G
George Mohler
Researcher at Indiana University – Purdue University Indianapolis
Publications - 93
Citations - 3658
George Mohler is an academic researcher from Indiana University – Purdue University Indianapolis. The author has contributed to research in topics: Computer science & Predictive policing. The author has an hindex of 22, co-authored 83 publications receiving 2760 citations. Previous affiliations of George Mohler include Santa Clara University & University of California, Los Angeles.
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Self-Exciting Point Process Modeling of Crime
TL;DR: The implementation of self-exciting point process models in the context of urban crime is illustrated using residential burglary data provided by the Los Angeles Police Department to gain insight into the form of the space–time triggering function and temporal trends in the background rate of burglary.
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The challenges of modeling and forecasting the spread of COVID-19.
TL;DR: In this article, three regional-scale models for forecasting and assessing the course of the coronavirus disease 2019 (COVID-19) pandemic are presented. But, the authors focus on early-time data and provide an accessible framework for generating policy-relevant insights into its course.
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Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis.
George Mohler,Andrea L. Bertozzi,Jeremy G. Carter,Martin B. Short,Daniel Sledge,George E. Tita,Craig D. Uchida,P. Jeffrey Brantingham +7 more
TL;DR: Examination of the most recently available data from both Los Angeles, CA, and Indianapolis, IN, shows that social distancing has had a statistically significant impact on a few specific crime types, however, the overall effect is notably less than might be expected given the scale of the disruption to social and economic life.
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Randomized Controlled Field Trials of Predictive Policing
George Mohler,Martin B. Short,Sean Malinowski,Mark E. Johnson,George E. Tita,Andrea L. Bertozzi,P. J. Brantingham +6 more
TL;DR: In this article, the authors report results of two randomized controlled trials of near real-time epidemic-type aftershock sequence (ETAS) crime forecasting, one trial within three divisions of the Los Angeles Police Department and the other trial within two divisions of Kent Police Department (United Kingdom).
RESEARCH ARTICLE A Nonparametric EM algorithm for Multiscale Hawkes Processes
Erik Lewis,George Mohler +1 more
TL;DR: Maximum penalized likelihood estimation is proposed as a method for simultaneously estimating the background rate and the triggering density of Hawkes process intensities that vary over multiple time scales and used to examine self-excitation in Iraq IED event patterns.