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A Space‐Time Raster GIS Data Model for Spatiotemporal Analysis of Vegetation Responses to a Freeze Event
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Many past space‐time GIS data models viewed the world mainly from a spatial perspective. They attached a time stamp to each state of an entity or the entire area of study. This approach is less efficient for certain spatio‐temporal analyses that focus on how locations change over time, which require researchers to view each location from a temporal perspective. In this article, we present a data model to organize multi‐temporal remote sensing datasets and track their changes at the individual pixel level. This data model can also integrate raster datasets from heterogeneous sources under a unified framework. The proposed data model consists of several object classes under a hierarchical structure. Each object class is associated with specific properties and behaviors to facilitate efficient spatio‐temporal analyses. We apply this data model to a case study of analyzing the impact of the 2007 freeze in Knoxville, Tennessee. The characteristics of different vegetation clusters before, during, and after the 2007 freeze event are compared. Our findings indicate that the majority of the study area is impacted by this freeze event, and different vegetation types show different response patterns to this freeze. 相似文献
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Cumulative spatial impact layers: A novel multivariate spatio‐temporal analytical summarization tool
Lucy Romeo Jake Nelson Patrick Wingo Jennifer Bauer Devin Justman Kelly Rose 《Transactions in GIS》2019,23(5):908-936
Scientific inquiry often requires analysis of multiple spatio‐temporal datasets, ranging in type and size, using complex multi‐step processes demanding an understanding of GIS theory and software. Cumulative spatial impact layers (CSIL) is a GIS‐based tool that summarizes spatio‐temporal datasets based on overlapping features and attributes. Leveraging a recursive quadtree method, and applying multiple additive frameworks, the CSIL tool allows users to analyze raster and vector datasets by calculating data, record, or attribute density. Providing an efficient and robust method for summarizing disparate, multi‐format, multi‐source geospatial data, CSIL addresses the need for a new integration approach and resulting geospatial product. The built‐in flexibility of the CSIL tool allows users to answer a range of spatially driven questions. Example applications are provided in this article to illustrate the versatility and variety of uses for this CSIL tool and method. Use cases include addressing regulatory decision‐making needs, economic modeling, and resource management. Performance reviews for each use case are also presented, demonstrating how CSIL provides a more efficient and robust approach to assess a range of multivariate spatial data for a variety of uses. 相似文献
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Big Geo Data promises tremendous benefits to the GIS Science community in particular and the broader scientific community in general, but has been primarily of use to the relatively small body of GIScientists who possess the specialized knowledge and methods necessary for working with this class of data. Much of the greater scientific community is not equipped with the expert knowledge and techniques necessary to fully take advantage of the promise of big spatial data. IPUMS-Terra provides integrated spatiotemporal data to these scholars by simplifying access to thousands of raster and vector datasets, integrating them and providing them in formats that are useable to a broad array of research disciplines. IPUMS-Terra exemplifies a new class of National Spatial Data Infrastructure because it connects a large spatial data repository to advanced computational resources, allowing users to access the needle of information they need from the haystack of big spatial data. The project is trailblazing in its commitment to the open sharing of spatial data and spatial tool development, including describing its architecture, process development workflows, and openly sharing its products for the general use of the scientific community. 相似文献
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随着对地立体观测体系的建立,遥感大数据不断累积。传统基于文件、景/幅式的影像组织方式,时空基准不够统一,集中式存储不利于大规模并行分析。对地观测大数据分析仍缺乏一套统一的数据模型与基础设施理论。近年来,数据立方体的研究为对地观测领域大数据分析基础设施提供了前景。基于统一的分析就绪型多维数据模型和集成对地观测数据分析功能,可构建一个基于数据立方的对地观测大数据分析基础设施。因此,本文提出了一个面向大规模分析的多源对地观测时空立方体,相较于现有的数据立方体方法,强调多源数据的统一组织、基于云计算的立方体处理模式以及基于人工智能优化的立方体计算。研究有助于构建时空大数据分析的新框架,同时建立与商业智能领域的数据立方体关联,为时空大数据建立统一的时空组织模型,支持大范围、长时序的快速大规模对地观测数据分析。本文在性能上与开源数据立方做了对比,结果证明提出的多源对地观测时空立方体在处理性能上具有明显优势。 相似文献
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Linwang Yuan Zhaoyuan Yu Shaofei Chen Wen Luo Yongjun Wang Guonian Lü 《Transactions in GIS》2010,14(Z1):59-83
Introducing Clifford algebra as the mathematical foundation, a unified spatio‐temporal data model and hierarchical spatio‐temporal index are constructed by linking basic data objects, like pointclouds and Spatio‐Temporal Hyper Cubes of different dimensions, within the multivector structure of Clifford algebra. The transformation from geographic space into homogeneous and conformal space means that geometric, metric and many other kinds of operators of Clifford algebra can be implemented and we then design the shortest path, high‐dimensional Voronoi and unified spatial‐temporal process analyses with spacetime algebra. Tests with real world data suggest these traditional GIS analysis algorithms can be extended and constructed under Clifford Algebra framework, which can accommodate multiple dimensions. The prototype software system CAUSTA (Clifford Algebra based Unified Spatial‐Temporal Analysis) provides a useful tool for investigating and modeling the distribution characteristics and dynamic process of complex geographical phenomena under the unified spatio‐temporal structure. 相似文献
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Integrating Spatial Data Linkage and Analysis Services in a Geoportal for China Urban Research
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Many geoportals are now evolving into online analytical environments, where large amounts of data and various analysis methods are integrated. These spatiotemporal data are often distributed in different databases and exist in heterogeneous forms, even when they refer to the same geospatial entities. Besides, existing open standards lack sufficient expression of the attribute semantics. Client applications or other services thus have to deal with unrelated preprocessing tasks, such as data transformation and attribute annotation, leading to potential inconsistencies. Furthermore, to build informative interfaces that guide users to quickly understand the analysis methods, an analysis service needs to explicitly model the method parameters, which are often interrelated and have rich auxiliary information. This work presents the design of the spatial data linkage and analysis services in a geoportal for China urban research. The spatial data linkage service aggregates multisource heterogeneous data into linked layers with flexible attribute mapping, providing client applications and services with a unified access as if querying a big table. The spatial analysis service incorporates parameter hierarchy and grouping by extending the standard WPS service, and data‐dependent validation in computation components. This platform can help researchers efficiently explore and analyze spatiotemporal data online. 相似文献
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Fei Hu Yongyao Jiang Yun Li Weiwei Song Daniel Q. Duffy 《International Journal of Digital Earth》2020,13(3):410-428
ABSTRACTEarth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency. 相似文献
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Input/output (I/O) of geospatial raster data often becomes the bottleneck of parallel geospatial processing due to the large data size and diverse formats of raster data. The open‐source Geospatial Data Abstraction Library (GDAL), which has been widely used to access diverse formats of geospatial raster data, has been applied recently to parallel geospatial raster processing. This article first explores the efficiency and feasibility of parallel raster I/O using GDAL under three common ways of domain decomposition: row‐wise, column‐wise, and block‐wise. Experimental results show that parallel raster I/O using GDAL under column‐wise or block‐wise domain decomposition is highly inefficient and cannot achieve correct output, although GDAL performs well under row‐wise domain decomposition. The reasons for this problem with GDAL are then analyzed and a two‐phase I/O strategy is proposed, designed to overcome this problem. A data redistribution module based on the proposed I/O strategy is implemented for GDAL using a message‐passing‐interface (MPI) programming model. Experimental results show that the data redistribution module is effective. 相似文献
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A geospatial cyberinfrastructure is needed to support advanced GIScience research and education activities. However, the heterogeneous and distributed nature of geospatial resources creates enormous obstacles for building a unified and interoperable geospatial cyberinfrastructure. In this paper, we propose the Geospatial Service Web (GSW) to underpin the development of a future geospatial cyberinfrastructure. The GSW excels over the traditional spatial data infrastructure by providing a highly intelligent geospatial middleware to integrate various geospatial resources through the Internet based on interoperable Web service technologies. The development of the GSW focuses on the establishment of a platform where data, information, and knowledge can be shared and exchanged in an interoperable manner. Theoretically, we describe the conceptual framework and research challenges for GSW, and then introduce our recent research toward building a GSW. A research agenda for building a GSW is also presented in the paper. 相似文献
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多源地理矢量空间数据融合研究 总被引:2,自引:2,他引:0
随着地理信息大数据时代的到来,地理信息与多个行业领域日益深度融合,产生了海量的多源异构数据。多源丰富的空间数据为经济社会发展提供基础支撑作用的同时,其异构性也给数据共享和应用带来了问题与挑战。针对地理空间数据多源、异构、不一致性的现状及特点,本文提出了数据库模式融合与数据库实例融合为一体的多源地理空间数据融合流程与方法,并以基础地理信息、地理国情普查等实例数据进行了分析,为多源空间数据共享与应用提供了解决思路。 相似文献
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Seung-Hyun Jeong Norman W. Paton Alvaro A. A. Fernandes Tony Griffiths 《Transactions in GIS》2005,9(2):129-156
Many applications capture, or make use of, spatial data that changes over time. This requirement for effective and efficient spatio‐temporal data management has given rise to a range of research activities relating to spatio‐temporal data management. Such work has sought to understand, for example, the requirements of different categories of application, and the modelling facilities that are most effective for these applications. However, at present, there are few systems with fully integrated support for spatio‐temporal data, and thus developers must often construct custom solutions for their applications. Developers of both bespoke solutions and of generic spatio‐temporal platforms will often need to support the fusion of large spatio‐temporal data sets. Supporting such requests in a database setting involves the use of join operations with both spatial and temporal conditions – spatio‐temporal joins. However, there has been little work to date on spatio‐temporal join algorithms or their evaluation. This paper presents an evaluation of several approaches to the implementation of spatio‐temporal joins that build upon widely available indexing techniques. The evaluation explores how several algorithms perform for databases with different spatial and temporal characteristics, with a view to helping developers of generic infrastructures or custom solutions in the selection and development of appropriate spatio‐temporal join strategies. 相似文献
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Diverse studies have shown that about 80% of all available data are related to a spatial location. Most of these geospatial data are available as structured and semi‐structured datasets, and often use distinct data models, are encoded using ad‐hoc vocabularies, and sometimes are being published in non‐standard formats. Hence, these data are isolated within silos and cannot be shared and integrated across organizations and communities. Spatial Data Infrastructures (SDIs) have emerged and contributed to significantly enhance data discovery and accessibility based on OGC (Open Geospatial Consortium) Web services. However, finding, accessing, and using data disseminated through SDIs are still difficult for non‐expert users. Overcoming the current geospatial data challenges involves adopting the best practices to expose, share, and integrate data on the Web, that is, Linked Data. In this article, we have developed a framework for generating, enriching, and exploiting geospatial Linked Data from multiple and heterogeneous geospatial data sources. This proposal allows connecting two interoperability universes (SDIs, more specifically Web Feature Services, WFS, and Semantic Web technologies), which is evaluated through a study case in the (geo)biodiversity domain. 相似文献
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互联网与大数据技术的高速发展为地理信息服务的互联共享与广泛应用提供了技术基础。面对资源海量、多源异构的地理信息服务,如何实现服务的有效组织并提供合理高效的服务组合,拓展地理信息服务的应用范围,满足更高层次的应用需求受到研究者的广泛关注。构建地理信息服务网络并通过语义实现服务协同,是一种可能的解决方案。本文分析了当前地理信息服务和地理信息服务网络研究的现状,依据服务网络领域的研究成果,从服务网络的表达和优化、服务协同的构建和优化两个方面探讨了基于网络构建的地理信息服务协同方法的发展潜力,进而提出了地理信息服务网络及协同面临的挑战与研究方向。 相似文献
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《International Journal of Digital Earth》2013,6(1):13-53
ABSTRACTBig Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications. 相似文献