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Modelling and Assessing Spatial Big Data: Use Cases of the OpenStreetMap Full-History Dump
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Author(s): Alexey Noskov (Heidelberg University, Germany), A. Yair Grinberger (Heidelberg University, Germany), Nikolaos Papapesios (University College London, UK), Adam Rousell (Heidelberg University, Germany), Rafael Troilo (Heidelberg University, Germany)and Alexander Zipf (Heidelberg University, Germany)
Copyright: 2019
Pages: 29
Source title:
Spatial Planning in the Big Data Revolution
Source Author(s)/Editor(s): Angioletta Voghera (Politecnico di Torino, Italy)and Luigi La Riccia (Politecnico di Torino, Italy)
DOI: 10.4018/978-1-5225-7927-4.ch002
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Abstract
Many methods for intrinsic quality assessment of spatial data are based on the OpenStreetMap full-history dump. Typically, the high-level analysis is conducted; few approaches take into account the low-level properties of data files. In this chapter, a low-level data-type analysis is introduced. It offers a novel framework for the overview of big data files and assessment of full-history data provenance (lineage). Developed tools generate tables and charts, which facilitate the comparison and analysis of datasets. Also, resulting data helped to develop a universal data model for optimal storing of OpenStreetMap full-history data in the form of a relational database. Databases for several pilot sites were evaluated by two use cases. First, a number of intrinsic data quality indicators and related metrics were implemented. Second, a framework for the inventory of spatial distribution of massive data uploads is discussed. Both use cases confirm the effectiveness of the proposed data-type analysis and derived relational data model.
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