Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments

Steadily increasing urbanization is causing significant economic and social transformations in urban areas, posing several challenges related to city management and services. In particular, in cities with higher crime rates, effectively providing for public safety is an increasingly complex undertaking. To handle this complexity, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends. These technologies have potentially to increase the efficient deployment of police resources within a given territory and ultimately support more effective crime prevention. This paper presents a predictive approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and to reliably forecast crime trends in each region. The algorithm result is a spatio-temporal crime forecasting model, composed of a set of crime-dense regions with associated crime predictors, each one representing a predictive model for estimating the number of crimes likely to occur in its associated region. The experimental evaluation was performed on two real-world datasets collected in the cities of Chicago and New York City. This evaluation shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.     

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.     

Dynamic road crime risk prediction with urban open data

Crime risk prediction is helpful for urban safety and citizens’life quality.However,existing crime studies focused on coarse-grained prediction,and usually failed to capture the dynamics of urban crimes.The key challenge is data sparsity,since that 1)not all crimes have been recorded,and 2)crimes usually occur with low frequency.In this paper,we propose an effective framework to predict fine-grained and dynamic crime risks in each road using heterogeneous urban data.First,to address the issue of unreported crimes,we propose a cross-aggregation soft-impute(CASI)method to deal with possible unreported crimes.Then,we use a novel crime risk measurement to capture the crime dynamics from the perspective of influence propagation,taking into consideration of both time-varying and location-varying risk propagation.Based on the dynamically calculated crime risks,we design contextual features(i.e.,POI distributions,taxi mobility,demographic features)from various urban data sources,and propose a zero-inflated negative binomial regression(ZINBR)model to predict future crime risks in roads.The experiments using the real-world data from New York City show that our framework can accurately predict road crime risks,and outperform other baseline methods.     

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.     

The strongest does not attract all but it does attract the most – evaluating the criminal attractiveness of shopping malls using fuzzy logic

Crime attractors are locations (e.g. shopping malls) that attract criminally motivated offenders because of the presence of known criminal opportunities. Although there have been many studies that explore the patterns of crime in and around these locations, there are still many questions that linger. In recent years, there has been a growing interest to develop mathematical models in attempts to help answer questions about various criminological phenomena. In this paper, we are interested in applying a formal methodology to model the relative attractiveness of crime attractor locations based on characteristics of offenders and the crime they committed. To accomplish this task, we adopt fuzzy logic techniques to calculate the attractiveness of crime attractors in three suburban cities in the Metro Vancouver region of British Columbia, Canada. The fuzzy logic techniques provide results comparable with our real‐life expectations that offenders do not necessarily commit significant crimes in the immediate neighbourhood of the attractors, but travel towards it, and commit crimes on the way. The results of this study could lead to a variety of crime prevention benefits and urban planning strategies.     

基于聚类的数据加权优化在犯罪预测中的应用

近年来，我国传统暴力犯罪与成年人犯罪呈下降态势，但是，犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力，打击各类违法犯罪事件,本文针对犯罪数据，提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类，然后进行数据降维提取关键属性生成特征数据，继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习，从而预测犯罪案由。实验结果表明，与传统方法相比，本文方法具有更好的预测效果，为公安实践工作中类似案件的侦破和预防，提供新的路径支撑。     

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.     

RETRACTED ARTICLE: Comparative analysis of time series model and machine testing systems for crime forecasting

Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.

     

利用虚拟电话实施诈骗犯罪之案例分析

侦破利用虚拟电话诈骗案件需要认真总结成功个案的技战法,把侦查思路具体化、标准化,为将来建立此类案件侦查的数学模型打下基础。虚拟电话诈骗作为可防性犯罪,构筑覆盖全社会的预防网络是减少此类犯罪最有效的手段。     

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.     

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.     

新农村建设中贪污贿赂犯罪案件实证分析 对57起涉农职务犯罪特点、原因及预防对策的解析

本文通过对一个地区在社会主义新农村建设中出现的贪污贿赂案件的实证分析,提炼出此类犯罪的主要特点、剖析此类犯罪产生原因;并从检察机关的法律监督职能作用出发,提出了打防结合的方针,服务与预防相结合,打击是一种特殊的预防形式,服务与预防是构建和谐农村的有效法治保障,为维护农村和谐稳定,保障农民合法权益,促进新农村全面建设提供稳定的发展环境。     

Using active learning to adapt remote sensing image classifiers

The validity of training samples collected in field campaigns is crucial for the success of land use classification models. However, such samples often suffer from a sample selection bias and do not represent the variability of spectra that can be encountered in the entire image. Therefore, to maximize classification performance, one must perform adaptation of the first model to the new data distribution. In this paper, we propose to perform adaptation by sampling new training examples in unknown areas of the image. Our goal is to select these pixels in an intelligent fashion that minimizes their number and maximizes their information content. Two strategies based on uncertainty and clustering of the data space are considered to perform active selection. Experiments on urban and agricultural images show the great potential of the proposed strategy to perform model adaptation.     

基于DCGRU-RF模型的路网短时交通流预测

随着城市化进程的加快,我国城市机动车数量快速增加,使得现有路网容量难以满足交通运输需求,交通拥堵、环境污染、交通事故等问题与日俱增。准确高效的交通流预测作为智能交通系统的核心,能够有效解决交通出行和管理方面的问题。现有的短时交通流预测研究往往基于浅层的模型方法,不能充分反映交通流特性。文中针对复杂的交通网络结构,提出了一种基于DCGRU-RF(Diffusion Convolutional Gated Recurrent Unit-Random Forest)模型的短时交通流预测方法。首先,使用DCGRU(Diffusion Convolutional Gated Recurrent Unit)网络刻画交通流时间序列数据中的时空相关性特征;在获取数据中的依赖关系和潜在特征后,选择RF(Random Forest)模型作为预测器,以抽取的特征为基础构建非线性预测模型,得出最终的预测结果。实验以两条城市道路中的38个检测器为实验对象,选取了5周工作日的交通流数据,并将所提方法与其他常见交通流量预测模型进行比较。结果表明,DCGRU-RF模型能够进一步提高预测精度,准确度可达95%。     

论网络犯罪形成的心理因素与防控

现代生活中计算机网络已经越来越普及,在人们享受网络科技带来的快捷方便的同时,网络犯罪也悄悄潜入了人们的生活,黑客病毒、网络诈骗以及色情暴力信息随着网络如潮水般涌来,对网络犯罪进行防控已经刻不容缓。本文简要介绍了目前网络犯罪的特点,然后从心理学的角度分析了网络犯罪形成的原因,最后针对网络犯罪心理的成因,从政府部门的角度提出了一些预防措施。     

A study of urban expansion of prefectural-level cities in South China using night-time light images

Because of its rapid economic development, China has been undergoing a dramatic urbanization process in recent decades. Such a process can be reflected by urban expansion, which can be represented by the change in urban built-up areas. In the literature, very little has been discussed on the extraction of built-up areas with a high frequency of acquisition for a large region. This article introduces a methodology to extract built-up areas using night-time stable light data. An improved calibration method is formulated to first eliminate the discrepancies across different satellites and years. A thresholding technique utilizing the sudden jump method is then employed to extract built-up areas for each prefectural city in each year. This method selects the best threshold by scrutinizing the sudden jump in the natural logarithms of the areas under different digital number (DN) thresholds. Moreover, the urbanization process of South China is examined using the extracted time series of built-up areas. The results show that such extracted time series represent changes in urban areas rather well when compared with the Thematic Mapper (TM) images, and a significant linear relationship between the extracted built-up areas and those of the land-use map and the China City Statistical Yearbook (CCSY) has also been established. Moreover, the empirical analyses also reveal that urban expansion took place in all cities from 1992 to 2010, especially in coastal cities, capital cities, and cities in the special economic zones.     

多参数的城市时空热点查询

城市时空热点指城市居民来往次数较多且交通流量较大的时空区域。确定城市时空热点在城市基础设施建设、交通规划、商铺选址、打击犯罪等公共服务领域有大量的应用。目前的热点检测通常是在收集到的全部出租车轨迹上，采用Getis-Ord统计学方法，把轨迹按照时空立方单元进行划分，计算所有轨迹数据覆盖下的热点单元，作为城市时空热点。由于积累的轨迹数量庞大且计算复杂，现有检测算法的重点放在了如何应对海量的数据上。但随着实际应用的扩展，很多需求下的热点检测不需要用到全部数据，适当的数据组织可以使热点检测变得高效。针对实际应用的需要，时空热点查询可以按照用户指定参数（地理范围、日期范围、城市热点大小和时间组织方式），计算时空区域的热度，返回TOP-K热度单元作为时空热点。针对不同的查询参数，时空热点查询需要处理的数据不同，小粒度三维网格索引的轨迹数据组织方法能够快速提取需要处理的轨迹数据。用纽约市出租车轨迹数据集在Spark集群进行查询实验，结果证明这样的索引方法和存储策略能够满足指定参数，并大幅减少查询响应时间。     

BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

For a long time, legal entities have developed and used crime prediction methodologies. The techniques are frequently updated based on crime evaluations and responses from scientific communities. There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level. Child maltreatment is not adequately addressed because children are voiceless. As a result, the possibility of developing a model for predicting child abuse was investigated in this study. Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events. The data set was balanced using the Borderline-SMOTE technique, and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse. The proposed approach successfully predicted crime types with 93% of accuracy, precision, recall, and F1-Score. The AUC value of the same was 0.989. However, when compared to the Extra Trees model (17.55), which is the second best, the proposed model’s execution time was significantly longer (476.63). We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse. The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model (BS-SC Model) would be effective in the real-time child abuse prediction and prevention process.     

城市燃气短期负荷预测的神经网络等维新息模型

应用人工神经网络理论和灰色预测理论中的等维息建模思想,建立了既反映其时间序列的周期性增长趋势,又包括天气、气温等非线性影响因素在内的短期负荷预测的BP神经网络等维新息模型。通过改进BP神经网络,对哈尔滨市燃气管网系统的小时燃气用量进行了预测,所建立的东仅有较高的收敛速度和精度,同时也具有较强的适应性和灵活性,可应用于工程实践。     

The Crime Crisis and Preventable Crimino-Genesis

Strong research evidence implicates housing design as a powerful factor in crimino-genesis during the formative years of early childhood. Detailed and large-scale investigations have revealed 38 design and layout variables that favour existing criminals and breed many new ones by impeding normal child-rearing practices. The average age of criminals has progressively decreased and crimes have become more sadistic. It is well established that changing the deleterious designs responsible for social breakdown can halt and reverse the decay of local societies. The police report that the relevant type of design improvement allows natural community formation to replace the pre-existing ethos of anonymity and alienation, so that crime may virtually cease, indeed, in some cases, completely so. Young children are then integrated into the community and do not grow up delinquent. This is criminal prevention, as opposed to crime prevention, which allows delinquency to develop and then tries to control its results. This paper discusses the specific design variables that are influential; the results obtained by changing them in ten estates; the reasons why government (apart from former British Prime Minister Thatcher) turns a blind eye to the design solution to crime; and the evidence for thinking that the escalating urban crisis is close to the sort of sudden change postulated by catastrophe theory.