survey

A Survey of Measures and Methods for Matching Geospatial Vector Datasets

Authors:

Emerson M. A. Xavier,

Francisco J. Ariza-López,

Manuel A. Ureña-CámaraAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 49, Issue 2

Article No.: 39, Pages 1 - 34

https://doi.org/10.1145/2963147

Published: 13 August 2016 Publication History

Abstract

The field of Geographical Information Systems (GIS) has experienced a rapid and ongoing growth of available sources for geospatial data. This growth has demanded more data integration in order to explore the benefits of these data further. However, many data providers implies many points of view for the same phenomena: geospatial features. We need sophisticated procedures aiming to find the correspondences between two vector datasets, a process named geospatial data matching. Similarity measures are key-tools for matching methods, so it is interesting to review these concepts together. This article provides a survey of 30 years of research into the measures and methods facing geospatial data matching. Our survey presents related work and develops a common taxonomy that permits us to compare measures and methods. This study points out relevant issues that may help to discover the potential of these approaches in many applications, like data integration, conflation, quality evaluation, and data management.

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Index Terms

A Survey of Measures and Methods for Matching Geospatial Vector Datasets
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Similarity measures
  2. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 49, Issue 2

June 2017

747 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2966278

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Publication History

Published: 13 August 2016

Accepted: 01 June 2016

Revised: 01 March 2016

Received: 01 October 2015

Published in CSUR Volume 49, Issue 2

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