580 research outputs found
Latent Self-Exciting Point Process Model for Spatial-Temporal Networks
We propose a latent self-exciting point process model that describes
geographically distributed interactions between pairs of entities. In contrast
to most existing approaches that assume fully observable interactions, here we
consider a scenario where certain interaction events lack information about
participants. Instead, this information needs to be inferred from the available
observations. We develop an efficient approximate algorithm based on
variational expectation-maximization to infer unknown participants in an event
given the location and the time of the event. We validate the model on
synthetic as well as real-world data, and obtain very promising results on the
identity-inference task. We also use our model to predict the timing and
participants of future events, and demonstrate that it compares favorably with
baseline approaches.Comment: 20 pages, 6 figures (v3); 11 pages, 6 figures (v2); previous version
appeared in the 9th Bayesian Modeling Applications Workshop, UAI'1
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
There is often latent network structure in spatial and temporal data and the
tools of network analysis can yield fascinating insights into such data. In
this paper, we develop a nonparametric method for network reconstruction from
spatiotemporal data sets using multivariate Hawkes processes. In contrast to
prior work on network reconstruction with point-process models, which has often
focused on exclusively temporal information, our approach uses both temporal
and spatial information and does not assume a specific parametric form of
network dynamics. This leads to an effective way of recovering an underlying
network. We illustrate our approach using both synthetic networks and networks
constructed from real-world data sets (a location-based social media network, a
narrative of crime events, and violent gang crimes). Our results demonstrate
that, in comparison to using only temporal data, our spatiotemporal approach
yields improved network reconstruction, providing a basis for meaningful
subsequent analysis --- such as community structure and motif analysis --- of
the reconstructed networks
Towards understanding crime dynamics in a heterogeneous environment:A mathematical approach
Crime data provides information on the nature and location of the crime but, in general, does not include information on the number of criminals operating in a region. By contrast, many approaches to crime reduction necessarily involve working with criminals or individuals at risk of engaging in criminal activity and so the dynamics of the criminal population is important. With this in mind, we develop a mechanistic, mathematical model which combines the number of crimes and number of criminals to create a dynamical system. Analysis of the model highlights a threshold for criminal efficiency, below which criminal numbers will settle to an equilibrium level that can be exploited to reduce crime through prevention. This efficiency measure arises from the initiation of new criminals in response to observation of criminal activity; other initiation routes - via opportunism or peer pressure - do not exhibit such thresholds although they do impact on the level of criminal activity observed. We used data from Cape Town, South Africa, to obtain parameter estimates and predicted that the number of criminals in the region is tending towards an equilibrium point but in a heterogeneous manner - a drop in the number of criminals from low crime neighbourhoods is being offset by an increase from high crime neighbourhoods
Crime Topic Modeling
The classification of crime into discrete categories entails a massive loss
of information. Crimes emerge out of a complex mix of behaviors and situations,
yet most of these details cannot be captured by singular crime type labels.
This information loss impacts our ability to not only understand the causes of
crime, but also how to develop optimal crime prevention strategies. We apply
machine learning methods to short narrative text descriptions accompanying
crime records with the goal of discovering ecologically more meaningful latent
crime classes. We term these latent classes "crime topics" in reference to
text-based topic modeling methods that produce them. We use topic distributions
to measure clustering among formally recognized crime types. Crime topics
replicate broad distinctions between violent and property crime, but also
reveal nuances linked to target characteristics, situational conditions and the
tools and methods of attack. Formal crime types are not discrete in topic
space. Rather, crime types are distributed across a range of crime topics.
Similarly, individual crime topics are distributed across a range of formal
crime types. Key ecological groups include identity theft, shoplifting,
burglary and theft, car crimes and vandalism, criminal threats and confidence
crimes, and violent crimes. Though not a replacement for formal legal crime
classifications, crime topics provide a unique window into the heterogeneous
causal processes underlying crime.Comment: 47 pages, 4 tables, 7 figure
Routine Crime in Exceptional Times: The Impact of the 2002 Winter Olympics on Citizen Demand for Police Services
Despite their rich theoretical and practical importance, criminologists have paid scant attention to the patterns of crime and the responses to crime during exceptional events. Throughout the world large-scale political, social, economic, cultural, and sporting events have become commonplace. Natural disasters such as blackouts, hurricanes, tornadoes, and tsunamis present similar opportunities. Such events often tax the capacities of jurisdictions to provide safety and security in response to the exceptional event, as well as to meet the “routine” public safety needs. This article examines “routine” crime as measured by calls for police service, official crime reports, and police arrests in Salt Lake City before, during, and after the 2002 Olympic Games. The analyses suggest that while a rather benign demographic among attendees and the presence of large numbers of social control agents might have been expected to decrease calls for police service for minor crime, it actually increased in Salt Lake during this period. The implications of these findings are considered for theories of routine activities, as well as systems capacity
Semi-Supervised First-Person Activity Recognition in Body-Worn Video
Body-worn cameras are now commonly used for logging daily life, sports, and
law enforcement activities, creating a large volume of archived footage. This
paper studies the problem of classifying frames of footage according to the
activity of the camera-wearer with an emphasis on application to real-world
police body-worn video. Real-world datasets pose a different set of challenges
from existing egocentric vision datasets: the amount of footage of different
activities is unbalanced, the data contains personally identifiable
information, and in practice it is difficult to provide substantial training
footage for a supervised approach. We address these challenges by extracting
features based exclusively on motion information then segmenting the video
footage using a semi-supervised classification algorithm. On publicly available
datasets, our method achieves results comparable to, if not better than,
supervised and/or deep learning methods using a fraction of the training data.
It also shows promising results on real-world police body-worn video
Early Identification of Violent Criminal Gang Members
Gang violence is a major problem in the United States accounting for a large
fraction of homicides and other violent crime. In this paper, we study the
problem of early identification of violent gang members. Our approach relies on
modified centrality measures that take into account additional data of the
individuals in the social network of co-arrestees which together with other
arrest metadata provide a rich set of features for a classification algorithm.
We show our approach obtains high precision and recall (0.89 and 0.78
respectively) in the case where the entire network is known and out-performs
current approaches used by law-enforcement to the problem in the case where the
network is discovered overtime by virtue of new arrests - mimicking real-world
law-enforcement operations. Operational issues are also discussed as we are
preparing to leverage this method in an operational environment.Comment: SIGKDD 201
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