Abstract:
Anyone can contribute geographic information to OpenStreetMap (OSM), regardless of
their level of experience or skills, which has raised concerns about quality. When reference data
is not available to assess the quality of OSM data, intrinsic methods that assess the data and its
metadata can be used. In this study, we applied unsupervised machine learning for analysing OSM
history data to get a better understanding of who contributed when and how in Mozambique. Even
though no absolute statements can be made about the quality of the data, the results provide valuable
insight into the quality. Most of the data in Mozambique (93%) was contributed by a small group of
active contributors (25%). However, these were less active than the OSM Foundation’s definition of
active contributorship and the Humanitarian OpenStreetMap Team (HOT) definition for intermediate
mappers. Compared to other contributor classifications, our results revealed a new class: contributors
who were new in the area and most likely attracted by HOT mapping events during disaster relief
operations in Mozambique in 2019. More studies in different parts of the world would establish
whether the patterns observed here are typical for developing countries. Intrinsic methods cannot
replace ground truthing or extrinsic methods, but provide alternative ways for gaining insight about
quality, and they can also be used to inform efforts to further improve the quality. We provide
suggestions for how contributor-focused intrinsic quality assessments could be further refined.
