Crime incidents embedding using restricted Boltzmann machines

We present a new approach for detecting related crime series, by unsupervised learning of the latent feature embeddings from narratives of crime record via the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM). This is a drastically different approach from prior work on crime analysis, which typically considers only time and location and at most category information. After the embedding, related cases are closer to each other in the Euclidean feature space, and the unrelated cases are far apart, which is a good property can enable subsequent analysis such as detection and clustering of related cases. Experiments over several series of related crime incidents hand labeled by the Atlanta Police Department reveal the promise of our embedding methods.Comment: 5 pages, 3 figure

Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns. The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%

Review of current practices in recording road traffic incident data: with specific reference to spatial analysis and road policing policy

Road safety involves three major components: the road system, the human factor and the vehicle element. These three elements are inter-linked through geo-referenced traffic events and provide the basis for road safety analyses and attempts to reduce the number of road traffic incidents and improve road safety. Although numbers of deaths and serious injuries are back to approximately the 1950s levels when there were many fewer vehicles on the road, there are still over 100 fatalities or serious injuries every day, and this is a considerable waste of human capital. It is widely acknowledged that the location perspective is the most suitable methodology by which to analyse different traffic events, where by in this paper, I will concentrating on the relationship between road traffic incidents and traffic policing. Other methods include studying road and vehicle engineering and these will be discussed later. It is worth noting here that there is some division within the literature concerning the definitions of ‘accident’ and ‘incident’. In this paper I will use ‘incident’ because it is important to acknowledge a vast majority of ‘road accidents’ are in fact crimes. However I will use the term ‘accident’ where it is referred to in the literature or relevant reports. It is important to mention here that a road traffic accident can be defined as ‘the product of an unwelcome interaction between two or more moving objects, or a fixed and moving object’ (Whitelegg 1986). Road safety and road incident reduction relates to many other fields of activity including education, driver training, publicity campaigns, police enforcement, road traffic policing, the court system, the National Health Service and Vehicle engineering. Although the subject of using GIS to analyse road traffic incidents has not received much academic attention, it lies in the field of crime mapping which is becoming increasingly important. It is clear that studies have been attempted to analyse road traffic incidents using GIS are increasingly sophisticated in terms of hypotheses and statistical technique (for example see Austin, Tight and Kirby 1997). However it is also clear that there is considerable blurring of boundaries and the analysis of road accidents sits uncomfortably in crime mapping. This is due to four main reasons: - Road traffic incidents are associated with road engineering, which is concerned with generic solutions while road traffic analysis is about sensitivity to particular contexts. - Not all road traffic incidents are crimes - It is not just the police who have an interest in reducing road traffic incidents, other partners include local authorities, hospitals and vehicle manufacturers - The management of road traffic incidents is not just confined to the police GIS has been used for over thirty years however it has only been recently been used in the field of transportation. The field of transportation has come to embrace Geographical Information Systems as a keytechnology to support its research and operational need. The acronym GIS-T is often employed to refer to the application and adaptation of GIS to research, planning and management in transportation. GIS-T covers a broad arena of disciplines of which road traffic incident detection is just one theme. Others include in vehicle navigation systems. Initially it was only used to ask simple accident enquiries such as depicting the relative incidence of accidents in wet weather or when there is no street lighting, or to flag high absolute or relative incidences of accidents (see Anderson 2002). Recently however there has been increased acknowledgement that there is a requirement to go beyond these simple questions and to extend the analyses. It has been widely claimed by academics and the police alike that knowing where road accidents occur must lead to better road policing, in order to ensure that road policing becomes better integrated with other policing activities. This paper will be used to explore issues surrounding the analysis of road traffic accidents and how GIS analysts, police and policy makers can achieve a better understanding of road traffic incidents and how to reduce them. For the purpose of this study I will be trying to achieve a broader overview of the aspects concerning road accident analysis with a strong emphasis on data quality and accuracy with concern to GIS analysis. Data quality and accuracy are seen as playing a pivotal role in the road traffic management agenda because they assist the police and Local Authorities as to the specific location whereby management can be undertaken. Part one will consider the introduction to road incidents and their relationship with geography and spatial analysis and how this were initially applied to locating ‘hotspots’ and the more recent theory of ‘accident migration’. Part two will address current data issues of the UK collection procedure. This section will pay particular reference to geo-referencing and the implication of data quality on the procedure of analysing road incidents using GIS. Part three addresses issues surrounding the spatial analysis of road traffic incidents, including some techniques such as spatial autocorrelation, time-space geography and the modifiable area unit problem. Finally part four looks at the role of effective road traffic policing and how this can be achieved due to better understanding of the theory and issues arising from analysing road traffic incidents. It will also look at the diffusion and use of GIS within the police and local authorities

Reign Mobile Application for Hotspot Detection

Reign mobile hotspot detection system is a cross platform mobile application developed to help warn its users of hostile areas (i.e., areas prone to accident, flooding, kidnapping, civil unrest, etc.). It also has functionalities that allow users to report hazardous areas through a preconfigured e-mail, which includes the users current location and a description of the hazard being reported. The goal of this project is to explore the use of mobile computing, by means of mobile apps, to address some of the social and developmental challenges being experienced in Nigeria. Thus, we could adapt technology to improve social conditions as well as, possibly, save lives. The motivation for this project is the ubiquity of mobile computing, particularly when we consider that Nigeria with a population of over 140 million people is currently estimated to have a mobile broadband Internet penetration equivalent of about 30%. These users mostly connect through mobile devices with at least 100million unique mobile communication lines registered. The app was developed with HTML5 and JAVA programming languages, uses GPS coordinates to map locations and a push server to send alerts to registered users. Currently, the Android version of the app has been developed and is being tested. During the development and testing, we interacted with security and paramilitary institutions  like the Police, Federal Road Safety Service (FRSC) and the Nigerian Metrological Agency (NIMET) in order to ascertain areas that are prone to hazards. Preliminary tests in Lagos and Abuja confirm the functionality and usefulness of the app. Keywords: Mobile computing, hotspot detection, security hazards, crime detection and prevention, alerts

CRIME RATE PREDICTION USING THE RANDOM FOREST ALGORITHM

  An act that creates crimes punishable by law is characterized as a crime. Rape, fraud, terrorism, kidnapping, burglary, murder, and other crimes are common in Nigeria. Examples are cybercrime, bribery and corruption, robbery, money laundering, among other crimes. Crime is a harmful and widespread social issue that affects individuals all around the world. The rate of crime has risen dramatically in recent years. To cut down on crime, at any rate, law enforcements must take preventative actions. To protect society against crime, modern systems and new technologies are required. Although accurate real-time crime study is on aid in reducing crime rates, they are nonetheless useless. As crime occurrences are dependent on, this is a difficult subject for the scientific community to solve. Therefore, this paper proposes machine learning algorithm to indicate the frequency and pattern of crimes based on the data collected and to show the extent of crime in a particular region. Various visualization approaches and machine learning algorithms are used in this study to anticipate the crime distribution over a large area. In the first stage, raw datasets were processed and visualized according to the requirements. Then, to extract knowledge from these massive datasets, machine learning methods were deployed and uncover hidden patterns in the data, which were then utilized to investigate and report on crime patterns, It is beneficial to crime analysts. Investigate these crime networks using a variety of interactive crime visualizations. As a result, it is helpful in crime prevention

Predicting Crime Using Spatial Features

Our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime. The reverse geocoding technique is applied to retrieve open street map (OSM) spatial data. This study also proposes finding hotpoints extracted from crime hotspots area found by Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). A spatial distance feature is then computed based on the position of different hotpoints for various types of crime and this value is used as a feature for classifiers. We test the engineered features in crime data from Royal Canadian Mounted Police of Halifax, NS. We observed a significant performance improvement in crime prediction using the new generated spatial features.Comment: Paper accepted to 31st Canadian Conference in Artificial Intelligence, 201

Detecting and Monitoring Hate Speech in Twitter

Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Union’s Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge

CrimeTelescope: crime hotspot prediction based on urban and social media data fusion

Crime is a complex social issue impacting a considerable number of individuals within a society. Preventing and reducing crime is a top priority in many countries. Given limited policing and crime reduction resources, it is often crucial to identify effective strategies to deploy the available resources. Towards this goal, crime hotspot prediction has previously been suggested. Crime hotspot prediction leverages past data in order to identify geographical areas susceptible of hosting crimes in the future. However, most of the existing techniques in crime hotspot prediction solely use historical crime records to identify crime hotspots, while ignoring the predictive power of other data such as urban or social media data. In this paper, we propose CrimeTelescope, a platform that predicts and visualizes crime hotspots based on a fusion of different data types. Our platform continuously collects crime data as well as urban and social media data on the Web. It then extracts key features from the collected data based on both statistical and linguistic analysis. Finally, it identifies crime hotspots by leveraging the extracted features, and offers visualizations of the hotspots on an interactive map. Based on real-world data collected from New York City, we show that combining different types of data can effectively improve the crime hotspot prediction accuracy (by up to 5.2%), compared to classical approaches based on historical crime records only. In addition, we demonstrate the usability of our platform through a System Usability Scale (SUS) survey on a full prototype of CrimeTelescope