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LauNuts: A Knowledge Graph to Identify and Compare Geographic Regions in the European Union
Abstract
The Nomenclature of Territorial Units for Statistics (NUTS) is a classification that represents countries in the European Union (EU). It is published at intervals of several years and organized in a hierarchical system where geographical areas are subdivided according to their population sizes. In addition to NUTS, there is a further subdivided hierarchy level, named Local Administrative Units (LAU), whose data are updated annually by EU member states. While both datasets are published by Eurostat as Excel files, an additional RDF dataset is available for NUTS up to the 2016 scheme. With this work, we provide the Linked Data community with an up-to-date Knowledge Graph in which NUTS and LAU data are linked and which contains population numbers as well as area sizes. We also publish an Open Source generator software for future released versions that will naturally arise due to changes in population numbers. These contributions can be used to enrich other datasets and allow comparisons among regions in the European Union. All resources are available at https://w3id.org/launuts.KeywordsEUEuropean UnionEurostatKnowledge GraphLAULauNutsLinked DataNUTS
... GADM is published as Linked Data, named GADM-RDF [20]. NUTS, the Nomenclature of Units for Territorial Statistics, provides geospatial regions in the European Union as Linked Data for statistical and policy purposes [21]. Table 2. Geospatial data sources and related statistics. ...
The Web of Data has been fueled significantly by geospatial data over the last few years. In the current link discovery frameworks, there is still a lack of robust support for finding geospatial-aware links between geospatial data sources in the Web of Data. They are also limited in efficient association capabilities for large-scale datasets. This paper extends the data integration capability based on the spatial metrics in the open geospatial engine OGE. These metrics include topological relationships and spatial matching between geospatial entities within multiple geospatial data sources. Thus, the tool can be employed by data publishers to set geospatial-aware links to facilitate geospatial data and knowledge discovery in the Web of Data. Several geospatial data sources are used to demonstrate the usability and effectiveness of the approach and tool implementation.
Farm-scale and agent-based models draw typically on detailed and preferably spatially explicit single farm data. Data protection standards however restrict or exclude their access, as for example in Germany. We provide data on a synthetic farm population of the German federal state of North Rhine-Westphalia, mainly based on the German Farm Structure Survey 2016 and plot specific crop data from 2019/2020. The population is derived from farm typology at administrative unit level to which the observed plots are allocated afterwards. The data contains 25,858 farms and covers 1.3 million ha of agricultural land, provided at plot scale in a geospatial vector and at farm scale in tabular format. For each plot, the managing farm (including the estimated farm's location), the number of livestock, the cultivated crop, as well as the corresponding administration units are indicated. Furthermore, spatial data such as yield information, soil characteristics, as well as monitoring data on environmental status are attached. The provided data allows for diverse analysis on the farm population in the federal state of North Rhine Westphalia with farm, agent-based or different bio-physical models. Furthermore, it can serve as a test data set for models which require detailed and spatially explicit farm data.
The home-sharing economy is growing on a global scale, enabling homeowners to short-term lease through online platforms such as Airbnb. These practices bear both positive and negative externalities at both regional and national levels. Positive externalities include job creation, rises in income and in the dwelling supply for touristic purposes, as well as increase in tax revenues. Negative externalities involve disruptions to local
communities by raising rental prices and crowding-out long-term tenants, implying income redistribution from homeowners to renters, and the overall ‘touristification’ of traditional neighbourhoods (Gurran, 2018). The regional graphic in this paper
illustrates the fact that short-term listings in Greece are unevenly distributed, mainly concentrated in touristic regions and the capital city, but not in continental areas.
In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use different schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities. This knowledge graph does not yet readily exist, though, or at least, not a single one: so, we integrate and interlink several datasets to construct our (extensible) base geo-entities knowledge graph: (i) the openly available geospatial data repository GeoNames, (ii) the map service OpenStreetMap, (iii) country-specific sets of postal codes, and (iv) the European Union’s classification system NUTS. As a second step, this base knowledge graph is used to add semantic labels to the open datasets, i.e., we heuristically disambiguate the geo-entities in CSV columns using the context of the labels and the hierarchical graph structure of our base knowledge graph. Finally, in order to interact with and retrieve the content, we index the datasets and provide a demo user interface. Currently we indexed resources from four Open Data portals, and allow search queries for geo-entities as well as full-text matches at http://data.wu.ac.at/odgraph/.
The Semantic Web eases data and information integration tasks by providing an infrastructure based on RDF and ontologies. In this paper, we contribute to the development of a spatial Data Web by elaborating on how the collaboratively collected OpenStreetMap data can be interactively transformed and represented adhering to the RDF data model. This transformation will simplify information integration and aggregation tasks that require comprehensive background knowledge related to spatial features such as ways, structures, and landscapes. We describe how this data is interlinked with other spatial data sets, how it can be made accessible for machines according to the Linked Data paradigm and for humans by means of several applications, including a faceted geo-browser. The spatial data, vocabularies, interlinks and some of the applications are openly available in the LinkedGeoData project.
The recent publication of public sector information (PSI) data sets has brought to the attention of the scientific community the redundant presence of location based context. At the same time it stresses the inadequacy of current Linked Data services for exploiting the semantics of such contextual dimensions for easing entity retrieval and browsing. In this paper describes our approach for supporting the publication of geographical subdivisions in Linked Data format for supporting the e-government and public sector in publishing their data sets. The topological knowledge published can be reused in order to enrich the geographical context of other data sets, in particular we propose an exploitation scenario using statistical data sets described with the SCOVO ontology. The topological knowledge is then exploited within a service that supports the navigation and retrieval of statistical geographical entities for the EU territory. Geographical entities, in the extent of this paper, are linked data resources that describe objects that have a geographical extension. The data and services presented in this paper allows the discovery of resources that contain or are contained by a given entity URI and their representation within map widgets. We present an approach for a geography based service that helps in querying qualitative spatial relations for the EU statistical geography (proper containment so far). We also provide a rationale for publishing geographical information in Linked Data format based on our experience, within the EnAKTing project, in publishing UK PSI data.
The publication of public sector information (PSI) data sets has brought to the attention of the scientific community the redundant presence of location based context. At the same time it stresses the inadequacy of current Linked Data services for exploiting the semantics of such contextual dimensions for easing entity retrieval and browsing. In this paper we describe our Linked Data representation of the NUTS European statistical subdivision, created to support the e-government and public sector in publishing their data sets. The topological knowledge published in the Linked NUTS can be reused in order to enrich the geographical context of other data sets, in particular in a scenario where statistical data sets describe information that have strong ties with the territory, and therefore with its geography.
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