Melissa Tobin and I discuss the prediction of crime using twitter data and the role this data could play in police planning, public policy, and public service delivery.
Category Archives: Analysis
A Review of the Data Broker Industry: Collection, Use, and Sale of Consumer Data for Marketing Purposes
The United States Senate Committee on Commerce, Science, and Transportation’s inquiry sought answers to four basic questions:
- What data about consumers does the data broker industry collect?
- How specific is this data?
- How does the data broker industry obtain consumer data?
- Who buys this data and how is it used?
Based on review of the company responses and other publicly available information, this Committee Majority staff report finds:
- Data brokers collect a huge volume of detailed information on hundreds of millions of consumers.
- Data brokers sell products that identify financially vulnerable consumers.
- Data broker products provide information about consumer offline behavior to tailor online outreach by marketers.
- Data brokers operate behind a veil of secrecy.
California Department of Motor Vehicles to regulate self-driving vehicles by 2015
My discussion with Stephen Quinn on CBC Radio Vancouver’s Early Edition regarding efforts by the California Department of Motor Vehicles to regulate self-driving vehicles by 2015.
California Preparing for Self-Driving Cars by 2015
California Preparing for Self-Driving Cars by 2015
Self-driving cars sound like fantasy to many, but regulators are laying the groundwork for the technology to hit the roads next year.
Predicting crime using Twitter and kernel density estimation
Predicting crime using Twitter and kernel density estimation
Research by Matthew S. Gerber:
Abstract
Twitter is used extensively in the United States as well as globally, creating many opportunities to augment decision support systems with Twitter-driven predictive analytics. Twitter is an ideal data source for decision support: its users, who number in the millions, publicly discuss events, emotions, and innumerable other topics; its content is authored and distributed in real time at no charge; and individual messages (also known as tweets) are often tagged with precise spatial and temporal coordinates. This article presents research investigating the use of spatiotemporally tagged tweets for crime prediction. We use Twitter-specific linguistic analysis and statistical topic modeling to automatically identify discussion topics across a major city in the United States. We then incorporate these topics into a crime prediction model and show that, for 19 of the 25 crime types we studied, the addition of Twitter data improves crime prediction performance versus a standard approach based on kernel density estimation. We identify a number of performance bottlenecks that could impact the use of Twitter in an actual decision support system. We also point out important areas of future work for this research, including deeper semantic analysis of message content, temporal modeling, and incorporation of auxiliary data sources. This research has implications specifically for criminal justice decision makers in charge of resource allocation for crime prevention. More generally, this research has implications for decision makers concerned with geographic spaces occupied by Twitter-using individuals.
NSA TURBINE
Discussing the NSA TURBINE initiative with Rick Cluff on CBC Radio Vancouver’s Early Edition