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Semantic Graphs to Reflect the Evolution of Geographic Divisions

In: Handbook of Big Geospatial Data

Author

Listed:
  • C. Bernard

    (Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG)

  • C. Plumejeaud-Perreau

    (LIttoral ENvironnement et Sociétés (LIENSs) - UMR)

  • M. Villanova-Oliver

    (Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG)

  • J. Gensel

    (Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG)

  • H. Dao

    (University of Geneva, Department of Geography and Environment Geneva)

Abstract

Nowadays, the volume of data coming from the public sector is growing rapidly on the Open Data Web. Most of these data come from governmental agencies such as Statistical and Mapping Agencies. Together, these public institutions publish territorial statistics that are of utmost importance for policy-makers to conduct various analyses of their jurisdiction, in time and space, and observe its evolution over time. However, through times, all over the world, the geographic divisions that serve as a reference for recording territorial statistical values, are subject to change: their name, their belonging or their boundaries change for political or administrative reasons and at several subdivision levels (e.g., regions, districts, sub-districts). These changes lead to breaks in time-series and are source of both misinterpretations, and statistical biases when not properly documented. In this chapter, we investigate solutions relying on the Semantic Web technologies for the description of the evolution of geographic divisions over time. We investigate how these technologies may enhance the understanding of the territorial dynamics over time, providing statisticians, researchers, citizens with well-documented descriptions of territorial changes to conduct various analyses of the territories.

Suggested Citation

  • C. Bernard & C. Plumejeaud-Perreau & M. Villanova-Oliver & J. Gensel & H. Dao, 2021. "Semantic Graphs to Reflect the Evolution of Geographic Divisions," Springer Books, in: Martin Werner & Yao-Yi Chiang (ed.), Handbook of Big Geospatial Data, chapter 0, pages 135-159, Springer.
  • Handle: RePEc:spr:sprchp:978-3-030-55462-0_6
    DOI: 10.1007/978-3-030-55462-0_6
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