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Geographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in International Journal of Health Geographics, vol inconnu (2021)
Titre : Geographically masking addresses to study COVID-19 clusters Type de document : Article/Communication Auteurs : Walid Houfaf-Khoufaf, Auteur ; Guillaume Touya Année de publication : 2021 Projets : 1-Pas de projet / Note générale : bibliographie
10.21203/rs.3.rs-128679/v1 DOI d'attenteLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes descripteurs IGN] adresse postale
[Termes descripteurs IGN] anonymisation
[Termes descripteurs IGN] carte sanitaire
[Termes descripteurs IGN] classification barycentrique
[Termes descripteurs IGN] surveillance sanitaire
[Termes descripteurs IGN] traitement de données localiséesRésumé : (auteur) The spatio-temporal analysis of cases is a good way an epidemic, and the recent COVID-19 pandemic unfortunately generated a huge amount of data. But analysing this raw data, with for instance the address of the people who contracted COVID-19, raises some privacy issues, and geomasking is necessary to preserve both people privacy and the spatial accuracy required for analysis. This paper proposes dierent geomasking techniques adapted to this COVID-19 data. Methods: Different techniques are adapted from the literature, and tested on a synthetic dataset mimicking the COVID-19 spatio-temporal spreading in Paris and a more rural nearby region. Theses techniques are assessed in terms of k-anonymity and cluster preservation. Results: Three adapted geomasking techniques are proposed: aggregation, bimodal gaussian perturbation, and simulated crowding. All three can be useful in different use cases, but the bimodal gaussian perturbation is the overall best techniques, and the simulated crowding is the most promising one, provided some improvements are introduced to avoid points with a low k-anonymity. Conclusions: It is possible to use geomasking techniques on addresses of people who caught COVID-19, while preserving the important spatial patterns. Numéro de notice : A2021-065 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.21203/rs.3.rs-128679/v1 En ligne : https://doi.org/10.21203/rs.3.rs-128679/v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96857
in International Journal of Health Geographics > vol inconnu (2021)[article]
Titre : Volunteered geographic information systems: Technological design patterns Type de document : Article/Communication Auteurs : Jose Pablo Gómez‐Barrón, Auteur ; Miguel Angel Manso-Callejo, Auteur ; Ramón Alcarria, Auteur Année de publication : 2019 Article en page(s) : pp 976 - 1007 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes descripteurs IGN] acquisition de données
[Termes descripteurs IGN] approche participative
[Termes descripteurs IGN] communauté virtuelle
[Termes descripteurs IGN] données localisées des bénévoles
[Termes descripteurs IGN] GeoWeb
[Termes descripteurs IGN] Infrastructure de données
[Termes descripteurs IGN] interface mobile
[Termes descripteurs IGN] réseau social
[Termes descripteurs IGN] système d'information géographique
[Termes descripteurs IGN] traitement de données localiséesRésumé : (auteur) Volunteered geographic information (VGI) has been studied extensively within various contexts, however, generally focusing on isolated aspects of data. Therefore, we still need to advance the structured knowledge of the mechanisms that affect and support its creation. This article aims to contribute to the integrated study of VGI systems (VGIS) in order to enhance design processes that lead to a more systematic and efficient VGI management. We define VGIS, their main components, and general architectures. Then, we use the service blueprinting technique to conduct the case study of several systems, identifying system actions to find the processes and technologies forming the different functional components of VGIS: elements of the technological infrastructure that implement diverse processes to facilitate participation and data management. Integrated in the data collection component, we also propose a typology of crowdsourcing tasks that—coupled with the functional components—establishes a foundation for VGIS research and design. We characterize these components, arguing that they are necessary in the development of VGIS, however, there is still a need for a more detailed study of the flow of processes and subprocesses and how they work under the criteria of different types of projects to continue facilitating their design. Numéro de notice : A2019-550 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12544 date de publication en ligne : 13/06/2019 En ligne : https://doi.org/10.1111/tgis.12544 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94200
in Transactions in GIS > Vol 23 n° 5 (October 2019) . - pp 976 - 1007[article]
Titre : Cognitive aspects of human-computer interaction for GIS Type de document : Monographie Auteurs : Dieter Fritsch, Editeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 196 p. ISBN/ISSN/EAN : 978-3-03921-569-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes descripteurs IGN] Androïd
[Termes descripteurs IGN] base de données statistiques
[Termes descripteurs IGN] carte d'intérieur
[Termes descripteurs IGN] classification par forêts aléatoires
[Termes descripteurs IGN] cognition
[Termes descripteurs IGN] état de l'art
[Termes descripteurs IGN] géovisualisation
[Termes descripteurs IGN] informatique en nuage
[Termes descripteurs IGN] interface homme-machine
[Termes descripteurs IGN] IOS
[Termes descripteurs IGN] monde virtuel
[Termes descripteurs IGN] oculométrie
[Termes descripteurs IGN] représentation mentale spatiale
[Termes descripteurs IGN] traitement de données localisées
[Termes descripteurs IGN] visualisation de donnéesRésumé : (éditeur) The book is dealing with recent progress in human–computer interaction (HCI) related to geographic information science (GIS). The Editorial starts with an overview about the evolution of the Internet and first HCI concepts and stimulates recent HCI developments using 3D and 4D apps, running on all mobile devices with OS Android, iOS, Linus, and Windows. Eight research articles present the state-of-the-art in HCI–GIS-related issues, starting with gender and age differences in using indoor maps via the estimation of building heights from space to an efficient visualization method for polygonal data with dynamic simplification. The review article deals with progress and challenges on entity alignment of geographic knowledge bases. Note de contenu : Editorial “Cognitive aspects of human-computer interaction for GIS”
1- Gender and age differences in using indoor maps for wayfinding in real environments
2- Collaborative Immersive Virtual Environments for Education in Geography
3- Evaluation of User Performance in Interactive and Static 3D Maps
4- Determining optimal video length for the estimation of building height through radial displacement measurement from space
5- 4D time density of trajectories: Discovering spatiotemporal patterns in movement data
6- Two-Dimensional Priority R-Tree Algorithm for Spatial Partitioning in SpatialHadoop
7- Estimating the performance of random forest versus multiple regression for predicting prices of the apartments
8- An efficient visualization method for polygonal data with dynamic simplification
9- Progress and challenges on entity alignment of geographic knowledge basesNuméro de notice : 25883 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03921-569-0 En ligne : https://doi.org/10.3390/books978-3-03921-569-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95748
Titre : Google Earth Engine applications Type de document : Monographie Auteurs : Lalit Kumar, Editeur scientifique ; Onisimo Mutanga, Editeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 420 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-03897-885-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Information géographique
[Termes descripteurs IGN] base de données d'images
[Termes descripteurs IGN] Google Earth Engine
[Termes descripteurs IGN] image 3D
[Termes descripteurs IGN] image aérienne
[Termes descripteurs IGN] image satellite
[Termes descripteurs IGN] information géographique numérique
[Termes descripteurs IGN] informatique en nuage
[Termes descripteurs IGN] moteur de recherche
[Termes descripteurs IGN] surveillance écologique
[Termes descripteurs IGN] système d'information environnementale
[Termes descripteurs IGN] traitement de données localiséesRésumé : (éditeur) In a rapidly changing world, there is an ever-increasing need to monitor the Earth's resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth's surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales. Note de contenu : 1- Google Earth Engine applications since inception: usage, trends, and potential
2- Global estimation of biophysical variables from Google Earth Engine platform
3- An operational before-after-control-impact (BACI) designed platform for vegetation monitoring at planetary scale
4- Mapping vegetation and land use types in Fanjingshan national nature reserve using Google Earth Engine
5- A dynamic Landsat derived Normalized Difference Vegetation Index (NDVI) product for the conterminous United States
6- High spatial resolution visual band imagery outperforms medium resolution spectral imagery for ecosystem assessment in the semi-arid Brazilian Sert˜ao
7- Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016
8- Towards global-scale seagrass mapping and monitoring using Sentinel-2 on Google Earth Engine: The case study of the Aegean and Ionian Seas
9- BULC-U: Sharpening resolution and improving accuracy of land-use/land-cover classifications in Google Earth Engine
10- Monitoring the impact of land cover change on surface urban heat island through Google
Earth Engine: Proposal of a global methodology, first applications and problems
11- Regional crop gross primary productivity and yield estimation using fused Landsat-MODIS data
12- The first wetland inventory map of Newfoundland at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform
13- A cloud-based multi-temporal ensemble classifier to map smallholder farming systems
14- Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine
15- SnowCloudHydro — A new framework for forecasting streamflow in snowy, data-scarce regions
16- Flood prevention and emergency response system powered by Google Earth Engine
17- Leveraging the Google Earth Engine for drought assessment using global soil moisture data
18- Multitemporal cloud masking in the Google Earth Engine
19- Historical and operational monitoring of surface sediments in the lower Mekong basin using Landsat and Google Earth Engine cloud computing
20- Mapping mining areas in the Brazilian Amazon using MSI/Sentinel-2 imagery (2017)
21- Estimating satellite-derived bathymetry (SDB) with the Google Earth Engine and Sentinel-2
22- Mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potentialNuméro de notice : 25887 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03897-885-5 En ligne : https://doi.org/10.3390/books978-3-03897-885-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95788 Machine learning techniques applied to geoscience information system and remote sensing / Saro Lee (2019)
Titre : Machine learning techniques applied to geoscience information system and remote sensing Type de document : Monographie Auteurs : Saro Lee, Editeur scientifique ; Hyung-Sup Jung, Editeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 438 p. ISBN/ISSN/EAN : ISBN 978-3-03921-215-6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes descripteurs IGN] analyse spatiale
[Termes descripteurs IGN] apprentissage automatique
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] géosciences
[Termes descripteurs IGN] réseau neuronal convolutif
[Termes descripteurs IGN] système d'information géographique
[Termes descripteurs IGN] télédétection
[Termes descripteurs IGN] traitement de données localiséesRésumé : (éditeur) As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing. Numéro de notice : 25831 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif En ligne : https://www.mdpi.com/books/pdfview/book/1533 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95158 
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