兰州市城市治安时空特征分析

治安问题防治是保障城市社会安全与稳定的重要环节。本文以兰州市主城区2014年警情数据为数据来源,选择发案率高且具有代表性的盗窃、两抢(抢劫类和抢夺类)、扒窃及诈骗4类案件为研究对象,运用空间面模式和空间点模式的GIS分析方法,分析了兰州市公共安全空间结构。结果表明:1)研究时段内,案发率呈季节性变化,夏秋季节案发率比较稳定,而冬春季节案发率变化幅度较大。2)4类案件总体在街道上表现为空间聚集分布。从区际尺度来看,城关区治安形势最为严峻;从街道尺度来看,张掖路、火车站等是案发"热点"区域;从案发点分布来看,西关十字、火车站等地属于犯罪"热点"。针对上述情况,宜加强热点附近的警力部署,加大巡逻力度,保障城市公共安全。     

风险地形建模在犯罪风险评估中的应用

社会环境中的犯罪风险无处不在,监测和评估不同地点的犯罪风险有利于优化警务资源,提高公安机关打击和防控犯罪能力。如何衡量犯罪风险在地理空间的分布,成为公安机关亟待解决的难题。本文以真实案件数据,基于风险地形建模方法,验证了基于风险地形建模的犯罪风险评估的可靠性,并以2015年全年台北市住宅盗窃案件为例,通过风险地形建模、回归分析等统计分析方法,对住宅盗窃案件相关的因素创建加权风险地图,绘制犯罪风险地图。通过2016年第一季度台北市实际发生的住宅盗窃案件评估,验证了该模型具有优异的效能,可满足实战应用的需求。     

应用GIS的城市中心区定界方法

城市中心区等空间范围是城市研究中必须面对的基本问题。由于社会经济要素集聚的相对性和过渡性,城市中心区的位置和界线范围通常是不明确的,只有一个大致的范围,缺乏一个简单的确定方法。依据市政公用设施、地价、交通量等统计数据及其空间分布确定城市范围的传统方法工作量大、欠缺可比性。本研究以广州市城市道路网为数据基础,利用ArcGIS空间分析的密度分析方法圈定并验证了广州市城市中心区的边界范围。     

美国内布拉斯加州林肯市犯罪行为的聚类及热点分布分析

抢劫和入室盗窃犯罪行为是日常生活中最长见的违法行为,而这两种违法行为的发生有一定的规律可循。例如抢劫行为主要发生在市中心、CBD和商业中心的人群、商铺密集区域,而入室盗窃则多发生于生活社区及其周边地带。然而,想要防范这两种犯罪行为需要根据实际情况进行分析和比对,结合分析结果提出准确合理的警力部署。美国内布拉斯加州林肯市警察局针对该市抢劫和入室盗窃犯罪行为高发的情况,需要针对具体分析结果对于警力进行部署,主要针对徒步巡逻和是否需要对住户进行入户警示进行分析,根据分析结果进行具体部署。本文中使用的分析方法为紧邻层空间聚类分析(NNH)和针对犯罪的空间和时间分析法(SPAC),分别对林肯市入室行窃和抢劫的数据进行分析并尝试将两种方法进行对比以便给出更加科学的分析报告。     

基于POI数据的城市功能区识别及主要交通枢纽空间分析

研究利用机器学习中的TF-IDF统计方法,基于POI数据识别北京五环范围内的城市用地功能区。实验从道路网和格网两个层面开展,首先,将两结果与相同地区的遥感影像进行对比与验证,并从中提取属于交通用地范畴中的主要交通枢纽;其次,基于空间服务范围和空间连接强度两个视角对火车站和机场的地理特征进行分析,具体包括空间分布范围的特点、受区域影响的强弱、空间联系强度的差异等内容;最后,进一步对比各重要交通枢纽所在空间单元作为出租车行程起始点和终止点的共性和差异性。     

基于CA-Markov模型的土地利用格局变化研究

以广州市1990年、2000年TM影像为数据源，通过分析宽块和景观两个层次的格局指数变化，研究了20世纪90年代广州市土地利用格局的时空特征。利用马尔柯夫模型和元胞自动机模型，对广州市2010年土地利用空间格局进行了预测。     

顾及空间约束的犯罪时空关联分析组合算法

犯罪时空数据关联分析结果有助于相关部门在关键时段和关键区域进行警力重点配置.提出一种基于空间约束的lightGBM/Apriori组合算法,对犯罪数据中的时空属性特征进行简约处理,通过对犯罪类别的预测分析,识别时空关联特征较为显著的犯罪类型及关键影响要素,寻找热点细化研究区域,抽取犯罪时空关键特征,建立犯罪强关联规则挖掘模型.以美国费城2015年犯罪数据集为例,利用提出的组合算法进行了犯罪类别预测、时空分布模式分析和关联规则挖掘,将挖掘结果与真实发生的犯罪进行对比,盗窃犯罪事件在每天4个时段的预测准确率为64.29％～90.20％.     

基于FME的DSM变化检测提取方法研究

本文基于FME平台,采用目标级变化检测方法,基于同一区域不同时相影像,设计了一个DSM变化检测范围提取的模板。对同一位置不同时相的DSM进行叠置,计算高程差值,得到变化的点,并通过高程差值阈值和面积阈值过滤后,进一步处理提取变化区域。该方法为大面积的DSM变化检测和地理国情监测提供了新的技术方法。     

基于GIS的北京市治安案件空间特征分析

为了有效地在复杂城市环境中预防犯罪,本文基于地理信息系统空间统计方法和环境犯罪学原理,对北京市几类典型治安案件的空间分布特征进行了研究。研究结果发现,基于区域面的治安案件空间分布具有不均衡的特点,特别是按街道分布的治安案件热点区域集中在二环以外;具体的治安案件热点区域主要集中在中关村商务区、六里桥和木樨园客运站等少数地段;空间区位分析结果则证明了诈骗与扒窃类治安案件在二环以内的区域比例异常偏高,而抢劫与盗窃案件则主要分布在二环以外区域。     

确定射线空间相邻关系的两个非角度算法的时间复杂度分析

以建立节点上弧段之间的拓扑关系为例，对比分析了两个非角度算法在确定射线的空间相邻关系时的时间复杂度，探讨了进一步将应用范围拓展到确定点集的空间关系时两种算法的有效性。研究表明，在这一类空间分析中基子Qi(xi,yi)函数的Qi算法是一个时间复杂度低、可靠性高的算法。     

城市犯罪时空同现模式的非参数检验方法

采用时空同现模式分析方法挖掘多元犯罪事件之间的关联关系，可为犯罪事件防控问题提供科学指导。现有方法依赖人为设置的频繁度阈值，应用部门若缺乏先验知识则可能导致决策错误。因此，基于非参数统计思想，提出一种面向城市犯罪的时空同现模式显著性检验方法。首先通过重建每类犯罪事件的时空分布，构建多元犯罪事件分布独立的零模型；然后根据零模型下多元犯罪事件同现频率的试验分布，判别候选时空同现模式的显著性。最后设计具有预设模式的模拟数据实验验证该方法的有效性；在多个分析尺度（时空半径）下识别S市2016年13种犯罪事件间时空同现模式，并以时空同现模式{扰乱治安，盗窃电动自行车，扒窃}为例，结合公共设施空间分布，对该模式形成机理进行深入分析。结果表明:①该方法充分顾及了单元犯罪事件自相关特征的影响，能够有效识别具有统计特性的时空同现模式；②犯罪事件时空同现模式随分析尺度的变化而存在差异；③具有相似建成环境和社会环境的犯罪事件容易形成时空同现模式。     

Modeling the risk of robbery in the city of Tshwane,South Africa

ABSTRACT

In this study, we model the risk of robbery in the City of Tshwane in South Africa. We use the collective knowledge of two prominent spatial theories of crime (social disorganization theory, and crime pattern theory) to guide the selection of data and employ rudimentary geospatial techniques to create a crude model that identifies the risk of future robbery incidents in the city. The model is validated using actual robbery incidences recorded for the city. Overall the model performs reasonably well with approximately 70% of future robbery incidences accurately identified within a small subset of the overall model. Developing countries such as South Africa are in dire need of crime risk intensity models that are simple, and not data intensive to allocate scarce crime prevention resources in a more optimal fashion. It is anticipated that this model is a first step in this regard.     

Street‐level Spatial Scan Statistic and STAC for Analysing Street Crime Concentrations

This study develops new types of hotspot detection methods to describe the micro‐space variation of the locations of crime incidents at the street level. It expands on two of the most widely used hotspot detection methods, Spatial and Temporal Analysis of Crime and Spatial Scan Statistic, and applies them to the analysis of the network space. The study first describes the conceptual and the methodological framework of the new methods followed by analyses using: (1) a simulated distribution of points along the street network; and (2) real street‐crime incident data. The simulation study using simulated point distributions confirms that the proposed methods is more accurate, stable and sensitive in detecting street‐level hotspots than their conventional counterparts are. The empirical analysis with real crime data focuses on the distribution of the drug markets and robberies in downtown Buffalo, NY in 1995 and 1996. The drug markets are found to form hotspots that are dense, compact and stable whereas hotspots of the robberies are observed more thinly across a wider area. The study also reveals that the location of the highest risk remains on the same spot over time for both types of crimes, indicating the presence of hotbeds which requires further attention.     

Spatial Information Divergence: Using Global and Local Indices to Compare Geographical Masks Applied to Crime Data

Advances in Geographic Information Science (GISc) and the increasing availability of location data have facilitated the dissemination of crime data and the abundance of crime mapping websites. However, data holders acknowledge that when releasing sensitive crime data there is a risk of compromising the victims' privacy. Hence, protection methodologies are primarily applied to the data to ensure that individual privacy is not violated. This article addresses one group of location protection methodologies, namely geographical masks that are applicable for crime data representations. The purpose is to identify which mask is the most appropriate for crime incident visualizations. A global divergence index (GDi) and a local divergence index (LDi) are developed to compare the effects that these masks have on the original crime point pattern. The indices calculate how dissimilar the spatial information of the masked data is from the spatial information of the original data in regards to the information obtained via spatial crime analysis. The results of the analysis show that the variable radius mask and the donut geomask should be primarily used for crime representations as they produce less spatial information divergence of the original crime point pattern than the alternative local random rotation mask and circular mask.     

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Population at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public.     

The Impact of Hurricanes on Crime: A Spatio-Temporal Analysis in the City of Houston,Texas

The impact that natural disasters have on crime is not well understood. In general, it is assumed that crime declines shortly after the disaster and slowly increases to pre-disaster levels over time. However, this assumption is not always confirmed by the few empirical studies that have been conducted to date. In this paper we analyze the impacts that Hurricane Rita, and for the purpose of comparison, Hurricane Katrina had on the temporal and spatial distributions of reported crimes in the city of Houston, TX. Crime data were collected before, during, and after the landfall of both hurricanes. The modeling part of this paper focused on primarily spatio-temporal and local regression models at the local scale. Spatio-temporal models were applied to identify potential spatio-temporal crime clusters associated with the hurricanes. A local regression model in the form of a geographically weighted regression was applied to explore relationships between crime clusters and possible underlying factors leading to the creation of said clusters.

The results show that while Hurricane Katrina did not have any apparent impact on crime, Hurricane Rita led to a significant short-term increase in burglaries and auto thefts. The post important result was the identification of a large, highly significant spatio-temporal burglary cluster located in the northeastern part of Houston. This cluster lasted from a few days before to a few days after the landfall of Hurricane Rita. Empirical evidence was found that the mandatory evacuation order that was issued prior to the arrival of Hurricane Rita led to a short-time spike in burglaries. It was assumed that these crimes were committed by individuals who did not follow the evacuation order, but instead burglarized the residences of individuals who did evacuate. No mandatory evacuation order was issued for Hurricane Katrina. Altogether, three variables including the percentage of African Americans, the percentage of persons living below the poverty level, and the distance to the nearest police station was identified as having a positive relationship with the increase in burglaries associated with Hurricane Rita     

Diversity and burglary: Do community differences matter?

Diversity within a population has been linked to levels of both social cohesion and crime. Neighborhood crimes are the result of a complex set of factors, one of which is weak community cohesion. This article seeks to explore the impacts of diversity on burglary crime in a range of neighborhoods, using Leeds, UK as a case study. We propose a new approach to quantifying the correlates of burglary in urban areas through the use of diversity metrics. This approach is useful in unveiling the relationship between burglary and diversity in urban communities. Specifically, we employ stepwise multiple regression models to quantify the relationships between a number of neighborhood diversity variables and burglary crime rates. The results of the analyses show that the variables that represent diversity were more significant when regressed against burglary crime rates than standard socio‐demographic data traditionally used in crime studies, which do not generally use diversity variables. The findings of this study highlight the importance of neighborhood cohesion in the crime system, and the key place for diversity statistics in quantifying the relationships between neighborhood diversities and burglary. The study highlights the importance of policy planning aimed at encouraging community building in promoting neighborhood safety.     

Knox时空交互检验空间阈值确定方法

Knox检验是一种常用的城市犯罪时空交互模式分析方法,但其阈值需要人为指定,这种主观的阈值确定方法存在一定随意性,因此需探索更为合理的阈值确定方法。提出利用点对平均最邻近距离作为Knox检验空间阈值的确定方法,并通过城市入室盗窃、盗窃电动车和扒窃3类事件进行实验验证。结果表明,与常见的几种阈值确定方法相比,所提方法检测出了最大数量的显著性交互事件对数,能更加充分地了解事件的真实时空交互模式,为基于Knox检验的事件时空聚集模式分析提供了一种有效的空间阈值确定手段。     

基于Web新闻的案(事)件抽取与时空分析系统

新闻,自古以来便是人们了解社会动态的重要途径,大数据时代,由于Web新闻自身所具有的客观性和真实性,其蕴含的数据价值凸显。针对新闻网站中案(事)件信息丰富、易采集等优点,研究开发一套基于Web新闻的案(事)件抽取与时空分析系统,抓取各个新闻网站对发生于福州的案(事)件相关信息的报道,对新闻信息进行判别清洗与解析,采用支持向量机进行案(事)件类别分类,多类别分类精度达75%,抽取经分类处理之后的案(事)件文本中的案(事)件时空信息并进行时空分析,以毒品案(事)件为例,将解析结果与公安毒品案(事)件分别做核密度估计,结果表明,福州毒品事件集中发生于茶园派出所和象园派出所等辖区。该系统有利于分析福州社会动态,也为公安部门提供了信息辅助。     

Development of a Composite Model of Quality of Life: A Case Study in Austin,Texas

This research examines how quality of life (QOL) may be studied empirically for Austin, Texas by using social, economic, and environmental variables at the census tract level. In addition to common factors examined by previous researchers, this paper examined the inclusion of crime rate and commute time in modeling QOL. The results from factor analysis evaluated six factors related to QOL for the study area, including education and commute time (factor 1), housing and population density (factor 2), property crimes (factor 3), environmental quality (factor 4), some college education (factor 5), and violent crimes (factor 6). Using the percentage of variance for each variable as a weight, a synthesized index was developed to assess QOL in Austin, Texas.