Ben Carleton: Historical Crime Reports Analysis
The goal of this project is to help smaller law enforcement agencies leverage their existing databases of historical crime reports to visualize how trends in crime change in their areas of responsibility. The application analyzes the crime data using a spatial autocorrelation algorithm which identifies hotspots in the dataset given a range of dates and a specific type of crime type. The application imports data from the existing source of record, eliminating the need to manually move data between systems. The application could also be configured to connect directly to the target database, though this was not done due to privacy and confidentiality considerations. The user interface requires minimal training to use, and it is our hope that the project will be useful across law enforcement agencies, from executive managers who make budgetary and staffing decisions to individual officers patrolling on the street.
Bio:
Ben Carleton is a senior at the George Washington University, majoring in Computer Science with a minor in History. He works on the systems engineering team at the Office of Technology Services within GW’s Columbian College of Arts and Sciences, where he manages Windows and Linux infrastructure and endpoints as well as SaaS applications utilized by the College and across the University.
Documentation
- System Requirements
- CentOS, Fedora, or other Red Hat derivative
- PostgresSQL with PostGIS extensions installed
- Python 3.x installed in addition to system default (2.7)
- Django 1.7, NumPy, SciPy, PySAL, and other libraries (see requirements.txt)
- Homework
- Final Presentation Slides
Presentation Screencast