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Geographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in Cartography and Geographic Information Science, vol 49 n° inconnu (2022)  

Public [article]  inCartography and Geographic Information Science > vol 49 n° inconnu (2022) Titre : Geographically masking addresses to study COVID-19 clusters Type de document : Article/Communication Auteurs : Walid Houfaf-Khoufaf, Auteur ; Guillaume Touya , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] adresse postale [Termes IGN] anonymisation [Termes IGN] carte sanitaire [Termes IGN] classification barycentrique [Termes IGN] surveillance sanitaire [Termes IGN] traitement de données localisées Résumé : (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- ) Autre URL associée : vers RSquare Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.1977709 Date de publication en ligne : 08/10/2021 En ligne : https://doi.org/10.1080/15230406.2021.1977709 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96857 [article]

Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach / Bisong Hu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 466 - 489 Titre : Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach Type de document : Article/Communication Auteurs : Bisong Hu, Auteur ; Pan Ning, Auteur ; Yi Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 466 - 489 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] carte sanitaire [Termes IGN] Chine [Termes IGN] entropie maximale [Termes IGN] filtre de Kalman [Termes IGN] géostatistique [Termes IGN] modèle dynamique [Termes IGN] régression Résumé : (auteur) In this work, a synthesis of the Bayesian maximum entropy (BME) and the Kalman filter (KF) methods, which enhances their individual strengths and overcomes certain of their weaknesses for spatiotemporal mapping purposes, is proposed in a spatiotemporal disease mapping context. The proposed BME-Kalman synthesis allows BME to use information from both parametric regression modeling and KF estimation leading to enhanced knowledge bases. The BME-Kalman synthetic approach is used to study the space-time incidence mapping of the hand, foot and mouth disease (HFMD) in Shandong province (China) during the period May 1st, 2008 to March 19th, 2009. The results showed that the BME-Kalman approach exhibited very good regressive and predictive accuracies, maintained a very good performance even during low-incidence and extremely low-incidence periods, offered an improved description of hierarchical disease characteristics compared to traditional mapping techniques, and provided a clear explanation of the spatial stratified incidence heterogeneity at unsampled locations. The BME-Kalman approach is versatile and flexible so that it can be modified and adjusted according to the needs of the application. Numéro de notice : A2021-165 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1795177 Date de publication en ligne : 22/07/2021 En ligne : https://doi.org/10.1080/13658816.2020.1795177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97098 [article]

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Crowdsourcing without data bias: Building a quality assurance system for air pollution symptom mapping / Marta Samulowska in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 2 (February 2021) . - n° 46 Titre : Crowdsourcing without data bias: Building a quality assurance system for air pollution symptom mapping Type de document : Article/Communication Auteurs : Marta Samulowska, Auteur ; Szymon Chmielewski, Auteur ; Edwin Raczko, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] assurance qualité [Termes IGN] carte sanitaire [Termes IGN] données localisées des bénévoles [Termes IGN] erreur systématique [Termes IGN] pollution atmosphérique [Termes IGN] production participative [Termes IGN] qualité de l'air [Termes IGN] qualité des données [Termes IGN] science citoyenne [Termes IGN] surveillance sanitaire [Termes IGN] zone urbaine Résumé : (auteur) Crowdsourcing is one of the spatial data sources, but due to its unstructured form, the quality of noisy crowd judgments is a challenge. In this study, we address the problem of detecting and removing crowdsourced data bias as a prerequisite for better-quality open-data output. This study aims to find the most robust data quality assurance system (QAs). To achieve this goal, we design logic-based QAs variants and test them on the air quality crowdsourcing database. By extending the paradigm of urban air pollution monitoring from particulate matter concentration levels to air-quality-related health symptom load, the study also builds a new perspective for citizen science (CS) air quality monitoring. The method includes the geospatial web (GeoWeb) platform as well as a QAs based on conditional statements. A four-month crowdsourcing campaign resulted in 1823 outdoor reports, with a rejection rate of up to 28%, depending on the applied. The focus of this study was not on digital sensors’ validation but on eliminating logically inconsistent surveys and technologically incorrect objects. As the QAs effectiveness may depend on the location and society structure, that opens up new cross-border opportunities for replication of the research in other geographical conditions. Numéro de notice : A2021-153 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10020046 Date de publication en ligne : 22/01/2021 En ligne : https://doi.org/10.3390/ijgi10020046 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97064 [article]

Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique / Hao Li in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 41-51 Titre : Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique Type de document : Article/Communication Auteurs : Hao Li, Auteur ; Benjamin Herfort, Auteur ; Wei Huang, Auteur Année de publication : 2020 Article en page(s) : pp 41-51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse de groupement [Termes IGN] analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] carte sanitaire [Termes IGN] cartographie collaborative [Termes IGN] données localisées des bénévoles [Termes IGN] géographie sociale [Termes IGN] inventaire du bâti [Termes IGN] Mozambique [Termes IGN] OpenStreetMap [Termes IGN] qualité des données [Termes IGN] Twitter Résumé : (auteur) Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while the availability and quality of OSM remains a major concern. The majority of existing works in assessing OSM data quality focus on either extrinsic or intrinsic analysis, which is insufficient to fulfill the humanitarian mapping scenario to a certain degree. This paper aims to explore OSM missing built-up areas from an integrative perspective of social sensing and remote sensing. First, applying hierarchical DBSCAN clustering algorithm, the clusters of geo-tagged tweets are generated as proxies of human active regions. Then a deep learning based model fine-tuned on existing OSM data is proposed to further map the missing built-up areas. Hit by Cyclone Idai and Kenneth in 2019, the Republic of Mozambique is selected as the study area to evaluate the proposed method at a national scale. As a result, 13 OSM missing built-up areas are identified and mapped with an over 90% overall accuracy, being competitive compared to state-of-the-art products, which confirms the effectiveness of the proposed method. Numéro de notice : A2020-350 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.007 Date de publication en ligne : 07/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95233 [article]

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Using GIS for disease mapping and clustering in Jeddah, Saudi Arabia / Abdulkader Murad in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 22 p. Titre : Using GIS for disease mapping and clustering in Jeddah, Saudi Arabia Type de document : Article/Communication Auteurs : Abdulkader Murad, Auteur ; Bandar Fuad Khashoggi, Auteur Année de publication : 2020 Article en page(s) : 22 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse de groupement [Termes IGN] Arabie Saoudite [Termes IGN] carte sanitaire [Termes IGN] distribution spatiale [Termes IGN] estimation par noyau [Termes IGN] modélisation environnementale [Termes IGN] modélisation spatiale [Termes IGN] surveillance sanitaire [Termes IGN] zone à risque Résumé : (auteur) Geographic information systems (GIS) can be used to map the geographical distribution of the prevalence of disease, trends in disease transmission, and to spatially model environmental aspects of disease occurrence. The aim of this study is to discuss a GIS application created to produce mapping and cluster modeling of three diseases in Jeddah, Saudi Arabia: diabetes, asthma, and hypertension. Data about these diseases were obtained from health centers’ registered patient records. These data were spatially evaluated using several spatial–statistical analytical models, including kernel and hotspot models. These models were created to explore and display the disparate patterns of the selected diseases and to illustrate areas of high concentration, and may be invaluable in understanding local patterns of diseases and their geographical associations. Numéro de notice : A2020-300 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050328 Date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.3390/ijgi9050328 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95140 [article]

La géomatique comme aide à la décision dans le domaine de la santé / Dorothée Dumay (2018) 

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La géomatique comme aide à la décision dans le domaine de la santé / Lise Le Lann (2017)

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Développement d’un outil de webmapping pour l’optimisation de l’offre de soins en dialyse / Clémentine Chasles (2016) 

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Environnement, santé, territoire : le tryptique d'avenir / Françoise de Blomac in DécryptaGéo le mag, n° 173 (janvier 2016)

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Regionalization of youth and adolescent weight metrics for the continental United States using contiguity-constrained clustering and partitioning / Samuel Adu-Prah in Cartographica, vol 50 n° 2 (Summer 2015)  

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Conception et réalisation d'un atlas relatif au parcours de santé des personnes âgées : une approche comparative multisite et multiéchelle / Constance Lecomte (2014)

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Quand les satellites s'attaquent aux moustiques / M. Siron in CNES Magazine, n° 53 (01/04/2012)

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La géomatique au service de la santé / Anthony Necha (2012)

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Design and implementation of a Model, Web-based, GIS-enabled cancer atlas / Alan M. MacEachren in Cartographic journal (the), vol 45 n° 4 (November 2008)  

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Des moucherons sous l'œil de SPOT / Françoise de Blomac in SIG la lettre, n° 91 (novembre 2007)

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