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.     

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.     

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.     

Street level urban design qualities for walkability: Combining 2D and 3D GIS measures

Much of the physical activity and built environment literature has focused on composite walkability indices based on the D variables– design, density, diversity, destination accessibility, and distance to transit. This literature, however, has largely ignored the microscale streetscape features that affect the pedestrian experience. Five street level urban design qualities were recently identified and defined for quantitative measures although these measures are mostly through subjective field observation. View related features such as long sight line and proportion of sky have not yet been objectively measured due to the limitation of data and method. This study uses both 2D and 3D GIS to objectively measure street level urban design qualities in Buffalo, New York and tests their correlation with observed pedestrian counts and Walk Scores. Our results showed that 3D GIS helped to generate objective measures on view related features. These objective measures can help us better understand the influence of street level urban design features on walkability for designing and planning healthy cities.     

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.     

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.     

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.     

Decoding urban landscapes: Google street view and measurement sensitivity

While Google Street View (GSV) has been increasingly available for large-scale examinations of urban landscapes, little is known about how to use this promising data source more cautiously and effectively. Using data for Santa Ana, California, as an example, this study provides an empirical assessment of the sensitivity of GSV-based streetscape measures and their variation patterns. The results show that the measurement outcomes can vary substantially with changes in GSV acquisition parameter settings, specifically spacing and direction. The sensitivity is found to be particularly high for some measurement targets, including humans, objects, and sidewalks. Some of these elements, such as buildings and sidewalks, also show highly correlated patterns of variation indicating their covariance in the mosaic of urban space.     

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.     

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.     

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.     

Capturing what human eyes perceive: A visual hierarchy generation approach to emulating saliency-based visual attention for grid-like urban street networks

Visual hierarchy is an important notion in urban imagery research. As the skeletons of cities, urban streets attract more attention from urban residents and street network hierarchies are important references for urban planning and urban studies. However, due to the characteristic of over-regularization, it is often difficult for humans to differentiate visual salience for grid-like street networks, resulting in the hierarchies of grid-like streets yielded by existing methods being prone to cause visual cognitive confusion. Therefore, in this study, we proposed a novel model to quantify the extent to which a street attracts human visual attention through emulating the visual attention mechanism that can capture the focus of relatively significant elements at different levels of perception. Using the natural street (also known as the stroke) as the sensor unit, the comprehensive visual salience (CVS) index combining the geometric competitive factors of natural streets at the local scale and psychological competitive factors of natural streets at the global scale is designed. Finally, the visual salience of the urban natural streets is ranked by these CVS scores and the visual hierarchy is derived by the head/tail breaks scheme. The model was applied to eight typical grid-like street networks and the results show that the performance of visual discrimination on street hierarchies is greatly improved. Our hierarchy generation method could effectively detect visually prominent streets for grid-like street networks and generate the visual hierarchies of grid-like street networks that conform to the hierarchies perceived by human eyes. These results would provide helpful suggestions in practical urban street network applications.     

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.     

Places for play: Understanding human perception of playability in cities using street view images and deep learning

Play benefits childhood development and well-being, and is a key factor in sustainable city design. Though previous studies have examined the effects of various urban features on how much children play and where they play, such studies rely on quantitative measurements of play such as the precise location of play and the duration of play time, while people's subjective feelings regarding the playability of their environment are overlooked. In this study, we capture people's perception of place playability by employing Amazon Mechanical Turk (MTurk) to classify street view images. A deep learning model trained on the labelled data is then used to evaluate neighborhood playability for three U.S. cities: Boston, Seattle, and San Francisco. Finally, multivariate and geographically weighted regression models are used to explore how various urban features are associated with playability. We find that higher traffic speeds and crime rates are negatively associated with playability, while higher scores for perception of beauty are positively associated with playability. Interestingly, a place that is perceived as lively may not be playable. Our research provides helpful insights for urban planning focused on sustainable city growth and development, as well as for research focused on creating nourishing environments for child development.     

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。     

城市生活性街道的空间营造——以天津市鞍山道北段为例

生活性街道是城市居民在城市生活中不可缺少的场所。文章以天津市和平区鞍山道北段为例,通过实地详细调研,运用街道空间理论分析其空间结构、细部要素及市民活动,探讨如何才能营造出宜人的城市生活性街道环境。     

Crime hotspot mapping using the crime related factors—a spatial data mining approach

The technique of Hotspot Mapping is widely used in analysing the spatial characteristics of crimes. The spatial distribution of crime is considered to be related with a variety of socio-economic and crime opportunity factors. But existing methods usually focus on the target crime density as input without utilizing these related factors. In this study, we introduce a new crime hotspot mapping tool—Hotspot Optimization Tool (HOT). HOT is an application of spatial data miming to the field of hotspot mapping. The key component of HOT is the Geospatial Discriminative Patterns (GDPatterns) concept, which can capture the differences between two classes in a spatial dataset. Experiments are done using a real world dataset from a northeastern city in the United States and the pros and cons of utilizing related factors in hotspot mapping are discussed. Comparison studies with the Hot Spot Analysis tool implemented by Esri ArcMap 10.1 validate that HOT is capable of accurately mapping crime hotspots.     

Detecting and mapping traffic signs from Google Street View images using deep learning and GIS

Street traffic sign infrastructure remains an extremely difficult asset for local government to manage due to its diverse physical structure and geographical distribution. A spatial registrar of traffic infrastructure is currently a required component of local government councils' mandatory road management plans. Recent advancements of object detection technology in machine learning have presented an automated approach for the detection and classification of street signage captured by Google's Street View (GSV) imagery. This paper explores the possibility of using deep learning to produce an autonomous system for detecting traffic signs on GSV images to assist in traffic assets monitoring and maintenance. By leveraging Google's Street View API, this research offers an economic approach of building purposeful street sign computer vision datasets. A custom object detection model was trained to detect and classify Stop and Give Way signs from images captured at intersection approaches. Considering the output detected bounding box coordinates, photogrammetry approach was applied to calculate the approximate location of each detected sign in two-dimensional geographical space. The newly located and classified street signs can be combined with relevant spatial data for implementation into an asset management system. By combining GIS and the GSV API, the process is completely scalable to any level of street sign classification scope. The experiments conducted on the road network of study area recorded a detection accuracy of 95.63% and classification accuracy of 97.82%. Our proposed automated approach to the detection and localisation of street sign infrastructure has displayed a promising potential for its use by local government authorities. Our workflow can be used to detect other traffic signs and applied to other road sections and other cities. Of primary importance, this approach takes an entirely free and open-source approach throughout. The continuation of Google's Street View program will account for the spatiotemporal representation of street sign infrastructure for the ongoing maintenance and renewal programs of this valuable asset.     

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.     

基于BERT的盗窃罪构成要件识别方法

随着人工智能技术的发展,人工智能技术在生活中被广泛使用,并逐步深入到司法审理中.但在实际应用中存在着可解释性不足,不能有效的辅助审理这一问题.针对这一问题,本文结合刑事案件审理过程中依据犯罪构成采用的四要件理论,从犯罪构成的四要件角度,设计了构成要件识别任务.筛选了盗窃罪中一些构成要件,构建盗窃罪构成要件数据集.并基于...