Data integration across borders: a case study of the Abbotsford‐Sumas aquifer (British Columbia/Washington State) 1
Data integration across borders: a case study of the Abbotsford‐Sumas aquifer (British Columbia/Washington State) 1
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跨境数据整合：阿伯茨福德-苏马斯含水层案例研究（不列颠哥伦比亚省/华盛顿州）1
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
D. Allen
中科院分区：
文献类型：
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作者：
N. Schuurman;A. Deshpande;D. Allen
翻译后摘要：整合来自不同来源的空间数据集是至关重要的跨境环境调查和决策。 这是一个很少研究的主题，对空间数据的可用性和可靠性有着深远的影响。目前，加拿大或美国（美国）都有地下水水文地层模型。含水层的一部分，但很少有跨越边界的综合。在本文中，我们描述了集成多个源，大型数据集的Abbotsford-Sumas含水层的地下水水文地层模型的发展所面临的挑战。加拿大日益关注边界以南的过度开采，美国日益关注边界以北的硝酸盐污染，这使这一特殊含水层成为国际利益之一。虽然GIScience的重点是数据集成的理论解决方案，如当前的本体研究，本研究解决了跨国界数据集成的实用方法。许多互操作性挑战，包括数据的可用性，元数据，数据格式和质量，数据库结构，语义，政策和合作被确定为跨境研究数据集成的抑制因素。本文的最后一节概述了两种可能的解决方案，用于标准化地下水模型的分类方案-一旦数据异质性得到解决。
Abstract: Integrating spatial datasets from diverse sources is essential for cross‐border environmental investigations and decision‐making. This is a little investigated topic that has profound implications for the availability and reliability of spatial data. At present, ground‐water hydrostratigraphic models exist for both the Canadian or for the United States (U.S.) portion of the aquifer but few are integrated across the border. In this paper, we describe the challenges of integrating multiple source, large datasets for development of a ground‐water hydrostratigraphic model for the Abbotsford‐Sumas Aquifer. Growing concerns in Canada regarding excessive withdrawal south of the border and in the U.S. regarding nitrate contamination originating north of the border make this particular aquifer one of international interest. While much emphasis in GIScience is on theoretical solutions to data integration, such as current ontology research, this study addresses pragmatic ways of integrating data across borders. Numerous interoperability challenges including the availability of data, metadata, data formats and quality, database structure, semantics, policies, and cooperation are identified as inhibitors of data integration for cross‐border studies. The final section of the paper outlines two possible solutions for standardizing classification schemes for ground‐water models – once data heterogeneity has been addressed.