A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation
Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/11/2498 |
| _version_ | 1811306873856458752 |
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| author | Amin Mobasheri |
| author_facet | Amin Mobasheri |
| author_sort | Amin Mobasheri |
| collection | DOAJ |
| description | Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets. |
| first_indexed | 2024-04-13T08:54:34Z |
| format | Article |
| id | doaj.art-8205ef63724948afaf5be547af98006a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:54:34Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8205ef63724948afaf5be547af98006a2022-12-22T02:53:22ZengMDPI AGSensors1424-82202017-10-011711249810.3390/s17112498s17112498A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and NavigationAmin Mobasheri0GIScience research group, Institute of Geography, Heidelberg University, Im Neuenheimer Feld 348, 69120 Heidelberg, GermanyFinding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets.https://www.mdpi.com/1424-8220/17/11/2498data miningOpenStreetMapdata quality enrichmentroutingcrowdsourced geographic informationVGI |
| spellingShingle | Amin Mobasheri A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation Sensors data mining OpenStreetMap data quality enrichment routing crowdsourced geographic information VGI |
| title | A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation |
| title_full | A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation |
| title_fullStr | A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation |
| title_full_unstemmed | A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation |
| title_short | A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation |
| title_sort | rule based spatial reasoning approach for openstreetmap data quality enrichment case study of routing and navigation |
| topic | data mining OpenStreetMap data quality enrichment routing crowdsourced geographic information VGI |
| url | https://www.mdpi.com/1424-8220/17/11/2498 |
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