Understanding Threats to Crowdsourced Geographic Data Quality Through a Study of OpenStreetMap Contributor Bans

Abstract

In recent years, the crowdsourced OpenStreetMap (OSM) database has gained attention as a potentially rich source of spatial data for governments and businesses, but some are hesitant to use OSM due to concerns about quality and lack of institutional control. Preventing intentional and accidental damage to OSM is critical for increasing the credibility of this data source. In this chapter, we study the ways that OSM data incurs damage by examining descriptions of incidents where OSM contributors were banned by project administrators. These publicly available ban descriptions represent an underutilized qualitative source of information about threats to OSM data integrity. We identify four main themes from the ban descriptions, which we term nefariousness, obstinance, ignorance, and mechanical problems. We describe subcategories of bans identified under each theme and discuss how these incidents can inform strategies for preempting future damage to the project and increasing crowdsourced data quality.

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