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1.
采用核密度估计和Aoristic analysis等方法分析了2015年A市CP区入户盗窃警情的时空分布热点,并通过热点矩阵分类进一步对热点内的犯罪时空分布格局及其内在犯罪机理进行分析。结果显示：CP区共有3个犯罪热点区域和2个集中时段;3个热点区域分别为时间集中-空间聚集型、时间集中-空间分散型、时间集中-空间热点型;同时,影响入户盗窃犯罪的机理在于犯罪主体关联要素、犯罪客体关联要素、犯罪环境关联要素在时空上的密切耦合,共同影响入户盗窃犯罪活动的形成;提示在预防及控制犯罪“热点”中,需要采取更有针对性的防范措施和对策。 相似文献
2.
Borrowing methods from epidemiology, studies of spatiotemporal regularities of crime have been booming in various industrialized countries. However, few such attempts are empirical studies using crime data in developing countries due to a lack of data availability. Utilizing a recent burglary dataset in Wuhan, the fourth largest city in China, current research applied the sequential kernel density estimation and the space–time K-function methods to analyze the spatiotemporal changes of hotspots of residential burglaries. The results show that, both spatial and spatiotemporal clustering exists. The hotspots were relatively stable over time. The space–time clustering, however, shows significant concentrations both in space and over time. In addition, analytic results show significant effects of distance decay in terms of occurrences of burglary incidents along the spatial and temporal dimensions. Moreover, findings from the research provide critical information on the space–time rhythm of crime, and therefore can be utilized in crime prevention practice. Finally, the implications of the findings and limitations are discussed. 相似文献
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毒品犯罪是全球共同关注的犯罪问题,许多学者从不同的专业领域开展了大量研究。已有文献对毒品犯罪的成因进行分析,由于数据的局限性,较少从微观尺度对毒品犯罪热点的时空分布进行研究。论文以SZ市NH、DM街道内社区为例,基于毒品犯罪案件数据,利用探索性数据分析和时空扫描识别毒品犯罪时空热点分布,结合用地类型、动态人流量等数据定量分析毒品犯罪案件的时空分布影响因素。研究结果如下：① 毒品犯罪主要分布在商业发达地区和城中村地区,且城中村的毒品犯罪时空热点分布的起始时间较商业发达地区更早,影响范围也更大;② 毒品犯罪在不同用地类型分布是不均匀的,其中“住宿旅游娱乐”“商业百货批发零售”“餐饮经营服务品牌”3类用地类型与毒品犯罪具有高度相关性;③ 人流量高热区的面积占比与毒品犯罪的发生有一定的相关性,高热区面积占比大于5%或为0时,能够抑制毒品犯罪的发生;高热区面积占比在0~5%之间,能够促进毒品犯罪的发生。 相似文献
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基于风险地形建模的毒品犯罪风险评估和警务预测 总被引：2，自引：2，他引：0
犯罪具有明显的时空特征,研究犯罪问题离不开时间和空间维度分析,以及产生犯罪的社会、地理、生态、环境等因素。风险地形建模是美国学者研发的空间风险评估和警务预测技术,已在全球六大洲45个国家和美国35个州得到了独立测试和验证,被广泛应用于警务预测、国土安全、交通事故、公共医疗、儿童虐待、环境污染、城市发展等多个领域。在毒品、纵火、爆炸、强奸、抢劫、盗窃等犯罪研究领域更是取得了显著成果。本文运用犯罪热点分析和风险地形建模,以长三角地区N市毒品犯罪为研究对象,对该市2015年毒品犯罪的危险因子、空间盲区、风险地形进行分析,探索毒品犯罪的生成机理和演化规律,并对2016年毒品犯罪进行预测。研究结果表明,N市毒品犯罪呈现明显的犯罪热点和冷点;出租屋、酒店、车站、ATM机、停车场、娱乐场所、城市快速路、网吧是N市毒品犯罪的风险性因素。风险地形建模能较好地预测毒品犯罪。公安机关禁毒部门应据此进行严密管控,逐步限制、消除犯罪产生地、犯罪吸引地、犯罪促进地的生存土壤和条件。 相似文献
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基于轨迹数据场的热点区域提取及空间交互分析
——以深圳市为例 总被引：2，自引：0，他引：2
以深圳市出租车GPS数据为基础，运用时空拓展的轨迹数据场聚类方法提取城市交通热点区域，结合城市POI（Point of Interest）数据和地理实况对热点区域加以理解和分析。基于复杂网络的视角，计算交互分析指标并可视化热点区域的空间交互网络，探究城市交通和居民出行的时空规律。结果表明：1）交通枢纽（机场、火车站和口岸）、综合性商圈、城市重要主干道周边和城市商务中心在节假日和工作日均表现为持续热点区域；2）节假日热点区域分布较“发散”，主要反映了居民个性化出行需求；3）工作日热点区域分布较“收敛”，主要表现为职住分离的通勤模式；4）不同热点区域在空间交互网络中的重要性存在明显差异，其空间交互体现了距离衰减效应和局部抱团现象，居民出行的热点区域网络本身具有小世界效应和无标度特征。 相似文献
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近年来,以盗窃为代表的接触型犯罪和以电信网络诈骗为代表的非接触型犯罪均呈多发态势,严重影响社会治安稳定。已有研究对不同类型犯罪分布模式的时空稳定性关注仍不够,且未能提出不同类型犯罪的空间联合防控策略。本文以ZG市HT区为例,以社区为分析单元,运用核密度估计、时空跃迁测度法等方法,对比分析2017年盗窃犯罪和电信网络诈骗犯罪的时空分布特征及其空间分布模式的月度稳定性,并从犯罪防控角度改进时空跃迁测度法,结合二阶聚类法识别两类犯罪联合防控空间类型。研究发现：① 两类犯罪时空稳定性差异大,盗窃犯罪的空间分布模式稳定,月度变化小;而电信网络诈骗犯罪空间稳定性整体波动起伏大,月度变化较大;② 识别出两类犯罪的四种联合防控空间类型,分别是“两类犯罪无需防控社区”“两类犯罪邻域防控社区”“盗窃犯罪热点防控、电信网络诈骗犯罪无需防控社区”“盗窃犯罪连片防控、电信网络诈骗综合防控社区”。该研究有助于了解接触型犯罪和非接触型犯罪时空特征的共性和差异性,给警务联合防控提供决策支持。 相似文献
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DP半岛街头抢劫犯罪案件热点时空模式 总被引：10，自引：4，他引：6
选取H市中心城区DP半岛作为研究区域,以岛上2006-2011 年发生的街头抢劫案件（共373 起） 作为研究对象,将DP半岛内街头抢劫案件的时空分布特征分别从宏观和局部微观两个尺度层面进行系统的分析。首先,对岛上的街头抢劫案件按年、月和小时进行统计分析,总结其在不同时间尺度上的变化规律：2007 年开始的严打使案件数量逐年减少,直到2010 年才略有回升;春节期间（二月前后） 的案件数量明显高于其他月份;晚上22:00-23:00 期间是案件高发时段。其次,利用Kernel 密度方法对研究区街头抢劫犯罪的宏观空间分布进行整体的辨别,剥离出犯罪热点空间分布,分析热点与道路网和土地利用的关联性,结果表明热点多分布于主干道、通达性高的节点或土地利用混合度高的地方。最后,选出4 个最主要的热点从微观尺度进行分析,PAI 指数表明这4 个热点在时间上是稳定的,从2006 年到2011 年一直存在。依据“热点时空类型矩阵”的时间分布和空间分布模式,将这4 个稳定热点归类到不同微观时空模式,并对每类模式下的街头抢劫犯罪提出有针对性的防控对策,以便优化警力资源的配置、最大限度抑制和减少犯罪的发生。 相似文献
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基于空间句法的武汉城区“两抢一盗”犯罪分布环境 总被引：8，自引：0，他引：8
结合空间组构与环境犯罪学,初步建构基于空间句法的犯罪分布环境阐释理论。以武汉市中心城区为案例区域,以立案判决的2013年盗窃罪、抢劫罪和抢夺罪案件分布地点为数据源,以典型犯罪空间为实证研究对象,解读城市“两抢一盗”犯罪现象的分布环境。宏观尺度下,空间组构自发涌现的城市节点容易成为犯罪吸引场,与犯罪热点区分布形成密切关系;节点可达性衔接宏微观空间,影响犯罪人到达、逃逸犯罪地点成本的大小以及犯罪失败风险的高低;微观尺度上,局部空间与整体空间割裂形成的“空间缝隙”为犯罪人的空间探索创造了可能,空间的高集成度与空间使用者的单一化强化了犯罪集聚;空间拓扑深度则通过塑造社区人流的社会结构与领域感影响犯罪的空间防卫。空间句法为分析犯罪人的滋生环境、犯罪动机的刺激环境、犯罪人“到达”、“实施”、“逃逸”的活动环境以及防卫犯罪活动的约束环境提供了有力支撑。 相似文献
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Bo Yang Minxuan Lan Zengli Wang Hanlin Zhou Hongjie Yu 《International journal of geographical information science》2020,34(9):1740-1764
ABSTRACT Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test. 相似文献
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以“Web of ScienceTM核心合集”和CNKI核心文献库为数据源,运用CiteSpace软件进行文献计量分析,从发文时间、地区分布、学科分布、研究机构、关键词共现与高被引文献等方面,总结比较了2000年以来中外犯罪地理研究进展,并展望了未来的研究趋势。结果发现：1）国内外犯罪地理发文量整体呈现持续增长态势,美国发文量首位度明显。学科分布国外较为广泛,国内相对集中,且存在较大发展空间。研究机构之间的合作网络国外较强,国内机构联系较弱,后续研究力量正处于培育发展阶段。2）不同时期国内外研究关注的热点不同：国外侧重于暴力犯罪、恐怖主义犯罪、因种族歧视和性别歧视等引发的多类型犯罪研究,从微观到宏观,涉及地区、国家甚至全球层面;国内聚焦于城市社区“两抢一盗”犯罪、省域拐卖儿童犯罪和毒品犯罪等类型,微观和宏观并举,实证案例研究逐渐增多。3）随着多学科的交叉融合发展,国内外犯罪地理发展势头良好。犯罪分布模式、空间防控对策与犯罪风险模拟仍是当下较为活跃的研究议题,“3S”技术开发和大数据应用将成为犯罪地理研究的两条并行趋势线。未来需要以综合性思维审视犯罪地理环境,持续关注犯罪地理研究的潜在领域。同时,信息技术发展与计量模型应用为犯罪地理带来新契机,必须立足于当下国际社会环境,加强个人、组织和团体机构之间的研究合作,交流和分享经验成果,探索多样化的犯罪防控模式,并采取全球合作的方式应对区域所面临的犯罪挑战。 相似文献
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基于时空临近重复效应的犯罪热点特征及成因分析——以北京市抢劫案件为例 总被引：1，自引：0，他引：1
时空临近重复效应是犯罪活动的一种重要时空特征。为深入研究犯罪热点的特征及其形成原因,论文以北京市内城六区2012—2014年抢劫案件为例,通过核密度估计、时空临近重复计算及定义时空临近重复案件链等方法分析了犯罪热点的案件构成,并从犯罪人因素和环境因素等方面对犯罪热点内的案件特征结构进行了分析。结果表明：北京市内城六区的抢劫案件存在有“a”“b”“c”三个主要的空间热点,并且热点内的大部分案件均具有显著的时空临近重复效应;其中热点“a”位于双井、劲松一带,热点“c”位于南四环大红门桥一带,且2个热点内案件的犯罪人特征在一致性程度上高于环境类特征,表明热点的形成源于犯罪人在热点区域内重复作案的可能性较大;而热点“b”位于东南三环的分钟寺地区,热点内案件的环境类特征在一致性程度上高于犯罪人特征,表明该热点的形成为不同犯罪人在热点区域内集中作案的可能性较高。研究对警务部门开展针对性的犯罪打击和防控有一定的支撑作用。 相似文献
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商业空间结构是城市经济活动的重要载体,识别商业中心和商业热点区以及探究其影响因素对于商业资源优化配置显得尤为必要,进而指导城市有序发展。论文以乌鲁木齐主城区为例,利用开放平台大数据兴趣点(point of interest, POI),采用地理学空间统计方法定量识别商业中心和商业热点区,对商业分布和空间集聚特征进行分类和解读,并利用地理探测器方法探寻其影响因素。主要结论如下：① 乌鲁木齐市商业高值区分布在吐乌大高速—和平渠沿线地带,大型商业中心主要有南湖商圈、中山路商圈、友好商圈、会展商圈、米东商圈、铁路局商圈。② 商业热点区呈现“T型”双轴分布,北部新城商业地带与南部传统商业地带共同构成乌鲁木齐市最具活力的商业地带;6类商业热点区的分布可归纳为3种类型,商务和金融服务类为单一点状型,住宿和餐饮服务类为带状延伸型,生活与购物服务类为带状双核型。③地价、集聚效应、路网密度等是影响商业宏观分布的主要因素,其次为人口密度和中心可达性,自然因素如高程、地形起伏度等对商业布局影响有限;各因素对不同类型商业业态的影响程度各异,如人口密度、路网密度对购物类影响较大,中心可达性和地价对于商务、金融类影响较大;就各业态类型网点间的关系而言,商务和金融类协同作用强,餐饮与购物类协同效应较强,共同影响城市商业空间。 相似文献
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基于GIS场模型的城市餐饮服务热点探测及空间格局分析 总被引：1，自引：0，他引：1
餐饮服务是城市生活的重要组成部分,提取城市餐饮服务热点并识别其空间分布模式,对于理解城市形态结构具有重要意义。针对过去基于POI进行城市形态特征定量分析的不足,利用GIS场模型对城市特征要素的空间分布模式进行识别,并采用地学信息图谱对其模式进行可视化和分析。以济南市主城区4.71万个餐饮服务POI作为主要数据源,首先基于密度场热点探测模型提取餐饮服务热点并按照密度值进行等级划分;然后采用广义对称结构图谱和数字场层次结构图谱表达餐饮服务热点的空间分布结构特征和规模等级结构特征,并构建其分布模式图谱;最后对结果展开讨论。研究表明：① 数字场热点探测模型能够有效地从POI中识别出不同等级的热点。② 广义对称结构图谱和基于GIS场模型的层级结构图谱能够分别从纵横两个方面分析和表达餐饮热点的空间分布结构和层次等级结构特征。综上所述,本研究为基于POI的城市特征要素提取和城市形态研究提供了一种有效的定量分析思路,其方法也可以推广至其他城市特征要素的提取、分析和表达当中。 相似文献
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基于面域汇总数据的犯罪地理分析不仅存在MAUP局限,还会制约理论发展。本文将基于距离测度方法的DO指数用于犯罪地理研究,在连续空间上揭示2013—2018年北京盗窃、抢夺抢劫、暴力犯罪与32类城市功能的分布关系。研究表明：① 98%的“犯罪-功能”组合呈共聚分布,单一尺度分析极易低估犯罪发生地的种类数;② 由于罪犯在中等尺度上选择收益、风险、成本适中的概率最高,“犯罪-功能”组合的共聚尺度与程度多为倒U型关系,该规律有助补足日常活动理论和理性选择理论的空间视角;③ 随着监管加强,三种犯罪与所有功能的总体共聚程度渐趋下降,暴力犯罪的共聚尺度大于“两抢一盗”;④ 较之犯罪模式理论中的单一共聚类型,共聚组合可细分成大、中、小尺度强共聚型以及弱共聚型等小类。本研究将犯罪空间形成机制简化为犯罪点与功能点的几何关系,未来需克服混淆因素干扰、功能点均质化假设等。 相似文献
16.
Luliang Tang Xia Zhang Fei Sun Xue Yang Qingquan Li 《International journal of geographical information science》2016,30(9):1717-1737
ABSTRACTKernel Density Estimation (KDE) is an important approach to analyse spatial distribution of point features and linear features over 2-D planar space. Some network-based KDE methods have been developed in recent years, which focus on estimating density distribution of point events over 1-D network space. However, the existing KDE methods are not appropriate for analysing the distribution characteristics of certain kind of features or events, such as traffic jams, queue at intersections and taxi carrying passenger events. These events occur and distribute in 1-D road network space, and present a continuous linear distribution along network. This paper presents a novel Network Kernel Density Estimation method for Linear features (NKDE-L) to analyse the space–time distribution characteristics of linear features over 1-D network space. We first analyse the density distribution of each linear feature along networks, then estimate the density distribution for the whole network space in terms of the network distance and network topology. In the case study, we apply the NKDE-L to analyse the space–time dynamics of taxis’ pick-up events, with real road network and taxi trace data in Wuhan. Taxis’ pick-up events are defined and extracted as linear events (LE) in this paper. We first conduct a space–time statistics of pick-up LE in different temporal granularities. Then we analyse the space–time density distribution of the pick-up events in the road network using the NKDE-L, and uncover some dynamic patterns of people’s activities and traffic condition. In addition, we compare the NKDE-L with quadrat method and planar KDE. The comparison results prove the advantages of the NKDE-L in analysing spatial distribution patterns of linear features in network space. 相似文献
17.
Monsuru Adepeju Gabriel Rosser 《International journal of geographical information science》2016,30(11):2133-2154
Many physical and sociological processes are represented as discrete events in time and space. These spatio-temporal point processes are often sparse, meaning that they cannot be aggregated and treated with conventional regression models. Models based on the point process framework may be employed instead for prediction purposes. Evaluating the predictive performance of these models poses a unique challenge, as the same sparseness prevents the use of popular measures such as the root mean squared error. Statistical likelihood is a valid alternative, but this does not measure absolute performance and is therefore difficult for practitioners and researchers to interpret. Motivated by this limitation, we develop a practical toolkit of evaluation metrics for spatio-temporal point process predictions. The metrics are based around the concept of hotspots, which represent areas of high point density. In addition to measuring predictive accuracy, our evaluation toolkit considers broader aspects of predictive performance, including a characterisation of the spatial and temporal distributions of predicted hotspots and a comparison of the complementarity of different prediction methods. We demonstrate the application of our evaluation metrics using a case study of crime prediction, comparing four varied prediction methods using crime data from two different locations and multiple crime types. The results highlight a previously unseen interplay between predictive accuracy and spatio-temporal dispersion of predicted hotspots. The new evaluation framework may be applied to compare multiple prediction methods in a variety of scenarios, yielding valuable new insight into the predictive performance of point process-based prediction. 相似文献
18.
Giorgos Mountrakis 《International journal of geographical information science》2013,27(11):1389-1412
Moose–vehicle collisions (MVCs) pose a serious safety and environmental concern in many regions of Europe and North America. For example, in the state of Vermont, one‐third of all reported MVCs resulted in motorist injury or fatality while collisions have increased from two in 1982 to 164 in 2002. Our work used a MVC dataset from 1983 to 1999 in the Northeastern Highlands of Vermont (four major roads) to perform space, time and spatiotemporal analyses and guide future mitigation strategies. An adapted kernel density estimator was implemented for exploratory analyses to detect high density collision hotspots on roads. The kernel in space showed seven major density peaks which varied in magnitude and spread between roads. The kernel estimator in time for all roads showed an exponentially increasing trend with annual periodicity and a seasonal cyclic component, where the majority of collisions occurred from May to October. Spatiotemporal kernel estimation exhibited discontinuous density hotspots in time and space suggesting changing animal movement patterns across roads. We used an adapted Ripley's K‐function to test the hypothesis that MVCs clustering occurred at multiple scales in space, in time and in space–time combined. Statistically significant spatial clustering was evident on all roads at spatial scales from 2 to 10 km. A more consistent clustering in time occurred on all roads at a scale distance of 5 years. Similar to the kernel estimation, annual periodicity was also evident. Positive space–time clustering was present at small spatial (5 km) and temporal scales (2 years) indicating that where MVCs occur is also influenced by when they occur. In retrospect, using multiple road lengths, and the combined kernel estimation and Ripley's K‐function in time and space, provided a powerful methodology to study varying spatiotemporal patterns of wildlife collisions along roads. This can greatly assist transportation planners in identifying optimal mitigation strategies along specific roads, such as deciding on location and spatial length for permanent and expensive measures (e.g. crossing structures and associated fencing) versus less permanent and inexpensive structures (e.g. wildlife signage and reduced speed limits). 相似文献
19.
The prevailing pattern in much of the social sciences, including geography and criminology, relies on count data. “Hotspots” — geospatial areas with disproportionally more crime than the rest of the city — are usually identified by the number of events in these areas. Yet no attention is given to their severity, or any other weighting system of harm, despite the common-sense view that not all crimes are created equal. To illustrate the value of focusing on harm in addition to count data, we turn to a spatial analysis of crime by observing crime concentrations (hotspots) against harm concentrations (harmspots), across fifteen councils in the United Kingdom. The definition of “harm” is based on the Sentencing Guidelines for England and Wales, as each crime category (n = 415) attracts a different severity weight. Both “hotspots” and “harmspots” are defined as being at least 2 standard deviations from the mean distribution within each city: This procedure creates comparable datasets. The data suggest that half of all crime events are concentrated within 3% of all street segments in the selected councils, yet harm is even more heavily concentrated, with half of all harm located in just 1% of each council [OR = 3.49; 95% CI 3.268–3.728]. The intra-unit variance was also reduced by approximately half — from 0.75% to 0.45%. We discuss the implications of using harm, in addition to counts, for research and policy by arguing that a shift in focus is required both for the development of theories and for cost-effective prevention strategies. 相似文献
20.
Police databases hold a large amount of crime data that could be used to inform us about current and future crime trends and patterns. Predictive analysis aims to optimize the use of these data to anticipate criminal events. It utilizes specific statistical methods to predict the likelihood of new crime events at small spatiotemporal units of analysis. The aim of this study is to investigate the potential of applying predictive analysis in an urban context. To this end, the available crime data for three types of crime (home burglary, street robbery, and battery) are spatially aggregated to grids of 200 by 200 m and retrospectively analyzed. An ensemble model is applied, synthesizing the results of a logistic regression and neural network model, resulting in bi-weekly predictions for 2014, based on crime data from the previous three years. Temporally disaggregated (day versus night predictions) monthly predictions are also made. The quality of the predictions is evaluated based on the following criteria: direct hit rate (proportion of incidents correctly predicted), precision (proportion of correct predictions versus the total number of predictions), and prediction index (ratio of direct hit rate versus proportion of total area predicted as high risk). Results indicate that it is possible to attain functional predictions by applying predictive analysis to grid-level crime data. The monthly predictions with a distinction between day and night produce better results overall than the bi-weekly predictions, indicating that the temporal resolution can have an important impact on the prediction performance. 相似文献