Abstract
OpenStreetMap and other Volunteered Geographic Information datasets have been explored in the last years, with the aim of understanding how their meaning is rendered, of assessing their quality, and of understanding the community-driven process that creates and maintains the data. Research mostly focuses either on the data themselves while ignoring the social processes behind, or solely discusses the community-driven process without making sense of the data at a larger scale. A holistic understanding that takes these and other aspects into account is, however, seldom gained. This article describes a server infrastructure to collect and process data about different aspects of OpenStreetMap. The resulting data are offered publicly in a common container format, which fosters the simultaneous examination of different aspects with the aim of gaining a more holistic view and facilitates the results’ reproducibility. As an example of such uses, we discuss the project OSMvis. This project offers a number of visualizations, which use the datasets produced by the server infrastructure to explore and visually analyse different aspects of OpenStreetMap. While the server infrastructure can serve as a blueprint for similar endeavours, the created datasets are of interest themselves too.
Highlights
Introduction and backgroundThe way geographical information is collected and used, and the characteristics of the data themselves, has changed in the last years and decades
We examine infrastructure to collect and mine data from and about the OSM project in order to explore and analyse them holistically, but the results can be transferred to other Volunteered Geographic Information (VGI) projects
The interpretation and analysis of VGI data is only possible when considering the social process that leads to their creation
Summary
Introduction and backgroundThe way geographical information is collected and used, and the characteristics of the data themselves, has changed in the last years and decades. The data are rather edited and the folksonomy and tools created on a voluntary basis by individuals who coordinate their activities, leading to strong heterogeneity among the users and their mapping behaviour [11, 12]; among the choice of what to map; among the chosen representation; and among the sources used to map, for example, aerial images, local knowledge, and GPS tracks This heterogeneity implies that some regions are mapped more complete than others [11, 12]; concepts are used differently [13]; the data are not always up to date [12]; and locations are described with differing precision [12]
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