Channeling the water data deluge: a system for flexible integration and analysis of hydrologic data

The hydrologic cycle and understanding the relationship between rainfall and runoff is an important component of earth system science, sustainable development, and natural disasters caused by floods. With this in mind, the integration of digital earth data for hydrologic sciences is an important area of research. Currently, it takes a tremendous amount of effort to perform hydrologic analysis at a large scale because the data to support such analyses are not available on a single system in an integrated format that can be easily manipulated. Furthermore, the state-of-the-art in hydrologic data integration typically uses a rigid relational database making it difficult to redesign the data model to incorporate new data types. The HydroCloud system incorporates a flexible document data model to integrate precipitation and stream flow data across spatial and temporal dimensions for large-scale hydrologic analyses. In this paper, a document database schema is presented to store the integrated data-set along with analysis tools such as web services for data access and a web interface for exploratory data analysis. The utility of the system is demonstrated based on a scientific workflow that uses the system for both exploratory data analysis and statistical hypothesis testing.     

A novel method for discovering spatio-temporal clusters of different sizes,shapes, and densities in the presence of noise

The discovery of spatio-temporal clusters in complex spatio-temporal data-sets has been a challenging issue in the domain of spatio-temporal data mining and knowledge discovery. In this paper, a novel spatio-temporal clustering method based on spatio-temporal shared nearest neighbors (STSNN) is proposed to detect spatio-temporal clusters of different sizes, shapes, and densities in spatio-temporal databases with a large amount of noise. The concepts of windowed distance and shared nearest neighbor are utilized to define a novel spatio-temporal density for a spatio-temporal entity with definite mathematical meanings. Then, the density-based clustering strategy is employed to uncover spatio-temporal clusters. The spatio-temporal clustering algorithm developed in this paper is easily implemented and less sensitive to density variation among spatio-temporal entities. Experiments are undertaken on several simulated data-sets to demonstrate the effectiveness and advantage of the STSNN algorithm. Also, the real-world applications on two seismic databases show that the STSNN algorithm has the ability to uncover foreshocks and aftershocks effectively.     

基于时间语义的地籍管理信息系统数据组织与实现

地籍管理信息系统属于GIS范畴,具有很强的时态特性,如何高效地组织时空数据是系统成败的关键。本文在分析地籍管理信息系统必要性以及时间概念及其具体含义的基础上,引入时间语义,提出基于时间语义的数据模型。根据语义对地籍管理信息系统的时空数据组织方法进行了研究。并根据地籍管理对模型的要求,分别从时间点、时间段角度对历史信息进行查询和回溯,验证了模型的可行性。     

鄱阳湖生态环境动态监测服务系统

针对目前生态环境监测中多源多尺度数据获取、定量遥感模型半自动化条件制约海量数据的快速处理与分析以及静态的时空快照服务难以表达动态变化过程等问题,该文从基于传感网的数据多元化实时感知获取、基于数据仓库的生态信息主题化动态汇聚、基于多尺度WFS的智能化服务、基于动态数据驱动的仿真动态模拟知识化应用方面考虑,设计湖泊流域生态环境动态监测服务系统。以鄱阳湖为例进行验证,为鄱阳湖流域的生态监测以及江西省生态文明试验区建设提供服务。     

多源数据协同定量遥感产品生产系统的领域模型

大数据技术中的一个难点是如何统筹多源异构的数据。在多源数据协同定量遥感产品生产系统中,不同传感器数据的协同使用为生产系统的各个环节都带来了许多的问题,例如异构数据文件的统一表示和调度,繁杂生产流程的统一抽象等。领域驱动设计是一种应对软件核心复杂性的设计方式。领域驱动是指在设计过程中经过不断的迭代,逐渐提炼出一套灵活优雅的领域模型。领域模型专注于分析问题领域本身,发掘重要的业务领域概念,并建立业务领域概念之间的关系。本文在不断实践的基础上提出了一组较成熟的领域模型,该模型用一种统一的方式解决了多源数据协同生产系统中各方面的问题,并显著地降低了系统集成的难度和工作量,且为新数据源的加入预留了灵活性。     

Blending Aggregation and Selection: Adapting Parallel Coordinates for the Visualization of Large Datasets

Abstract

Many of the traditional data visualization techniques, which proved to be supportive for exploratory analysis of datasets of moderate sizes, fail to fulfil their function when applied to large datasets. There are two approaches to coping with large amounts of data: data selection, when only a portion of data is displayed, and data aggregation, i.e. grouping data items and considering the groups instead of the original data. None of these approaches alone suits the needs of exploratory data analysis, which requires consideration of data on all levels: overall (considering a dataset as a whole), intermediate (viewing and comparing collective characteristics of arbitrary data subsets, or classes), and elementary (accessing individual data items). Therefore, it is necessary to combine these approaches, i.e. build a tool showing the whole set and arbitrarily defined subsets (object classes) in an aggregated way and superimposing this with a representation of arbitrarily selected individual data items.

We have achieved such a combination of approaches by modifying the technique of parallel coordinate plot. These modifications are described and analysed in the paper.     

Developing a geospatial web-GIS system for landscape and urban planning

This study discusses a geographical information system (GIS) for operating a local government's landscape and urban planning activities via a website. Implementing this web-GIS system will help build a more realistic landscape and urban planning model that includes citizen participation and city marketing. The approach is applicable to ubiquitous city (u-city) development based on geospatial web and its related systems. The approach presented is built on six selected elements of a u-city system. The outcome of the study includes sustainable analysis, environmental planning, urban planning, and city marketing. The outcome is applicable to cities that are planning to adopt the u-city system or advanced telecommunication or planning tools into their urban frameworks.     

基于时空数据挖掘的城市体检评估方法与实践——以无锡市为例

时空数据挖掘作为当前研究的热点之一,为城市研究提供了新的思路和技术手段。本文以无锡市为例,利用手机信令数据、车辆轨迹实时数据、基础地理信息等多源异构时空数据,构建相应的数据挖掘模型,进行了职住平衡、区域联系度、交通拥堵等城市特征的挖掘与分析,构建多源数据挖掘的城市评估体系,探索了时空数据挖掘在城市体检评估中的应用,为城市规划和运行管理提供了科学评估与决策。     

电子地籍图库动态更新的设计与实现

介绍了广州市房地产测绘所电子地籍图库动态更新系统。该系统依靠广州市房地产测绘所负责广州市的地籍测绘工作的有利条件 ,建立实时动态更新的电子地籍图库。随着地籍测绘成果的不断纳入 ,电子地籍图库作为广州市国土房管局基础图库系统的基础 ,将实现广州市国土房管局基础图库系统数据的不断更新     

土地数据集成方法探讨

土地信息系统的迅速发展和广泛应用导致了土地空间数据多源性的产生,为土地数据综合利用和数据共享带来不便。探讨土地空间数据多源性的产生和表现,指出多数据格式是多源土地数据集成的瓶颈;提出了多源空间数据集成的原则与方法,展望了土地多源数据集成的发展方向,并将其在土地管理中的应用做了有益的探索。     

土地数据集成方法探讨

土地信息系统的迅速发展和广泛应用导致了土地空间数据多源性的产生,为土地数据综合利用和数据共享带来不便.探讨土地空间数据多源性的产生和表现,指出多数据格式是多源土地数据集成的瓶颈;提出了多源空间数据集成的原则与方法,展望了土地多源数据集成的发展方向,并将其在土地管理中的应用做了有益的探索.     

Geolokit: An interactive tool for visualising and exploring geoscientific data in Google Earth

Virtual globes have been developed to showcase different types of data combining a digital elevation model and basemaps of high resolution satellite imagery. Hence, they became a standard to share spatial data and information, although they suffer from a lack of toolboxes dedicated to the formatting of large geoscientific dataset. From this perspective, we developed Geolokit: a free and lightweight software that allows geoscientists – and every scientist working with spatial data – to import their data (e.g., sample collections, structural geology, cross-sections, field pictures, georeferenced maps), to handle and to transcribe them to Keyhole Markup Language (KML) files. KML files are then automatically opened in the Google Earth virtual globe and the spatial data accessed and shared. Geolokit comes with a large number of dedicated tools that can process and display: (i) multi-points data, (ii) scattered data interpolations, (iii) structural geology features in 2D and 3D, (iv) rose diagrams, stereonets and dip-plunge polar histograms, (v) cross-sections and oriented rasters, (vi) georeferenced field pictures, (vii) georeferenced maps and projected gridding.Therefore, together with Geolokit, Google Earth becomes not only a powerful georeferenced data viewer but also a stand-alone work platform. The toolbox (available online at http://www.geolokit.org) is written in Python, a high-level, cross-platform programming language and is accessible through a graphical user interface, designed to run in parallel with Google Earth, through a workflow that requires no additional third party software. Geolokit features are demonstrated in this paper using typical datasets gathered from two case studies illustrating its applicability at multiple scales of investigation: a petro-structural investigation of the Ile d’Yeu orthogneissic unit (Western France) and data collection of the Mariana oceanic subduction zone (Western Pacific).     

DCAD: a dual clustering algorithm for distributed spatial databases

Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.     

DCAD： a Dual Clustering Algorithm for Distributed Spatial Databases

Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clus- tering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.     

变形监测信息管理系统集成化的实现

开发一套对变形监测数据进行集中管理和快速、准确、全面计算分析的信息管理系统,对地表变形的稳定性评价和地球深部动力学预报工作质量具有重要的意义。本文采用系统集成的思想,通过集成MATLAB、MapObjects和Offiece等优秀软件和功能,初步实现了变形监测信息管理系统可视化的数据库管理、监测网优化设计和平差计算等功能,为有效获取和使用适时准确的变形监测信息提供了实用平台。     

时空大数据服务平台建设模式及应用研究——以重庆市为例

近年来,国家提出大数据战略,并大力发展数字经济,时空大数据作为推动大数据智能化、数字经济发展的重要基础,对时空数据汇聚、快速更新、智能处理、多源融合、高性能服务、支撑多应用场景等方面都提出了更高的要求。本文围绕如何构建时空大数据资源,实现时空大数据智能化处理,提供时空大数据服务等多个方面提出了时空大数据平台的建设方法、模式及技术思路,并结合已开展的智慧应用,对新时代的时空大数据平台建设服务模式进行了探索。     

工程建设项目全生命周期“多测合一”综合服务平台研究及应用

为实现对工程建设项目海量多源异构数据的高效管理与共享,更好地服务审批监管,本文从工程建设项目审批、管理、服务和监管的全局角度,提出建立工程建设项目全生命周期“多测合一”综合服务平台,深入研究了平台实施过程中涉及的时空大数据、多源异构数据融合、GIS+BIM及三维等关键技术,并给出应用案例,为工程建设项目联合测绘相关平台建设提供了参考。     

城市地下管网系统数据编码的改进方案

为促进城市地下管线规范化、科学化管理,配合城市地下管线探测和地下管线信息管理系统的建设,实现数据共享,需要建立统一的数据编码规则。本文从目前广泛应用的几种编码方法出发,提出了一种便于数据管理、查询、网络分析及满足系统实际需求的编码方法,为管网系统的规范化建立提供参考依据。     

智能化土地定级估价信息系统设计与实现

总结分析了当前系统中存在的主要问题,提出将GIS、SDM、ES和可视化等多种信息技术进行有机结合,共同构建具有智能特性和辅助决策功能的土地定级估价信息系统。给出了GIS、SDM、ES和可视化等技术集成的基本框架,设计了基于多种信息技术集成的土地定级估价信息系统的基本结构。采用VC++6.0和MO2.0结合开发了基于多种技术集成的土地定级估价信息系统。研究表明,系统定级估价工作流程简单,结果可靠,且具有良好的移植性、复用性、扩展性和广泛适应性,能较好地解决土地定级估价中土地信息缺失、定性因子量化困难等半结构和非结构化问题。     

Improvement of a location-aware recommender system using volunteered geographic information

Abstract

Recommender systems (RS), as supportive tools, filter information from a massive amount of data based on the determined preferences. Most of the RS require information about the context of users such as their locations. In such cases, location-aware recommender systems (LARS) can be employed to suggest more personalized items to the users. The most current research projects on LARS focus on the development of algorithms, evaluation methods and applications. However, the role of up-to-date spatial databases in LARS is not a well-researched area. The up-to-date spatial information would potentially improve the accuracy of items which are recommended by LARS. Volunteered geographic information (VGI) could be a low-cost source of up-to-date spatial information for LARS. This article proposes an approach to enrich spatial databases of LARS by VGI. Since not all records of VGI are fitted for use in LARS, a mechanism is developed to identify useful information. Some VGI data sets refer to existing spatial data in the database while other VGI data sets are shared for the first time. Therefore, the proposed method assessed the quality of VGI with reference source (for VGI which is existed in the database) and VGI without reference source (for VGI which is shared for the first time). To demonstrate the feasibility of the proposed approach, a mobile application has been developed to recommend suitable restaurants to the users based on their geospatial locations. The evaluation of the method indicates that VGI can potentially enhance the functionality of the LARS in predicting the users’ interests.