The Reginald Golledge Distinguished Lecture in Geography (March 14, 2013 )

The Reginald Golledge Distinguished Lecture in Geography

March 14, 2013 - 3:30-4:30 - Buchanan 1930, UCSB

“On Mental Clocks and Mental Maps: Contributions of Behavioral Geography
to a Theory of Geospatial Change”

Gilberto Câmara
National Institute for Space Research, Brazil

Citation Map: Visualizing the Spread of Scientific Ideas Through Space and Time

Team members: Yingjie Hu, Grant McKenzie, and Song Gao
We designed a Citation Map for the Robert Raskin Mashup Mapping competition. This web mapping application is a novel approach to visualize research topics, authors, publications, as well as their citation relations on a world map. By displaying the geographic distribution of research paper citations, this dynamic web map shows how a scientific idea spreads through space and time (i.e. How a scientific publication is accepted and cited by researchers in different countries over the years). This Citation Map also shows the researchers who has cited a particular paper most frequently, and where are these researchers.

Place and Location on the Web of Linked Data

Geodata or more specifically places and locations play a key role on the Web of Linked Data by serving as nexuses that interconnect different data and data sources. Geonames, for instance, is one of the most linked hubs. In fact, most Linked Data are either directly or indirectly linked through various spatial and non-spatial relations to locations. Thus, it makes sense to dive deeper into investigating the role that place and location play, how many degrees (links) it takes before all Linked Data is connected to some sort of geo-feature, how these geo-features are represented, how their density is distributed, how to clean them up, and so forth. We started to do research on these topics some time ago and are preparing a paper. In the meantime we would like to show you some maps that illustrate the current state of locations on the Linked Data Web and the amount and types of errors we encountered. It turns out that more than 10% of these data has wrong or even impossible locations. We have found systematic errors and are beginning to figure out methods for cleaning them up. In case you are interested in Geospatial Semantics, Linked Spatiotemporal Data, and Geo-Ontologies you may also check out this short overview as starting point.