SDI 4 Apps - Uptake of open geographic information through innovative services based on linked data

The SDI4Apps’s Point of Interest (SPOI) dataset is a specific set of POIs which could be “a data fuel” for applications and services of the Open Smart Tourist Data pilot. The SPOI data set is created as a harmonized combination of global data (selected points from OpenStreetMap and GeoNames.org) and several local data sets. These local data are provided by the SDI4Apps partners (for example data from Posumavi region, Czech Republic and Sicily, Italia or experimental ontologies developed in the University of West Bohemia including ski regions in Europe or historical sights in Rome).
The added value of the SDI4Apps approach in comparison to other similar solutions such as OpenPOIs consists in implementation of linked data approach (several objects are connected to DBpedia or GeoNames.org), using of universal RDF format, using of standardized and respected properties or vocabularies (for example FOAF, GeoSPARQL) and development of the completely harmonized data set with uniform data model and common classification (not only a copy of original resources).
The current version of SPOI (October 2015) contains 4 206 573 POIs covering almost the whole Europe and Africa. The data model describes seven types of information of each POI – identifier, description, geometry, classification (as a link to the vocabulary based on categories used in Waze navigation), contact information, tourist information and links to other data (for example topological relations related to relevant countries). The SPOI data are published as SPARQL endpoint (to query) as well as in the map client (to view). All information on SPOI are available on the web page (sdi4apps.eu/spoi).
There are several further step of development of the SPOI data: extension of information resources, refining of existing data, optimization of the data model (for example adding metadata)and harmonization process, licensing issues or improvement of cartographic presentation of the data (for example clustering).

Author: Otakar Cerba (University of West Bohemia)

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