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Multi-resolution representation using graph database / Yizhi Huang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2022 (2022 edition) . - pp 173 - 180 Titre : Multi-resolution representation using graph database Type de document : Article/Communication Auteurs : Yizhi Huang, Auteur ; Emmanuel Stefanakis, Auteur Année de publication : 2022 Article en page(s) : pp 173 - 180 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] base de données de graphes [Termes IGN] interface utilisateur [Termes IGN] objet géographique [Termes IGN] représentation multiple [Termes IGN] requête spatiale Mots-clés libres : Neo4j Résumé : (auteur) Multi-resolution representation has always been an important and popular data source for many research and applications, such as navigation, land cover, map generation, media event forecasting, etc. With one spatial object represented by distinct geometries at different resolutions, multi-resolution representation is high in complexity. Most of the current approaches for storing and retrieving multi-resolution representation are either complicated in structure, or time consuming in traversal and query. In addition, supports on direct navigation between different representations are still intricate in most of the paradigms, especially in topological map sets. To address this problem, we propose a novel approach for storing, querying, and extracting multi-resolution representation. The development of this approach is based on Neo4j, a graph database platform that is famous for its powerful query and advanced flexibility. Benefited from the intuitiveness of the proposed database structure, direct navigation between representations of one spatial object, and between groups of representations at adjacent resolutions are both available. On top of this, collaborating with the self-designed web-based interface, queries within the proposed approach truly embraced the concept of keyword search, which lower the barrier between novice users and complicate queries. In all, the proposed system demonstrates the potential of managing multi-resolution representation data through the graph database and could be a time-saver for related processes. Numéro de notice : A2022-425 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-4-2022-173-2022 Date de publication en ligne : 18/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2022-173-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100730 [article]

Early warning of COVID-19 hotspots using human mobility and web search query data / Takahiro Yabe in Computers, Environment and Urban Systems, vol 92 (March 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 92 (March 2022) . - n° 101747 Titre : Early warning of COVID-19 hotspots using human mobility and web search query data Type de document : Article/Communication Auteurs : Takahiro Yabe, Auteur ; Kota Tsubouchi, Auteur ; Yoshihide Sekimoto, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101747 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aide à la localisation [Termes IGN] analyse de données [Termes IGN] analyse de groupement [Termes IGN] épidémie [Termes IGN] exploration de données [Termes IGN] maladie virale [Termes IGN] mobilité urbaine [Termes IGN] modèle de simulation [Termes IGN] prévention des risques [Termes IGN] requête spatiale [Termes IGN] ressources web [Termes IGN] surveillance sanitaire [Termes IGN] Tokyo (Japon) Résumé : (auteur) COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical interventions (e.g., lockdowns) through population density analysis. However, predicting the locations of potential outbreak occurrence is difficult using mobility data alone. Meanwhile, web search queries have been shown to be good predictors of the disease spread. In this study, we utilize a unique dataset of human mobility trajectories (GPS traces) and web search queries with common user identifiers (> 450 K users), to predict COVID-19 hotspot locations beforehand. More specifically, web search query analysis is conducted to identify users with high risk of COVID-19 contraction, and social contact analysis was further performed on the mobility patterns of these users to quantify the risk of an outbreak. Our approach is empirically tested using data collected from users in Tokyo, Japan. We show that by integrating COVID-19 related web search query analytics with social contact networks, we are able to predict COVID-19 hotspot locations 1–2 weeks beforehand, compared to just using social contact indexes or web search data analysis. This study proposes a novel method that can be used in early warning systems for disease outbreak hotspots, which can assist government agencies to prepare effective strategies to prevent further disease spread. Human mobility data and web search query data linked with common IDs are used to predict COVID-19 outbreaks. High risk social contact index captures both the contact density and COVID-19 contraction risks of individuals. Real world data was collected from 200 K individual users in Tokyo during the COVID-19 pandemic. Experiments showed that the index can be used for microscopic outbreak early warning. Numéro de notice : A2022-114 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101747 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101747 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99637 [article]

A survey on semantic question answering systems / Christina Antoniou in The Knowledge Engineering Review, vol 37 (2022)  

Public [article]  inThe Knowledge Engineering Review > vol 37 (2022) . - n° e2 Titre : A survey on semantic question answering systems Type de document : Article/Communication Auteurs : Christina Antoniou, Auteur ; Nick Bassiliades, Auteur Année de publication : 2022 Article en page(s) : n° e2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] entrepôt de données [Termes IGN] interface web [Termes IGN] requête (informatique) [Termes IGN] SPARQL [Termes IGN] web des données [Termes IGN] web sémantique Résumé : (auteur) Recently, many question answering systems that derive answers from linked data repositories have been developed. The purpose of this survey is to identify the common features and approaches of the semantic question answering (SQA) systems, although many different and prototype systems have been designed. The SQA systems use a formal query language like SPARQL and knowledge of a specific vocabulary. This paper analyses different frameworks, architectures, or systems that perform SQA and classifies SQA systems based on different criteria. Numéro de notice : A2022-323 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1017/S0269888921000138 Date de publication en ligne : 20/01/2022 En ligne : https://doi.org/10.1017/S0269888921000138 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100458 [article]

Spatial dataset search: Building a dedicated knowledge graph / Mehdi Zhral (2021)  

Public Titre : Spatial dataset search: Building a dedicated knowledge graph Type de document : Article/Communication Auteurs : Mehdi Zhral , Auteur ; Bénédicte Bucher , Auteur ; Marie-Dominique Van Damme , Auteur ; Fayçal Hamdi , Auteur Editeur : AGILE Alliance Année de publication : 2021 Projets : 1-Pas de projet / Conférence : AGILE 2021, 24th AGILE Conference on Geographic Information Science 19/07/2021 22/07/2021 Aurora - Colorado Etats-Unis OA Proceedings Importance : 5 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] découverte de connaissances [Termes IGN] données massives [Termes IGN] données ouvertes [Termes IGN] graphe [Termes IGN] INSPIRE [Termes IGN] jeu de données localisées [Termes IGN] précision sémantique [Termes IGN] recherche d'information géographique [Termes IGN] requête spatiale [Termes IGN] réseau sémantique [Termes IGN] ressources web [Termes IGN] service web géographique [Termes IGN] terminologie [Termes IGN] web des données [Termes IGN] web sémantique géolocalisé Résumé : (auteur) A growing number of spatial datasets are published every year. These can usually be found in dedicated web portals with different structures and specificities. However, finding the dataset that fits user needs is a real challenge as prior knowledge of these portals is needed to retrieve it efficiently. In this article, we present the problem of spatial dataset search and how the use of a geographic Knowledge Graph could improve it. A proposed direction for future work, ex-tending these contributions, is then presented. Numéro de notice : C2021-008 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : https://agile-giss.copernicus.org/articles/2/43/2021/agile-giss-2-43-2021.pdf Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/agile-giss-2-43-2021 En ligne : https://doi.org/10.5194/agile-giss-2-43-2021 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97855

A lightweight ensemble spatiotemporal interpolation model for geospatial data / Shifen Cheng in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1849 - 1872 Titre : A lightweight ensemble spatiotemporal interpolation model for geospatial data Type de document : Article/Communication Auteurs : Shifen Cheng, Auteur ; Peng Peng, Auteur ; Feng Lu, Auteur Année de publication : 2020 Article en page(s) : pp 1849 - 1872 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] coefficient de corrélation [Termes IGN] distance pondérée [Termes IGN] données localisées [Termes IGN] erreur absolue [Termes IGN] interpolation spatiale [Termes IGN] lissage de données [Termes IGN] modélisation spatio-temporelle [Termes IGN] requête spatiotemporelle Résumé : (auteur) Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations. Numéro de notice : A2020-484 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1725016 Date de publication en ligne : 12/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1725016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95651 [article]

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