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scientifique

From data to narratives: Scrutinising the spatial dimensions of social and cultural phenomena through lenses of interactive web mapping / Tian Lan in Journal of Geovisualization and Spatial Analysis, vol 6 n° 2 (December 2022)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 6 n° 2 (December 2022) . - n° 22 Titre : From data to narratives: Scrutinising the spatial dimensions of social and cultural phenomena through lenses of interactive web mapping Type de document : Article/Communication Auteurs : Tian Lan, Auteur ; Oliver O'Brien, Auteur ; James Cheshire, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] carte interactive [Termes IGN] cartographie sensible [Termes IGN] culture [Termes IGN] données démographiques [Termes IGN] données socio-économiques [Termes IGN] impact social [Termes IGN] récit [Termes IGN] Royaume-Uni [Termes IGN] sciences sociales [Termes IGN] web mapping Mots-clés libres : narrative mapping Résumé : (auteur) Modern web mapping techniques have enhanced the storytelling capability of cartography. In this paper, we present our recent development of a web mapping facility that can be used to extract interesting stories and unique insights from a diverse range of socio-economic and demographic variables and indicators, derived from a variety of datasets. We then use three curated narratives to show that online maps are effective ways of interactive storytelling and visualisation, which allow users to tailor their own story maps. We discuss the reasons for the revival of the recent attention to narrative mapping and conclude that our interactive web mapping facility powered by data assets can be employed as an accessible and powerful toolkit, to identify geographic patterns of various social and economic phenomena by social scientists, journalists, policymakers, and the public. Numéro de notice : A2022-541 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s41651-022-00117-x Date de publication en ligne : 16/06/2022 En ligne : https://doi.org/10.1007/s41651-022-00117-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101105 [article]

LinkClimate: An interoperable knowledge graph platform for climate data / Jiantao Wu in Computers & geosciences, vol 169 (December 2022)  

Public [article]  inComputers & geosciences > vol 169 (December 2022) . - n° 105215 Titre : LinkClimate: An interoperable knowledge graph platform for climate data Type de document : Article/Communication Auteurs : Jiantao Wu, Auteur ; Fabrizio Orlandi, Auteur ; Declan O'Sullivan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105215 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] changement climatique [Termes IGN] données météorologiques [Termes IGN] données multisources [Termes IGN] historique des données [Termes IGN] interopérabilité sémantique [Termes IGN] National oceanic and atmospheric administration [Termes IGN] ontologie [Termes IGN] OpenStreetMap [Termes IGN] réseau sémantique [Termes IGN] site wiki [Termes IGN] SPARQL [Termes IGN] web sémantique Résumé : (auteur) Climate science has become more ambitious in recent years as global awareness about the environment has grown. To better understand climate, historical climate(e.g. archived meteorological variables such as temperature, wind, water, etc.) and climate-related data (e.g. geographical features and human activities) are widely used by today’s climate research to derive models for an explainable climate change and its effects. However, such data sources are often dispersed across a multitude of disconnected data silos on the Web. Moreover, there is a lack of advanced climate data platforms to enable multi-source heterogeneous climate data analysis, therefore, researchers must face a stern challenge in collecting and analyzing multi-source data. In this paper, we address this problem by proposing a climate knowledge graph for the integration of multiple climate data and other data sources into one service, leveraging Web technologies (e.g. HTTP) for multi-source climate data analysis. The proposed knowledge graph is primarily composed of data from the National Oceanic and Atmospheric Administration’s daily climate summaries, OpenStreetMap, and Wikidata, and it supports joint data queries on these widely used databases. This paper shows, with a use case in Ireland and the United Kingdom, how climate researchers could benefit from this platform as it allows them to easily integrate datasets from different domains and geographical locations. Numéro de notice : A2022-789 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.cageo.2022.105215 Date de publication en ligne : 30/08/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105215 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101897 [article]

A machine learning approach for detecting rescue requests from social media / Zheye Wang in ISPRS International journal of geo-information, vol 11 n° 11 (November 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 11 (November 2022) . - n° 570 Titre : A machine learning approach for detecting rescue requests from social media Type de document : Article/Communication Auteurs : Zheye Wang, Auteur ; Nina S.N. Lam, Auteur ; Mingxuan Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 570 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] apprentissage automatique [Termes IGN] code postal [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données issues des réseaux sociaux [Termes IGN] Etats-Unis [Termes IGN] filtrage d'information [Termes IGN] secours d'urgence [Termes IGN] tempête [Termes IGN] terminologie [Termes IGN] Twitter Résumé : (auteur) Hurricane Harvey in 2017 marked an important transition where many disaster victims used social media rather than the overloaded 911 system to seek rescue. This article presents a machine-learning-based detector of rescue requests from Harvey-related Twitter messages, which differentiates itself from existing ones by accounting for the potential impacts of ZIP codes on both the preparation of training samples and the performance of different machine learning models. We investigate how the outcomes of our ZIP code filtering differ from those of a recent, comparable study in terms of generating training data for machine learning models. Following this, experiments are conducted to test how the existence of ZIP codes would affect the performance of machine learning models by simulating different percentages of ZIP-code-tagged positive samples. The findings show that (1) all machine learning classifiers except K-nearest neighbors and Naïve Bayes achieve state-of-the-art performance in detecting rescue requests from social media; (2) using ZIP code filtering could increase the effectiveness of gathering rescue requests for training machine learning models; (3) machine learning models are better able to identify rescue requests that are associated with ZIP codes. We thereby encourage every rescue-seeking victim to include ZIP codes when posting messages on social media. This study is a useful addition to the literature and can be helpful for first responders to rescue disaster victims more efficiently. Numéro de notice : A2022-846 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11110570 Date de publication en ligne : 16/11/2022 En ligne : https://doi.org/10.3390/ijgi11110570 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102081 [article]

Identifying the key resources and missing elements to build a knowledge graph dedicated to spatial dataset search / Mehdi Zrhal in Procedia Computer Science, vol 207 (2022)  

Public [article]  inProcedia Computer Science > vol 207 (2022) . - pp 2911 - 2920 Titre : Identifying the key resources and missing elements to build a knowledge graph dedicated to spatial dataset search Type de document : Article/Communication Auteurs : Mehdi Zrhal , Auteur ; Bénédicte Bucher , Auteur ; Fayçal Hamdi , Auteur ; Marie-Dominique Van Damme , Auteur Année de publication : 2022 Conférence : KES 2022, 26th International Conference Knowledge-Based and Intelligent Information & Engineering Systems 07/09/2022 09/09/2022 Vérone Italie OA proceedings Article en page(s) : pp 2911 - 2920 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] jeu de données localisées [Termes IGN] recherche d'information géographique [Termes IGN] réseau sémantique Résumé : (auteur) The number of spatial datasets available online has increased exponentially in recent years. Therefore, the search for spatial datasets is becoming a fourishing research field. The use of knowledge graphs has become rampant in search engines and in information retrieval. In this article, we identify the main resources needed and those missing to allow a knowledge graph to support spatial dataset search. We then apply our approach to the water domain in France by building a dedicated knowledge graph and describe an evaluation method to measure its effectiveness. Numéro de notice : A2022-695 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.procs.2022.09.349 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1016/j.procs.2022.09.349 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101910 [article]

Machine learning and natural language processing of social media data for event detection in smart cities / Andrei Hodorog in Sustainable Cities and Society, vol 85 (October 2022)  

Public [article]  inSustainable Cities and Society > vol 85 (October 2022) . - n° 104026 Titre : Machine learning and natural language processing of social media data for event detection in smart cities Type de document : Article/Communication Auteurs : Andrei Hodorog, Auteur ; Ioan Petri, Auteur ; yacine Rezgui, Auteur Année de publication : 2022 Article en page(s) : n° 104026 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] apprentissage automatique [Termes IGN] classification bayesienne [Termes IGN] détection d'événement [Termes IGN] données issues des réseaux sociaux [Termes IGN] outil d'aide à la décision [Termes IGN] régression multiple [Termes IGN] taxinomie [Termes IGN] traitement du langage naturel [Termes IGN] ville intelligente Résumé : (auteur) Social media data analysis in a smart city context can represent an efficacious instrument to inform decision making. The manuscript strives to leverage the power of Natural Language Processing (NLP) techniques applied to Twitter messages using supervised learning to achieve real-time automated event detection in smart cities. A semantic-based taxonomy of risks is devised to discover and analyse associated events from data streams, with a view to: (i) read and process, in real-time, published texts (ii) classify each text into one representative real-world category (iii) assign a citizen satisfaction value to each event. To select the language processing models striking the best balance between accuracy and processing speed, we conducted a pre-emptive evaluation, comparing several baseline language models formerly employed by researchers for event classification. A heuristic analysis of several smart cities and community initiatives was conducted, with a view to define real-world scenarios as basis for determining correlations between two or more co-occurring event types and their associated levels of citizen satisfaction, while further considering environmental factors. Based on Multiple Regression Analysis (MRA), we established the relationships between scenario variables, obtaining a variance of 60%–90% between the dependent and independent variables. The selected combination of supervised NLP techniques leverages an accuracy of 88.5%. We found that all regression models had at least one variable below the 0.05 threshold of the , therefore at least one statistically significant independent variable. These findings ultimately illustrate how citizens, taking the role of active social sensors, can yield vital data that authorities can use to make educated decisions and sustainably construct smarter cities. Numéro de notice : A2022-764 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104026 Date de publication en ligne : 02/07/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101785 [article]

A geographical and content-based approach to prioritize relevant and reliable tweets for emergency management / A. Marcela Suarez in Cartography and Geographic Information Science, Vol 49 n° 5 (September 2022)  

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Point-of-interest detection from Weibo data for map updating / Xue Yang in Transactions in GIS, vol 26 n° 6 (September 2022)  

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Location-aware neural graph collaborative filtering / Shengwen Li in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)  

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Narrative cartography with knowledge graphs / Gengchen Mai in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)  

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The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events / Sidgley Camargo de Andrade in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)  

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ChineseTR: A weakly supervised toponym recognition architecture based on automatic training data generator and deep neural network / Qinjun Qiu in Transactions in GIS, vol 26 n° 3 (May 2022)  

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A GIS representation framework for location-based social media activities / Xuebin Wei in Transactions in GIS, vol 26 n° 3 (May 2022)  

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Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation / Yingjie Hu in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

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Spatially oriented convolutional neural network for spatial relation extraction from natural language texts / Qinjun Qiu in Transactions in GIS, vol 26 n° 2 (April 2022)  

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Volunteered geographic information mobile application for participatory landslide inventory mapping / Raden Muhammad Anshori in Computers & geosciences, vol 161 (April 2022)  

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