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Advancements in web‐mapping tools for land use and marine spatial planning / Ainhoa González in Transactions in GIS, Vol 24 n° 2 (April 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 2 (April 2020) . - pp 253-267 Titre : Advancements in web‐mapping tools for land use and marine spatial planning Type de document : Article/Communication Auteurs : Ainhoa González, Auteur ; Christina Kelly, Auteur ; Anna Rymszewicz, Auteur Année de publication : 2020 Article en page(s) : pp 253-267 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] aménagement du territoire [Termes IGN] analyse multicritère [Termes IGN] analyse spatiale [Termes IGN] état de l'art [Termes IGN] interface web [Termes IGN] milieu marin [Termes IGN] visualisation de données Résumé : (Auteur) Land use and marine spatial planning processes are increasingly supported by systematic assessment techniques, particularly by multi‐criteria spatial analysis methods. This has been facilitated by the growing release and uptake of web‐mapping tools, which contribute to transparent, consistent, and informed planning processes and decisions. This article reviews the usability, functionality, and applicability of contemporary planning web‐mapping tools to identify the state‐of‐the‐art and future prospects. The review reveals that interfaces are increasingly available and intuitively applicable by non‐specialized users. Basic map navigation and data querying functionality is being expanded to incorporate advanced map‐making and online data geoprocessing capabilities that enable deriving new data and insights. However, the majority of published planning web tools are one‐off solutions, and a disconnect between research and practice is rendering many of these inaccessible or obsolete. Despite the significant progress made in advancing their provision in the last decade, there is a need for developing transferable interfaces that are maintained beyond project end dates, for them to effectively and consistently support planning processes. Numéro de notice : A2020-173 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12603 Date de publication en ligne : 01/12/2019 En ligne : https://doi.org/10.1111/tgis.12603 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94894 [article]

Predictive mapping with small field sample data using semi‐supervised machine learning / Fei Du in Transactions in GIS, Vol 24 n° 2 (April 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 2 (April 2020) . - pp 315 - 331 Titre : Predictive mapping with small field sample data using semi‐supervised machine learning Type de document : Article/Communication Auteurs : Fei Du, Auteur ; A - Xing Zhu, Auteur ; Jing Liu, Auteur ; Lin Yang, Auteur Année de publication : 2020 Article en page(s) : pp 315 - 331 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage semi-dirigé [Termes IGN] covariance [Termes IGN] échantillon [Termes IGN] modèle de simulation [Termes IGN] représentation cartographique Résumé : (Auteur) Existing predictive mapping methods usually require a large number of field samples with good representativeness as input to build reliable predictive models. In mapping practice, however, we often face situations when only small sample data are available. In this article, we present a semi‐supervised machine learning approach for predictive mapping in which the natural aggregation (clustering) patterns of environmental covariate data are used to supplement limited samples in prediction. This approach was applied to two soil mapping case studies. Compared with field sample only approaches (decision trees, logistic regression, and support vector machines), maps using the proposed approach can better capture the spatial variation of soil types and achieve higher accuracy with limited samples. A cross validation shows further that the proposed approach is less sensitive to the specific field sample set used and thus more robust when field sample data are small. Numéro de notice : A2020-174 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12598 Date de publication en ligne : 04/12/2019 En ligne : https://doi.org/10.1111/tgis.12598 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94900 [article]

Improving geospatial query performance of an interoperable geographic situation‐awareness system for disaster response / Chuanrong Zhang in Transactions in GIS, Vol 24 n° 2 (April 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 2 (April 2020) . - pp 508 - 525 Titre : Improving geospatial query performance of an interoperable geographic situation‐awareness system for disaster response Type de document : Article/Communication Auteurs : Chuanrong Zhang, Auteur ; Tian Zhao, Auteur ; E. Lynn Usery, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 508 - 525 Note générale : Bibliographie Langues : Anglais (eng) Résumé : (Auteur) Disaster response operations require fast and coordinated actions based on real‐time disaster situation information. Although crowdsourced geospatial data applications have been demonstrated to be valuable tools for gathering real‐time disaster situation information, they only provide limited utility for disaster response coordination because of the lack of semantic compatibility and interoperability. To help overcome the semantic incompatibility and heterogeneity problems, we use Geospatial Semantic Web (GSW) technologies. We then combine GSW technologies with Web Feature Service requests to access multiple servers. However, a GSW‐based geographic information system often has poor performance due to the complex geometric computations required. The objective of this research is to explore how to use optimization techniques to improve the performance of an interoperable geographic situation‐awareness system (IGSAS) based on GSW technologies for disaster response. We conducted experiments to evaluate various client‐side optimization techniques for improving the performance of an IGSAS prototype for flood disaster response in New Haven, Connecticut. Our experimental results show that the developed prototype can greatly reduce the runtime costs of geospatial semantic queries through on‐the‐fly spatial indexing, tile‐based rendering, efficient algorithms for spatial join, and caching, especially for those spatial‐join geospatial queries that involve a large number of spatial features and heavy geometric computation. Numéro de notice : A2020-175 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12614 Date de publication en ligne : 17/02/2020 En ligne : https://doi.org/10.1111/tgis.12614 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94903 [article]