What is wrong about Robocops as consultants? A technology-centric critique of predictive policing

Fighting crime has historically been a field that drives technological innovation, and it can serve as an example of different governance styles in societies. Predictive policing is one of the recent innovations that covers technical trends such as machine learning, preventive crime fighting strategies, and actual policing in cities. However, it seems that a combination of exaggerated hopes produced by technology evangelists, media hype, and ignorance of the actual problems of the technology may have (over-)boosted sales of software that supports policing by predicting offenders and crime areas. In this paper we analyse currently used predictive policing software packages with respect to common problems of data mining, and describe challenges that arise in the context of their socio-technical application.     

Exploring associations between streetscape factors and crime behaviors using Google Street View images

Understanding the influencing mechanism of the urban streetscape on crime is fairly important to crime prevention and urban management. Recently, the development of deep learning technology and big data of street view images, makes it possible to quantitatively explore the relationship between streetscape and crime. This study computed eight streetscape indexes of the street built environment using Google Street View images firstly. Then, the association between the eight indexes and recorded crime events was revealed with a poisson regression model and a geographically weighted poisson regression model. An experiment was conducted in downtown and uptown Manhattan, New York. Global regression results show that the influences of Motorization Index on crimes are significant and positive, while the effects of the Light View Index and Green View Index on crimes depend heavily on the socio-economic factors. From a local perspective, the Pedestrian Space Index, Green View Index, Light View IndexandMotorization Index have a significant spatial influence on crimes, while the same visual streetscape factors have different effects on different streets due to the combination differences of socio-economic, cultural and streetscape elements. The key streetscape elements of a given street that affect a specific criminal activity can be identified according to the strength of the association. The results provide both theoretical and practical implications for crime theories and crime prevention efforts.     

A comparison between the cost effectiveness of CCTV and improved street lighting as a means of crime reduction

The effectiveness of CCTV and improved street lighting has been studied extensively in terms of their potential for reducing the number of crimes in a certain area. However, this does not take into account the cost of the interventions or the savings due to crime reduction. This paper presents a model, which takes the form of a cellular automaton to simulate the implementation of improved street lighting and CCTV cameras using a range of strategies. This permits an exploration of simulated options to find which is most cost effective and what the best strategy for implementation is. The results indicate that there are few situations where CCTV is more cost effective than improved street lighting as a way of reducing street crime. In addition, it is shown that the strategy of targeting locations with the highest crime rates, “hot spots”, has the greatest potential for maximising the cost effectiveness of interventions.     

A data-driven agent-based simulation to predict crime patterns in an urban environment

Spatial crime simulations contribute to our understanding of the mechanisms that drive crime and can support decision-makers in developing effective crime reduction strategies. Agent-based models that integrate geographical environments to generate crime patterns have emerged in recent years, although data-driven crime simulations are scarce. This article (1) identifies numerous important drivers of crime patterns, (2) collects relevant, openly available data sources to build a GIS-layer with static and dynamic geographical, as well as temporal features relevant to crime, (3) builds a virtual urban environment with these layers, in which individual offender agents navigate, (4) proposes a data-driven decision-making process using machine-learning for the agents to decide whether to engage in criminal activity based on their perception of the environment and, finally, (5) generates fine-grained crime patterns in a simulated urban environment. The novelty of this work lies in the various large-scale data layers, the integration of machine learning at individual agent level to process the data layers, and the high resolution of the resulting predictions. The results show that the spatial, temporal, and interaction layers are all required to predict the top street segments with the highest number of crimes. In addition, the spatial layer is the most informative, which means that spatial data contributes most to predictive performance. Thus, these findings highlight the importance of the inclusion of various open data sources and the potential of theory-informed, data-driven simulations for the purpose of crime prediction. The resulting model is applicable as a predictive tool and as a test platform to support crime reduction.     

An agent-based model for simulating urban crime with improved daily routines

We propose a scalable agent-based crime simulation model based on the routine activity theory. It uses census data and time geography to create a synthetic population with residences and job locations, commuting schedules, and daily routines constrained by disposable time that are more representative than existing models. The time and location of crime incidents in this model are determined by random encounters between vulnerable targets and motivated offenders when they travel or carry out scheduled activities on a road network. This model is applied to simulate robberies in January 2011 for Baton Rouge, Louisiana. Three scenarios are simulated to demonstrate its value for crime theory development and shed light on modeling issues in need of improvement before it can reliably assist policymaking or inform the public. Major findings from this study include the model's ability to replicate prominent robbery hotspots in the study area with various degrees of success and the consistent effects of target definition and offender strategy on model performance. The study also suggests missing model components to effectively constrain the displacement of crime opportunities under hotspot policing, which can be the key to resolving contradiction between our simulation results and other empirical researches and crime simulations.     

Developing an online cooperative police patrol routing strategy

A cooperative routing strategy for daily operations is necessary to maintain the effects of hotspot policing and to reduce crime and disorder. Existing robot patrol routing strategies are not suitable, as they omit the peculiarities and challenges of daily police patrol including minimising the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability to patrol routes. In this research, we propose a set of guidelines for patrol routing strategies to meet the challenges of police patrol. Following these guidelines, we develop an innovative heuristic-based and Bayesian-inspired real-time strategy for cooperative routing police patrols. Using two real-world cases and a benchmark patrol strategy, an online agent-based simulation has been implemented to testify the efficiency, flexibility, scalability, unpredictability, and robustness of the proposed strategy and the usability of the proposed guidelines.     

Built environment and violent crime: An environmental audit approach using Google Street View

Recent studies empirically support the role of the built environment in inducing or hindering violent crime. Particularly, studies of the broken window theory have provided evidence that physical disorder is an environmental correlate of crime. This includes broken windows, vacant/abandoned housings, abandoned cars on street, graffiti, and decayed street lighting, among other things. Current studies are limited by the difficulty involved in collecting fine-scale quantitative environmental data. The conventional environmental audit approach, which aims to assess environmental features, is costly, time-consuming, and burdensome. In this study, we use Google Street View to study the relationship between violent crime and physical features of urban residential environment. More concretely, a Poisson regression model with spatial filtering is used to identify socio-economic correlates of violent crime. Parting from the hypothesis that omission of built environmental factors results in systematic residual pattern, we proceed to analyze the spatial filter to select sites for virtual environmental audits. A series of physical environmental factors are identified using contingency table analysis. The results provide both theoretical and practical implications for several theories of crime and crime prevention efforts.     

Automatic latent street type discovery from web open data

Street categorization is an important topic in urban planning and in various applications such as routing and environment monitoring. Typically streets are classified as commercial, residential, and industrial. However, such broad categorization is insufficient to capture the rich properties a street may possess, and often cannot be used for specific applications. Previous works have proposed several advanced street categorization systems. However, most of these systems rely on manual analysis and design, which requires significant effort. In this paper, we propose a method for automatically discovering latent street types from multi-modal Web open data. We utilize data of different modalities including microblog tweets, Foursquare venues, and Google Street View images. The model we propose considers both coherence within each modality and association between modalities. Based on the San Francisco city data, our quantitative evaluation shows superiority of the proposed method in terms of coherence and association. In qualitative analysis, we show that the street types discovered by our method correspond to the official street plan. We also show an example application in which the discovered street types are used in crime prediction.     

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.     

基于信用量消耗边界探测的流量管制测试研究

针对航空电子全双工交换式以太网交换节点流量管制功能,采用信用量消耗边界探测机制实现流量管制功能测试,提出了数据帧转发模型、数据帧边界转发模型、数据帧管制模型和数据帧边界管制模型等4种流量管制测试模型,从微观角度观测交换节点对“违规”流量管制处理的结果.开发了流量管制功能测试系统,验证了流量管制模型边界探测机制的正确定和有效性,克服了基于数据帧输入和输出计数统计的宏观测试精确性不足的问题.与理论处理结果进行了对比,结果表明,该系统能够很好地实现交换节点流量管制功能的测试和评估.     

An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago

The sharp rise in urban crime rates is becoming one of the most important issues of public security, affecting many aspects of social sustainability, such as employment, livelihood, health care, and education. Therefore, it is critical to develop a predictive model capable of identifying areas with high crime intensity and detecting trends of crime occurrence in such areas for the allocation of scarce resources and investment in the prevention and reduction of criminal strategies. This study develops a predictive model based on K-means clustering, signal decomposition technique, and neural networks to identify crime distribution in urban areas and accurately forecast the variation tendency of the number of crimes in each area. We find that the time series of the number of crimes in different areas show a correlation in the long term, but this long-term effect cannot be reflected in the short period. Therefore, we argue that short-term joint law enforcement has no theoretical basis because data show that spatial heterogeneity and time lag cannot be timely reflected in short-term prediction. By combining the temporal and spatial effects, a high-precision anticrime information support system is designed, which can help the police to implement more targeted crime prevention strategies at the micro level.     

基于STM32的智慧路灯控制系统设计与实现

随着物联网科技的不断发展,智慧路灯成为了智慧城市发展过程中不可或缺的重要组成部分。传统的城市照明路灯仅仅只能满足简单的照明需求,并且在控制局部照明上无法实现实时以及自由控制,只能按照季度的日出日落时间来固定设置路灯的开关灯,不仅浪费了人力物力,而且对于能源也是极大的浪费。设计了基于STM32的路灯集中控制器,该集中控制器通过GPRS与后台通信服务器连接,实现实时数据的回传、在线命令和策略的下发,最后对系统进行测试与分析;实验结果表明,该方案不仅解决了路灯的智能化控制,而且具有高度的可扩展性,极大的方便了城市照明,更实现了高并发通信,从而使得更安全可靠的达到对城市照明的目的。     

UK Cyber-cop Highlights Key Computer Crimes

The story on everyone's lips in April was the launch of the Hi-Tech Crime Unit in the UK. A group of highly trained police officers who understand the strictures of keeping digital evidence and investigating crime across national borders has been unleashed to tackle cybercrime.Nigel Jones who is involved in the HTCU spoke to a forum on The Threat from Within about the nature of the relationship between computers and crime. The major threats are posed by the computer as a launch pad for attack, and as a communications and storage device.He also spoke about his successes in policing as part of the Fraud Squad.     

The future for the policing of cybercrime

What can we reasonably expect from law enforcement in terms of policing high tech crime? What can the police reasonably expect from us in terms of adequacy of prevention and co-operation when a suspected crime occurs? Cyber Crime Policing can only be understood within the broader issues facing the criminal justice system. It seems to me that both sides may be expecting too much of each other and a more realistic approach might be more productive. In this presentation I will concentrate on the situation here in the UK; however similar patterns occur in many other countries.     

Assessing the effects of bus stop relocation on street robbery

A number of studies have revealed a correlation between bus stops and crimes, especially street robberies. However, few have looked into the impact of bus stop location changes on the distribution of street robberies. Will newly added bus stops attract more street robberies? Will the removal of existing bus stops reduce street robberies? By assessing the change of street robberies in relation to the spatial change of bus stops of Cincinnati, OH, with the consideration of the controls from socioeconomic characteristics, point of interests (POI) and spatial heterogeneity, this study uses before-and-after comparisons and the difference-in-differences (DID) analysis in the context of quasi-experiment to answer these questions. This study assesses not only the influences of the relocation of bus stops, but also the influence on street robberies of the time elapsed from the addition or removal of bus stops. Besides the three typical variables representing the presence or absence of the intervention, before or after the intervention and the interaction of the two, we add the time from addition/removal to the DID analysis. Results suggest that, on average, adding bus stops to a new location significantly increases street robberies in the areas surrounding the stops. The longer the time from the addition of a new bus stop, the more the street robberies in its surrounding areas. Removing all bus stops from a location decreases street robberies in the areas nearby; however, this influence is not statistically significant. This suggests that the relationship between street robbery and time from removal may not be linear. There are multiple studies exploring the static relationship between the bus stop and street robbery, but none looked into their dynamic relationship. This study represents the first attempt to do so. Its findings add new evidence to the theories of rational choice, routine activity, crime pattern, and crime displacement.     

Urban navigation beyond shortest route: The case of safe paths

Advancements in mobile technology and computing have fostered the collection of a large number of civic datasets that capture the pulse of urban life. Furthermore, the open government and data initiative has led many local authorities to make these datasets publicly available, hoping to drive innovation that will further improve the quality of life for the city-dwellers. In this paper, we develop a novel application that utilizes crime data to provide safe urban navigation. Specifically, using crime data from Chicago and Philadelphia we develop a risk model for their street urban network, which allows us to estimate the relative probability of a crime on any road segment. Given such model we define two variants of the SafePaths problem where the goal is to find a short and low-risk path between a source and a destination location. Since both the length and the risk of the path are equally important but cannot be combined into a single objective, we approach the urban-navigation problem as a biobjective shortest path problem. Our algorithms aim to output a small set of paths that provide tradeoffs between distance and safety. Our experiments demonstrate the efficacy of our algorithms and their practical applicability.     

Internet Gambling

With the increase in crime that Internet gambling poses, a greater incidence of addiction by users and the problem of policing a global electronic marketplace, enforcement agencies need to be aware of measures that can be applied to counteract the problems Internet gambling, introduces. Since there are no outright legal or technological fixes to prevent the growth in Internet gambling what can enforcement authorities do?     

基于Windows 平台的动态取证系统

针对目前一些动态取证模型的不足,在分布式网络取证模型的基础上设计了一个基于Windows平台的动态取证系统,能够实现网络中的计算机作为作案目标和作案工具双重角色时的取证,具有实时获取多种数据源、取证过程隐秘、取证分析算法可扩展等特点。介绍了动态取证系统中各功能模块设计,并阐述了系统设计中涉及到的关键技术,最后通过模拟测试表明该系统能够在Windows网络下实现动态取证。     

A Structural Approach to the Model Generalization of an Urban Street Network*

This paper proposes a novel generalization model for selecting characteristic streets in an urban street network. This model retains the central structure of a street network. It relies on a structural representation of a street network using graph principles where vertices represent named streets and links represent street intersections. Based on this representation, so-called connectivity graph, centrality measures are introduced to qualify the status of each individual vertex within the graph. We show that these measures can be used for characterizing the structural properties of an urban street network, and for the selection of important streets. The proposed approach is validated by a case study applied to a middle-sized Swedish city.     

Using Google Street View imagery to capture micro built environment characteristics in drug places,compared with street robbery

The drug-related problem poses a serious threat to human health and safety. Previous studies have associated drug places with factors related to place management and accessibility, often at several scattered places, as data at the micro level are hard to obtain at a city-wide scale. Google Street View imagery presents a new source for deriving micro built environment characteristics, including place management and accessibility in larger areas. In this study, we calculate an overall safety score by the Streetscore algorithm and extract physical elements at the address location by the Pyramid Scene Parsing Network (PSPNet) model from every Google Street View image. Additionally, to distinguish drug activities from other types of crime, we compare drug-related calls for service (CFS) data with street robbery incident data. We build the binary logistic regression models to assess the impact of the micro built environment variables on drug activities after controlling for other criminological elements pertaining to drug places. Results show that the safety score, traffic lights, and poles make statistically significant and negative (or deterring) impacts on drug activities, whilst traffic signs and roads make statistically significant and positive (or contributing) impacts. The positive impact of buildings is also notable as its p-value is slightly over 0.05. This study provides evidence at the micro level that less place management and higher accessibility can increase the risk of drug activities. These street-view variables may be generally applicable to other types of crime research in the context of the micro built environment.