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Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods / Wolfgang B. Hamer in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)  

Public [article]  inISPRS International journal of geo-information > Vol 9 n° 1 (January 2020) Titre : Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods Type de document : Article/Communication Auteurs : Wolfgang B. Hamer, Auteur ; Tim Birr, Auteur ; Joseph-Alexander Verreet, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Allemagne [Termes IGN] apprentissage automatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] diffusion spatiale [Termes IGN] données localisées [Termes IGN] données météorologiques [Termes IGN] géostatistique [Termes IGN] maladie phytosanitaire [Termes IGN] modèle de simulation [Termes IGN] modèle dynamique [Termes IGN] rendement agricole [Termes IGN] risque environnemental [Termes IGN] temps réel Résumé : (auteur) Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted Numéro de notice : A2020-116 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9010044 Date de publication en ligne : 15/01/2020 En ligne : https://doi.org/10.3390/ijgi9010044 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94723 [article]

Revealing the Correlation between Population Density and the Spatial Distribution of Urban Public Service Facilities with Mobile Phone Data / Yi Shi in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)  

Public [article]  inISPRS International journal of geo-information > Vol 9 n° 1 (January 2020) Titre : Revealing the Correlation between Population Density and the Spatial Distribution of Urban Public Service Facilities with Mobile Phone Data Type de document : Article/Communication Auteurs : Yi Shi, Auteur ; Junyan Yang, Auteur ; Peiyu Shen, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] densité de population [Termes IGN] distribution spatiale [Termes IGN] données spatiotemporelles [Termes IGN] occupation du sol [Termes IGN] recensement démographique [Termes IGN] service public [Termes IGN] Shanghai (Chine) [Termes IGN] téléphone intelligent [Termes IGN] utilisation du sol [Termes IGN] zone urbaine Résumé : (auteur) Some studies have confirmed the association between urban public services and population density; however, other studies using census data, for example, have arrived at the opposite conclusion. Mobile signaling data provide new technological tools to investigate the subject. Based on the data of 20 million 2G mobile phone users in downtown Shanghai and the land use data of urban public service facilities, this study explores the spatiotemporal correlation between population density and public service facilities’ locations in downtown Shanghai and its variation laws. The correlation between individual population density at day vs. night and urban public service facilities distribution was also examined from a dynamic perspective. The results show a correlation between service facilities’ locations and urban population density at different times of the day. As a result, the average population density observed over a long period of time (day-time periodicity or longer) with census data or remote sensing data does not directly correlation with the distribution of public service facilities despite its correlation with public service facilities distribution. Among them, there is a significant spatial correlation between public service facilities and daytime population density and a significant spatial correlation between non-public service facilities and night-time population density. The spatial and temporal changes in the relationship between urban population density and service facilities is due to changing crowd behavior; however, the density of specific types of behavior is the real factor that affects the layout of urban public service facilities. The results show that mobile signaling data and land use data of service facilities are of great value for studying the spatiotemporal correlations between urban population density and service facilities Numéro de notice : A2020-115 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9010038 Date de publication en ligne : 13/01/2020 En ligne : https://doi.org/10.3390/ijgi9010038 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94725 [article]