Ground motion prediction equations derived from the Italian strong motion database

We present a set of ground motion prediction equations (GMPEs) derived for the geometrical mean of the horizontal components and the vertical, considering the latest release of the strong motion database for Italy. The regressions are performed over the magnitude range 4?C6.9 and considering distances up to 200?km. The equations are derived for peak ground acceleration (PGA), peak ground velocity (PGV) and 5%-damped spectral acceleration at periods between 0.04 and 2?s. The total standard deviation (sigma) varies between 0.34 and 0.38?log₁₀ unit, confirming the large variability of ground shaking parameters when regional data sets containing small to moderate magnitude events (M?<?6) are used. The between-stations variability provides the largest values for periods shorter than 0.2?s while, for longer periods, the between-events and between-stations distributions of error provide similar contribution to the total variability.     

街头抢劫者前犯罪经历对其后作案地选择的影响

作案地选择是犯罪地理学的研究主题。已有的重复作案地选择的研究表明,犯罪者“前案件”作案地选择对他们“后案件”作案地选择具有影响,但以往研究关注的是先前的犯罪时间和地点对其后续作案地选择的影响,仍未检验犯罪者在“前案件”中犯罪经历的具体作用。因此,论文以中国东南沿海ZG市为例,利用街头抢劫者的抓捕数据和混合Logit模型,聚焦探析街头抢劫者先前的个体犯罪经历对他们随后的作案地选择的影响。研究发现：街头抢劫者在“前案件”中的犯罪间隔、犯罪出行和当场被捕等个体犯罪经历对其“后案件”作案地选择具有强烈的影响,即“前后案件”的犯罪间隔越临近、“前案件”犯罪出行距离越短,以及“前案件”未当场被捕,则大大增加了街头抢劫者返回到先前抢劫区域再次犯罪的可能性。并通过警察访谈和结合理论分析,发现“前案件”未当场被捕是由犯罪者当场被捕的恐惧感、警察特殊的干预方式,以及社会凝聚力和犯罪防控的相互作用而形成。研究结论可为警务部门的“事前防控”与“主动处置”提供一定的参考。     

建成环境对星级酒店内被盗的影响——以ZG市中心城区为例

盗窃是中西方酒店内财产犯罪中最为高发的一种,但鲜有研究关注建成环境对酒店被盗的影响。论文以2012—2014年ZG市中心城区发生过被盗的星级酒店作为研究样本,在综合分析酒店被盗时空特征的基础上,选取酒店周围500 m范围内的建成环境指标,利用负二项回归模型,对分时段各类型的星级酒店被盗的影响因素进行系统建模分析。结果表明：被盗星级酒店的时空分布呈现典型的集聚分布特征。不同时段空间“主热点”分布相对稳定,“次热点”有所不同。整体来看,提高服务水平对降低星级酒店被盗的作用最为稳定,周围的兴趣点(Point of Interest, POI)数量会显著增加星级酒店被盗的机会,道路交叉口则对星级酒店被盗起显著监管作用。分模型结果显示,服务水平对三星级和五星级酒店被盗的抑制作用显著,道路交叉口则对以商务客人为主的四星级酒店被盗的抑制作用更强,而POI数量对等级较低的三星、四星酒店被盗作用更为明显;大型零售商业中心能显著增加旅游旺季及周末时段的酒店被盗的数量,道路交叉口数量则对旅游淡季、工作日2个时段的星级酒店被盗风险的监控作用显著。研究表明建成环境在影响ZG市星级酒店被盗的机会和成本方面作用显著。结果验证了日常活动理论在中国大城市星级酒店被盗方面研究的适用性,拓展了犯罪地理学在星级酒店被盗方面的研究成果,对酒店盗窃预防有指导意义。     

“雷霆扫毒”对贩卖毒品犯罪的影响及后续时空分布变化——以ZG市主城区为例

贩卖毒品是实现毒品犯罪经济利益的重要环节,是危害最为严重的毒品犯罪类型之一。现有研究主要关注毒品犯罪与建成环境之间的关系,没有顾及打击后贩卖毒品犯罪空间转移现象以及影响因素的变化。本文基于日常活动理论、犯罪模式理论和社会解组理论,以中国ZG市主城区为例,融合多源时空数据,分别对2013年8月开始的“雷霆扫毒”前后的贩卖毒品犯罪构建负二项回归模型,分析微观尺度下半公共空间、室外公共空间和室内私人空间对贩卖毒品犯罪影响的变化。研究发现“雷霆扫毒”行动后：① 贩卖毒品犯罪案件数量显著下降;② 半公共空间对贩卖毒品犯罪的影响作用减弱;③ 室外公共空间对贩卖毒品犯罪影响作用增强,室内私人空间对贩卖毒品犯罪的影响上升。结果表明：“雷霆扫毒”专项行动开展后一年,贩卖毒品犯罪的“主阵地”发生变化,由城市半公共空间逐渐向室外公共空间和室内私人空间转移。特别的是,“雷霆扫毒”对大毒枭及贩毒团伙的打击成效突出,促使2014年贩毒案件大幅度下降,实现了专项行动开展的目的。后续一系列专项行动进一步提升了对贩卖毒品犯罪的发现和查处能力,显示出专项行动对打击隐性犯罪的明显效果。研究表明公安执法部门必须对毒品贩卖犯罪进行持续的、有针对性的打击,对发生地的变动进行定期的监控,不能一蹴而就。     

居民日常活动对扒窃警情时空格局的影响

扒窃现象与居民的日常活动有较强的关系,已有文献研究了不同活动场所对扒窃的影响,但鲜有考虑不同类型居民活动出行量及其场所对扒窃影响的时间差异。本文基于日常活动理论,以南方某大城市为例,以派出所为分析单元,利用居民入户调查问卷、兴趣点（Point of Interest, POI）、扒窃报警数据,检验不同类型活动出行量与扒窃的时间相关性,并划分时间段,建立多个空间滞后负二项回归模型分析不同时段居民日常活动对应场所与扒窃的关系。研究发现,扒窃警情存在明显的时空集聚性,其中,凌晨和早上的集聚性最强,白天和晚上相对较弱;不同类型的日常活动对扒窃时空格局的作用存在差异。买菜活动出行量与扒窃的时间变化存在正相关关系,买菜场所在凌晨和早上容易吸引扒窃者;购物活动与扒窃亦存在较强的时间正相关,白天和晚上在购物场所附近被盗的风险较高;外出吃饭的出行量与扒窃行为无直接关系,但在凌晨和早上餐馆周边容易吸引扒窃者;娱乐活动与扒窃同样存在较强的时间正相关,不同类型的娱乐场所对扒窃的影响存在差异,电影院、酒吧在大部分时间段均会吸引犯罪者,凌晨时段KTV附近区域的被盗风险较高。场所对扒窃的影响主要与犯罪机会吸引（犯罪目标的丰富性、暴露性）及场所自身属性有关（是否为犯罪者的集聚地）。文中最后对时间地理学和犯罪地理学结合的可能性、研究的应用意义等方面进行了探讨。     

Influence of earthquakes on the stability of slopes

Earthquakes are a major trigger for instability of natural and man-made slopes. Often the instability of slopes due to an earthquake causes more destruction and kills more people than the actual earthquake itself. A comparison is made between different methodologies to analyze the potential stability of slopes during earthquakes. Theoretically, it seems simple to calculate the stability of a slope during an earthquake. In reality, however, the stability is influenced by so many parameters that are either not known or which influence is so poorly known that a decent estimation of stability cannot be made. Offshore the situation is worse because proper data required for stability calculations are even less available than onshore. On- and offshore, erosion and weathering create continuously slopes that may become unstable during a future earthquake, offshore also sedimentation creates continuously new slopes. Another fundamental problem in stability analysis is the complicated and largely unknown behavior of seismic waves in three-dimensions in natural materials. The lack of accurate data and the unknown behavior of seismic waves in three-dimensions make estimations of slope stability during an earthquake unreliable.     

A correlation between slope failures and accelerometric parameters: the 26 September 1997 earthquake (Umbria–Marche, Italy)

After the occurrence of the 26 September 1997 earthquake in Umbria–Marche an extensive survey was performed to individuate surface effects induced by the ground motion. Several types of effects occurred on bedrock, calcareous debris and sandy-clay deposits. Shallow soil slides and dry debris slides were mapped by field survey and aerial photograph interpretation in the epicentral area close to Nocera Umbra. This gave the opportunity for testing the prediction maps that can be obtained with the use of empirical laws, proposed by several authors, based on a simple method (Geotechnique 15(2) (1965) 139). Accelerometric records from the permanent and mobile seismic network of the Seismic Survey of Italy have been processed and interpolated to obtain strong ground motion parameters at each site, e.g. Peak Ground Acceleration, Spectral Intensity, Arias Intensity, etc. The different predictions have been tested with the real landslide data to verify their performances. Some of the proposed methods explain quite well the behaviour of slopes during the application of a time history and can therefore be employed for future landslide hazard zonation.     

Pan-European ground-motion prediction equations for the average horizontal component of PGA,PGV, and 5 %-damped PSA at spectral periods up to 3.0 s using the RESORCE dataset

This article presents a set of Ground-Motion Prediction Equations (GMPEs) for Europe and the Middle East, derived from the RESORCE strong motion data bank, following a standard regression approach. The parametric GMPEs are derived for the peak ground acceleration, peak ground velocity, and 5 %-damped pseudo-absolute acceleration response spectra computed over 23 periods between 0.02 and 3 s, considering the average horizontal-component ground-motions. The GMPEs are valid for distances less than 300 km, hypocentral depth up to 35 km and over the magnitude range 4–7.6. Two metrics for the source-to-station distance (i.e. Joyner-Boore and hypocentral) are considered. The selected dataset is composed by 2,126 recordings (at a period of 0.1 s) related to 365 earthquakes, that includes strong-motion data from 697 stations.The EC8 soil classification (four classes from A to D) discriminates recording sites and four classes (normal, reverse, strike-slip, and unspecified) describe the style of faulting. A subset which contains only stations with measured Vs30 and earthquakes with specified focal mechanism (1,224 records from 345 stations and 255 earthquakes) is used to test of the accuracy of the median prediction and the variability associated to the broader data set. A random effect regression scheme is applied and bootstrap analyses are performed to estimate the 95 % confidence levels for the parameters. The total standard deviation sigma is decomposed into between-events and within-event components, and the site-to-site component is evaluated as well. The results show that the largest contribution to the total sigma is coming from the within-event component. When analyzing the residual distributions, no significant trends are observed that can be ascribed to the earthquake type (mainshock-aftershock classification) or to the non-linear site effects. The proposed GMPEs have lower median values than global models at short periods and large distances, while are consistent with global models at long periods $(\hbox {T} > 1)$  s. Consistency is found with two regional models developed for Turkey and Italy, as the considered dataset is dominated by waveforms recorded in these regions.     

交互作用视角下人口流动性与住房类型对城市入室盗窃空间格局的影响

已有研究发现社区住房类型对城市入室盗窃的空间格局有重要影响,但鲜有学者探讨位于不同人口流动程度社区环境中的不同住房对入室盗窃影响的差异。本研究以中国东南沿海城市ZG市为例,基于日常活动和社会失序等理论,以社区为分析单元,利用入室盗窃警情数据、第六次人口普查数据和兴趣点数据（Point of Interest, POI）,运用空间滞后负二项回归方法,分析人口流动性与住房类型的交互作用对入室盗窃空间格局的影响。结果表明：人口流动性高的社区入室盗窃率较高;自建住房比例对入室盗窃率有显著的正向影响,原公有住房比例和商品房比例则呈显著的抑制作用;交互变量的结果发现,人口流动性与不同类型住房变量的交互作用存在差异,其中,人口流动性与自建住房、经济适用房、原公有住房的交互变量对入室盗窃的发生具有显著的促进作用,而与商品房的交互对入室盗窃的影响并不显著。说明在探讨不同类型住房与入室盗窃的关系时,应重视其所在社区的人口流动程度的影响。     

Wind storm risk management: sensitivity of return period calculations and spread on the territory

Modeling and forecasting damage from wind storms is a major issue for insurance companies. In this article, we focus on the sensitivity of estimations of return periods for extreme events with respect to modeling assumptions and the type of input data. Numerous variables play a role: the quality of data concerning the location of insured buildings and weather report homogeneity, missing updates for correcting non-stationarities concerning the insurance portfolio history, ground roughness or climate change, the evolution of the model after an unprecedented event such as the Lothar storm observed in 1999 in Europe, temporal aggregation of daily events over several days, where events could span over several days up to one week, and storm trajectories, which could change due to global warming or sweep larger areas. Our work explores three important aspects. First, we highlight the geographic heterogeneity of the spatial distribution of wind speeds and the resulting damages. Therefore, we propose to partition the French territory into 6 relatively homogeneous storm zones, based on the dependence among observed wind speeds and geographic distance. Second, we extend a storm index—defined in Mornet et al. (Risk Anal 35:2029–2056, 2015)—to take into account geographic heterogeneity, and we analyze its tail behavior to show the difficulties met to obtain reliable results on extreme events. Third, we explore the calculation of Solvency Capital Requirements based on a model that we propose for the annual claim amount distribution. The purpose of our analysis is to quantify and to point out the high level of uncertainty in the computation of return periods and of other quantities strongly influenced by extreme events.