Gamers as an extremist ideology
I play video games, but I would never identify myself as a gamer. Even worse, I argue that enough of those who do are turning the identity and adjacent ideology into a dangerous kind of extremism. I spoke with Matt Galloway, host of CBC’s Metro Morning about the subject.
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.
The United States Senate Committee on Commerce, Science, and Transportation’s inquiry sought answers to four basic questions:
Based on review of the company responses and other publicly available information, this Committee Majority staff report finds:
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
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
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.