safest route prediction in urban areas final presentation
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© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
1
E6895 Advanced Big Data Analytics:
Safest Route Prediction in New York City
May 12, 2016
Team Members: Gabriel Thomas (gtm2122), Anubha
Bhargava (ab3955)
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Motivation
● Develop a useful, problem solving tool that displays the safest walking
route in a city
● The application will be designed for those unfamiliar with a city or
uncomfortable walking at night.
The application will…
● Display the safest route on a map interface
● Provide walking directions to the user
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Datasets, Software Languages and APIs Used
Datasets: NYPD Major Felony Incidents Crime Dataset, spotcrime.com
Software Languages and packages: Python, JavaScript, HTML, Spark
APIs: Yelp, imaplib, GoogleMaps, MapQuest
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Front-End Interface
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Visualization Map
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Algorithm
1. Get 24 hour shops from Yelp API
2. Load historical crime datasets and Spotcrime.com data
3. Get circle around the origin and destination
4. Get all the coordinates for 24 hour shops within this area
5. Use Gaussian Mixture Modelling to fit the crime locations
6. Use the mixed Gaussian Multivariate Distribution on the locations of the
24 hour shops to check safety
7. Use these locations as waypoints to plot the route in Google Maps and
provide walking directions
8. Plot the crime data on using a visualization plot on Google Maps
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Safest Walking Route Prediction
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Safest Walking Route Prediction
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Safest Walking Route Prediction
Google Maps Original Route:
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Safest Walking Route Prediction
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Safest Walking Route Prediction
Google Maps Original Route:
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Opportunities for Future Development
● Instead of only using 24 hour shops, using additional waypoints would
aid in determining safety.
○ A more precise technique would be determining the safety at the
coordinates of each leg.
● The current algorithm holds each crime at the same weight, but a
more robust algorithm would hold more dangerous crimes with more
weight.
● Each crime is currently modeled as a independent and identically
distributed Gaussian mixture model.
○ It would be useful to experiment with different mixture models.
© 2015 CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics – Final Project
Presentation
Questions?
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