Self-Exciting Point Process Modeling of Crime

By George O. Mohler, Martin B. Short, P. Jeffrey Brantingham, Frederic Paik Schoenberg, George E. Tita. 2011

966 citations recorded by the source corpus.

Subfields: Applied Mathematics, Mathematical Physics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/32b246099484de62f623cf4f017395cbe76b1a21

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Citations in the corpus

Modeling financial contagion using mutually exciting jump processes (2015, positive): “…interactions to model the “viral propagation” of some phenomena, such as the viewing of YouTube videos (see Crane and Sornette (2008)), and to model the occurrence of certain types of crime, such as burglaries, which are empirically highly clustered in time and space (see Mohler et al. (2011).)”
An estimation procedure for the Hawkes process (2015, methodology): “hquake modeling in Ogata (1988), internet trac, e.g., youTube clicks in Crane and Sornette (2008), biology, e.g., genome analysis in Reynaud-Bouret and Schbath (2010), sociology, e.g., crime data in Mohler et al. (2011), or medicine, e.g., virus spreading in Kim (2011). A most active area of scienti”
Algorithmic realism: expanding the boundaries of algorithmic thought (2020, background): “From predictive policing [101] to healthcare [41] to fake news [152],”
The ethics of algorithms: Mapping the debate (2016, neutral): “(Mohler et al., 2011), and the tailoring of general tools for”
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (2015, methodology): “A major distinction between our model and existing methods based on Hawkes processes ( e.g. , [22, 23, 33, 34, 36]) is that we assume the process intensity λ t depends on another stochastic process p t , the post infectiousness.”
Self-exciting point process models of civilian deaths in Iraq (2011, neutral): “In one case, it has been found that self-exciting foreground processes account for ~ 13 per cent of burglaries, leaving ~ 87 per cent linked to stationary background processes ( Mohler et al , 2011 ).”
Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data (2014, methodology): “Similarly, Mohler et al. [25] applied the self-exciting point process model (previously developed for earthquake prediction) as a model of crime.”
Randomized Controlled Field Trials of Predictive Policing (2015, positive): “Whereas prospective hotspot maps only take into account short-term hotspot dynamics, the ETAS model estimates both long-term and short-term hotspots and systematically estimates the relative contribution to risk of each via expectation-maximization (EM)(Mohler et al. 2011; Mohler 2014).”
Learning to Detect Patterns of Crime (2013, background): “A simple way of dealing with a time window is to take the midpoint of it (e.g., [15]), which simpliﬁes the problem but may introduce bias.”
The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, background): “An example of a place-centric perspective is the detection, analysis, and interpretation of crime hotspots [16, 29, 53].”
Partially Generative Neural Networks for Gang Crime Classification with Partial Information (2018, background): “It is no surprise, therefore, that law enforcement agencies are interested in reducing crimes by leveraging crime prediction models (Mohler et al. 2011; Green, Horel, and Papachristos 2017) and intervention strategies optimized for deterrence (Loughran et al. 2011; Mohler et al. 2015).”
Exploring the Effects of COVID-19 Containment Policies on Crime: An Empirical Analysis of the Short-term Aftermath in Los Angeles (2020, background): “Additional caution should be made in interpreting the results obtained. As the extant criminological literature clearly demonstrates, crime is clustered in time and space, mean-ing that its geographical and temporal distribution is not random (Doran and Lees, 2005; Grubesic and Mack, 2008; Mohler et”
Is Gun Violence Contagious? A Spatiotemporal Test (2018, background): “In work on crime, Mohler and colleagues were the first to demonstrate the utility of these models to the problem of predicting spatiotemporal point processes (Mohler et al., 2011).”