Class time: TR 1345-1515
Location: MCNB 410
Name: Greg Ridgeway
Email: gridge@upenn.edu
Office: MCNB 560
Time: M 1300-1400
Location: MCNB 549
ZOOM Credentials: Email for credentials
This course covers the tools and techniques necessary to acquire, organize, and visualize complex data in order to answer questions about crime and the criminal justice system. The course is organized around key questions about police shootings, victimization rates, benchmarking justice system performance, identifying crime hotspots, calculating the cost of crime, and finding out what happens to crime when it rains. On the way to answer these questions, the course will cover topics including data sources, basic programming techniques, SQL, text mining, regular expressions, and geocoding. The course will use R, an open-source, object oriented scripting language with a large set of available add-on packages.
The design of the course is to learn about the data sources and tools on the way toward answering questions related to crime and criminal justice. While the main goal is to equip you with the knowledge of data sources and a variety of computational tools, we will work with each data source and tool because we need them in order to answer a key question of interest. Below lists some of the questions, data sources, and tools I intend to cover during the semester.
Where do crimes occur and where are there hotspots?
How many crimes do the police record and can I expect to get my stolen mobile phone back?
How much crime do victims report?
How much does crime cost a community?
What happens to crime when a summer blockbuster movie opens?
What happens to crime when it rains?
When the police shoot someone, do they transport them to the nearest hospital?
Which communities are impacted by gang injunctions?
Bureau of Justice Statistics
NCVS
LEMAS
NIS
FBI
LEOKA
NIBRS
Police data
Chicago crime reports
PPD officer-involved shootings
Web scraping
Movie data
Weather data
Census data
ACS
TIGER
R
Object-oriented programming
Regular expressions
Structured Query Language (SQL)
Survey weights
Working with geographic data