Crime_Date, and Crime_Location .
– Crime_Date = Crime_Month, Crime_Day, and
Crime_Time .

9. Data Processing
• Data Transformation and Discretization

10. Models Building
• Apriori Algorithm
– It scans the dataset to collect all itemsets that
satisfy a predefined minimum support
(optimum choice  Denver 0.0012 & Los
Angeles 0.0018).
– List of all crime hotspots.
– Location and time features, without the crime
type feature.
– Restriction, three specific itemsets  Location,
Day, Time.

11. • Naïve Bayesian Classifier
– Supervised learning algorithm.
– Statistical model that predicts class membership
probabilities based on Bayes’ theorem.
– P (CrimeType|Location,Day,Month,Time)
– Crime features contain  month, day, time,
location.
– Class Label  Crime type.

12. • Decision Tree Classifier
– Supervised Learning algorithm.
– Learning simple decision rules implied from the
data features  predict class label.
– Best tree  entropy & Information gain.
– generated tree was complex  restriction  ten
maximum leaf nodes.

13. EVALUATION
• Apriori Algorithm
– frequent crime patterns
– )+) readiness , easiness ,implement
– (-) very long running time

14. EVALUATION
• Naïve Bayesian and decision tree
classifiers
– crime type prediction
– NBC: 51% Denver- 54% for LA
– DTC: 42% Denver- 43% for LA
– very complex tree
– tree that cannot generalize

16. Key Results
• Crime Frequent Hotspots
• Crime Prediction
• Crime Hotspots Demographics Analysis

17. Crime Frequent Hotspots
• Apriori algorithm
• finding spatial and temporal criminal
hotspots
• Denver : 62 patterns
• Los Angeles : 59 patterns.

18. Crime Prediction
• Bayesian classifier
– Month, Day of the week, Time, Location
– All features in nominal values

20. Crime Hotspots Demographics
Analysis
• Relationship between crime rate and
demographics
– large population and large number of housing
– vacant houses and the dangerous locations
– people’s age and gender
– More man  more dangerous
– More female  more safe
– ages from 20 to 29

23. References
• https://arxiv.org/ftp/arxiv/papers/1
508/1508.02050.pdf