Using digital spatial archives effectively

Flewelling, Douglas M.

doi:10.1080/136588199241427

Cited by 15 publications

(5 citation statements)

References 6 publications

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“…Nevertheless, collections of data are not necessarily adequate for the needs of global-scale analysis, as merging data from various sources may raise several questions (FLEWELLING and EGENHOFER, 1999). As TOMLINSON (1988) observes, the way in which Earth observations are stored and integrated largely determines the degree to which spatial processes can be understood.…”

Section: Global Coastal Databases: An Overviewmentioning

confidence: 99%

A New Global Coastal Database for Impact and Vulnerability Analysis to Sea-Level Rise

Vafeidis

Nicholls

McFadden

et al. 2008

Journal of Coastal Research

241

176

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VAFEIDIS, A.T.; NICHOLLS, R.J.; MCFADDEN, L.; TOL, R.S.J.; HINKEL, J.; SPENCER, T.; GRASHOFF, P.S.; BOOT, G., and KLEIN, R.J.T., 2008. A new global coastal database for impact and vulnerability analysis to sea-level rise. Journal of Coastal Research, 24(4), 917-924. West Palm Beach (Florida), ISSN 0749-0208.A new global coastal database has been developed within the context of the DINAS-COAST project. The database covers the world's coasts, excluding Antarctica, and includes information on more than 80 physical, ecological, and socioeconomic parameters of the coastal zone. The database provides the base data for the Dynamic Interactive Vulnerability Assessment modelling tool that the DINAS-COAST project has produced. In order to comply with the requirements of the modelling tool, it is based on a data model in which all information is referenced to more than 12,000 linear coastal segments of variable length. For efficiency of data storage, six other geographic features (administrative units, countries, rivers, tidal basins or estuaries, world heritage sites, and climate grid cells) are used to reference some data, but all are linked to the linear segment structure. This fundamental linear data structure is unique for a global database and represents an efficient solution to the problem of representing and storing coastal data. The database has been specifically designed to support impact and vulnerability analysis to sea-level rise at a range of scales up to global. Due to the structure, consistency, user-friendliness, and wealth of information in the database, it has potential wider application to analysis and modelling of the world's coasts, especially at regional to global scales. ADDITIONAL INDEX WORDS:Segmentation, coastal geographic information system (GIS), data model, climate change, global change.

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Section: Global Coastal Databases: An Overviewmentioning

confidence: 99%

A New Global Coastal Database for Impact and Vulnerability Analysis to Sea-Level Rise

Vafeidis

Nicholls

McFadden

et al. 2008

Journal of Coastal Research

241

176

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“…In the strategy described here, the specific information models need to be carefully evaluated before incorporation into the global schema and subsequent exposure to client users, when including new catalogue services. Efforts such as the ISO Technical Committee (TC) 211 Geospatial Metadata Standard 19115, Content Standard for Digital Geospatial Metadata (FGDC, 1998) and Dublin Core (DCMI, 2006) are attempting to standardize the geospatial metadata information model, but, in many cases, their use is voluntary (Flewelling and Egenhofer, 1999). A metadata crosswalk could be of help when mapping two distinct models, but not very suitable for one-to-many mapping.…”

Section: Discussionmentioning

confidence: 99%

Towards a Geospatial Catalogue Federation Service

Bai¹,

Di²,

Chen³

et al. 2007

photogramm eng remote sensing

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“…As online spatial datasets grow both in number and sophistication, finding suitable datasets for a task becomes increasingly difficult. For spatial data archives, Flewelling and Egenhofer [7] identified several ways to evaluate the usefulness of an archive and pointed out that none of them offers an ideal solution. For spatial databases, the user's choices are even more limited, since downloading the complete dataset or part of it is no longer an option.…”

Section: Introductionmentioning

confidence: 99%

Exploring spatial datasets with histograms

Sun

Bandi

Agrawal

et al. 2006

Distrib Parallel Databases

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As online spatial datasets grow both in number and sophistication, it becomes increasingly difficult for users to decide whether a dataset is suitable for their tasks, especially when they do not have prior knowledge of the dataset. In this paper, we propose browsing as an effective and efficient way to explore the content of a spatial dataset. Browsing allows users to view the size of a result set before evaluating the query at the database, thereby avoiding zero-hit/mega-hit queries and saving time and resources. Although the underlying technique supporting browsing is similar to range query aggregation and selectivity estimation, spatial dataset browsing poses some unique challenges. In this paper, we identify a set of spatial relations that need to be supported in browsing applications, namely, the contains, contained and the overlap relations. We prove a lower bound on the storage required to answer queries about the contains relation accurately at a given resolution. We then present three storage-efficient approximation algorithms which we believe to be the first to estimate query results about these spatial relations. We evaluate these algorithms with both synthetic and real world datasets and show that they provide highly accurate estimates for datasets with various characteristics.

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Using digital spatial archives effectively

Cited by 15 publications

References 6 publications

A New Global Coastal Database for Impact and Vulnerability Analysis to Sea-Level Rise

A New Global Coastal Database for Impact and Vulnerability Analysis to Sea-Level Rise

Towards a Geospatial Catalogue Federation Service

Exploring spatial datasets with histograms

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