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Volume 98 - N°1 - janvier 2026 - The year the world changed (Bulletin de Geographical) / Royal geographical society (Royaume-Uni)  

Public [n° ou bulletin]  est un bulletin de Geographical / Royal geographical society (Royaume-Uni) (1935 -)   Titre : Volume 98 - N°1 - janvier 2026 - The year the world changed Type de document : Périodique Auteurs : Royal geographical society (Royaume-Uni), Auteur Année de publication : 2026 Importance : 82 p. Langues : Anglais (eng) Descripteur : [Termes IGN] anglais (langue) [Termes IGN] économie [Termes IGN] géographie [Termes IGN] géopolitique [Termes IGN] gestion de crise Note de contenu : Departments 1. Worldwatch 2. Front lines 3. Regulars - An abundance of space - Desert riches The year that changed the world - Trump 2.0 : project 2025 - Lesotho : the human cost of aid cuts - New York courtroom dram - Voices : speaking out Numéro de notice : 130-202601 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Numéro de périodique Accessibilité : Accessible via le SUDOC (sur demande au cdos) En ligne : https://reader.exacteditions.com/issues/154231/spread/1 Format de la ressource électronique : Sur navigateur Firefox Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=35320 [n° ou bulletin]

Modelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)  

Public [article]  inSustainable Cities and Society > vol 87 (December 2022) . - n° 104257 Titre : Modelling evacuation preparation time prior to floods: A machine learning approach Type de document : Article/Communication Auteurs : R. Sreejith, Auteur ; K.R. Sinimole, Auteur Année de publication : 2022 Article en page(s) : n° 104257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] apprentissage automatique [Termes IGN] chronométrie [Termes IGN] données spatiotemporelles [Termes IGN] gestion de crise [Termes IGN] inondation [Termes IGN] Kerala (Inde ; état) [Termes IGN] modèle de simulation [Termes IGN] plan de prévention des risques [Termes IGN] questionnaire [Termes IGN] risque naturel [Termes IGN] secours d'urgence Résumé : (auteur) Flooding is a significant hazard responsible for substantial damage and risks to human life worldwide. Effective emergency evacuation to a safer location remains a concern even though the crisis can be predicted and warnings were given. During a calamity, most residents cannot quickly and securely flee. As it is crucial to start evacuation at the right time to have a safe evacuation, this study focuses on a machine learning-based model for predicting a household's evacuation preparation time in the incident of a flood. The study is based on the data collected from flood-affected people from Kerala, India, through a questionnaire. The study indicates that people's demographic, geographical and behavioural aspects, awareness of natural hazards and management are the critical components for improved emergency actions. Further, the article also analysed the characteristics of the respondents and successfully created clusters in which the respondents broadly belong, which will help the rescue team operationalize the evacuation process. Numéro de notice : A2022-819 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104257 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104257 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101986 [article]

An improved optimization model for crowd evacuation considering individual exit choice preference / Fei Gao in Transactions in GIS, vol 26 n° 7 (November 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 7 (November 2022) . - pp 2850 - 2873 Titre : An improved optimization model for crowd evacuation considering individual exit choice preference Type de document : Article/Communication Auteurs : Fei Gao, Auteur ; Zhiqiang Du, Auteur ; Martin Werner, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2850 - 2873 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] comportement [Termes IGN] événement [Termes IGN] gestion de crise [Termes IGN] optimisation (mathématiques) [Termes IGN] optimisation par essaim de particules [Termes IGN] planification [Termes IGN] secours d'urgence Résumé : (auteur) Guidance-assisted crowd evacuation is a process of combining individual exit choice behavior with managers'exit assignment control. The knowledge of individual exit choice preference is of great significance for optimizing global exit assignment planning. This study proposes an improved optimization model for crowd evacuation by integrating the individual-level exit choice preference analysis with system-level exit assignment optimization to represent more realistic crowd evacuation decisions. First, the impact factors of individual exit choice behavior are considered in a mixed logit model to predict the probability of each individual choosing each exit in specific situations. Second, a preference-based exit filtering strategy is designed to analyze the sensible alternative exits for individuals or groups in multi-scale evacuation cells. Finally, to pursue optimal exit assignment planning, a multi-objective particle swarm optimization algorithm and an improved social force model are adopted to simulate the process of crowd evacuation and evaluate the performance of the specific exit assignment plans. The case study of an outdoor multiple-exit scenario in Xi'an, China, indicates that the proposed model can help managers to understand the heterogeneity of individual evacuation behaviors. Furthermore, it will support more reliable and realistic evacuation decisions in real-life situations than conventional plans that typically implement the top-n strategy. Numéro de notice : A2022-833 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12984 Date de publication en ligne : 04/09/2022 En ligne : https://doi.org/10.1111/tgis.12984 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102216 [article]

Exploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 36 n° 9 (September 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 9 (September 2022) . - pp 1741 - 1783 Titre : Exploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations Type de document : Article/Communication Auteurs : Kevin Chapuis, Auteur ; Pham Minh-Duc, Auteur ; Arthur Brugière, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1741 - 1783 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] gestion de crise [Termes IGN] gestion des risques [Termes IGN] inondation [Termes IGN] modèle de simulation [Termes IGN] modèle orienté agent [Termes IGN] prévention des risques [Termes IGN] secours d'urgence [Termes IGN] trafic routier [Termes IGN] Viet Nam [Termes IGN] zone urbaine Résumé : (auteur) At a time when the impacts of climate change and increasing urbanization are making risk management more complex, there is an urgent need for tools to better support risk managers. One approach increasingly used in crisis management is preventive mass evacuation. However, to implement and evaluate the effectiveness of such strategy can be complex, especially in large urban areas. Modeling approaches, and in particular agent-based models, are used to support implementation and to explore a large range of evacuation strategies, which is impossible through drills. One major limitation with simulation of traffic based on individual mobility models is their capacity to reproduce a context of mixed traffic. In this paper, we propose an agent-based model with the capacity to overcome this limitation. We simulated and compared different spatio-temporal evacuation strategies in the flood-prone landlocked area of the Phúc Xá district in Hanoi. We demonstrate that the interaction between distribution of transport modalities and evacuation strategies greatly impact evacuation outcomes. More precisely, we identified staged strategies based on the proximity to exit points that make it possible to reduce time spent on road and overall evacuation time. In addition, we simulated improved evacuation outcomes through selected modification of the road network. Numéro de notice : A2022-644 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2069774 Date de publication en ligne : 16/05/2022 En ligne : https://doi.org/10.1080/13658816.2022.2069774 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101455 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2022091	SL	Revue	Centre de documentation	En réserve L003	Disponible

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)  

Public [article]  inCartography and Geographic Information Science > Vol 49 n° 5 (September 2022) . - pp 443 - 463 Titre : A geographical and content-based approach to prioritize relevant and reliable tweets for emergency management Type de document : Article/Communication Auteurs : A. Marcela Suarez, Auteur ; Keith C. Clarke, Auteur Année de publication : 2022 Article en page(s) : pp 443 - 463 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] catastrophe naturelle [Termes IGN] classement [Termes IGN] données issues des réseaux sociaux [Termes IGN] Etats-Unis [Termes IGN] fiabilité des données [Termes IGN] filtrage d'information [Termes IGN] gestion de crise [Termes IGN] pertinence [Termes IGN] qualité des données [Termes IGN] secours d'urgence [Termes IGN] tempête [Termes IGN] Twitter Résumé : (auteur) Tweets posted by the general public during disaster events represent timely, up-to-date, and on-site data that may be useful for emergency responders. However, since Twitter data has been deemed to be unverifiable and untrustworthy, it is challenging to identify those reliable and relevant tweets that can inform emergency response operations. Although computational methods exist both to classify overwhelming amounts of tweets and to filter those relevant to emergency response, using contextual geographic information regarding the disaster event to filter tweets has been overlooked. We review the existing research on the quality of data contributed by the general public from a geographical perspective, and then propose an approach to prioritize tweets for emergency response based on their relevance and reliability. The novelty of the approach is twofold: a) the use of both authoritative data such as hazard-related information and on-the-ground reports provided by weather spotters and validated by the National Weather Service; and b) the fact that it leverages tweets content as well as their geographical context and location. Using Hurricane Harvey in 2017 as a case study, results show that by following the proposed approach 79% of tweets sent from post-identified flooded areas were classified as of high or medium relevance and reliability. This suggests that the proposed approach can provide an accurate prioritization of tweets to be used for real time emergency management. Numéro de notice : A2022-633 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2022.2081257 En ligne : https://doi.org/10.1080/15230406.2022.2081257 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101399 [article]

Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand / Kiatkulchai Jitt-Aer in Natural Hazards, vol 113 n° 1 (August 2022)  

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Simulation d'ouragans et de collectes de déchets sur QGIS pour l'amélioration de la collecte des déchets post-ouragan / Quy Thy Truong in Cartes & Géomatique, n° 247-248 (mars-juin 2022)

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Automated construction of a French Entity Linking dataset to geolocate social network posts in the context of natural disasters / Gaëtan Caillaut (2022)  

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Modeling transit-assisted hurricane evacuation through socio-spatial networks / Yan Yang in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)  

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Mask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan / Dirk Tiede in Transactions in GIS, Vol 25 n° 3 (June 2021)  

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COVID-19 pandemic, geospatial information, and community resilience / Abbas Rajabifard (2021)  

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Optimisations cartographiques pour la gestion des crises et des risques majeurs : le cas de la cartographie des dommages post-catastrophes / Thomas Candela (2021)  

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Crowdsource mapping of target buildings in hazard: the utilization of smartphone technologies and geographic services / Mohammad H. Vahidnia in Applied geomatics, vol 12 n° 1 (April 2020)  

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Mapping crisis / Doug Specht (2020)  

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Modélisation sémantique et programmation générative pour une simulation multi-agent dans le contexte de gestion de catastrophe / Claire Prudhomme in Revue internationale de géomatique, vol 30 n° 1-2 (janvier - juin 2020)  

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