NYC subway crime
Zheyan Liu, Chaoqi Wu, Baode Gao, Youyuan Kong, Zexu Yuan
Columbia University, Mailman School of Public Health
Motivation
New York subway, one of the main public transportations for New Yorkers, provides super convenience for local citizens, at the same time, brings potential danger to passengers, where criminals are attracted to busier subway stations for certain kinds of crime like pick pocketing, grand larceny and assault. The cloest train compartment can trigger cime and make victims harder to run.
11/21/2021, around 12:00 AM, at 34th Street-Penn Station in Manhattan, Alkeem Loney, a 32-year-old male, was stabbed in the neck during an unprovoked attack and was pronounced dead later as NYPD stated. The deadly incident is the latest in a pate of violence underground that comes as the MTA tries to get commuters back on mass transit. The horrible crime event raised lots of public concern about the safety at subway stations, the safety tightly related to almost every citizen who are living, working and studying in New York City.
As students who are living here in New York City, most of us will almost take subway to the campus in early morning and back to apartment in nights on weekdays, and hang out with friends on weekends. Keeping away from danger at subway stations is closely related to ourselves. We hope we are able to help citizens to find the comparatively safe and reliable routes when taking subways.
Data
Subway Crime
The orginal subway crime data has two parts.The first one contains all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department— NYPD. The second one includes similar crimes. We join these two data frames and only analyze crimes which happen in subway, NYC.
Subway Passenger
The orginal Subway passenger data is from MTA(Metropolitan Transportation Authority). The orginal data contains total entries and exits in each station in every 4 hours from 2010 to now.
What you can find in this website
- Exploratory Data Analysis of subway crime with time (year, month, hour)
- Exploratory Data Analysis of subway crime with location (coordiante-based Kmeans cluster, subway line, station)
- Analysis of passengers flow of subway and its relationship with the location; How we use coordinates-based Kmeans on this analysis.
- An interactive Shiny Dashboard about subway crime, people can look up crime rate in each location, distribution of each crime type.
- A shiny app for Subway passenger flow animation and info lookup on each line
- A GNN model for predicting existence and severity of subway crime
- No-crime Navigation shiny app based on Google Maps Api (give subway route), GNN model (predict crime existence) and historical passenger data (give crowdness of a route).
Team members
Zheyan Liu
MS in Biostatistics at Columbia University
Email: zheyan.liu@columbia.edu
Contributions
- 1 Cleaned and imputed subway passenger data; Built project website and orgnized final report
- 2 Conduct EDA on subway passenger data&location; Feature engineering on coordinates with K-means
- 3 Built subway naviagtion app based on Google Maps Apis and adapted GNN into crime prediction on each route
Chaoqi Wu
MS in Biostatistics at Columbia University
Email: cw3370@cumc.columbia.edu
Contributions
- 1 Cleaned the subway passenger data, clarify the relation between station and lines.
- 2 Conduct EDA on subway passenger data, analyze the relationship among passenger, line and time
- 3 Built subway passenger app providing search function based on line and time.
Baode Gao
MS in Biostatistics at Columbia University
Email: bg2715@cumc.columbia.edu
Contributions
- 1 Cleaned, grouped and transformed data to the form GNN accepted;
- 2 Implemented graph auto encoder to our data, evaluated it and adapted it into crime prediction on each route;
- 3 Contributed in model report in web.
Youyuan Kong
MS in Biostatistics at Columbia University
Email: yk2960@columbia.edu
Contributions
- 1 Clean and imputed crime by location data;create a crime rate shiny dashboard, and participate in writing report
- 2 Conduct EDA on subway passenger data&location
Zexu Yuan
MPH in Biostatistics at Columbia University
Email: zy2392@columbia.edu
Contributions
- 1 Conducted exploratory data analysis regarding the relationship between occurrence of crime events and time
- 2 Contributed to establishment of shiny dashboard with regard to crime events map