Spatial Data Quality: From Process to Decisions
Spatial Data Quality: From Process to Decisions
复制标题
空间数据质量：从流程到决策
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
R. Devillers
中科院分区：
文献类型：
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作者：
M. Delavar;R. Devillers
据认为，大约80%的数据都有空间成分。由于良好的数据对支持决策过程至关重要，因此空间数据的质量和不确定性长期以来一直是地理信息(GI)社区感兴趣的领域也就不足为奇了。虽然空间数据质量在过去三十年中一直是一个活跃的研究领域(见Devillers等人的文章)。从历史的角度看这一专题)，研究越来越多地探索数据质量和决策过程之间存在的关系，认识到空间数据在许多领域发挥着越来越大的作用。为此，本期特刊的主题是《空间数据质量：从过程到决策》。随着陆地、机载和空间传感器(例如高分辨率遥感图像、干涉合成孔径雷达、高光谱图像)和基于位置的系统(全球定位系统、移动地理信息系统、基于位置的服务、普适地理信息和地球传感器网络)的出现和广泛使用，数据质量的重要性增加。当涉及到测量、描述和交流数据质量信息时，集成数量不断增加的异类数据的需求导致了巨大的挑战。空间数据的质量和不确定性一直是一些研究方案的重点，例如国家地理信息和分析中心(NCGIA)联盟的第一项倡议、空间数据质量和准确性国际研讨会(ISSDQ)和欧洲地理信息实验室协会(AGILE)和国际制图协会(ICA)的专题讨论会和工作组。在学术领域之外，它还通过质量控制和质量保证计划以及ISO/TC211、开放地理空间联盟(OGC)、联邦地理数据委员会(FGDC)或欧洲标准化委员会(CEN)等组织的标准吸引了政府和行业的注意。史(2008)提到，质量仍然是空间数据用户面临的主要问题之一。数据生产者，被问及他们在地理信息系统交易中提供了什么，2010，14(4)：379-386
It is believed that about 80% of data have a spatial component. As good data are crucial to support decision-making processes, it is not surprising that quality and uncertainty of spatial data has long been an area of interest for the Geographic Information (GI) community. While spatial data quality has been an active field of research in the past three decades (see the article by Devillers et al. in this special issue for an historical perspective), research increasingly explores the relationship that exists between data quality and decision-making processes, recognizing the increasing role that spatial data plays in a large number of fields. For this reason, the theme of this special issue is “Spatial data quality: from process to decisions.” The importance of data quality increases as we witness the emergence and widespread use of terrestrial, airborne, and spaceborne sensors (e.g. high resolution remotely sensed images (HRSI), interferometric synthetic aperture radar (INSAR), hyperspectral images) and systems based on locations (global positioning systems (GPS), mobile GIS, locationbased services (LBS), ubiquitous geographic information (UBGI), and geo-sensor networks). The need to integrate an increasing amount of heterogeneous data results in significant challenges when it comes to measuring, describing, and communicating data quality information. Quality and uncertainty of spatial data have been the focus of a number of research programs, such as the first initiative of the National Center for Geographic Information and Analysis (NCGIA) consortium, the International Symposium of Spatial Data Quality (ISSDQ) and Accuracy symposia and working groups from the Association of Geographic Information Laboratories in Europe (AGILE) and from the International Cartographic Association (ICA). Beyond the academic field, it has also captured the attention of government and industry through quality control and quality assurance programs and through standards from organizations such as ISO/TC211, the Open Geospatial Consortium (OGC), the Federal Geographic Data Committee (FGDC) or the European Committee for Standardization (CEN). Shi (2008) mentioned that quality remains one the major issues faced by users of spatial data. Data producers, who are questioned about what they provide in terms of Transactions in GIS, 2010, 14(4): 379–386