The OpenStreetMap folksonomy and its evolution

Mocnik, Franz-Benjamin; Zipf, Alexander; Raifer, Martin

doi:10.1080/10095020.2017.1368193

Cited by 34 publications

(33 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The aim of OSMvis is to generate insights about the OSM community and its mapping behaviour as well as about the OSM data themselves by a visual exploration of the datasets. The usefulness of such visualizations, in particular when combining different aspects of the data from different datasets, has been demonstrated by Mocnik et al [8].…”

Section: Results and Discussion Of Use Casesmentioning

confidence: 99%

“…An understanding of how the folksonomy evolves over time can thus provide useful insights in how the data quality changes over time. The OSMvis visualizations, which are able to provide such insights, have already been discussed in literature [8] and are thus rather shortly summarized in this section.…”

Section: The History Of the Folksonomy And Its Effect On Data Qualitymentioning

confidence: 99%

“…Accordingly, the taxonomy is changing over time. As the taxonomy is created by its use in the data rather than by documentation efforts, it is referred to as a folksonomy [8]. The quality of OSM data is strongly interlinked with the evolution of the folksonomy.…”

Section: The History Of the Folksonomy And Its Effect On Data Qualitymentioning

confidence: 99%

“…The language versions are often direct translations, often shortened to the most important information or country specific information. The OSM wiki contains a documentation of the folksonomy of the OSM data, in particular of many concepts used for tagging OSM objects [8].…”

Section: Data Sourcesmentioning

confidence: 99%

“…The characteristics of VGI to be collected and maintained by a community implies that the advancement of the dataset and its underlaying folksonomy, that is, the semantics of the tags used in OSM, are not controlled by a single person or authority but rather the result of a community-driven process [8][9][10]. The tools and services used to produce and process the data are neither.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Open source data mining infrastructure for exploring and analysing OpenStreetMap

Mocnik

Mobasheri

Zipf

2018

Open geospatial data, softw. stand.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Results and Discussion Of Use Casesmentioning

confidence: 99%

Section: The History Of the Folksonomy And Its Effect On Data Qualitymentioning

confidence: 99%

Section: The History Of the Folksonomy And Its Effect On Data Qualitymentioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations