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Parallel cartographic modeling: a methodology for parallelizing spatial data processing / Eric Shook in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 11-12 (November - December 2016) . - pp 2355 - 2376 Titre : Parallel cartographic modeling: a methodology for parallelizing spatial data processing Type de document : Article/Communication Auteurs : Eric Shook, Auteur ; Michael E. Hodgson, Auteur ; Shaowen Wang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 2355 - 2376 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] langage de programmation [Termes IGN] Map Algebra [Termes IGN] modèle conceptuel de données localisées [Termes IGN] Python (langage de programmation) [Termes IGN] traitement de données localisées [Termes IGN] traitement parallèle [Termes IGN] WebSIG Résumé : (Auteur) This article establishes a new methodological framework for parallelizing spatial data processing called parallel cartographic modeling, which extends the widely adopted cartographic modeling framework. Parallel cartographic modeling adds a novel component called a Subdomain, which serves as the elemental unit of parallel computation. Four operators are also added to express parallel spatial data processing, namely scheduler, decomposition, executor, and iteration. A parallel cartographic modeling language (PCML) is developed based on the parallel cartographic modeling framework, which is designed for usability, programmability, and scalability. PCML is a domain-specific language implemented in Python for the domain of cyberGIS. A key feature of PCML is that it supports automatic parallelization of cartographic modeling scripts; thus, allowing the analyst to develop models in the familiar cartographic modeling language in a Python syntax. PCML currently supports more than 70 operations and new operations can be easily implemented in as little as three lines of PCML code. Experimental results using the National Science Foundation-supported Resourcing Open Geospatial Education and Research computational resource demonstrate that PCML efficiently scales to 16 cores and can process gigabytes of spatial data in parallel. PCML is shown to support multiple decomposition strategies, decomposition granularities, and iteration strategies that be generically applied to any operation implemented in PCML. Numéro de notice : A2016-755 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1172714 En ligne : http://dx.doi.org/10.1080/13658816.2016.1172714 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82420 [article]

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The socio-environmental data explorer (SEDE) : a social media–enhanced decision support system to explore risk perception to hazard events / Eric Shook in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)  

Public [article]  inCartography and Geographic Information Science > vol 43 n° 5 (November 2016) . - pp 427 - 441 Titre : The socio-environmental data explorer (SEDE) : a social media–enhanced decision support system to explore risk perception to hazard events Type de document : Article/Communication Auteurs : Eric Shook, Auteur ; Victoria K. Turner, Auteur Année de publication : 2016 Article en page(s) : pp 427 - 441 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse de données [Termes IGN] données environnementales [Termes IGN] Etats-Unis [Termes IGN] exploration de données [Termes IGN] neige [Termes IGN] outil d'aide à la décision [Termes IGN] perception [Termes IGN] réseau social [Termes IGN] risque environnemental [Termes IGN] risque technologique [Termes IGN] tempête [Termes IGN] temps réel Résumé : (Auteur) Social media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to social, environmental, and technological risk and hazard events. We introduce a prototype SME-DSS entitled socio-environmental data explorer (SEDE) to explore the opportunities and challenges of leveraging social media for decision support. We use a winter storm during 25–28 January 2015 that accumulated record amounts of snow along the East Coast of the United States as a case study to evaluate SEDE in helping assess social response to environmental risk and hazard events as well as evaluate social media as a theoretical component within the social amplification of risk framework (SARF) that serves as a theoretical foundation for SME-DSS. Numéro de notice : A2016-693 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1131627 En ligne : https://doi.org/10.1080/15230406.2015.1131627 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82030 [article]

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