社会空间视角下大遗址区乡村地域功能结构研究——以关中5处大遗址区为例

新时期国家大力推进遗产“活化”与遗产旅游,大遗址保护与利用过程既影响区域空间功能重构又涉及深刻的社会关系调整。研究选取大遗址数量多、分布密集的关中地区中5处大遗址区为案例,从社会与空间维度构建指标体系,采取因子生态分析划定社会区,以“社会—空间”综合交互视角解析乡村地域功能结构特征。研究发现大遗址区乡村社会空间存在文保限制因子、旅游带动因子、工业与商贸因子等8个主因子,各主因子空间分布体现典型的分异特征,并呈现出6种功能属性的片区。其地域功能结构特征表现为：①保护区划产生圈层式地域功能格局,②高附加值功能空间沿交通线嵌入,③核心与边缘区功能构成与遗址影响力及城乡区位相关,④各功能片区并未形成产业联动。在此基础上,进一步剖析了大遗址区乡村非均衡发展成因及优化路径。     

便携式X荧光光谱结合主成分分析鉴别不同产地的绿松石

国内外研究成果表明,便携式X荧光光谱仪(PXRF)在文物矿料产源研究中具有一定的可行性。基于此,利用PXRF检测陕西洛南河口、湖北竹山、河南淅川等东秦岭5个不同产地绿松石样品,结果显示,不同矿点绿松石样品在某些元素含量方面存在差异。随之建立主成分分析(PCA)鉴别模型。建模结果表明,三个主成分因子累计方差贡献率达86%,样本在主成分空间中大体聚集为不同的区域,结合Ba、V、U元素的散点图,基本实现不同产地绿松石的鉴别。提取模型的载荷因子,分析显示,5个产地绿松石的差异主要在Sn、Sb、Te、Sr、Ni、Ba、V、Mo、U九种元素上。运用该方法初步判定洛南河口绿松石古矿可能是二里头遗址出土绿松石的矿源之一。研究结果表明PXRF结合PCA分析,可以一定程度实现不同产地绿松石的鉴别。     

我国城市犯罪空间防控研究二十年

从4个方面透视了我国20年来城市犯罪空间防控的研究进展。(1)溯源-学习与批判。国外的犯罪原因传统理论、犯罪新理论和城市犯罪空间理论等,为我国城市犯罪空间防控研究提供了理论素养和反面教训。(2)历程-理念与实践。城市犯罪空间防控研究从介绍国外研究成果并蕴育科学理念,到结合中国国情尝试应用实践,再到初步提出理论体系,经历了一个短暂而并不缓慢的发展历程。(3)凝练-概念与框架。理清了空间防控的概念内涵、理论要点,阐明了空间防控的不可替代性。(4)展望-拓展与深化。我国城镇化的客观需求必将为空间防控研究提供强大的推动力,地理学必将在空间防控研究领域里取得重大而独特的成就。     

公共空间内犯罪被害性别差异的阐释:基于整合性的理论视角

激进女权主义以父权制为理论核心,为私人空间内发生的女性被害提供了直接的解释。然而,父权制却无法对公共空间内存在的被害性别差异提供完备的解释。基于女权主义犯罪学、日常活动理论和时间地理学,本文从整合的视角,以父权制作为结构性起点,以劳动分工和女性对犯罪的恐惧为中间环节,以女性日常活动中时空约制对其被害风险的影响为落脚点,阐释了公共空间内犯罪被害性别差异的原因。最后总结了本文的解释思路,并为进一步促进我国犯罪地理学发展提出了一些建议。     

城市舒适性视角下长春市创新产出的空间特征及影响因素

基于长春市2009—2018年专利授权数，以城市街道为创新产出基本分析单元，从城市舒适性视角探讨城市创新产出的空间格局及其影响因素。研究发现：2009—2018年长春市城市内部创新发展差异逐渐增大，局部区域相对均衡，整体呈现“西南集聚，东南分散”的空间特征；创新产出的空间单元数量逐渐增多，并形成“北低南高，四大核心”的空间格局；且伴随显著的高值集聚现象，企业、大学与研究机构的空间聚集性促进了知识溢出效应；城市舒适性对于长春市创新产出的空间集聚有一定影响，休闲舒适性因子与长春市创新产出的空间分布整体相关性较强。     

重庆主城区社会空间结构分析

以重庆市主城区社会空间结构作为研究对象,以五普人口普查数据为基础,利用因子生态分析手段,采用聚类分析方法,对重庆主城区社会区加以分析。结果表明影响2000年重庆主城区社会区形成的主因子有蓝领人口因子、白领人口因子、居住条件因子、外来人口因子4个;将2000年重庆主城区社会区划分为六个主要类型,其空间分布呈现出明显的"马赛克和圈层"结构,内圈表现为马赛克分布,被外圈农业人口分布区所包围。从主城区层面上看,社会区分布表现出一定非规律性;在组团内反映了一种内在的规律性。     

城市犯罪风险区位因子与风险地形分析——以武汉市主城区为例

从犯罪背景空间、场所空间、联接空间和聚集空间四个层次,构建城市犯罪风险区位因子体系。以武汉市主城区为研究区域,以立案判决的“两抢一盗”犯罪为数据源,综合运用空间句法、犯罪近重复分析和地理加权回归模型方法进行犯罪风险地形建模实证研究。结果表明,犯罪风险区位因子对犯罪空间分布的影响具有显著的空间异质性;依据多层次的犯罪风险区位因子体系及其对犯罪行为的影响机制,城市犯罪高风险区域可分为城市商业中心、火车站交通枢纽、城中村和城乡结合部等几大类型。基于犯罪地理学理论构建的犯罪风险区位因子体系模拟的犯罪风险地形对实际犯罪空间分布有良好的解释度,可为城市犯罪分布环境形成机制的研究提供相关借鉴。     

国内城市空间环境与犯罪关系研究评析

首先统计分析1980年代以来中国知网、维普以及万方三大数据库中我国城市空间环境与犯罪关系研究的文献数量和研究进展。进而基于城市规划的视角,将文献分为"犯罪空间分布研究-犯罪与空间环境关系研究-空间环境设计预防犯罪对策研究"三个方面进行评析,指出当前国内研究多为对国外理论或实践的介绍,实证研究匮乏,加之针对我国城市环境与犯罪关系的研究系统性欠缺,各环节研究存在脱节现象,导致提出的环境设计预防对策缺乏可操作性。最后指出国内这一研究领域存在的问题及未来有待发展的方向。     

基于手机信令数据的长春市活动空间特征与社区分异研究

居民时空行为成为透视城市空间的重要视角,本文基于手机信令数据研究长春市活动空间及其社区分异,轮廓性的揭示居住空间、就业空间和消费休闲空间的分布规律,从人地互动的过程与格局的视角认识城市空间。研究发现,居住活动对中心城区依赖明显高于就业和消费休闲活动,就业活动呈现多中心连片发展格局,近郊的欧亚卖场和郊区的净月潭成为人们消费休闲活动的重要选择。就业-居住距离和消费休闲-居住距离的社区分异显著,折射出三类活动空间关系复杂,由此感知城市生活空间质量和社会空间结构。通过对社区分异的空间聚类分析明晰各地域自身问题,从而有针对性的对长春的空间调整优化和居民行为引导提出建议,构建和谐宜居的城市空间。     

城市空间环境对城市犯罪的影响

城市空间环境的基本属性是影响城市犯罪的重要的空间因子。本文全面分析了城市空间环境的分异格局、布局形态、相互作用等基本属性的空间显现形式,系统论述了这些属性对城市犯罪种类、犯罪率、犯罪要素的直接与间接影响。     

基于家庭的广州市入室盗窃的特征分析

家庭是社会的细胞,以犯罪地理学的理论和方法作为依据,从家庭的视角研究入室盗窃的特征及其影响因素。选取广州市18个社区作为案例,以1332个家庭的问卷调查作为数据基础,分别分析不同类型家庭遭遇入室盗窃的频次特征。结果表明:不同类型家庭在遭遇入室盗窃的频次方面存在明显的差异,邻里之间是否愿意帮忙、是否是本地家庭、居住条件和家庭收入等因素都会对入室盗窃发生的频次产生影响。在此基础上把影响入室盗窃的影响因素划分为根本因素和非根本因素两大类,并分析了两类因素对入室盗窃的影响特征。     

THE IMPACT OF CRIME ON APARTMENT PRICES: EVIDENCE FROM STOCKHOLM,SWEDEN

This study uses data of about 9,000 apartment sales in Stockholm, Sweden, to assess the impact of crime on property prices. The study employs hedonic pricing modelling to estimate the impact of crime controlling for other factors (property and neighbourhood characteristics). Geographic Information System (GIS) is used to combine apartment sales by coordinates with offences, land use characteristics and demographic data of the population. The novelty of this research is threefold. First, it explores a set of land use attributes created by spatial techniques in GIS in combination with detailed geographical data in hedonic pricing modelling. Second, the effect of crime in neighbouring zones at one place can be measured by incorporating spatial lagged variables of offence rates into the model. Third, the study provides evidence of the impact of crime on housing prices in a capital city of a traditional welfare state, information otherwise lacking in the international literature. Our results indicate that apartment prices in a specific area are strongly affected by crime in its neighbouring zones, regardless of crime type. When offences were broken down by types, residential burglary, theft, vandalism, assault and robbery individually had a significant negative effect on property values. However, for residential burglary such an effect is not homogenous across space, and apartment prices in central areas are often less discounted by being exposed to crime than those in the city's outskirts.     

唐代官吏赃罪述论

《唐律》中官吏赃罪相当于现代刑法中的贪污、贿赂罪。《唐律》规定了六种赃罪,除强盗和窃盗两种赃罪外,受财枉法、受财不枉法、受所监临和坐赃的犯罪主体均为官吏。另外,属于窃盗赃罪范畴的监临主守自盗主体也是官吏。《唐律》对官吏赃罪规定了广泛的范围和严厉的刑罚。惩治官吏犯赃是唐代以法治吏的重点,唐代对官吏犯赃采取从重惩罚政策。由于唐代官吏赃罪的以上特点,使它成为排除异己、打击政敌的有效武器。     

Lost primacy: crime in Italy at the end of the twentieth century

This article attempts to answer four questions. First: has Italy today, or has it had in the last thirty years, rates of theft, robbery and homicide that are higher than those in other western European countries? Second: have crime statistics in Italy followed the same pattern as in other European countries in the same period? Third: have crime statistics become more or less comparable in the western European countries in this period? Fourth: what factors have determined changes in crime rates in Italy in recent decades?     

Theft and Gender in Late Medieval Bologna

This article aims to challenge the prevalent presentation and analysis of gender in studies of theft in the Middle Ages. Female thieving is usually differentiated from men's – by motivation and mode, by venue and value – and these differences are related either to women's physical inferiority or to their nurturing roles (thieving to support the domestic economy) or to inequalities of access to such elements as weapons (for robbery) or tools (for burglary). When studying theft, historians have focused on male thieving as the norm, and they have tended to differentiate thieving according to gender: women mainly commit petty theft, often for immediate consumption, and do not steal on the highways, in gangs or with weapons and violence. Men, on the other hand, operate in groups: their motivation is more diverse and they use a more varied repertoire of modus operandi, which includes highway robbery. To re‐examine these characterisations, I have taken data from three twenty‐year periods from the criminal court of the city of Bologna in northern Italy (one of the most complete series of judicial records for the period). These yield a total of over 700 prosecutions and over 1,300 accused. The focus of the analysis is on elements of differentiation, such as the numbers of individual and gang thefts, the nature of goods stolen, the use of violence, et cetera. These show that, while there was some differentiation between male and female thieving, this was mainly quantitative, and that qualitatively there were more shared features of behaviour. What are often seen to be the distinguishing features of male thieving – robbery, violence, gangs, professionals – turn out to be a small, untypical group. What is often seen to be the distinguishing features of female thieving – petty theft for immediate consumption – turns out to be equally or more true of male theft.     

Crime Risk Maps: A Multivariate Spatial Analysis of Crime Data

In crime analyses, maps showing the degree of risk help police departments to make decisions on operational matters, such as where to patrol or how to deploy police officers. This study statistically models spatial crime data for multiple crime types in order to produce joint crime risk maps. To effectively model and map the spatial crime data, we consider two important characteristics of crime occurrences: the spatial dependence between sites, and the dependence between multiple crime types. We reflect both characteristics in the model simultaneously using a generalized multivariate conditional autoregressive model. As a real‐data application, we examine the number of incidents of vehicle theft, larceny, and burglary in 83 census tracts of San Francisco in 2010. Then, we employ a Bayesian approach using a Markov chain Monte Carlo method to estimate the model parameters. Based on the results, we detect the crime hotspots, thus demonstrating the advantage of using a multivariate spatial analysis for crime data.     

基于环境犯罪学理论的入室盗窃时空分布研究——以北京市主城区案件的分析为例

以往关于犯罪时空特征的研究较多的侧重于对犯罪时空分布现象的描述分析以及从人文地理视角下的环境影响性分析,但缺少从环境要素与犯罪主体行为之间的相互作用机制下的犯罪时空特征研究。本文通过开展北京市入室盗窃案件的时空热点实证分析,总结和归纳了案件热点环境的时空要素类型,并基于环境犯罪学的基本原理,构建了基于犯罪主体与环境影响要素相互作用的犯罪时空分布机制,其中,环境对犯罪主体的影响主要表现为四类要素,即目标、交通、防范和作案时机。这些研究可以为下一步开展犯罪模拟以及犯罪预防提供借鉴。     

The homicidal nation: An investigation into violence in the United States

Violent crime occupies a central place in American political culture, both as an element of the national character and as the focus of continuing policy debates on issues as disparate as school integration and gun control. This paper seeks to identify the root of the American penchant for violent crime, and for homicide in particular, and sketches its political implications. An “opportunity” model, stressing reaction to social immobility in a society with pervasive achievement‐oriented symbols, provides an appealing approach. It fails to explain, however, the regional violence characterising the South. Institutional factors, notably the weaknesses in a fragmented, locally‐controlled system of law enforcement within a non‐parliamentary democracy, must also contribute to this American problem.     

Applying Geostatistical Analysis to Crime Data: Car-Related Thefts in the Baltic States

Geostatistical methods have rarely been applied to area-level offense data. This article demonstrates their potential for improving the interpretation and understanding of crime patterns using previously analyzed data about car-related thefts for Estonia, Latvia, and Lithuania in 2000. The variogram is used to inform about the scales of variation in offense, social, and economic data. Area-to-area and area-to-point Poisson kriging are used to filter the noise caused by the small number problem. The latter is also used to produce continuous maps of the estimated crime risk (expected number of crimes per 10,000 habitants), thereby reducing the visual bias of large spatial units. In seeking to detect the most likely crime clusters, the uncertainty attached to crime risk estimates is handled through a local cluster analysis using stochastic simulation. Factorial kriging analysis is used to estimate the local- and regional-scale spatial components of the crime risk and explanatory variables. Then regression modeling is used to determine which factors are associated with the risk of car-related theft at different scales.