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Parallel computing for fast spatiotemporal weighted regression / Xiang Que in Computers & geosciences, vol 150 (May 2021)  

Public [article]  inComputers & geosciences > vol 150 (May 2021) . - n° 104723 Titre : Parallel computing for fast spatiotemporal weighted regression Type de document : Article/Communication Auteurs : Xiang Que, Auteur ; Chao Ma, Auteur ; Xiaogang Ma, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104723 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] calcul matriciel [Termes IGN] étalonnage de modèle [Termes IGN] modèle de régression [Termes IGN] modélisation spatio-temporelle [Termes IGN] régression géographiquement pondérée [Termes IGN] traitement parallèle Résumé : (auteur) The Spatiotemporal Weighted Regression (STWR) model is an extension of the Geographically Weighted Regression (GWR) model for exploring the heterogeneity of spatiotemporal processes. A key feature of STWR is that it utilizes the data points observed at previous time stages to make better fit and prediction at the latest time stage. Because the temporal bandwidths and a few other parameters need to be optimized in STWR, the model calibration is computationally intensive. In particular, when the data amount is large, the calibration of STWR becomes heavily time-consuming. For example, with 10,000 points in 10 time stages, it takes about 2307 s for a single-core PC to process the calibration of STWR. Both the distance and the weighted matrix in STWR are memory intensive, which may easily cause memory insufficiency as data amount increases. To improve the efficiency of computing, we developed a parallel computing method for STWR by employing the Message Passing Interface (MPI). A cache in the MPI processing approach was proposed for the calibration routine. Also, a matrix splitting strategy was designed to address the problem of memory insufficiency. We named the overall design as Fast STWR (F-STWR). In the experiment, we tested F-STWR in a High-Performance Computing (HPC) environment with a total number of 204,611 observations in 19 years. The results show that F-STWR can significantly improve STWR's capability of processing large-scale spatiotemporal data. Numéro de notice : A2021-300 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104723 Date de publication en ligne : 05/03/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104723 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97413 [article]

Local terrain modification method considering physical feature constraints for vector elements / Jiangfeng She in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 452 - 470 Titre : Local terrain modification method considering physical feature constraints for vector elements Type de document : Article/Communication Auteurs : Jiangfeng She, Auteur ; Junyan Liu, Auteur ; Junzhong Tan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 452 - 470 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] altitude [Termes IGN] analyse vectorielle [Termes IGN] contrainte d'intégrité [Termes IGN] déformation de surface [Termes IGN] données vectorielles [Termes IGN] interpolation [Termes IGN] processeur graphique [Termes IGN] rastérisation [Termes IGN] relief [Termes IGN] superposition de données [Termes IGN] surface du sol [Termes IGN] terrain [Termes IGN] traitement parallèle [Termes IGN] zone tampon Résumé : (auteur) Many studies have been focused on rendering 2D vector elements on 3D terrain, and a series of algorithms have been proposed. Most of these algorithms struggle to provide a seamless overlay between vector elements and an irregular terrain surface. Despite their importance, the physical characteristics of vector elements are often ignored, which distorts the surface of vector elements. For example, if vector elements that represent roads and rivers are simply overlaid on terrain, the phenomena of uneven surfaces and rivers going uphill may occur because of elevation fluctuation. To correct these deficiencies, terrain should be modified according to the physical characteristics of vectors. We propose a local terrain modification method: First, the elevation of terrain covered by vector elements is recalculated according to vectors’ physical characteristics. Second, the multigrid method is used to realize a smooth transition between the modified terrain and its surrounding area. Finally, by setting different transition ranges and comparing the visualization effects, rules are given for the selection of a suitable range. After modification, the terrain conforms to vectors’ physical characteristics, and the overall relief is undamaged. The proposed method was applied to a CPU–GPU parallel heterogeneous model and demonstrated a high level of performance. Numéro de notice : A2020-489 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1770128 Date de publication en ligne : 06/07/2020 En ligne : https://doi.org/10.1080/15230406.2020.1770128 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95660 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2020051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Computational improvements to multi-scale geographically weighted regression / Ziqi Li in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 7 (July 2020) . - pp 1378 - 1397 Titre : Computational improvements to multi-scale geographically weighted regression Type de document : Article/Communication Auteurs : Ziqi Li, Auteur ; A. Stewart Fotheringham, Auteur Année de publication : 2020 Article en page(s) : pp 1378 - 1397 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse géovisuelle [Termes IGN] analyse multiéchelle [Termes IGN] implémentation (informatique) [Termes IGN] modélisation spatio-temporelle [Termes IGN] régression géographiquement pondérée [Termes IGN] traitement parallèle Résumé : (auteur) Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. MGWR is superior in capturing multi-scale processes over the traditional single-scale GWR model by using different bandwidths for each covariate. However, the multiscale property of MGWR brings additional computation costs. The calibration process of MGWR involves iterative back-fitting under the additive model (AM) framework. Currently, MGWR can only be applied on small datasets within a tolerable time and is prohibitively time-consuming to run with moderately large datasets (greater than 5,000 observations). In this paper, we propose a parallel implementation that has crucial computational improvements to the MGWR calibration. This improved computational method reduces both memory footprint and runtime to allow MGWR modelling to be applied to moderate-to-large datasets (up to 100,000 observations). These improvements are integrated into the mgwr python package and the MGWR 2.0 software, both of which are freely available to download. Numéro de notice : A2020-305 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1720692 Date de publication en ligne : 06/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1720692 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95147 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2020071	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Distributed and parallel architectures for spatial data / Alberto Belussi (2020)  

Public Titre : Distributed and parallel architectures for spatial data Type de document : Monographie Auteurs : Alberto Belussi, Éditeur scientifique ; Sara Migliorini, Éditeur scientifique ; Damiano Carra, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 170 p. ISBN/ISSN/EAN : 978-3-03936-751-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] base de données localisées [Termes IGN] collecte de données [Termes IGN] développement durable [Termes IGN] données localisées [Termes IGN] données massives [Termes IGN] entrepôt de données localisées [Termes IGN] géoportail [Termes IGN] Hadoop [Termes IGN] métadonnées [Termes IGN] modèle numérique de surface [Termes IGN] objet mobile [Termes IGN] OLAP [Termes IGN] OpenStreetMap [Termes IGN] PostGIS [Termes IGN] réseau social [Termes IGN] SQL [Termes IGN] système d'information géographique [Termes IGN] téléphone intelligent [Termes IGN] traitement parallèle [Termes IGN] zone tampon Résumé : (Editeur) [Préface] In recent years, an increasing amount of spatial data has been collected by different types of devices, such as mobile phones, sensors, satellites, space telescope, and medical tools for analysis, or is generated by social networks, such as geotagged tweets. The processing of this huge amount of information, including spatial properties, which are frequently represented in heterogeneous ways, is a challenging task that has boosted research in the big data area in an attempt to investigate cases and propose new solutions for dealing with its peculiarities. In the literature, many different proposals and approaches for facing the problem have been proposed, addressing different goals and different types of users. However, most are obtained by customizing existing approaches which were originally developed for the processing of big data of the alphanumeric type, without any specific support for spatial or spatiotemporal properties. Thus, the proposed solutions can exploit the parallelism provided by these kinds of systems, but without taking into account, in a proficient way, the space and time dimensions that intrinsically characterize the analyzed datasets. As described in the literature, current solutions include: (i) the on-top approach, where an underlying system for traditional big datasets is used as a black box while spatial processing is added through the definition of user-defined functions that are specified on top of the underlying system; (ii) the from-scratch approach, where a completely new system is implemented for a specific application context; and (iii) the built-in approach, where an existing solution is extended by injecting spatial data functions into its core. This book aims at promoting new and innovative studies, proposing new architectures or innovative evolutions of existing ones, and illustrating experiments on current technologies in order to improve the efficiency and effectiveness of distributed and cluster systems when they deal with spatiotemporal data. Note de contenu : Preface 1- Distributed Processing of Location-Based Aggregate Queries Using MapReduce 2- Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa 3- HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time 4- Mobility DataWarehouses 5- Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC 6- LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data 7- Mr4Soil: A MapReduce-Based Framework Integrated with GIS for Soil Erosion Modelling 8- High-Performance Geospatial Big Data Processing System Based on MapReduce Numéro de notice : 25884 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03936-751-1 En ligne : https://doi.org/10.3390/books978-3-03936-751-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95762

An automated and optimized approach for online spatial biodiversity model: a case study of OGC web processing service / Hariom Singh in Geocarto international, vol 34 n° 2 ([01/02/2019])  

Public [article]  inGeocarto international > vol 34 n° 2 [01/02/2019] . - pp 194 - 214 Titre : An automated and optimized approach for online spatial biodiversity model: a case study of OGC web processing service Type de document : Article/Communication Auteurs : Hariom Singh, Auteur ; Harish Chandra Karnatak, Auteur ; Rahul Dev Garg, Auteur Année de publication : 2019 Article en page(s) : pp 194 - 214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse de sensibilité [Termes IGN] biodiversité [Termes IGN] données localisées [Termes IGN] indicateur biologique [Termes IGN] indicateur de biodiversité [Termes IGN] interface web [Termes IGN] modèle conceptuel de données localisées [Termes IGN] processus de hiérarchisation analytique floue [Termes IGN] service web géographique [Termes IGN] SIG participatif [Termes IGN] traitement parallèle [Termes IGN] Uttarakhand (Inde ; état) [Termes IGN] Web Processing Service Résumé : (auteur) An online spatial biodiversity model (SBM) for optimized and automated spatial modelling and analysis of geospatial data is proposed, which is based on web processing service (WPS) and web service orchestration (WSO) in parallel computing environment. The developed model integrates distributed geospatial data in geoscientific processing workflow to compute the algorithms of spatial landscape indices over the web using free and open source software. A case study for Uttarakhand state of India demonstrates the model outputs such as spatial biodiversity disturbance index (SBDI) and spatial biological richness index (SBRI). In order to optimize and automate, an interactive web interface is developed using participatory GIS approaches for implementing fuzzy AHP. In addition, sensitivity analysis and geosimulation experiments are also performed under distributed GIS environment. Results suggest that parallel algorithms in SBM execute faster than sequential algorithms and validation of SBRI with biological diversity shows significant correlation by indicating high R2 values. Numéro de notice : A2019-222 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1381178 Date de publication en ligne : 06/10/2017 En ligne : https://doi.org/10.1080/10106049.2017.1381178 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92742 [article]

Calibration & optimisation / Sébastien Rey-Coyrehourcq (2019)  

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An efficient data organization and scheduling strategy for accelerating large vector data rendering / Mingqiang Guo in Transactions in GIS, vol 21 n° 6 (December 2017)  

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Experiments to distribute and parallelize map generalization processes / Guillaume Touya in Cartographic journal (the), Vol 54 n° 4 (November 2017)  

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Uncertain Voronoi cell computation based on space decomposition / Klaus Arthur Schmid in Geoinformatica, vol 21 n° 4 (October - December 2017)  

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A new GPU bundle adjustment method for large-scale data / Zhou Shunping ; Xiong Xiaodong ; Junfeng Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)  

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A parallel scheme for large-scale polygon rasterization on CUDA-enabled GPUs / Chen Zhou in Transactions in GIS, vol 21 n° 3 (June 2017)  

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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)  

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A parallel algorithm for coverage optimization on multi-core architectures / Ran Wei in International journal of geographical information science IJGIS, vol 30 n° 3-4 (March - April 2016)  

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Parallélisation des processus de traitement des données spatiales / Justin Berli (2016)

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Parallel performance of typical algorithms in remote sensing-based mapping on a multi-core computer / Jinghui Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 5 (May 2015)  

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