轨迹大数据: 数据处理关键技术研究综述

针对轨迹大数据的高效点-轨迹k近邻（point to trajectory k nearest neighbor, P2T_kNN）查询处理需求,提出了一种融合时空剖分和轨迹分段的轨迹组织方法,其核心思想是在对轨迹作时间剖分的基础上,利用离散全球网格系统（discrete global grid system, DGGS）在空间上进行再次剖分,从而利用两次剖分得到的时空单元编码来索引落入其中的轨迹片段。在此基础上利用分布式列式存储技术设计了面向轨迹大数据的P2T_kNN查询处理框架,提出了一种顾及轨迹数据空间分布的自适应空间单元搜索算法,即通过分析轨迹数据在给定时间约束下的空间分异特征,动态调整空间单元的搜索步长,从而提升了轨迹稀疏区域的处理效率。针对亿级轨迹的实验结果表明,该方法适用于轨迹大数据的P2T_kNN查询处理,在轨迹稠密与稀疏区域的平均查询响应时间均小于1 s。     

GIS中多幅地图的缩放漫游及其数据组织

本文在分析现存GIS软件中处理多幅不同比例尺或相同比例尺地图的放缩漫游时的不足及其原因的基础上,提出了一种使用地理逻辑窗口动态裁剪快速而安全的多幅地图无级缩放和全区域自由漫游的方法,并对其数据组织和管理作了探讨。     

Comparative assessment of the relationship of satellite data with the above ground biomass of Sal trees (<Emphasis Type="Italic">Shorea robusta</Emphasis>) determined from phenologically different time periods

The Above Ground Biomass(AGB) estimates of vegetation comprise both the bole biomass determined through a volumetric equation and litter biomass collected from the ground.For mature trees,the AGB estimated in phenologically different time periods is directly affected by the litter biomass since the Diameter at Breast Height(DBH) and height(H) of such trees that are used in the estimation of bole biomass would remain unchanged over a reasonable time period.In the present study,we have determined the AGB of Sal trees(Shorea robusta) in two contrasting seasons:the peak green period in October being devoid of lit-ter on the ground and the leaf shedding period in February with abundant amount of litter present on the ground.Estimation of AGB for the month of February included the litter biomass.In contrast,the AGB for October represented only the bole biomass.AGB was estimated for ten different plots selected in the study area.The AGB estimated from ten sampling plots for each time period was re-gressed with the individual tree parameters such as the average DBH and height of trees measured from the corresponding plots.The regression analysis exhibited a significantly stronger relationship between the AGB and DBH for the month of October as compared to February.Furthermore,the correlation between the remotely sensed derived data and AGB was also found to be significantly higher for the month of October than February.This observation indicates that inclusion of the litter biomass in AGB will tend to decrease the re-gression relationship between AGB and DBH and also between the remotely sensed data and AGB.Therefore,these conclusions invite careful consideration while estimating AGB from satellite data in phenologically different time periods.     

3维地理信息系统技术综述

文中对3维地理信息系统技术进行综述性介绍,扼要地讨论了国内外有关3维地球空间信息的获取、管理、分析与可视化等技术的进展与动态,并分别探讨了它们的优势和局限。     

一种基于信息网格的空间数据分解算法

针对空间数据库数据海量且分布的特点,为了能从这些空间数据中更加有效的收集信息和发现知识,介绍了基于信息网格的空间数据挖掘计算模型,在分析空间数据划分一般原则和方法的基础上,针对该计算模型给出了一个数据划分的初步算法,并通过模拟实验对算法的时间性能进行了评价与分析。     

Downward migration of seismic activity prior to some great shallow earthquakes in Japanese subduction zones—a possible intermediateterm precursor

Before the 1944 Tonankai earthquake along the Nankai Trough, seismic activity increased in the shallow depths, and then the activity gradually migrated downwards. When it reached its limit (a depth of approximatelty 70 km), the main shock occurred. Several deep earthquakes, including one ofM5.3, occurred several months prior to the Tonankai earthquake. A similar downward migration pattern also can be recognized regarding the 1952 Tokachi-oki earthquake. In this case the deepest earthquakes reached about 400 km. This may be one of the intermediate-term precursory phenomena of great thrusttype earthquakes in subduction zones. Recent observations in the Tokai district along the Suruga Trough, where a large earthquake is expected to occur in the future, suggest a similar downward migration pattern in the land area.     

北京城市高温遥感指标初探与时空格局分析

利用北京高温天气下的NOAA18/AVHRR卫星资料与气象台站资料,分析了日最高气温与遥感反演的城市地表温度的关系,初步确定了地表高温阈值并建立了高温遥感指标,并利用1989~2008年(缺2002年)6~9月NOAA/AVHRR资料开展了北京地区高温时空格局分析研究。指标初步研究表明:北京气温高温值为35、37、40°C对应的遥感地表高温值分别为44、47、52°C,可以较好地适用于北京平原地区;利用该阈值建立的地表高温强度指标(LSHI)对北京平原高温的监测与气象台站高温监测基本一致,而高温比例指数指标(LSHP)能有效反映出城市高温空间强弱和时间差异。北京遥感地表高温空间格局分析显示:夏季(6~8月)旬平均遥感地表温度≥44°C年出现概率不超过50%,广泛分布于城区和平原区;旬平均遥感地表温度≥47°C年出现概率不超过40%,集中于北京五环内;旬平均遥感地表温度≥52°C年出现概率不超过15%,集中于城区;6~9月高温出现的概率高值区一般都集中于五环内,其中旬平均遥感地表温度≥44°C、≥47°C、≥52°C的出现概率分别为80%~100%、60%~80%、10%~40%。     

欧氏聚类算法支持下的点云数据分割

欧氏聚类算法是多元统计中的一种重要分类方法,可以将其应用于测绘领域中点云数据的分割。本文首先计算点云数据中两点之间的欧氏距离,将距离小于指定阈值作为分为一类的判定准则;然后迭代计算,直至所有的类间距大于指定阈值,完成欧氏聚类分割。具体步骤为：①利用Octree法建立点云数据拓扑组织结构;②对每个点进行k近邻搜索,计算该点与k个邻近点之间的欧氏距离,最小归为一类;③设置一定的阈值,对步骤②迭代计算,直至所有类与类之间的距离大于指定阈值。试验证明,欧氏聚类算法对不同测量技术手段获取的点云数据均具有适用性,可以成功对点云数据进行分割,分割效果良好。     

时序分析在危岩体监测数据处理中的应用

引入时间序列分析进行危岩体监测数据的处理,建立了危岩体变形的动态模型,取得了较好的拟会与预报精度。