Next Article in Journal
Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks
Previous Article in Journal
Spatiotemporal Pattern Analysis of China’s Cities Based on High-Resolution Imagery from 2000 to 2015
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle

Shared Data Sources in the Geographical Domain—A Classification Schema and Corresponding Visualization Techniques

Institute of Geography, Heidelberg University, 69120 Heidelberg, Germany
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(5), 242; https://doi.org/10.3390/ijgi8050242
Received: 12 March 2019 / Revised: 5 April 2019 / Accepted: 15 May 2019 / Published: 27 May 2019
  |  
PDF [655 KB, uploaded 30 May 2019]
  |  

Abstract

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. View Full-Text
Keywords: Shared Data Source (SDS); Geographical Shared Data Source (GSDS); visualization; semantics; Volunteered Geographic Information (VGI); Ambient Geographic Information (AGI); Participatory Geographic Information (PGI); conceptual space Shared Data Source (SDS); Geographical Shared Data Source (GSDS); visualization; semantics; Volunteered Geographic Information (VGI); Ambient Geographic Information (AGI); Participatory Geographic Information (PGI); conceptual space
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Mocnik, F.-B.; Ludwig, C.; Grinberger, A.Y.; Jacobs, C.; Klonner, C.; Raifer, M. Shared Data Sources in the Geographical Domain—A Classification Schema and Corresponding Visualization Techniques. ISPRS Int. J. Geo-Inf. 2019, 8, 242.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top