Application of Spatiotemporal Fuzzy C-Means Clustering for Crime Spot Detection

The various sources generate large volume of spatiotemporal data of different types including crime events. In order to detect crime spot and predict future events, their analysis is important. Crime events are spatiotemporal in nature; therefore a distance function is defined for spatiotemporal events and is used in Fuzzy C-Means algorithm for crime analysis. This distance function takes care of both spatial and temporal components of spatiotemporal data. We adopt sum of squared error (SSE) approach and Dunn index to measure the quality of clusters. We also perform the experimentation on real world crime data to identify spatiotemporal crime clusters.

Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation—An Application in Crime Analysis

Cluster-based hot spot detection is applied in many disciplines to analyze the locations, concentrations, and evolution over time for a phenomenon occurring in an area of study. The hot spots consist of areas within which the phenomenon is most present; by detecting and monitoring the presence of hot spots in different time steps, it is possible to study their evolution over time. One of the most prominent problems in hot spot analysis occurs when measuring the intensity of a phenomenon in terms of the presence and impact on an area of study and evaluating its evolution over time. In this research, we propose a hot spot analysis method based on a fuzzy cluster hot spot detection algorithm, which allows us to measure the incidence of hot spots in the area of study. We analyze its variation over time, and in order to evaluate its reliability we use a well-known fuzzy entropy measure that was recently applied to measure the reliability of hot spots by executing fuzzy clustering algorithms. We apply this method in crime analysis of the urban area of the City of London, using a dataset of criminal events that have occurred since 2011, published by the City of London Police. The obtained results show a decrease in the frequency of all types of criminal events over the entire area of study in recent years

Model for Spatiotemporal Crime Prediction with Improved Deep Learning

Crime is hard to anticipate since it occurs at random and can occur anywhere at any moment, making it a difficult issue for any society to address. By analyzing and comparing eight known prediction models: Naive Bayes, Stacking, Random Forest, Lazy:IBK, Bagging, Support Vector Machine, Convolutional Neural Network, and Locally Weighted Learning – this study proposed an improved deep learning crime prediction model using convolutional neural networks and the xgboost algorithm to predict crime. The major goal of this research is to provide an improved crime prediction model based on previous criminal records. Using the Boston crime dataset, where our larceny crime dataset was extracted, exploratory data analysis (EDA) is used to uncover patterns and explain trends in crimes. The performance of the proposed model on the basis of accuracy, recall, and f-measure was 100% outperforming the other models used in this study. The analysis of the proposed model and prediction can aid security services in making better use of their resources, anticipating crime at a certain time, and serving the society better

Time Series from Clustering: An Approach to Forecast Crime Patterns

This chapter presents an approach to forecast criminal patterns that combines the time series from clustering method with a computational intelligence-based prediction. In this approach, clusters of criminal events are parametrized according to simple geometric prototypes. Cluster dynamics are captured as a set of time series. The size of this set corresponds to the number of clusters multiplied by the number of parameters per cluster. One of the main drawbacks of clustering is the difficulty of defining the optimal number of clusters. The paper also deals with this problem by introducing a validation index of dynamic partitions of crime events that relates the optimal number of clusters with the foreseeability of time series by means of non-linear analysis. The method as well as the validation index was tested over two cases of reported urban crime. Our results showed that crime clusters can be predicted by forecasting their representative time series using an evolutionary adaptive neural fuzzy inference system. Thus, we argue that the foreseeability of these series can be anticipated satisfactorily by means of the proposed index

Challenges to Spatiotemporal Analysis of Sub-State War

Territoriale Gewaltkontrolle durch (nicht-)staatliche Gewaltakteure ist Gegenstand einer großen Zahl grundlegender Konzepte der Konfliktforschung. Es existieren bereits erste Ansätze dieses Phänomen zu messen und raumzeitlich einzugrenzen, jedoch beruhen diese Messungen bislang entweder auf qualitativen Einschätzungen von Experten oder – im Zuge der zunehmenden Verfügbarkeit raumzeitlich desaggregierter Daten von Gewaltereignissen – der Bestimmung umkämpfter Areale auf Grundlage bewaffneter Auseinandersetzungen. Im vorliegenden Papier werden drei Ansätze zur näherungsweisen Bestimmung der Präsenz von Gewaltakteuren vorgestellt. Dabei wird auftretenden Hindernissen und Herausforderungen der Messung territorialer Gebietskontrolle durch den Einsatz von Informationen über den territorialen Wettbewerb begegnet. Hierzu werden raumzeitlich desaggregierte Ereignisdaten ausgewählter Untersuchungsländer in Sub-Sahara Afrika exemplarisch genutzt, um die Möglichkeiten der Visualisierung territorialen Wettbewerbs unter Gewaltakteuren und dem Einfluss der Auflösung der Daten nachzugehen. Darüber hinaus wird die Verfügbarkeit von gleichfalls raumzeitlich desaggregierten Kontextdaten und entsprechenden Analyseverfahren diskutiert.Territorial control by violent (non-)state actors (VNSA) in sub-state war features prominently in many fundamental concepts in conflict studies. Though there have been attempts to measure this phenomenon or at least delimit it from a spatiotemporal perspective, these have so far been based either primarily on qualitative expert assessments or rely on dyadic event data to determine contested areas. In this methodological research paper, I present three approaches that can be used to estimate actor presence on basis of spatiotemporal approximation. In doing so, I focus on challenges and obstacles that can be encountered when measuring territorial control via the proxy of territorial contestation. Spatiotemporally disaggregated violent incidence data is used to analyze a small subsample of countries in sub- Saharan Africa in order to determine various ways of visualizing territorial contest. Further points of discussion include the impact of data aggregation, the availability of context data and analytical methods used for these evaluations

Limits of control - challenges to spatiotemporal analysis of sub-state war

Territorial control by violent (non-)state actors (VNSA) in sub-state war features prominently in many fundamental concepts in conflict studies. Though there have been attempts to measure this phenomenon or at least delimit it from a spatiotemporal perspective, these have so far been based either primarily on qualitative expert assessments or rely on dyadic event data to determine contested areas. In this methodological research paper, I present three approaches that can be used to estimate actor presence on basis of spatiotemporal approximation. In doing so, I focus on challenges and obstacles that can be encountered when measuring territorial control via the proxy of territorial contestation. Spatiotemporally disaggregated violent incidence data is used to analyze a small subsample of countries in subSaharan Africa in order to determine various ways of visualizing territorial contest. Further points of discussion include the impact of data aggregation, the availability of context data and analytical methods used for these evaluations.Territoriale Gewaltkontrolle durch (nicht-)staatliche Gewaltakteure ist Gegenstand einer großen Zahl grundlegender Konzepte der Konfliktforschung. Es existieren bereits erste Ansätze dieses Phänomen zu messen und raumzeitlich einzugrenzen, jedoch beruhen diese Messungen bislang entweder auf qualitativen Einschätzungen von Experten oder - im Zuge der zunehmenden Verfügbarkeit raumzeitlich desaggregierter Daten von Gewaltereignissen - der Bestimmung umkämpfter Areale auf Grundlage bewaffneter Auseinandersetzungen. Im vorliegenden Papier werden drei Ansätze zur näherungsweisen Bestimmung der Präsenz von Gewaltakteuren vorgestellt. Dabei wird auftretenden Hindernissen und Herausforderungen der Messung territorialer Gebietskontrolle durch den Einsatz von Informationen über den territorialen Wettbewerb begegnet. Hierzu werden raumzeitlich desaggregierte Ereignisdaten ausgewählter Untersuchungsländer in Sub-Sahara Afrika exemplarisch genutzt, um die Möglichkeiten der Visualisierung territorialen Wettbewerbs unter Gewaltakteuren und dem Einfluss der Auflösung der Daten nachzugehen. Darüber hinaus wird die Verfügbarkeit von gleichfalls raumzeitlich desaggregierten Kontextdaten und entsprechenden Analyseverfahren diskutiert

ConvGRU-CNN: Spatiotemporal Deep Learning for Real-World Anomaly Detection in Video Surveillance System

Video surveillance for real-world anomaly detection and prevention using deep learning is an important and difficult research area. It is imperative to detect and prevent anomalies to develop a nonviolent society. Realworld video surveillance cameras automate the detection of anomaly activities and enable the law enforcement systems for taking steps toward public safety. However, a human-monitored surveillance system is vulnerable to oversight anomaly activity. In this paper, an automated deep learning model is proposed in order to detect and prevent anomaly activities. The real-world video surveillance system is designed by implementing the ResNet-50, a Convolutional Neural Network (CNN) model, to extract the high-level features from input streams whereas temporal features are extracted by the Convolutional GRU (ConvGRU) from the ResNet-50 extracted features in the time-series dataset. The proposed deep learning video surveillance model (named ConvGRUCNN) can efficiently detect anomaly activities. The UCF-Crime dataset is used to evaluate the proposed deep learning model. We classified normal and abnormal activities, thereby showing the ability of ConvGRU-CNN to find a correct category for each abnormal activity. With the UCF-Crime dataset for the video surveillance-based anomaly detection, ConvGRU-CNN achieved 82.22% accuracy. In addition, the proposed model outperformed the related deep learning models

A Survey of Operations Research and Analytics Literature Related to Anti-Human Trafficking

Human trafficking is a compound social, economic, and human rights issue occurring in all regions of the world. Understanding and addressing such a complex crime requires effort from multiple domains and perspectives. As of this writing, no systematic review exists of the Operations Research and Analytics literature applied to the domain of human trafficking. The purpose of this work is to fill this gap through a systematic literature review. Studies matching our search criteria were found ranging from 2010 to March 2021. These studies were gathered and analyzed to help answer the following three research questions: (i) What aspects of human trafficking are being studied by Operations Research and Analytics researchers? (ii) What Operations Research and Analytics methods are being applied in the anti-human trafficking domain? and (iii) What are the existing research gaps associated with (i) and (ii)? By answering these questions, we illuminate the extent to which these topics have been addressed in the literature, as well as inform future research opportunities in applying analytical methods to advance the fight against human trafficking.Comment: 28 pages, 6 Figures, 2 Table