NYC crime time analysis
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R
data_exploration
indicator_correlations
time_series_analysis
.gitignore
README.md
report.pdf
requirements.txt
whodidwhat.md

README.md

NYC crime time analysis

Data

  1. NYPD Complaint Data Historic

  2. CoreData.nyc

Run

Python scripts

virtualenv -p python3 env/
source env/bin/activate
pip install -r requirements.txt

Jupyter notebooks

There are two Jupyter notebooks in the data_exploration/ folder, crime_analyses.ipynb and crime_data_prep.ipynb.

Related work

  • S. H. Huddleston and D. E. Brown, "A Statistical Threat Assessment," in IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 39, no. 6, pp. 1307-1315, Nov. 2009.

  • 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.

  • E. Cesario, C. Catlett and D. Talia, "Forecasting Crimes Using Autoregressive Models," 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), Auckland, 2016, pp. 795-802.

  • 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.

  • Al Boni, Mohammad & Gerber, Matthew. (2016). Area-Specific Crime Prediction Models. 671-676. 10.1109/ICMLA.2016.0118.

  • Seth R. Flaxman, "A General Approach to Prediction and Forecasting Crime Rates with Gaussian Processes", 2014.

Project repo

A preview of the project is available on GitHub