SexTant

Although tools for visualizing linked geospatial data have started to emerge, these tools focus on browsing a single dataset or the content of a single SPARQL endpoint. Although such tools are very useful for exploring the data offered by a single SPARQL endpoint, they present severe limitations when they are faced with the task of exploring the linked geospatial data cloud and capturing its temporal dimension.

With the aim of filling this gap and going beyond data exploration to map creation and sharing, we designed and developed SexTant. SexTant is a web-based tool that makes the task of map creation and sharing, as well as the task of browsing linked spatiotemporal data easy. Similar to well-known GIS tools (e.g., ArcGIS, QGIS), SexTant can be used to produce thematic maps by layering spatiotemporal information which exists in a number of data sources ranging from standard SPARQL endpoints, to SPARQL endpoints following OGC standards for the modelling and querying of geospatial information (i.e., GeoSPARQL), or even other standards or well-adopted geospatial file formats, such as KML and GeoJSON. The feature that distinguishes SexTant from other semantic web or GIS tools is that map creation and sharing, as well as exploration of data can be done in a declarative way using the query languages stSPARQL or GeoSPARQL. In this sense, SexTant is able to create useful thematic maps by layering information coming from the evaluation of SPARQL queries.

SexTant can be used to produce a map that is similar to the one produced by organizations, such as ZKI and NOA. The major difference between the two maps is that the one produced by SexTant is based solely on Linked Open Geospatial Data that are publicly available on the web. In the fire monitoring use case for example, this allows an emergency response manager to quickly compile a map based on “open source intelligence” until more precise, detailed data becomes available, i.e., after contacting local authorities.

The features of SexTant have been presented in the following demo papers:

  1. Charalampos Nikolaou, Kallirroi Dogani, Kostis Kyzirakos, and Manolis Koubarakis: "Sextant: Browsing and Mapping the Ocean of Linked Geospatial Data." In the 10th Extended Semantic Web Conference (ESWC 2013), Montpellier, France, May 26-30, 2013.

  2. Konstantina Bereta, Charalampos Nikolaou, Manos Karpathiotakis, Kostis Kyzirakos, and Manolis Koubarakis: “SexTant: Visualizing Time-Evolving Linked Geospatial Data.” In the 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, October 21-25, 2013.

The ESWC2013 demo won the best demo award (http://2013.eswc-conferences.org/).

SexTant also addresses the need for the visualization of the evolution of geospatial features through time. In the EO domain for example, data is constantly produced by satellite sensors and is associated with metadata containing, among others, temporal attributes, such as the time that an image was acquired. Satellite acquisitions are utilized in related applications such as the CORINE Land Cover programme operated by the European Environment Agency that makes available as a cartographic product the land cover of European areas over time.

SexTant can be used to combine information spanning multiple datasets and, by executing stSPARQL or GeoSPARQL queries that use several thematic, spatial and temporal criteria. In this way the full capabilities of endpoints using the spatiotemporal RDF store Strabon can be exploited. Map layers that contain solely geospatial information will be retrieved by evaluating a GeoSPARQL query on a Strabon, Oracle, Parliament, or Virtuoso endpoint, while layers that contain spatial and temporal information will be retrieved by evaluating an stSPARQL query on Strabon. The results that contain both spatial and temporal information can then be visualized on a map and on a timeline simultaneously, providing a user-friendly way to study the temporal evolution of geospatial features, e.g., the evolution of the land cover of an area though time, as well as to discover implicit links among the involved datasets due to their spatial and temporal correlation.

You can find more about SexTant here.