Articles | Volume 5
https://doi.org/10.5194/agile-giss-5-34-2024
https://doi.org/10.5194/agile-giss-5-34-2024
30 May 2024
 | 30 May 2024

Data Quality of OpenStreetMap for Industrial Sites in the Arctic

Daniel Kwakye, Sabrina Marx, Benjamin Herfort, Moritz Langer, and Sven Lautenbach

Keywords: OpenStreetMap, Intrinsic Quality Assessment, Permafrost, Volunteered Geographic Information, Ohsome Quality API

Abstract. Climate change is causing rapid warming in the Arctic region, resulting in the thawing of permafrost. This has substantial environmental implications, such as the release and mobilisation of contaminants from past and present industrial activities. However, freely accessible public geographical information is scarce on industrial sites and activities in much of the Arctic, which makes scientific research such as impact assessment difficult. OpenStreetMap (OSM) can be a valuable resource for identifying and assessing industrial sites for contamination. However, OSM data quality is not uniform across regions necessitating our evaluation of its reliability for identifying industrial sites and contamination hotspots in the areas most susceptible to permafrost thawing. Therefore, we examined in our study the object and attribute completeness as well as the currentness of OSM data on industrial sites. Our study focused on the regions defined by the presence of either discontinuous or continuous permafrost located in Canada, the USA, Denmark, Russia, and Norway, as these regions are expected to show strongest impacts of rising temperatures with respect to industrial pollution. The highest object completeness and currentness were obtained in Denmark (99% and 48% respectively). Russia had the lowest completeness (68%) and Canada had the lowest currentness (30%). Despite the promising average completeness of 86% and the average currentness of 35%, only 5.6% of industrial sites mapped in OSM contained information on the type of industry. This finding highlights the need for efforts to enhance attribute completeness gaps to maximize the use of OSM data in comprehensive environmental analyses.

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