Spatio-temporal prediction of crimes using network analytic approach

It is quite evident that majority of the population lives in urban area today than in any time of the human history. This trend seems to increase in coming years. A study [5] says that nearly 80.7% of total population in USA stays in urban area. By 2030 nearly 60% of the population in the world will live in or move to cities. With the increase in urban population, it is important to keep an eye on criminal activities. By doing so, governments can enforce intelligent policing systems and hence many government agencies and local authorities have made the crime data publicly available. In this paper, we analyze Chicago city crime data fused with other social information sources using network analytic techniques to predict criminal activity for the next year. We observe that as we add more layers of data which represent different aspects of the society, the quality of prediction is improved. Our prediction models not just predict total number of crimes for the whole Chicago city, rather they predict number of crimes for all types of crimes and for different regions in City of Chicago

Spatio-temporal prediction of Baltimore crime events using CLSTM neural networks

Crime activity in many cities worldwide causes significant damages to the lives of victims and their surrounding communities. It is a public disorder problem, and big cities experience large amounts of crime events. Spatio-temporal prediction of crimes activity can help the cities to have a better allocation of police resources and surveillance. Deep learning techniques are considered efficient tools to predict future events analyzing the behavior of past ones; however, they are not usually applied to crime event prediction using a spatio-temporal approach. In this paper, a Convolutional Neural Network (CNN) together with a Long-Short Term Memory (LSTM) network (thus CLSTM-NN) are proposed to predict the presence of crime events over the city of Baltimore (USA). In particular, matrices of past crime events are used as input to a CLSTM-NN to predict the presence of at least one event in future days. The model is implemented on two types of events: ‘‘street robbery’’ and ‘‘larceny’’. The proposed procedure is able to take into account spatial and temporal correlations present in the past data to improve future prediction. The prediction performance of the proposed neural network is assessed under a number of controlled plausible scenarios, using some standard metrics (Accuracy, AUC-ROC, and AUC-PR

Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150 articles to explore the various machine learning and deep learning algorithms applied to predict crime. The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities. Additionally, the paper highlights potential gaps and future directions that can enhance the accuracy of crime prediction. Finally, the comprehensive overview of research discussed in this paper on crime prediction using machine learning and deep learning approaches serves as a valuable reference for researchers in this field. By gaining a deeper understanding of crime prediction techniques, law enforcement agencies can develop strategies to prevent and respond to criminal activities more effectively.Comment: 35 Pages, 6 tables and 11 figures. Consists of Dataset links used for crime prediction. Review Pape

CPC: Crime, Policing and Citizenship - Intelligent Policing and Big Data

Crime, Policing and Citizenship (CPC) – Space-Time Interactions of Dynamic Networks has been a major UK EPSRC-funded research project. It has been a multidisciplinary collaboration of geoinformatics, crime science, computer science and geography within University College London (UCL), in partnership with the Metropolitan Police Service (MPS). The aim of the project has been to develop new methods and applications in space-time analytics and emergent network complexity, in order to uncover patterning and interactions in crime, policing and citizen perceptions. The work carried out throughout the project will help inform policing at a range of scales, from the local to the city-wide, with the goal of reducing both crime and the fear of crime. The CPC project is timely given the tremendous challenging facing policing in big cities nationally and globally, as consequences of changes in society, population structure and economic well-being. It addresses these issues through an intelligent approach to data-driven policing, using daily reported crime statistics, GPS traces of foot and vehicular patrols, surveys of public attitudes and geo-temporal demographic data of changing community structure. The analytic focus takes a spatio-temporal perspective, reflecting the strong spatial and temporal integration of criminal, policing and citizen activities. Street networks are used throughout as a basis for analysis, reflecting their role as a key determinant of urban structure and the substrate on which crime and policing take place. The project has presented a manifesto for ‘intelligent policing’ which embodies the key issues arising in the transition from Big Data into actionable insights. Police intelligence should go beyond current practice, incorporating not only the prediction of events, but also how to respond to them, and how to evaluate the actions taken. Cutting-edge network-based crime prediction methods have been developed to accurately predict crime risks at street segment level, helping police forces to focus resources in the right places at the right times. Methods and tools have been implemented to support senior offices in strategic planning, and to provide guidance to frontline officers in daily patrolling. To evaluate police performance, models and tools have been developed to aid identification of areas requiring greater attention, and to analyse the patrolling behaviours of officers. Methods to understand and model confidence in policing have also been explored, suggesting strategies by which confidence in the police can be improved in different population segments and neighbourhood areas. A number of tools have been developed during the course of the project include data-driven methods for crime prediction and for performance evaluation. We anticipate that these will ultimately be adopted in daily policing practice and will play an important role in the modernisation of policing. Furthermore, we believe that the approaches to the building of public trust and confidence that we suggest will contribute to the transformation and improvement of the relationship between the public and police

Large-scale and Deep Spatiotemporal Point-Process Models

Many accurate spatiotemporal data sets have recently become available for research. Real-world applications create strong demands for a better multivariate point-process modeling. In this thesis, we develop new multivariate models with generalization ability and scalability. The first two chapters provide a research background, real-world problems and a mathematical introduction to point-process models. In chapter 3, we develop a nonparametric method for multivariate spatiotemporal Hawkes processes with applications on network reconstruction. In contrast to prior work, which has often focused on exclusively temporal information, our approach uses spatiotemporal information and does not assume a specific parametric form. Our results demonstrate that, in comparison to using only temporal data, our approach yields improved network reconstruction, providing a basis for meaningful subsequent analysis---such as examinations of community structure and motifs---of the reconstructed networks. In chapter 4, we present a fast and accurate estimation method for multivariate Hawkes processes. Our method, with guaranteed consistency, combines two estimation approaches. Extensive numerical experiments, with synthetic data and real-world social network data, show that our method improves the accuracy, scalability and computational efficiency of prevailing estimation approaches. Moreover, it greatly boosts the performance of Hawkes process-based models on social network reconstruction and helps to understand the spatiotemporal triggering dynamics over social media.In chapter 5, we focus on multivariate spatial point processes, which can describe heterotopic data over space. However, highly multivariate intensities are computationally challenging due to the curse of dimensionality. To bridge this gap, we introduce a declustering-based hidden-variable model that leads to an efficient inference via a variational autoencoder (VAE). We also prove that this model is a generalization of the VAE-based model for collaborative filtering. This leads to an interesting application of spatial point-process models to recommender systems. Experimental results show the method's utility on both synthetic data and real-world data. Finally, in chapter 6, we show how multivariate point processes can be applied to opioid overdose events and real-time prediction of the hourly crime rate. In chapter 7, we discuss future directions and conclude the thesis

Spatio-Temporal Analysis of Crime Incidents for Forensic Investigation

Crime analysis and mapping has been routinely employed to gather intelligence which informs security efforts and forensic investigations. Traditionally, geographic information systems in the form of third-party mapping applications are used for analysis of crime data but are often expensive and lack flexibility, transparency, or efficiency in uncovering associations and relationships in crime. Each crime incident and article of evidence within that incident has an associated spatial and temporal component which may yield significant and relevant information to the case. Wide variations exist in the techniques that departments use and commonly spatial and temporal components of crime are evaluated independently, if at all. Thus, there is a critical need to develop and implement spatio-temporal investigative strategies so police agencies can gain a foundational understanding of crime occurrence within their jurisdiction, develop strategic action for disruption and resolution of crime, conduct more informed investigations, better utilize resources, and provide an overall more effective service. The purpose of this project was to provide foundational knowledge to the investigative and security communities and demonstrate the utility of empirical spatio-temporal methods for the assessment and interpretation of crime incidents. Two software packages were developed as an open source (R) solution to expand current techniques and provide an implementable spatio-temporal methodology for crime analysis. Additionally, an actionable method for near repeat analysis was developed. Firstly, the premise of the near repeat phenomenon was evaluated across crime types and cities to discern optimal parameters for spatial and temporal bandwidths. Using these parameters, a method for identifying near repeat series was developed which draws inter-incident linkages given the spatio-temporal clustering of the incidents. Resultant crime networks and maps provide insight regarding near repeat crime incidents within the landscape of their jurisdiction for targeted investigation. Finally, a new approach to the geographic profiling problem was developed which assesses and integrates the travel environment of road networks, beliefs and assumptions formed through the course of the investigation process about the perpetrator, and information derived from the analysis of evidence. Each piece of information is evaluated in conjunction with spatio-temporal routing functions and then used to update prior beliefs about the anchor point of the perpetrator. Adopting spatio-temporal methodologies for the investigation of crime offers a new framework for forensic operations in the investigation of crime. Systematic consideration about the value and implications of the relationship between space, time, and crime was shown to provide insight regarding crime. In a forward-looking sense this work shows that the interpretation of crime within a spatio-temporal context can provide insight into crime occurrence, linkage of crime incidents, and investigations of those incidents

CRIME DATA PREDICTION BASED ON GEOGRAPHICAL LOCATION USING MACHINE LEARNING

This project employs machine learning methods like K Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Decision Tree algorithms to monitor crime data based on location and pinpoint areas with risks. The project implements and tunes the four models to improve the precision of predicting crime levels. These models collaborate to offer a trustworthy evaluation of crime patterns. K Nearest Neighbors (KNN) categorizes locations by examining the proximity of data points considering coordinates and other factors to identify trends linked to increased crime data. Logistic Regression gauges the likelihood of crime incidents by studying the connection, between factors (like location and time ) and the crime activity, assisting in forecasting crimes in various regions. Decision Tree Classifier uses a tree structure to make decisions based on feature values dividing the data into branches representing decision paths. This approach is particularly useful for identifying high-risk areas using crime data. Random Forest Classifier constructs decision trees and combines their results for classification purposes, resulting in enhanced prediction accuracy and robustness by merging outcomes from multiple trees, thus reducing the risks of overfitting and improving generalization to unseen data. The system’s efficiency is assessed using a crime dataset that includes information, about crime occurrences, geographical locations, and time-related data. Metrics, like accuracy, precision, and recall are employed to assess the model’s ability to anticipate crimes and identify hotspots accurately

Identification of patterns for space-time event networks

This paper provides new tools for analyzing spatio-temporal event networks. We build time series of directed event networks for a set of spatial distances, and based on scan-statistics, the spatial distance that generates the strongest change of event network connections is chosen. In addition, we propose an empirical random network event generator to detect significant motifs throughout time. This generator preserves the spatial configuration but randomizes the order of the occurrence of events. To prevent the large number of links from masking the count of motifs, we propose using standardized counts of motifs at each time slot. Our methodology is able to detect interaction radius in space, build time series of networks, and describe changes in its topology over time, by means of identification of different types of motifs that allows for the understanding of the spatio-temporal dynamics of the phenomena. We illustrate our methodology by analyzing thefts occurred in Medellín (Colombia) between the years 2003 and 2015.Work supported by Red de Violencia y Criminalidad - Universidad Nacional Abierta y a Distancia UNAD, Bogotá Colombia and Universidad Nacional de Colombia sede Bogotá

Predictive Crime Mapping: Arbitrary Grids or Street Networks?

OBJECTIVES: Decades of empirical research demonstrate that crime is concentrated at a range of spatial scales, including street segments. Further, the degree of clustering at particular geographic units remains noticeably stable and consistent; a finding that Weisburd (Criminology 53:133–157, 2015) has recently termed the ‘law of crime concentration at places’. Such findings suggest that the future locations of crime should—to some extent at least—be predictable. To date, methods of forecasting where crime is most likely to next occur have focused either on area-level or grid-based predictions. No studies of which we are aware have developed and tested the accuracy of methods for predicting the future risk of crime at the street segment level. This is surprising given that it is at this level of place that many crimes are committed and policing resources are deployed. METHODS: Using data for property crimes for a large UK metropolitan police force area, we introduce and calibrate a network-based version of prospective crime mapping [e.g. Bowers et al. (Br J Criminol 44:641–658, 2004)], and compare its performance against grid-based alternatives. We also examine how measures of predictive accuracy can be translated to the network context, and show how differences in performance between the two cases can be quantified and tested. RESULTS: Findings demonstrate that the calibrated network-based model substantially outperforms a grid-based alternative in terms of predictive accuracy, with, for example, approximately 20 % more crime identified at a coverage level of 5 %. The improvement in accuracy is highly statistically significant at all coverage levels tested (from 1 to 10 %). CONCLUSIONS: This study suggests that, for property crime at least, network-based methods of crime forecasting are likely to outperform grid-based alternatives, and hence should be used in operational policing. More sophisticated variations of the model tested are possible and should be developed and tested in future research