大数据背景下地质云的构建与应用

地质学属于数据密集型科学,并与地球科学面临的问题息息相关。已经收集的和将要收集的大量数字国土相关数据,由于科学研究的需要,正在不断加以检验和扩充。大数据时代背景下,中国国土资源数字化、信息化的战略行动具有深远意义,大数据的相关技术应用为实现地质工作的现代化和信息化提供了有效的支撑。重点介绍大数据背景下的“地质云”构建理念与方法,以及大数据在地学领域的应用。大数据为非结构化、半结构化的地质数据带来了新的处理方法与理念。地质云的构建旨在探索以需求带动的地质核心数据的应用,挖掘非结构化数据的新数据信息,以支撑国土资源管理决策。     

Predicting the visualization intensity for interactive spatio-temporal visual analytics: a data-driven view-dependent approach

The continually increasing size of geospatial data sets poses a computational challenge when conducting interactive visual analytics using conventional desktop-based visualization tools. In recent decades, improvements in parallel visualization using state-of-the-art computing techniques have significantly enhanced our capacity to analyse massive geospatial data sets. However, only a few strategies have been developed to maximize the utilization of parallel computing resources to support interactive visualization. In particular, an efficient visualization intensity prediction component is lacking from most existing parallel visualization frameworks. In this study, we propose a data-driven view-dependent visualization intensity prediction method, which can dynamically predict the visualization intensity based on the distribution patterns of spatio-temporal data. The predicted results are used to schedule the allocation of visualization tasks. We integrated this strategy with a parallel visualization system deployed in a compute unified device architecture (CUDA)-enabled graphical processing units (GPUs) cloud. To evaluate the flexibility of this strategy, we performed experiments using dust storm data sets produced from a regional climate model. The results of the experiments showed that the proposed method yields stable and accurate prediction results with acceptable computational overheads under different types of interactive visualization operations. The results also showed that our strategy improves the overall visualization efficiency by incorporating intensity-based scheduling.     

Bimodal distribution of area-weighted latitude of sunspots and solar North-South asymmetry

We study the latitudinal distribution of sunspots observed from 1874 to 2009 using the center-of-latitude (COL). We calculate COL by taking the area-weighted mean latitude of sunspots for each calendar month. We then form the latitudinal distribution of COL for the sunspots appearing in the northern and southern hemispheres separately, and in both hemispheres with unsigned and signed latitudes, respectively. We repeat the analysis with subsets which are divided based on the criterion of which hemisphere is dominant for a given solar cycle. Our primary findings are as follows: (1) COL is not monotonically decreasing with time in each cycle. Small humps can be seen (or short plateaus) around every solar maxima. (2) The distribution of COL resulting from each hemisphere is bimodal, which can well be represented by the double Gaussian function. (3) As far as the primary component of the double Gaussian function is concerned, for a given data subset, the distributions due to the sunspots appearing in two different hemispheres are alike. Regardless of which hemisphere is magnetically dominant, the primary component of the double Gaussian function seems relatively unchanged. (4) When the northern (southern) hemisphere is dominant the width of the secondary component of the double Gaussian function in the northern (southern) hemisphere case is about twice as wide as that in the southern (northern) hemisphere. (5) For the distribution of the COL averaged with signed latitude, whose distribution is basically described by a single Gaussian function, it is shifted to the positive (negative) side when the northern (southern) hemisphere is dominant. Finally, we conclude by briefly discussing the implications of these findings on the variations in the solar activity.     

利用决策级融合进行遥感影像分类

提出了一种基于决策级融合的遥感影像分类方法。该方法对遥感影像特征以最大似然分类器进行预分类,应用Adaboost算法将分类的结果进行决策级融合,实现影像的分类。实验结果表明,该方法的分类精度较传统分类方法有明显的提高。     

基于Web Service的空间数据共享平台

分析传统GIS软件存在的数据共享、集成困难和应用间互操作复杂的问题,提出了基于Web Service的数据共享平台的简单原型和实现技术。阐述如何基于开放的规范和协议构建开放的数据存储一体化模型,并在此基础上,提出了分布式环境下基于Web Service数据节点集群、互操作方案,以及上层充一的应用开发模式,包括提供以面向对象的思想定义的、构建上层应用的组件支持。重点研究了在空间数据文本化之后数据的索引、查询问题,提出了结合元数据和GML的两阶段数据查询方案。并对该平台下空间数据的语义共享方式做初步探讨。     

杭州城市生活质量的定量评价

以乡镇、街道为基本空间统计单元,选取人口密度、住宅基准地价、大专以上学历人口比重为社会经济指标,建设用地比重、NDVI、地表温度为生物物理指标,采用主成分分析和GIS叠加法,定量评价了杭州城市生活质量的空间分布状况。结果表明：NDVI和地表温度 (相关系数r = -0.63)、城镇用地比重 (相关系数r = -0.83) 呈明显的负相关,地表温度和城镇建设用地是影响杭州“绿色环境”两个的最重要因素。NDVI和住宅基准地价 (r = -0.66)、大学毕业生比例 (相关系数r = -0.59)、人口密度 (相关系数r = -0.29) 也呈较明显的负相关,“绿色环境”并未成为高学历人群和高收入家庭的主要决定因素,较繁华或较便利地段仍是高学历人群和高收入家庭选择居住环境的首要选择。高生活质量值 (前20%) 区域主要位于分布于城市中心区,即杭州上城区、下城区及西湖区的部分单元,而郊区的生活质量相对较低。     

Assimilating data into open ocean tidal models

The problem of deriving tidal fields from observations by reason of incompleteness and imperfectness of every data set practically available has an infinitely large number of allowable solutions fitting the data within measurement errors and hence can be treated as ill-posed. Therefore, interpolating the data always relies on some a priori assumptions concerning the tides, which provide a rule of sampling or, in other words, a regularization of the ill-posed problem. Data assimilation procedures used in large scale tide modeling are viewed in a common mathematical framework as such regularizations. It is shown that they all (basis functions expansion, parameter estimation, nudging, objective analysis, general inversion, and extended general inversion), including those (objective analysis and general inversion) originally formulated in stochastic terms, may be considered as utilizations of one of the three general methods suggested by the theory of ill-posed problems. The problem of grid refinement critical for inverse methods and nudging is discussed.     

Head/Tail Breaks: A New Classification Scheme for Data with a Heavy-Tailed Distribution

This article introduces a new classification scheme—head/tail breaks—to find groupings or hierarchy for data with a heavy-tailed distribution. The heavy-tailed distributions are heavily right skewed, with a minority of large values in the head and a majority of small values in the tail, commonly characterized by a power law, a lognormal, or an exponential function. For example, a country's population is often distributed in such a heavy-tailed manner, with a minority of people (e.g., 20 percent) in the countryside and the vast majority (e.g., 80 percent) in urban areas. This new classification scheme partitions all of the data values around the mean into two parts and continues the process iteratively for the values (above the mean) in the head until the head part values are no longer heavy-tailed distributed. Thus, the number of classes and the class intervals are both naturally determined. I therefore claim that the new classification scheme is more natural than the natural breaks in finding the groupings or hierarchy for data with a heavy-tailed distribution. I demonstrate the advantages of the head/tail breaks method over Jenks's natural breaks in capturing the underlying hierarchy of the data.     

基于多源数据的中国地形海拔分级指标调整研究

使用SRTM DEM、土地覆被、冻融侵蚀、河流沟谷等多源数据,对区分海拔等级的指标地物作了具体分析,以指标地物的平均海拔为依据,通过分级指标对象(如冰川、林线)分布高程重合或贴近的多条等高线圈形成分级等高平面,对陆地地貌进行切割划分,用每组指标对象偏离等高平面的值作为控制量(控制点),插值重构分级参考曲面。该方案最大限度突出指标的地理意义,形成的海拔分级结果与中国地理系统分布特征更为贴近,可以为中国大尺度地貌基本形态的划分提供参考。     

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。