A space-time varying graph for modelling places and events in a network

Modelling topological relationships between places and events is challenging especially because these relationships are dynamic, and their evolutionary analysis relies on the explanatory power of representing their interactions across different temporal resolutions. In this paper, we introduce the Space-Time Varying Graph (STVG) based on the whole graph approach that combines directed and bipartite subgraphs with a time-tree for representing the complex interaction between places and events across time. We demonstrate how the proposed STVG can be exploited to identify and extract evolutionary patterns of traffic accidents using graph metrics, ad-hoc graph queries and clustering algorithms. The results reveal evolutionary patterns that uncover the places with high incidence of accidents over different time resolutions, reveal the main reasons why the traffic accidents have occurred, and disclose evolving communities of densely connected traffic accidents over time.     

中国极端降水事件时空特征及其对夏季温度响应

基于中国1951-2014年728个气象站点日降水数据,利用POT抽样、变异点分析、趋势分析、分段回归等方法全面分析中国极端降水事件（量级、频率与发生时间）非平稳性特征及其对夏季温度响应。研究结果发现中国极端降水量级有明显变异特征,但无显著趋势变化,中国极端降水频率则相反。并且中国极端降水次数在全国大部分区域有显著增加趋势。另外,研究还表明变异点对中国极端降水量级和频率趋势特征有明显的改变,对极端降水发生时间趋势特征改变较弱;中国极端降水发生时间在中南部呈显著上升趋势,其他区域趋势性不显著。中国大部分区域夏季温度呈上升或显著上升趋势,且变异特征显著。在转折点前,中国大部分区域如西部干旱区东南部、东部干旱区西南部、华北区、华中区和西南区北部夏季温度呈下降或显著下降趋势;在转折点后,上述区域夏季温度转为上升或显著上升趋势。     

基于网络时空核密度的交通事故多发点鉴别方法

交通事故多发点是道路交通安全管理的重要治理对象,如何利用空间统计方法对其进行高效鉴别是研究热点。以华东某地为研究区域,以2013~2015年该研究区域的道路交通事故数据为研究对象,以时空道路网络为视角,通过路网匹配和路网裁剪形成事故时空子路段,提出一种基于交通事故场景的网络时空核密度估计值作为鉴别指标,用累计频率法和零膨胀的负二项回归模型确定鉴别阈值的事故多发点鉴别方法。     

北极水域船舶事故特征及关联规则分析

北极海冰的大量融化,加速了北极航道开发和利用,对北极水域船舶事故风险管理提出了更高的要求。本文以2008—2017年的北极水域船舶事故数据为样本,运用数据挖掘技术和方法,对北极水域船舶事故进行了时空特征分析,并对船舶属性与事故属性之间的潜在映射关系进行了关联规则挖掘。结果表明:北极水域事故数逐年呈上升的趋势,事故主要发生在巴伦支海东部的俄罗斯摩尔曼斯克港口水域和挪威北部水域;北极水域严重事故受船舶总吨、船旗国、船舶类型、事故类型、事故水域等变量的影响,船舶尺度较大的船舶更容易造成严重事故;北极水域俄罗斯籍渔船的严重事故,很多是由机损事故引发的,但很少造成环境污染。研究结论可辅助海事管理部门和航运企业开展北极水域船舶事故预防和风险管理工作。     

基于日SPEI的近55 a西南地区极端干旱事件时空演变特征

利用1960~2014年中国西南地区141个气象台站的逐日气象资料,引入一个新的干旱指数——逐日标准化降水蒸散指数（日SPEI）,对极端干旱事件的年代际、年际、季节内变化及持续性特征进行了分析,结果表明：空间上,近55 a西南春季和年极端干旱程度呈一致的减弱趋势,重庆、四川与贵州的交界处及四川西北部极端干旱程度明显缓解,而夏、秋两季极端干旱表现出增强的趋势并有一定的区域性特征。时间上,春季和全年极端干旱频率、强度和持续天数逐渐减少,春季极端干旱的减弱程度较全年明显;夏、秋两季极端干旱频率、强度和持续天数呈增加趋势,夏季极端干旱的加重趋势比秋季明显。从极端干旱事件的持续性来看,20世纪60年代和21世纪初（2000~2014年）西南遭受的极端干旱最严重,持续期达60 d以上的站点分别占到站点总数的60%和73%。     

Policy-relevant indicators for mapping the vulnerability of urban populations to extreme heat events: A case study of Philadelphia

The recent global increase in extreme heat events linked to climate change is projected to continue. The additive effect of urban heat islands from impervious surfaces and urban heat emissions (e.g., from transportation and building cooling) exacerbates extreme heat events in urban areas, exposing dense populations to extreme heat with implications for human health. Ground- and satellite-based data on urban and suburban temperatures and vegetation over a historical period can help identify temporal and geospatial trends in heat exposure. A set of indicators has been developed to map the exposure, social sensitivity, and vulnerability of urban populations to heat wave health impacts. Guided by an Advisory Group of local planners in the pilot city of Philadelphia, localized trends of increasing urban extreme heat events using MODIS Land Surface Temperature (LST) data, confirmed with urban and non-urban temperature monitor data were identified. For the Philadelphia study area, the number of heat-event days in the urban setting has increased from approximately 4 days in 1980 to almost 12 days in 2013, while the non-urban setting has consistently experienced 5 days of heat events per year across the time period. Warmer micro-climates with limited vegetative cooling and elevated LSTs were also identified. The exposure indicator was combined with areas of high social sensitivity (e.g., low-income and elderly) to create a vulnerability indicator, showing significant overlap between highly exposed and highly sensitive populations. As a measure of the adaptive capacity of local governments to reduce the urban heat island, evidence of targeted vegetation increases or reduced localized temperatures linked to urban greening and cooling programs were sought, though none were of a scale to be identified by the 1 km satellite data utilized. The indicators have helped local decision makers to understand patterns of vulnerability, and may be used in the future to target adaptation actions and measure results (LST reduction or vegetation increase) from existing adaptation actions.     

东北地区暴雪天气的统计学划分方法及其时空分布特征

应用近49 a东北地区200个气象站冬半年降水资料和相对应的NCEP/NCAR 2.5o×2.5o温度场再分析资料,提出了基于降水相态变化特征的东北地区暴雪天气划分的统计学方法,将东北地区暴雪分为纯雪型暴雪(Ⅰ型暴雪)和雨雪混合型暴雪(Ⅱ型暴雪)两大类,并根据影响范围将其划分为大范围暴雪、区域暴雪和局地暴雪3类。在此基础上分析了不同类型东北区暴雪的时空分布特征。结果发现:两型暴雪出现频次基本相当,Ⅱ型大范围暴雪明显多于Ⅰ型,局地暴雪则相反;东北地区暴雪的年际变化较大且具有明显的年代际特征,20世纪70年代后期至80年代中期Ⅰ型暴雪明显多于Ⅱ型,21世纪初期Ⅱ型暴雪多于Ⅰ型,表明全球变暖在中国东北的区域响应显著;东北区暴雪主要集中在秋末和初春两季,但两型暴雪起止时间和峰值都不相同;两型暴雪出现频次都呈东多西少的分布,其中Ⅱ型暴雪频次自东南向西向北减小的梯度更大。从结果来看所提出的划分办法很好的反映了不同降水相态的东北地区暴雪的发生和变化规律具有很大差别,这种差异与以往东北地区暴雪个例的分析工作中反映的情况是一致的;证明了本划分方法是合理的,具有实际工作意义。     

城市交通消散期事故高峰现象及成因

学界对于交通事故高峰出现在交通高峰期还是之后的消散期存在争论,以惠州市中心城区为例,对城市交通事故进行研究,判别交通消散期事故高峰现象的存在性。以交通事故空间点数据为研究对象,使用核心密度估算法,分析交通事故的结构性特征和时空分布特征,并针对三个交通消散期研究其事故的具体特征及其成因。研究表明:惠州市中心城区交通消散期存在事故高峰现象,主要发生在上午和下午;机动车在交通事故中占主导地位;临近商业设施的交通消散期事故高峰现象最为明显;支路事故在晚间交通消散期后段出现了事故高峰;从上午到晚上,交通事故黑点从交通相关设施转移到商业设施。交通消散期事故高峰现象的形成与居民集中在交通消散期的非通勤交通行为、特定路段的小时交通量高峰出现在交通消散期,以及交通消散期内松懈的交通管制和驾驶行为等有一定的关系。     

内蒙古牧区暴风雪风险评估研究

暴风雪天气是内蒙古草原牧区危害严重的气象灾害之一。为了更确切的了解暴风雪灾害天气的特征,分季节对暴风雪天气进行了类型,即秋末初春暴风雪天气、隆冬暴风雪天气、春末初夏湿雪冷雨型暴风雪天气;通过因子分析法、灰色关联分析对三种类型暴风雪天气进行分析,发现上述三种类型暴风雪天气都为风、雪、寒潮灾害群天气;每个季节最主要的影响因子不同,导致的灾害程度不同;因此掌握各个季节暴风雪天气的特征对预报员提高预报预警水平很有实际意义。尝试使用BP神经网络法、支持向量机对暴风雪灾害等级进行评估,通过与根据灾情评定的灾害等级对比分析,发现SVM方法的评估效果优于BP神经网络法,因此,基于数值预报产品通过SVM方法做暴风雪灾害预警产品成为可能,可为暴风雪灾害预报预警业务提供客观参考依据,能够提升预报服务效果,减少灾害损失。     

新疆阿勒泰地区冬季大到暴雪气候变化特征

基于新疆阿勒泰地区7个观测站1961-2010年冬季逐日降水量资料,定义了大到暴雪特征量,并对其时间序列进行标准化,然后运用线性趋势、Cubic函数、M-K突变检验、Morlet小波变换、R/S分析等方法,研究了该区冬季大到暴雪的气候变化特征。结果表明：大到暴雪量对冬季降水总量的方差贡献较大。在气候分布上,大到暴雪特征量、冬季总降水量的大值区均位于北部东部,大值中心位于富蕴或阿勒泰站,方差贡献大值中心位于青河站;小值中心位于福海站。在年际尺度上,该区各特征量有7 a的显著周期变化特征;在年代际尺度上,有13～15 a、17～23 a、28～29 a的显著周期变化。在时间域上,大到暴雪量和频次在20世纪90年代之前以负距平为主,变化相对平稳,90年代之后以正距平为主,变化相对剧烈;大到暴雪强度60-70年代初、80年代中期到21世纪最初10 a波动幅度较大,70年代中期到80年代初变化相对平稳。Cubic函数拟合表明,大到暴雪特征量在80年代末90年代初发生了由少到多的转型,但没有突变点。在空间域上,各年代大到暴雪量大值区所在范围比较稳定,位于北部东部,小值区出现在河谷平原;值中心在60和90年代出现在富蕴站,其它年代出现在阿勒泰站;小值中心70年代出现在布尔津站,其它年代均出现在福海站;大到暴雪频次的变化与其不同的是大值中心90年代出现在青河站;大到暴雪强度在60和70年代大值中心位于阿勒泰站,80和90年代位于富蕴站,21世纪最初10 a位于哈巴河站,大值区分布也较复杂。大到暴雪特征量全区及各站均呈增多趋势;大到暴雪量是阿勒泰和福海站、大到暴雪频次和强度是阿勒泰、福海、富蕴站没有通过显著性检验,其它均通过了显著性检验。R/S分析表明,在未来该地区大到暴雪特征量将逐渐转为减少的趋势,尤其是吉木乃站。通过分析揭示了阿勒泰地区大到暴雪变化规律,对指导该地区经济生产布局、防灾减灾、暴雪灾害风险评估提供参考,同时也为短期气候预测提供依据。     

西北干旱区极端气候水文事件特征分析

中国西北干旱区是对全球变化响应最敏感地区之一。气候变化导致气候水文系统的不稳定性加剧,极端气候水文事件的频度和强度增大、重现期缩短,灾害程度加重。借助资料分析和文献阅读,对过去50 a中国西北干旱区极端气候/水文事件的发生规律、影响机制及未来趋势进行了梳理总结,主要结论如下：（1）西北干旱区的极端气候/水文事件呈逐年增加趋势,特别是20世纪70年代以来增加显著;气温和降水极值都表现为一致的增强趋势。降水量的增多是降雨频率和强度共同增加的结果。（2）中国西北干旱区低温、降水极值在1986年左右发生了明显的突变,高温极值在1996年左右发生突变。突变后,气温和降水极值均发生了显著增强变化。（3）北半球极涡面积指数和青藏高原指数对西北干旱区气候极值变化具有重要影响,冬季极值还受冬季北极涛动和北大西洋涛动等影响。（4）新疆地区有变暖湿趋势,而河西走廊东部则为变干趋势。强大的西伯利亚高压和增强的贝加尔湖气流造成新疆地区降水增加,而河西走廊干旱增加是由东亚夏季风减弱引起的。     

乌鲁木齐典型暴雪天气机理及成因分析

利用常规地面、高空观测资料、NCEP/NCAR 1°×1°再分析和FY卫星资料,针对乌鲁木齐1990年以来的3场典型暴雪天气,从高低空环流和天气系统配置、不稳定条件、水汽、动力及黑体亮温（TBB）变化等方面综合对比分析暴雪成因。结果表明：（1） 3场暴雪均发生在欧洲高压脊东南衰退,推动西西伯利亚低槽东移南下,与中纬度短波槽结合的环流形势下,高低空系统呈“后倾槽”结构,乌鲁木齐处在925~600 hPa西北急流与600~200 hPa强西南急流叠置区,且天山山脉的地形强迫抬升有利于暴雪的维持和加强。（2） 暴雪前850~700 hPa均有东南风存在,微差平流作用有利于平流逆温生成和加强,使得能量不断积聚,后期冷空气进入,冷锋锋生,层结不稳定发展,为暴雪天气提供热力条件,东南风和平流逆温维持时间越长,储存能量越多,降雪越强。（3） 暴雪区存在西南和偏西2条水汽输送通道,中层水汽输送对乌鲁木齐暴雪至关重要,850~600 hPa存在较强的水汽辐合,且700 hPa最强。水汽输送、辐合强度及持续时间共同决定暴雪强度。（4） TBB与降雪强弱有一定的对应关系,TBB越低,云顶高度越高,中尺度云团发展越旺盛,降雪越强,降雪前TBB（云顶高度）的第一次迅速降低（升高）预示降雪开始,降雪过程中TBB降低对应降雪强度增强,且TBB降幅越大、低TBB值维持时间越长,降雪越强。     

Intra-event characteristics of extreme erosive rainfall on Mauritius

Mauritius is a volcanic island with a raised interior where extreme rainfall events dominate rainfall erosivity. Intra-event characteristics of the 120 highest erosive events at six selected locations between 2004 and 2008 were analyzed to provide the first detailed intra-storm data for a tropical island environment. On Mauritius, spatial variation is evident in the characteristics of extreme erosive rainfall recorded at the stations, with a noticeable increase in rainfall depth, duration, kinetic energy, and erosivity of extreme events with altitude. Extreme events in the raised interior (central plateau) show high variability of peak intensity over time as well as a higher percentage of events in which the greatest intensity occurs in the latter part of the event. Intra-event distribution of rainfall in the interior of the island shows that rainfall there has a higher potential to exceed infiltration rates as well as the ability to generate high peak runoff rates and cause substantial soil loss. The study suggests that even though within-event rainfall characteristics are complex, they have implications for soil erosion risk, and that, in tropical island environments, the within-storm distribution of rainfall should be incorporated in soil-loss modeling.     

Flow Cross K-function: a bivariate flow analytical method

Spatial flow data represent meaningful interaction activities between pairs of corresponding locations, such as daily commuting, animal migration, and merchandise shipping. Despite recent advances in flow data analytics, there is a lack of literature on detecting bivariate or multivariate spatial flow patterns. In this paper we introduce a new spatial statistical method called Flow Cross K-function, which combines the Cross K-function that detects marked point patterns and the Flow K-function that detects univariate flow clustering patterns. Flow Cross K-function specifically assesses spatial dependence of two types of flow events, in other words, whether one type of flows is spatially associated with the other, and if so, whether this is according to a clustering or dispersion trend. Both a global version and a local version of Flow Cross K-function are developed. The former measures the overall bivariate flow patterns in the study area, while the latter can identify anomalies at local scales that may not follow the global trend. We test our method with carefully designed synthetic data that simulate the extreme situations. We exemplify the usefulness of this method with an empirical study that examines the distributions of taxi trip flows in New York City.     

宁夏一次春季突发寒潮极值暴雪过程的等熵位涡分析

利用常规观测、地面加密站逐时观测和欧洲中期数值预报中心0.125 °×0.125 °逐6 h再分析资料,从天气系统演变、冷空气路径、灾害性天气预报指标等方面,利用315K等熵位涡对2016年4月2～3日宁夏突发寒潮极值暴雪过程进行了诊断分析。结果表明：（1）此次过程属于高空小槽东移合并型,地面有冷高压分裂且主体快速南下,并有锋面相配合,导致强冷空气入侵和锋后降雪。（2）前期环流形势稳定,后期天气系统突变,常规气象资料难以预报,315K等熵位涡图可作为短时、局地的春季寒潮降雪过程的有效分析和预报工具。（3）等熵位涡清楚地示踪冷空气的来源和传播路径：咸海区域对流层顶冷空气东移南扩与青藏高原对流层中层冷空气合并加强,且新地岛平流层下部的冷空气在前期缓慢东移后,自贝加尔湖加速南下对其补充引发寒潮。（4）等熵位涡异常大值可定量、清晰地表述关键影响系统西风带小槽的演变,具有更好的指示意义。（5）等熵位涡高值区随时间变化与寒潮演变一致,可提前6 h指示冷空气活动,且等熵位涡大于1.0 PVU区域与寒潮及降雪落区一致,大于0.8 PVU区域与强降温区域一致,可作为精细定量预报的重要指标。     

近50 年来淮河流域极端降水的时空变化及统计特征

以淮河流域27 个气象站点1960-2009 年逐日降水观测资料为基础,选取年最大降水量序列(AM) 和超门限峰值序列(POT),分析淮河流域年极端降水事件的时空变化趋势,研究淮河流域降水极值的统计特征。研究发现：过去50 年,淮河流域大多数站点年最大日降水量有增加的趋势,少数站点有减少的趋势,但增加和减少的趋势均不明显。从单个气象站点50 年降水序列来看,年最大日降水事件发生的时间大多集中于20 世纪60-70 年代,且以汛期居多。利用L-矩法、K-S 检验等方法,发现GEV和GP分布分别能够较好的拟合AM和POT序列。通过计算比较在不同重现期水平下的降水量,发现POT序列及其对应的GP分布能够更好的模拟淮河流域极端降水序列。     

1470-1912年西北东部地区极端干旱事件的复原及分形特征研究

通过对西北东部地区1470-1912年干旱灾害历史资料的搜集整理,根据干旱发生强度、持续时间、发生范围,定义了强旱事件,建立了极旱因子模型,复原了443年西北东部地区极端干旱事件,并根据时间分形原理,挖掘出极旱事件时间序列的分维特征。经过对比分析得到以下结论：建立的模型结果与记载结果具有显著的相似性;全序列旱灾时间分维值随极旱强度的增加而降低,与极旱发生周期呈反位相关系;各类别极旱有自己的时间重演律,极旱程度越轻,无标度区越宽,分维值越高,短周期比较明显。     

A trajectory clustering approach based on decision graph and data field for detecting hotspots

Spatial clustering can be used to discover hotspots in trajectory data. A trajectory clustering approach based on decision graph and data field is proposed as an effective method to select parameters for clustering, to determine the number of clusters, and to identify cluster centers. Synthetic data and real-world taxi trajectory data are utilized to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can automatically determine the parameters for clustering as well as perform efficiently in trajectory clustering. Hotspots are identified and visualized during different times of a single day and at the same times on different days. The dynamic patterns of hotspots can be used to identify crowded areas and events, which are crucial for urban transportation planning and management.     

Modeling Commuting Patterns in Chicago in a GIS Environment: A Job Accessibility Perspective

Using the newly released Census for Transportation Planning Package (CTPP) 1990, this research utilizes GIS tools to explain intraurban variations of commuting in Chicago at the Traffic Analysis Zone (TAZ) level. While commute times are directly available from the CTPP, commute distances are derived from the shortest time distances through real-world road networks. GIS surface modeling is used to help identify the major employment center (CBD) and other subcenters. The explanations of commute times and distances are attempted by various measures of job accessibility: the jobs-housing balance ratio defined in a floating catchment area, distances from the CBD and subcenters, and a gravity-based index that takes the effects of all jobs into account. These measurements better explain how far people commute than how much time they spend on commuting. The best model explains over 50% of the variation of commuting among 7,835 TAZs. This is a significant improvement over existing literature, which at best explained less than 30 percent.     

Spatial and temporal variabilities of rainstorms over China under climate change

Rainstorms are one of the extreme rainfall events that cause serious disasters, such as urban flooding and mountain torrents. Traditional studies have used rain gauge observations to analyze rainstorm events, but relevant information is usually missing in gauge-sparse areas. Satellite-derived precipitation datasets serve as excellent supplements or substitutes for the gauge observations. By developing a grid-based rainstorm-identification tool, we used the Tropical Rainfall Measurement Mission(TRMM) Multi-satellite Precipitation Analysis(TMPA) time series product to reveal the spatial and temporal variabilities of rainstorms over China during 1998–2017. Significant patterns of both increasing and decreasing rainstorm occurrences were detected, with no spatially uniform trend being observed across the whole country. There was an increase in the area being affected by rainstorms during the 20-year period, with rainstorm centers shifting along the southwest–northeast direction. Rainstorm occurrence was found to be correlated with local total precipitation. By comparing rainstorm occurrence with climate variables such as the El Ni?o-Southern Oscillation and Pacific Decadal Oscillation, we also found that climate change was likely to be the primary reason for rainstorm occurrence in China. This study complements previous studies that used gauge observations by providing a better understanding of the spatiotemporal dynamics of China's rainstorms.