Shared data sources in the geographical domain: a classification schema and corresponding visualization techniques
People share data in different ways. Many of them contribute on a voluntary basis, while others are unaware of their contribution. They have differing intentions, collaborate in different ways, and they contribute data about differing aspects. Shared Data Sources have been explored individually in t...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
27 May 2019
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| In: |
ISPRS International Journal of Geo-Information
Year: 2019, Volume: 8, Issue: 5 |
| ISSN: | 2220-9964 |
| DOI: | 10.3390/ijgi8050242 |
| Online Access: | Verlag, Volltext: https://doi.org/10.3390/ijgi8050242 Verlag, Volltext: https://www.mdpi.com/2220-9964/8/5/242 |
| Author Notes: | Franz-Benjamin Mocnik, Christina Ludwig, A. Yair Grinberger, Clemens Jacobs, Carolin Klonner and Martin Raifer |
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| 245 | 1 | 0 | |a Shared data sources in the geographical domain |b a classification schema and corresponding visualization techniques |c Franz-Benjamin Mocnik, Christina Ludwig, A. Yair Grinberger, Clemens Jacobs, Carolin Klonner and Martin Raifer |
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| 520 | |a People share data in different ways. Many of them contribute on a voluntary basis, while others are unaware of their contribution. They have differing intentions, collaborate in different ways, and they contribute data about differing aspects. Shared Data Sources have been explored individually in the literature, in particular OpenStreetMap and Twitter, and some types of Shared Data Sources have widely been studied, such as Volunteered Geographic Information (VGI), Ambient Geographic Information (AGI), and Public Participation Geographic Information Systems (PPGIS). A thorough and systematic discussion of Shared Data Sources in their entirety is, however, still missing. For the purpose of establishing such a discussion, we introduce in this article a schema consisting of a number of dimensions for characterizing socially produced, maintained, and used ‘Shared Data Sources,’ as well as corresponding visualization techniques. Both the schema and the visualization techniques allow for a common characterization in order to set individual data sources into context and to identify clusters of Shared Data Sources with common characteristics. Among others, this makes possible choosing suitable Shared Data Sources for a given task and gaining an understanding of how to interpret them by drawing parallels between several Shared Data Sources. | ||
| 650 | 4 | |a Ambient Geographic Information (AGI) | |
| 650 | 4 | |a conceptual space | |
| 650 | 4 | |a Geographical Shared Data Source (GSDS) | |
| 650 | 4 | |a Participatory Geographic Information (PGI) | |
| 650 | 4 | |a semantics | |
| 650 | 4 | |a Shared Data Source (SDS) | |
| 650 | 4 | |a visualization | |
| 650 | 4 | |a Volunteered Geographic Information (VGI) | |
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