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Articles | Volume XLII-4/W8
https://doi.org/10.5194/isprs-archives-XLII-4-W8-147-2018
https://doi.org/10.5194/isprs-archives-XLII-4-W8-147-2018
11 Jul 2018
 | 11 Jul 2018

AN OPEN SOURCE APPROACH FOR THE INTRINSIC ASSESSMENT OF THE TEMPORAL ACCURACY, UP-TO-DATENESS AND LINEAGE OF OPENSTREETMAP

M. Minghini, M. A. Brovelli, and F. Frassinelli

Keywords: Data Quality, Lineage, Open Source, OpenStreetMap, Temporal Accuracy, Thematic Accuracy, Up-to-dateness

Abstract. OpenStreetMap (OSM) is the most popular crowdsourced geographic information project. The main factor that still limits the practical use of OSM is the lack of quality assurance. OSM quality assessment is thus a well-studied topic in literature, with most of the studies evaluating the quality by comparison against reference datasets. In contrast to these extrinsic approaches, OSM intrinsic assessment evaluates the quality by only analysing OSM itself. This study contributes to OSM intrinsic assessment by introducing an open source procedure to evaluate the temporal accuracy, up-to-dateness and lineage of OSM. Two workflows are presented: the first allows accessing the historical evolution of single OSM objects through an interactive web application, while the second aggregates and stores results on a user-defined grid to enable further GIS processing. The methodology is applied on the OSM nodes in the city of Dar es Salaam, Tanzania, by computing the following measures on the cells of an hexagonal grid: total number of nodes, average date of creation and last edit of nodes, average update frequency of nodes, average number of versions of nodes, average and total number of different contributors on nodes. Results highlight the mapping dynamics driven by the Dar Ramani Huria project, focused on increasing flood preparedness and resilience. When moving from the peripheral areas to the city centre, OSM is characterized by a progressively higher density of nodes, created earlier in time and updated by a higher number of contributors, which are all indexes of a general higher data quality.