{"title":"Querying Geospatial Data Expressed in RDF","authors":"D. Pantazi, D. Bilidas","doi":"10.1145/3581906.3581914","DOIUrl":"https://doi.org/10.1145/3581906.3581914","url":null,"abstract":"In this chapter, we will show how to query geospatial data expressed in RDF using the query languages GeoSPARQL, stSPARQL, GeoSPARQL+, and GeoSPARQL 1.1. The queries will be posed on the datasets of Chapter 6 and can be run on the demo available on the SPARQL endpoint that can be found at the Web site of the book. Since GeoSPARQL, stSPARQL, and GeoSPARQL+ simply add spatial extension functions to SPARQL, all SPARQL queries are expressible in these languages as well. For example, we can write the following query. Query 1: Give me the population of the municipality of Piraeus (“Πειραιάς” in Greek). This query can be expressed as follows:","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126939648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Legacy Geospatial Data Technologies","authors":"Manolis Koubarakis, D. Pantazi","doi":"10.1145/3581906.3581910","DOIUrl":"https://doi.org/10.1145/3581906.3581910","url":null,"abstract":"dard for encoding geospatial data. These have been used by the query languages GeoSPARQL, stSPARQL, and GeoSPARQL+ that we will present in Chapter 7. We also present popular geospatial data formats in which geospatial data have histor ically been made available (shapefiles, GeoTIFF files, and NetCDF files). Finally, we discuss the functionality offered by two kinds of very successful geospatial technologies that came before the technologies presented in this book: geospa tial relational DBMS and GIS systems. One can see the technologies presented in this book as an alternative to these legacy technologies or as technologies that can coexist with them. Both of these approaches are explored in later chapters. Like Chapter 2, this chapter is also a background chapter that can be skipped by readers that are familiar with the material presented. Legacy Geospatial Data Technologies","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127922105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Interlinking Geospatial Data Sources","authors":"G. Papadakis","doi":"10.1145/3581906.3581917","DOIUrl":"https://doi.org/10.1145/3581906.3581917","url":null,"abstract":"∙ Consumers can discover more related data while consuming the data. ∙ Consumers can directly learn about the data schema. ∙ Publishers can make their data discoverable. ∙ Publishers can increase the value of their data. ∙ Publishers will gain the same benefits from the links as consumers. The Best Practice 3 of Tandy et al. [2017] urges us to link features together to create the Web of geospatial data.","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131106450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Ontologies and Linked Data","authors":"D. Bilidas","doi":"10.1145/3581906.3581911","DOIUrl":"https://doi.org/10.1145/3581906.3581911","url":null,"abstract":"4.1 The Data Model RDF RDF is the data model of linked data. It is an acronym for Resource Description Framework, a standard of the World Wide Web Consortium (W3C), currently in version 1.1.1 RDF is built on ideas coming from decades of work in Logic and Knowledge Representation. It is a very simple data model based on the concept of a triple to represent simple sentences such as “Athens is the capital of Greece.” An RDF triple (s, p, o) has three parts: the subject s, the predicate p, and the object o. The sub ject and predicate of a triple are IRIs2 while the object can be an IRI or a literal. Everything denoted by an IRI is called a resource in RDF. 3 If we want to use RDF to model the GADM database that we saw in Chapter 3, we should assign an IRI to each feature existing in GADM. For example, we can use the IRI http://ai.di.uoa.gr/gadm/Athens to represent the city of Athens. To represent datatype values in RDF, we use literals. Each lit eral has an associated data type defined by an IRI. For example,","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128062733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visualizing Linked Geospatial Data","authors":"G. Stamoulis","doi":"10.1145/3581906.3581915","DOIUrl":"https://doi.org/10.1145/3581906.3581915","url":null,"abstract":"8.1 The Tool Sextant Sextant is a Web-based platform for visualizing, exploring, and interacting with linked geospatial data. Sextant focuses on creating a user-friendly environment that would allow both domain experts and non-experts to take advantage of Semantic Web technologies. The core feature of Sextant is the ability to create thematic maps by combin ing geospatial and temporal information that exists in heterogeneous data sources ranging from widely adopted geospatial file formats like KML, GML, GeoJSON, WMS, and GeoTIFF to standard SPARQL endpoints to GeoSPARQL endpoints. In this manner, we provide functionality to domain experts from different fields in creating thematic maps, which emphasize the spatial variation of one or a small number of geographic distributions. Moreover, Sextant introduces a map ontology that assists on modeling these maps in RDF and allow for easy sharing, editing, and search mechanisms over existing maps. Another important feature is the utilization of the temporal dimension. Imple mentation of the valid time component of stRDF and stSPARQL in the system Strabon allows us to query both the spatial and the temporal dimensions. Enrich ing our results with temporal information allows us to create layers with valid time. Using a timeline, we can make these layers appear and disappear from the map according to their valid time. This feature allows the creation of thematic maps that","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126839100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Conclusions","authors":"Manolis Koubarakis","doi":"10.1080/02681307.2022.2030971","DOIUrl":"https://doi.org/10.1080/02681307.2022.2030971","url":null,"abstract":"At the outset of this paper, the authors set out to present the current balance of power in the Arctic region. The paper was structured to achieve two subordinate objectives: assess the present Russia-NATO balance of power in the Arctic; and analyse the level of ambition that Russian investment in the region can support. A key finding that emerges from the research is the degree to which the Arctic is a region in which a balance of power exists, but is heavily tilted towards offence. In particular, the weaknesses of both sides over the defensive components of subsurface warfare render aggressive submarine activity an increasingly appealing option for NATO. This dynamic extends to the surface, however, with NATO paying a heavier price for maintaining a reactive posture than adopting a forward maritime posture. The regional balance of power is also based on temporal and geographical factors. In the Western Arctic and the High North, where the bulk of Russia’s military and economic interests lie, it appears to have effective escalation dominance. On the ground, in the air and, in many cases, at sea, Russian forces can achieve a dominant position, at least in the early stages of a conflict. The ability of Russian submarines to penetrate NATO barriers will make the reinforcement of the High North difficult, should conflict erupt there. If NATO has sufficient warning times to build up to the force levels seen during Exercise Trident Juncture and demanded by the NRI, by contrast, the balance of power is more contested, the Alliance enjoying offensive advantages but struggling when Russian forces take the operational initiative. Russia’s position in the central and eastern parts of the Arctic is more tenuous. While it can exert a significant level of influence over the NSR, Russia’s air-defence network here is less dense than on the Kola Peninsula. This could be crucial should NATO choose to escalate horizontally using US strategic bombers. In the eastern parts of the Arctic, a relatively thin Russian air and sea defence network would be vulnerable to operations from Alaska. In both cases, this might","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1985-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121214995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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