Presentation on Deep Learning for Public Safety using open data sets from the cities of San Francisco and Chicago. - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

1. DEEP LEARNING FOR PUBLIC SAFETY:
FIGHTING CRIME WITH OPEN CITY DATA
AlexTellez, Michal Malohlava, and H2O.ai team

2. OPEN CITIES
Many major cities
around the world
provide easily accessible
public data sets
with years of historical
data
Currently this data is
underused

3. CHICAGO

4. OPEN CRIME DATA
Crime Dataset: Crimes from 2001 - Present Day
~ 4.6 million crimes

5. THE WINDY CITY
Harvest Chicago Weather data since 2001

6. SOCIOECONOMIC FACTORS
Crimes segmented into Community Area IDs
Percent of households below poverty, unemployed, etc.

7. SPARK + H2O
Weather CrimesCensusWeatherWeather
Data munging
Spark SQL join
Deep
Learning
Evaluate models
GOAL:
For a given crime,
predict if an
arrest is
more / less
likely to be made!

8. LOAD DATA INTO H2O
Weather Data
5k rows
Census Data
78 rows
Crime Data
~4.5 Mn rows

9. JOIN DATASETS
crime
data
weather
data
census
data
Using Spark, we join 3 datasets together
to make one mega dataset!

10. CHICAGOVISUALIZATIONS
arrest rate season of
crime
temperature
during crime
community
crime is
committed in

11. ARREST RATE BYTYPES OF CRIME

12. ARREST RATE VS % OF TOTAL CRIMES
Arrest Rate
% of all crimes recorded
A large proportion of crimes are thefts
Unfortunately, there is a much lower
arrest rate for thefts than for less
prevalent crimes like gambling

13. SPLIT DATA INTOTEST/TRAIN SETS
training set arrest rate test set arrest rate
train model on this segment, 80% of data
validate the model on this segment (remaining 20%)
~40% of crimes lead to arrest

14. DEEP LEARNING
Problem:
For a given crime, is an arrest more / less likely?
Deep Learning:
A multi-layer feed-forward
neural network that starts
w/ an input layer
(crime + weather data)
followed by
multiple layers of
non-linear transformations

15. DEEP LEARNING MODEL
Deep Neural Network w/ 2 layers of non-linear transformations
Binomial prediction: Is an arrest
made?Yes/No
AUC onTraining Data ~ 0.91!
~ 3.5 Million Crimes

16. HOW’D WE DO?
Train AUC ~ 0.91 Test AUC ~ 0.91

17. GEO-MAPPED PREDICTIONS
Because each of the
crimes reported comes
with latitude-longitude
coordinates, we scored
our hold out data using
the trained model and
plotted the predictions
on a map of Chicago -
specifically, the
Downtown district.

18. SAN FRANCISCO

19. OPEN CITY, OPEN DATA
Crime Dataset: SFPD Incidents from 1/1/2003 - Present
~1 Million Crimes

20. WEATHER ANYONE?
Harvest weather data from 1/1/2003

21. DATA INGESTION
Weather Data: Temp,Visibility,
Precipitation, Cloud Cover
Crime Data: Category,
Description,
Weekend,Arrest, etc

22. SFVISUALIZATIONS
Most common crimes?
When is crime happening most?
…midnight, noon, 6 PM

23. DEEP LEARNING MODEL
Deep Neural Network w/ 3 layers of non-linear transformations
Total RunTime: 6 mins. 42 sec.
AUC ~ 0.95 onTraining Data

24. VALIDATIONTEST
Model ‘trained’ on 80% of data, validated against remaining 20%
AUC = 0.95 on
validation data

25. WHAT’S NEXT?
Can deploy each model in real-time to increase public safety
and help police departments.
Map of Model Accuracy - For each point on the map (place of crime)
we can have different colors based on model prediction (0.999 =
green, arrest likely vs. 0.67 = orange)
Run prediction for specific subsets of the data (i.e. most dangerous
area)
We plan on doing all of the above!
Ensemble - Model average by running prediction models for Chicago +
San Francisco which may increase accuracy more?