Analysing the Impact of Large Data Imports in OpenStreetMap

Bibliographic Data

ID22032047
AuthorsRaphael Witt (0000-0003-2144-9029, HeiGIT gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany, corresponding author), Lukas Loos (0000-0002-1691-4064, HeiGIT gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany), Alexander Zipf (0000-0003-4916-9838, Heidelberg University)
Year2021
Volume10
Issue8
Pages528
Publication date2021-08-06
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10080528
OpenAlexW3191725647
LanguageEN
Citations received6
References cited30

OpenStreetMap (OSM) is a global mapping project which generates free geographical information through a community of volunteers. OSM is used in a variety of applications and for research purposes. However, it is also possible to import external data sets to OpenStreetMap. The opinions about these data imports are divergent among researchers and contributors, and the subject is constantly discussed. The question of whether importing data, especially large quantities, is adding value to OSM or compromising the progress of the project needs to be investigated more deeply. For this study, OSM’s historical data were used to compute metrics about the developments of the contributors and OSM data during large data imports which were for the Netherlands and India. Additionally, one time period per study area during which there was no large data import was investigated to compare results. For making statements about the impacts of large data imports in OSM, the metrics were analysed using different techniques (cross-correlation and changepoint detection). It was found that the contributor activity increased during large data imports. Additionally, contributors who were already active before a large import were more likely to contribute to OSM after said import than contributors who made their first contributions during the large data import. The results show the difficulty of interpreting a heterogeneous data source, such as OSM, and the complexity of the project. Limitations and challenges which were encountered are explained, and future directions for continuing in this field of research are given

Data mining · Data science · Data source · Geography · World Wide Web · Computer Science · Data Management and Algorithms · Data-Driven Disease Surveillance · Geographic Information Systems Studies · Mathematics · Artificial Intelligence

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