Category Archives: Maps

The Triangle of Sustainability Awarded

Triangle of Sustainability show is a way to interact with large amounts of Linked Spatiotemporal Data to support understanding of the ecological, social and economical dimensions of sustainable development.

The Triangle of Sustainability (in German “Dreieck der Nachhaltigkeit”) by  Thomas Bartoscheck and Tomi Kauppinen from the Institute for Geoinformatics at the University of Münster was  awarded with a Finalist Position and is competing for the first prize  in Wissenschaft interaktiv 2012, June 2–6, 2012, Lübeck, Germany.

The Triangle of Sustainability is an interactive show to explore observations about deforestation of rainforests and related phenomena such as road networks, political situation, and market prices of agricultural products on maps and timelines.  The Triangle thus connects three important aspects–ecological, economical and social–of sustainability. By doing this the Triangle serves as a show of what is achievable by  interconnecting different scientific assets via the Linked Science approach. The goal is to raise the awareness, and understanding of different factors of sustainability. The Triangle thus serves as an example of how the research field of Geoinformatics, and more generally Geographic Information Science can serve the society in these tasks.

The resulting information can be explored on three screens (see the figure above). The interaction is made extremely simple yet powerful, no additional tools are required for the participants. All the spatial and temporal information can be zoomed and panned simply by making gestures using hands.

The technological basis is built on the power of Linked Data techniques for interconnecting these very heterogenous data about different environmental and social phenomena. The data used by the show is the Linked Brazilian Amazon Rainforest published at LinkedScience.org.


Analyzing and Visualizing Productivity of a University

Originally posted by Carsten Keßler at Lodum.de.

One of the main goals of Linked Open Data University of Muenster (LODUM) project is to open up the university’s data silos, integrate the data, and make it easy to build applications on top of the data collection. This productivity map for Google Earth is an example of such an application. It renders the university buildings in 3D – the building height indicates the number of publications written by researchers working in the respective building.

The absolute number of papers is normalized by the number of researchers working in the given building for a more balanced impression. The buildings are split in two parts:

  1. the lower part indicates the number of journal papers, and
  2. the upper part represents all other publications.

Clicking either of these two parts opens a pop-up with the actual numbers. The distribution of publications between the different institutions in a building is visualized as a pie chart (generated by the Google Chart Tools). The pop-ups also include links to the SPARQL queries to pull the data for the given building out of our store, so that interested developers can learn how we built this map.

The KML file is also available for download—just open it up in Google Earth to explore the productivity map.

The following publication explains the visualization. Please use it for citing the project.

Carsten Keßler and Tomi KauppinenLinked Open Data University of Münster – Infrastructure and ApplicationsIn Demos of the 9th Extended Semantic Web Conference (ESWC2012), Heraklion, Greece, May, 2012. [BibTeX]

Tutorial on SPARQL Package for R

Tools are major enablers of Linked Science. One crucial aspect is how to access and analyze data, and especially how to get only that part of data which is of interest for a given research question.  Linked Data solves the access part, and SPARQL allows to query only a subset of the data. For statistical computing there are tools like R. As a solution to bridge the two communities, those of statistical computing and  semantic web, there is now a SPARQL Package for R, which enables to get data from Linked Data services to R for analysis. At LinkedScience.org you may now find a tutorial on SPARQL Package for R. By following it you can learn:

  1. how to access and query Linked Spatiotemporal Data of the  deforestation statistics related to the Brazilian Amazon Rainforest, and
  2. how to analyze it within R (which is a free software environment for statistical computing).