Abstract
The type of data an individual contributor adds to OpenStreetMap (OSM) varies by region. The local knowledge of a data contributor allows for the collection and editing of detailed features such as small trails, park benches or fire hydrants, as well as adding attribute information that can only be accessed locally. As opposed to this, satellite imagery that is provided as background images in OSM data editors, such as ID, Potlatch or JOSM, facilitates the contribution of less detailed data through on-screen digitizing, oftentimes for areas the contributor is less familiar with. Knowing whether an area is part of a contributor’s home region or not can therefore be a useful predictor of OSM data quality for a geographic region. This research explores the editing history of nodes and ways for 13 highly active OSM members within a two-tiered clustering process to delineate an individual mapper’s home region from remotely mapped areas. The findings are evaluated against those found with a previously introduced method which determines a contributor’s home region solely based on spatial clustering of created nodes. The comparison shows that both methods are able to delineate similar home regions for the 13 contributors with some differences.
Highlights
The evolution of voluntarily collected geodata and its distribution on the internet has led to a significant increase in research on Volunteered Geographic Information (VGI) [1] in recent years
The results showed that GIS designers can rely on a level of detail in VGI in selected regions that is unlikely to arise through professional geographic information (PGI)
The presented areal delineation approach was evaluated by comparing the extent of the identified home regions with those extracted from a previously introduced method based on a Delaunay triangulation which utilizes the centroids of all changesets created by the contributor under consideration [17]
Summary
The evolution of voluntarily collected geodata and its distribution on the internet has led to a significant increase in research on Volunteered Geographic Information (VGI) [1] in recent years. Whereas an earlier approach identified a contributor’s primary activity area solely based on the position of node contributions or the mean positions of changesets for that contributor [17], so far no method utilized the additional information about the type of edits made to OSM data to identify a contributor’s home region. To test this assumption we use a two-tiered clustering approach which analyzes the editing patterns on nodes and ways for 13 selected active OSM contributors This method spatially delineates a contributor’s data collection efforts into a home region and areas the contributor is presumably not as familiar with (from here on referred to as external region).
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