
Michael D. PorterUniversity of Virginia | UVa · Department of Systems and Information Engineering
Michael D. Porter
PhD
About
23
Publications
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424
Citations
Introduction
Additional affiliations
August 2018 - present
August 2018 - present
July 2013 - August 2018
Education
August 2003 - July 2006
January 2001 - July 2003
August 1994 - May 1998
Publications
Publications (23)
A model based on the cluster process representation of the self‐exciting process model is derived to allow for variation in the excitation effects for terrorist events in a self‐exciting or cluster process model. The model's derivation and implementation details are given and applied to data from the Global Terrorism Database (National Consortium f...
Background: The 2021 NIJ recidivism forecasting challenge asks participants to construct predictive models of recidivism while balancing false positive rates across groups of Black and white individuals through a multiplicative fairness score. We investigate the performance of several models for forecasting 1-year recidivism and optimizing the NIJ...
Background
Crime, traffic accidents, terrorist attacks, and other space-time random events are unevenly distributed in space and time. In the case of crime, hotspot and other proactive policing programs aim to focus limited resources at the highest risk crime and social harm hotspots in a city. A crucial step in the implementation of these strategi...
Bayesian model-based clustering is a widely applied procedure for discovering groups of related observations in a dataset. These approaches use Bayesian mixture models, estimated with MCMC, which provide posterior samples of the model parameters and clustering partition. While inference on model parameters is well established, inference on the clus...
Crime, traffic accidents, terrorist attacks, and other space-time random events are unevenly distributed in space and time. In the case of crime, predictive policing algorithms aim to focus limited resources at the highest risk crime hotspots in a city. A crucial step in the implementation of these strategies is the construction of scoring models u...
Bayesian model-based clustering is a widely applied procedure for discovering groups of related observations in a dataset. These approaches use Bayesian mixture models, estimated with MCMC, which provide posterior samples of the model parameters and clustering partition. While inference on model parameters is well established, inference on the clus...
Crime forecasts are sensitive to the spatial discretizations on which they are defined. Furthermore, while the Predictive Accuracy Index (PAI) is a common evaluation metric for crime forecasts, most crime forecasting methods are optimized using maximum likelihood or other smooth optimization techniques. Here we present a novel methodology that join...
Variation in rates of terrorist activity over time is explained via contagion or diffusion. Models for social contagion and diffusion are shown to be cases of the cluster process representation of the Hawkes self-exciting process model. Contagion and diffusion models exploring variations in endogenous and exogenous effects are fitted to data from t...
Across the nation, researchers and transportation engineers are developing safety performance functions (SPFs) to predict crash rates and develop crash modification factors to improve traffic safety at roadway segments and intersections. Generalized linear models (GLMs), such as Poisson or negative binomial regression, are most commonly used to dev...
The object of this paper is to develop a statistical approach to criminal linkage analysis that discovers and groups crime events that share a common offender and prioritizes suspects for further investigation. Bayes factors are used to describe the strength of evidence that two crimes are linked. Using concepts from agglomerative hierarchical clus...
http://cran.r-project.org/web/packages/crimelinkage/
Statistical clustering of criminal events can be used by crime analysts to create lists of potential suspects for an unsolved crime, to identify groups of crimes that may have been committed by the same individuals or group of individuals, for offender profiling and for predicting future events. We propose a Bayesian model-based clustering approach...
PurposeBehavioural crime linkage is underpinned by two assumptions: (a) that offenders exhibit some degree of consistency in the way they commit offences (their modus operandi [MO]); and, (b) that offenders can be differentiated on the basis of their offence behaviour. The majority of existing studies sample at most three crimes from an offender's...
The use of graphical processing unit (GPU) parallel processing is becoming a part of mainstream statistical practice. The reliance of Bayesian statistics on Markov Chain Monte Carlo (MCMC) methods makes the applicability of parallel processing not immediately obvious. It is illustrated that there are substantial gains in improved computational time...
Discussion of "Estimating the historical and future probabilities of large
terrorist events" by Aaron Clauset and Ryan Woodard [arXiv:1209.0089].
One aspect of tactical crime or terrorism analysis is predicting the location of the next event in a series. The objective of this article is to present a methodology to identify the optimal parameters and to test the performance of temporally weighted kernel density estimation models for predicting the next event in a criminal or terrorist event s...
Objective
This article explores patterns of terrorist activity over the period from 2000 through 2010 across three target countries: Indonesia, the Philippines and Thailand.
Methods
We use self-exciting point process models to create interpretable and replicable metrics for three key terrorism concepts: risk, resilience and volatility, as defined...
A predictive model of terrorist activity is developed by examining the daily
number of terrorist attacks in Indonesia from 1994 through 2007. The dynamic
model employs a shot noise process to explain the self-exciting nature of the
terrorist activities. This estimates the probability of future attacks as a
function of the times since the past attac...
Early detection of disease outbreaks is of paramount importance to
implementing intervention strategies to mitigate the severity and duration of
the outbreak. We build methodology that utilizes the characteristic profile of
disease outbreaks to reduce the time to detection and false positive rate. We
model daily counts through a Poisson distributio...
In order to assess the effectiveness of counter-terrorism interventions, terrorism must First be quantitatively measured and
appropriate statistical tests developed. By combining aspects of both the frequency and impact of terrorist attacks, we describe
how a marked point process framework can establish a comprehensive measure of terrorism. In addi...
We present a technique to represent the structure of large social networks through ego-centered network neighborhoods. This provides a local view of the network, focusing on the vertices and their kth order neighborhoods allowing discovery of interesting patterns and features of the network that would be hidden in a global network analysis. We pres...
A method is presented for detecting changes to the distribution of a criminal or terrorist point process between two time periods using a non-model-based approach. By treating the criminal/terrorist point process as an intelligent site selection problem, changes to the process can signify changes in the behavior or activity level of the criminals/t...