2. Data Sources and Pipeline
NYC Open Data
Major Crimes
Dataset
2005-2016
(~1,000,000)
3. Algorithms
● Dijkstra Algorithm: between Start and End, find
the shortest path minimizing sum of weigths
● K-nearest neighours classification to associate
each crime with a road
d1
d4
d5
d2
d9
d3 d6
d8
d10
d12
d7
d11
Start
End
4. Cost Model for Road Lengths
Personal fear of crime Crime Costs depending
on hour and type of
crime
● De Sota Road, NY at 0300
5. App Landing Page
● Personal Fear of Crime: Rational, Almost rational, Borderline
irrational, Irrational, Paranoid
6. Brooklyn Safe Route at 0100
● Crime Avoidance Level : Neutral, Personal Fear of Crime: Rational
7. Cost Model Validation (3rd
Party)
● Courtesy of Trulia
https://www.trulia.com/local/new-york-ny/tiles:1|
points:0_crime
8. Personal Fear of Crime: Rational
● Crime Avoidance Level: Vigilant
9. Personal Fear of Crime: Paranoid
● Crime Avoidance Level: Vigilant
12. Personal Fear of Crime
● Personal fear of crime affects each route slightly differently but there exists some
saturation point
13. Safety Rating
● approximates probability of experiencing no
crime on route
● Probablity of crime on road i.
● Assume crimes on each road are
independent