Rafael Troilo

OpenStreetMap (OSM) is a collaborative project collecting geographical data of the entire world. The level of detail of OSM data and its data quality vary much across different regions and domains. In order to analyse such variations it is often necessary to research the history and evolution of the OSM data. The OpenStreetMap History Database (OSHDB) is a new data analysis tool for spatio-temporal geographical vector data. It is specifically optimized for working with OSM history data on a global scale and allows one to investigate the data evolution and user contributions in a flexible way. Benefits of the OSHDB are for example: to facilitate accessing OSM history data as a research subject and to assess the quality of OSM data by using intrinsic measures. This article describes the requirements of such a system and the resulting technical implementation of the OSHDB: the OSHDB data model and its application programming interface.

This paper examines OpenStreetMap data quality at different stages of a participatory mapping process in seven slums in Africa and Asia. Data were drawn from an OpenStreetMap-based participatory mapping process developed as part of a research project focusing on understanding inequalities in healthcare access of slum residents in the Global South. Descriptive statistics and qualitative analysis were employed to examine the following research question: What is the spatial data quality of collaborative remote mapping achieved by volunteer mappers in morphologically complex urban areas? Findings show that the completeness achieved by remote mapping largely depends on the morphology and characteristics of slums such as building density and rooftop architecture, varying from 84% in the best case, to zero in the most difficult site. The major scientific contribution of this study is to provide evidence on the spatial data quality of remotely mapped data through volunteer mapping efforts in morphologically complex urban areas such as slums; the results could provide insights into how much fieldwork would be needed in what level of complexity and to what extent the involvement of local volunteers in these efforts is required.

The original notion of volunteered geographical information (VGI) offers a vision of democratizing geographical information systems (GIS) via the contributions of non-expert individuals, replacing authoritative episetemologies with more open and local geographical representations.Recent studies have questioned this vision, with empirical and conceptual investigations pointing to the effects of data production procedures on the resulting representation.In practice, many organizations and social institutions hold important roles in the production of VGI, thus integrating institutional epistemologies into VGI.This paper explores the role of such institutions in the production of OpenStreetMap (OSM) data by identifying and analysing large-scale contribution events, such as data imports or organized mapping efforts.The paper deploys a global event-identification query on the historical OSM database.The results show that large-scale events are responsible for a significant portion of OSM activities, especially in relation to the creation of data.The procedure identifies several event hotspots, prevalent in either highly developed regions or developing ones.Characterizing the events according to the institutional context that drives them, the paper suggests a relation between socio-economic contexts and the integration of specific institutional perspective into local representations.Hence, the paper contributes to our understanding of VGI as a product of complex interactions of social and institutional perspectives and offers a method towards considering these in research and practice.