MULTI-RESOLUTION SEAMLESS IMAGE DATABASE

This paper presents the basic concepts and principles,data structure and high efficient spatial index for multi_resolution image database.The database is characterized by arrangement of multi_resource image data and seamless mosaic,distribution_based storage and management,integration with other spatial database software such as GeoStar and GeoGrid developed by Wuhan Technical University of Surveying and Mapping.

A unified representation method for interdisciplinary spatial earth data

Unified representation of spatial earth data is an essential scientific issue.The analysis and mining of interdisciplinary spatial earth data resources can help discover hidden scientific knowledge,and even reveal the intrinsic relationship among different disciplines.However,the different description methods and inner structures among interdisciplinary spatial earth data bring significant chal-lenges to unified data management and collaborative analysis in earth environment research.To address this issue,this paper pro-poses a unified representation method for interdisciplinary spatial earth data.First,this paper establishes a general metadata model and realizes the unified description of interdisciplinary data.Second,an entity data organization model is presented,which can realize the unified organization of entity data with different inner structures.Finally,we introduce the Spatial Earth Data Format(SEDF),a data format based on HDF5 for implementing the data organization model of interdisciplinary spatial earth data.Data representation experiments and validation are conducted to verify the availability and practicability of the proposed data representa-tion method.The results suggest the powerful ability to represent spatial earth data efficiently and ensure data integrity,which is convenient for data management and application.

Predict the Future Hospitalized Patients Number Based on Patient’s Temporal and Spatial Fluctuations Using a Hybrid ARIMA and Wavelet Transform Model

Relative to hospitalized patient information, outpatient admission information is relatively simple. It only includes the patient admission time, place of residence and other information. Traditionally, the excavation of this information is not sufficient. However, when a large number of patients admitted time and residence information combined to consider, and add some data mining technology, some of the previously ignored regular information is likely to be found. Using 5 years of data mining research and admission data from a paediatric department at a large women’s and children’s hospital in China, we found important fluctuation rules regarding admissions using wavelet analysis on hospital admission data among different scales of cyclical fluctuations. Method: Seasonal distribution of patient number was analysed based on Haar wavelet transformation, and level 3 and level 2 of wavelets were extracted out to fit the data. The distribution function of hospitalized patients was visualized by kernel density estimation. Using linear regression and ARIMA (autoregressive integrated moving average model) predict the seasonally number of patients in the future. Results: The data analysis demonstrates the total surge of inpatients was decomposed into one mother wavelet and five small wavelets, each of which represents different time frequency. Besides, as distance from hospital increases, the number of patients decreased exponentially. The seasonal factors are the largest time factor influencing the number changes of patients. Conclusion: By wavelet analysis and the improved prediction model, we could make forecast on the future inpatient number trend and prove factors such as geographic position is influential on inpatient amount. Additionally, the concept of data mining based on spatial distribution and spectral analysis could be applied to other aspects of social management.

基于主动表征数据的城市虚拟空间意象测度研究--以南京中心城区为例

城市意象作为民众对建成环境特征要素的集体记忆,表征着城市的在地性文化和市民的集体认同,是构建人本城市的重要基石,成为高质量发展背景下的重要议题。在网络社会背景下,城市意象呈现出“基于现实世界的体验式感知”“基于虚拟空间的泛在化感知”两种不同的感知模式;虚拟空间意象的相关研究成为热点。以南京中心城区为例,文章基于主动表征数据,采用地理信息系统(GIS)空间分析、OPTICS算法和自然语言处理(NLP)方法,构建虚拟空间意象测度模型,发现虚拟空间意象热点呈现“核心+边缘”的分布模式和“历史城区集聚,外围散点分布”的集聚特征。基于“由点到面”的聚类思想,采用OPTICS聚类方法测度区域要素,填补相关研究空白;创新性地将情绪性分析引入虚拟空间意象的评估,结合感知热度和情绪强度,划分节点和标志要素;将识别结果与南京市国土空间总体规划进行对比,验证本方法的准确有效性和研究价值。

Sparse representation-based correlation analysis of non-stationary spatiotemporal big data

As the basic data of digital city and smart city research,Spatiotemporal series data contain rich geographic information.Alongside the accumulation of spatial time-series data,we are also encountering new challenges related to analyzing and mining the correlations among the data.Because the traditional methods of analysis also have their own suitable condition restrictions for the new features,we propose a new analytical framework based on sparse representation to describe the time,space,and spatial-time correlation.First,before analyzing the correlation,we discuss sparse representation based on the K-singular value decomposition(K-SVD)algorithm to ensure that the sparse coefficients are in the same sparse domain.We then present new computing methods to calculate the time,spatial,and spatial-time correlation coefficients in the sparse domain;we then discuss the functions’properties.Finally,we discuss change regulations for the gross domestic product(GDP),population,and Normalized Difference Vegetation Index(NDVI)spatial time-series data in China’s Jing-Jin-Ji region to confirm the effectiveness and adaptability of the new methods.

A spatial multi-scale integer coding method and its application to three-dimensional model organization

With the rapid development of digital earth,smart city,and digital twin technology,the demands of three-dimensional model data’s application is getting higher and higher.These data tend to be multi-objectification,multi-type,multi-scale,complex spatial relationship,and large amount,which brings great challenges to the efficient organization of them.This paper mainly studies the organization of three-dimensional model data,and the main contributions are as follows:1)A integer coding method of three dimensional multi-scale grid is proposed,which can reduce the four-dimensional(spatial dimension and scale dimension)space into one-dimensional,and has better space and scale clustering characteristics by comparing with various types of grid coding.2)The binary algebra calculation method is proposed to realize the basic spatial relationship calculation of three-dimensional grid,which has higher spatial relationship computing ability than 3D-Geohash method;3)The multi-scale integer coding method is applied to the data organization of three-dimensional city model,and the experiment results show that:it is more efficient and stable than the threedimensional R-tree index and Geohash coding method in the establishment of index and the query of three dimensional space.

一种用于空间数据多尺度表达的R树索引结构

针对现有R树无法支持空间数据多尺度表达的问题,提出了一种用于空间数据多尺度表达的R树变形索引结构:(1)允许空间对象出现在非叶结点上;(2)利用树的深度反映空间分辨率的变化,提供分辨率维的支持;(3)树的分支结构考虑对自动制图综合算法的支持.分析了该变形R树索引结构的空间数据多尺度查询过程,并着重对该索引结构生成算法中的约束条件、插入算法和分裂算法进行了讨论.针对相同数据源,使用该方法与基于四叉树的空间数据多尺度索引方法进行了对比实验,结果表明,该索引方法能有效检索多分辨率形式组织的空间数据,具有综合结果记忆功能,效率明显.

多代表点特征树与空间聚类算法

空间数据具有海量、复杂、连续、空间自相关、存在缺损与误差等的特点,要求空间聚类算法具有高效率,能处理各种复杂形状的簇,聚类结果与数据空间分布顺序无关,并且对离群点是健壮的等性能,已有的算法难以同时满足要求。本文提出了一个适合处理海量复杂空间数据的数据结构-多代表点特征树。基于多代表点特征树提出了适合挖掘海量复杂空间数据聚类算法CAMFT,该算法利用多代表点特征树对海量的数据进行压缩,结合随机采样的方法进一步增强算法处理海量数据的能力;同时,多代表点特征树能够保存复杂形状的聚类特征,适合处理复杂空间数据。实验表明了算法CAMFT能够快速处理带有离群点的复杂形状聚类的空间数据,结果与对象空间分布顺序无关,并且效率优于已有的同类聚类算法BIRCH与CURE。

空间数据网络渐进传输的概念、关键技术与研究进展

首先阐述空间数据网络渐进传输的概念、关键支撑技术以及意义。其次,总结了国内外相关研究的现状,分析了空间数据网络渐进传输研究方面的技术难点。最后结合目前国内该方面最新的研究成果对空间数据网络渐进传输的研究趋势做了进一步展望。

一种匹配分类的空间数据多尺度表达与变换模型

空间数据"连续"表达是多尺度表达与变换的理想状态。针对多尺度表达与变换中地理要素的不同几何形态和生命期特点,建立了动静态结合的尺度变换模型。通过空间数据匹配技术,根据生命期不同,将空间数据分为匹配数据和消亡数据。对于匹配数据,通过综合算法生成关键比例尺插值数据,并预先静态存储;对于消亡数据,依据重要程度的顺序统计量,动态"选取、化简"生成插值尺度数据。实验表明该方案可以缓解传统尺度变换的跳跃感问题,变换效率高,可以快速、动态实现连续的尺度变换。

空间数据多尺度表达不确定性研究进展

阐述了多尺度空间数据不确定性的研究意义,分析了空间数据多尺度表达不确定性的来源、研究内容、关键技术,最后从空间数据多尺度表达不确定性的研究历程、相关问题、分类及其可视化4个方面综述了空间数据多尺度表达不确定性的研究动态.

矿山地质勘察三维可视化管理系统与建模技术研究

以城市三维地质勘察管理信息系统为例,分析了地形三维数据模型的可视化表达与建模方法;基于组件GIS技术,通过将二维和三维空间信息统一在一个平台和数据模型下,建立了矿山三维地质勘察可视化管理系统。该管理系统可实现对数据的可视化管理及实时更新,可打印输出和三维显示地质图,并具有多种查询功能。对矿山地质体的空间数据管理和可视化表达,从理论、模型及技术实现上作了探讨。

空间数据的多尺度特征与自动综合

尺度是地理现象、地理数据所共有的重要特征之一。就地理现象的多尺度描述和数据的多尺度特征进行了简要的分析,介绍了GIS中空间数据多尺度特征的表示方法,并对空间数据自动综合的理论、方法问题进行了探讨。

多尺度空间对象拓扑相似关系的表达与计算

研究了空间数据多尺度表达中,在进行模型综合时空间对象拓扑关系的表达、变化和相似性。基于空间对象之间拓扑关系的概念邻域,给出了空间对象拓扑关系相似性的定义以及在数字景观模型中拓扑关系相似性的度量方法,为空间数据的多尺度表达提供了拓扑关系分析和评价手段。

基于GeoSOT-3D的三维数据表达研究

基于面向地球空间,且具有边界不重叠、网格正交、经纬一致、与传统数据规格兼容性好等特点的Geo SOT-3D立体网格,提出一种三维数据表达方法。该方法设计剖分预处理和剖分表达两个步骤来完成空间数据的真三维表达,并对大气观测数据进行三维展示实验。实验结果验证了该方法的可行性,为全球空间数据的表达提供一种新的解决思路。

基于特征的异形弹簧几何形状的描述和表达

分析和总结了异形弹簧的形状特征,提取出4种基本结构基元,并用链表记录这些基元的组合关系,解算了特征的空间位置,建立了异形弹簧完整的形状信息模型。这种描述和表达方式,为异形弹簧的三维建模和数控卷绕成形工艺信息的自动生成提供了重要基础。

基于尺度的GIS空间资料表达模型

介绍了基于层的GIS空间资料表达方法易于进行空间查询与空间分析处理的优点 ,针对其引起的地图难以进行扩展与图像显示转换困难的缺点 ,提出了基于尺度 (比例尺 )的空间资料表达新方法。在基于尺度的空间资料表达方法中 ,地图可以由空间对象 ,区域 ,尺度视图三级尺度模型表达。对尺度方法下的地图结构 ,地图组织 ,指标结构 ,地图扩展等在理论上作了探讨 ,提出了基于尺度的方法与已广为使用的基于层的方法的有效集合 ,将有可能成为地理信息表达研究的最重要研究方向。

WSN中基于压缩感知的数据收集方案

提出一种基于压缩感知的数据收集方案。依据感知数据的空间相关性分析,计算出事件发生的区域范围。基于剩余能量的成簇算法对区域范围内的节点进行分簇。各个节点将感知到的原始数据,基于压缩感知理论,进行数据的稀疏表示并采用随机高斯矩阵进行观测,将其观测值发送和存储在簇头节点上,当有移动收集者进入簇头的通信范围后,进行数据收集。理论分析和仿真实验结果表明,该方案能有效延长网络生命周期。

时空变化分类与表达研究

为了解决空间数据库更新过程中地理对象时空变化类型不明晰的问题,利用语义、空间、时态和属性4个组件描述地理对象,并以地理对象集合、地理对象、对象组件以及对象组件属性4个具有层次关系的因子实现地理对象时空变化的分类与表达,通过分析确定了单一对象的17种变化类型、多个对象的4种变化类型,以及每种变化对应的数据库操作。最后,以一个实例说明空间数据变化类型的判定过程。

基于ArcEngine和C/S构架的矿产资源管理系统开发

基于组件式GIS(Geographical Information System)思想,在Java平台下,以Oracle为后台数据库,通过以下技术的实施:ArcEngine实现地图表现、Java存储过程实现Oracle远程逻辑备份与恢复、ArcSDE实现空间数据存储,成功开发了攀枝花市矿产资源管理系统,并取得良好应用效果。系统的应用和实践促进了该市矿政管理信息化建设进程。