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Constructing and analyzing spatial-social networks from location-based social media data / Xuebin Wei in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 3 (May 2021) . - pp 258 - 274 Titre : Constructing and analyzing spatial-social networks from location-based social media data Type de document : Article/Communication Auteurs : Xuebin Wei, Auteur ; Xiaobai Yao, Auteur Année de publication : 2021 Article en page(s) : pp 258 - 274 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de données [Termes IGN] collecte de données [Termes IGN] données issues des réseaux sociaux [Termes IGN] données spatiotemporelles [Termes IGN] Facebook [Termes IGN] géolocalisation [Termes IGN] réseau social géodépendant [Termes IGN] santé [Termes IGN] système d'information géographique [Termes IGN] urbanisme Résumé : (auteur) People interact with each other in space and time. Improved understanding of human interactions in spatial, temporal, and social dimensions are highly beneficial for research and practices in public health, urban planning, and other fields. Traditional methods of collecting social interaction data are time-intensive and resource-consuming, resulting in relatively small sample sizes and limited information. Furthermore, traditional methods often oversimplify the dynamics of human interactions and fail to capture the characteristics of places where the interactions occur. With the popularity of location-based social media (LBSM) platforms, people can publish information about their social events such as time, location, and other participants. This research introduces a framework that formalizes terminologies and concepts related to spatial-social connections for the construction of spatial-social networks from LBSM data in GIS. Supported by the framework, the study presents methods of collecting, analyzing, and visualizing LBSM data in spatial-social dimensions. The methods are implemented and tested in a case study with Facebook data. The case study demonstrates that location-based social media data can be transformed into spatial-social networks and then be analyzed and visualized to answer innovative types of scientific inquiries. Numéro de notice : A2021-316 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.1891974 date de publication en ligne : 09/04/2021 En ligne : https://doi.org/10.1080/15230406.2021.1891974 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97538 [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]

Geo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan / Muhammad Imran in Geocarto international, vol 36 n° 2 ([01/02/2021])  

Public [article]  inGeocarto international > vol 36 n° 2 [01/02/2021] . - pp 197 - 211 Titre : Geo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan Type de document : Article/Communication Auteurs : Muhammad Imran, Auteur ; Yasra Hamid, Auteur ; Abeer Mazher, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 197 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] cartographie des risques [Termes IGN] diptère [Termes IGN] image Landsat [Termes IGN] maladie tropicale [Termes IGN] modélisation spatiale [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Pakistan [Termes IGN] régression géographiquement pondérée [Termes IGN] régression logistique [Termes IGN] risque sanitaire [Termes IGN] série temporelle [Termes IGN] zone intertropicale [Termes IGN] zone urbaine Résumé : (auteur) The study objective is to predict the epidemiological impact of dengue fever arbovirosis in urban tropical areas of Pakistan. To do so, we used the GPS-based data of the Aedes larvae collected during 2014–2015 in Lahore. We developed a Geographically Weighted Logistic Regression (GWLR) model for Geospatially predicting larvae presence or absence in Lahore. Data on rainfall, temperature are included along with time series of the normalized difference vegetation index (NDVI) derived from Landsat imagery. We observed a high spatial variability of the GWLR parameter estimates of these variables in the study area. The GWLR model significantly (R2a = 0.78) explained the presence or absence of Aedes larvae with temperature, rainfall and NDVI variables in South and Southeast of the study area. In the North and North-West, however, GWLR relationships were observed weak in highly populated areas. Interpolating GWLR coefficients generate more accurate maps of Aedes larvae presence or absence. Numéro de notice : A2021-474 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1614100 date de publication en ligne : 10/06/2020 En ligne : https://doi.org/10.1080/10106049.2019.1614100 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96932 [article]

Geographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in International Journal of Health Geographics, vol inconnu (2021)  

Public [article]  inInternational 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 , Auteur Année de publication : 2021 Projets : 1-Pas de projet / Note générale : bibliographie 10.21203/rs.3.rs-128679/v1 DOI d'attente 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- ) 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 [article]

Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation / George Grekousis in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 152 - 174 Titre : Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation Type de document : Article/Communication Auteurs : George Grekousis, Auteur Année de publication : 2021 Article en page(s) : pp 152 - 174 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] classification floue [Termes IGN] données démographiques [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] optimisation par essaim de particules [Termes IGN] pondération [Termes IGN] régression géographiquement pondérée [Termes IGN] santé [Termes IGN] segmentation [Termes IGN] voisinage (topologie) Résumé : (auteur) Fuzzy geographically weighted clustering has been proposed as an approach for improving fuzzy c-means algorithm when applied to geodemographic analysis. This clustering method allows a spatial entity to belong to more than one cluster with varying degrees, namely, membership values. Although fuzzy geographically weighted clustering attempts to create geographically aware clusters, it partially fails to trace spatial dependence and heterogeneity because, as a global metric, the membership values are calculated across the entire set of spatial entities. Here we introduce the first local version of fuzzy geographically weighted clustering, ‘local fuzzy geographically weighted clustering.’ In local fuzzy geographically weighted clustering, the membership values of a spatial entity are updated only according to the membership values of the spatial entities within its neighborhood and not across the entire set of entities, as originally proposed by the global metric. Additionally, we apply particle swarm optimization meta-heuristic to overcome the random initialization problem regarding the fuzzy c-means algorithm. To evaluate our method we compare local fuzzy geographically weighted clustering to global fuzzy geographically weighted clustering using a cancer incident benchmark dataset for Manhattan, New York. The results show that local fuzzy geographically weighted clustering outperforms the global version in all experimental settings. Numéro de notice : A2021-022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808221 date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808221 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96525 [article]

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Radio base stations and electromagnetic fields: GIS applications and models for identifying possible risk factors and areas exposed. Some exemplifications in Rome / Cristiano Pesaresi in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)  

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Exploratory bivariate and multivariate geovisualizations of a social vulnerability index / Georgianna Strode in Cartographic perspectives, n° 95 (July 2020)  

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Integration of spatialization and individualization: the future of epidemic modelling for communicable diseases / Meifang Li in Annals of GIS, vol 26 n° 3 (July 2020)  

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Spatiotemporally Varying Coefficients (STVC) model: a Bayesian local regression to detect spatial and temporal nonstationarity in variables relationships / Chao Song in Annals of GIS, vol 26 n° 3 (July 2020)  

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A web-based spatial decision support system for monitoring the risk of water contamination in private wells / Yu Lan in Annals of GIS, vol 26 n° 3 (July 2020)  

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Estimating and interpreting fine-scale gridded population using random forest regression and multisource data / Yun Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data / Rochelle Schneider dos Santos in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

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Mapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics / E. Banzhaf in Geocarto international, vol 35 n° 6 ([01/05/2020])  

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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)  

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Assessment of malaria hazard, vulnerability, and risks in Dire Dawa City Administration of eastern Ethiopia using GIS and remote sensing / Abdinasir Moha in Applied geomatics, vol 12 n° 1 (April 2020)  

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