Authors: Sohil Shah, Melanie Tosik, Jason Chang, Yang, Jae Hyun
This project gives an overview of crime time analysis in New York City . We have created Python Jupyter notebooks for spatial analysis of different crime types in the city using Pandas, Numpy, Plotly and Leaflet packages. As a second part to this analysis, we worked on ARIMA model on R for predicting the crime counts across various localities in the city based on correlations of various demographics correlation in each locality.
virtualenv -p python3 env/
source env/bin/activate
pip install -r requirements.txt
There are two Jupyter notebooks in the data_exploration/ folder, crime_analyses.ipynb and crime_data_prep.ipynb.
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M. A. Tayebi, M. Ester, U. Glässer and P. L. Brantingham, "CRIMETRACER: Activity space based crime location prediction," 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), Beijing, 2014, pp. 472-480.
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M. A. Tayebi, U. Gla¨sser and P. L. Brantingham, "Learning where to inspect: Location learning for crime prediction," 2015 IEEE International Conference on Intelligence and Security Informatics (ISI), Baltimore, MD, 2015, pp. 25-30.
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Seth R. Flaxman, "A General Approach to Prediction and Forecasting Crime Rates with Gaussian Processes", 2014.
A preview of the project is available on GitHub