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Online flu epidemiological deep modeling on disease contact network / Liang Zhao in Geoinformatica, vol 24 n° 2 (April 2020)  

Public [article]  inGeoinformatica > vol 24 n° 2 (April 2020) . - pp 443 – 475 Titre : Online flu epidemiological deep modeling on disease contact network Type de document : Article/Communication Auteurs : Liang Zhao, Auteur ; Jiangzhuo Chen, Auteur ; Feng Chen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 443 – 475 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] classification par réseau neuronal [Termes IGN] classification semi-dirigée [Termes IGN] données localisées des bénévoles [Termes IGN] épidémie [Termes IGN] itération [Termes IGN] maladie infectieuse [Termes IGN] maladie virale [Termes IGN] modélisation [Termes IGN] optimisation (mathématiques) [Termes IGN] réseau social [Termes IGN] surveillance sanitaire Résumé : (auteur) The surveillance and preventions of infectious disease epidemics such as influenza and Ebola are important and challenging issues. It is therefore crucial to characterize the disease progress and epidemics process efficiently and accurately. Computational epidemiology can model the progression of the disease and its underlying contact network, but as yet lacks the ability to process of real-time and fine-grained surveillance data. Social media, on the other hand, provides timely and detailed disease surveillance but is insensible to the underlying contact network and disease model. To address these challenges simultaneously, this paper proposes a novel semi-supervised neural network framework that integrates the strengths of computational epidemiology and social media mining techniques for influenza epidemiological modeling. Specifically, this framework learns social media users’ health states and intervention actions in real time, regularized by the underlying disease model and contact network. The learned knowledge from social media can then be fed into the computational epidemic model to improve the efficiency and accuracy of disease diffusion modeling. We propose an online optimization algorithm that iteratively processes the above interactive learning process. he extensive experimental results provided demonstrated that our approach can not only outperform competing methods by a substantial margin in forecasting disease outbreaks, but also characterize the individual-level disease progress and diffusion effectively and efficiently. Numéro de notice : A2020-359 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-019-00376-9 Date de publication en ligne : 25/07/2019 En ligne : https://doi.org/10.1007/s10707-019-00376-9 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95264 [article]

Techniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network / Adil Alim in Geoinformatica, vol 24 n° 2 (April 2020)  

Public [article]  inGeoinformatica > vol 24 n° 2 (April 2020) . - pp 269 – 299 Titre : Techniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network Type de document : Article/Communication Auteurs : Adil Alim, Auteur ; Aparna Joshi, Auteur ; Feng Chen, Auteur ; Catherine T. Lawson, Auteur Année de publication : 2020 Article en page(s) : pp 269 – 299 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] corrélation [Termes IGN] détection d'événement [Termes IGN] détection de changement [Termes IGN] données spatiotemporelles [Termes IGN] entretien du réseau [Termes IGN] hiver [Termes IGN] météorologie [Termes IGN] prévision météorologique [Termes IGN] réseau routier [Termes IGN] sécurité routière [Termes IGN] trafic routier Résumé : (auteur) Adverse weather conditions have a significant impact on the safety, mobility, and efficiency of highway networks. Weather contributed to 23 percent of all non-reoccurring delay and approximately 544 million vehicle hours of delay each year (2014). Nearly 2.3 billion dollars each year are spent by transportation agencies for winter maintenance that contribute to close to 20 percent of most DOT’s yearly budgets (2014). These safety and mobility factors make it important to develop new and more effective methods to address road conditions during adverse weather conditions. Given weather and traffic sensors installed along side of the highway networks, how can we automatically detect weather and traffic change events and prevent from the traffic delay or harsh weather accidents? To this end, we propose a novel framework to address this problem. This paper develops techniques for efficiently detecting rapid weather change events and analyzing their impacts on the traffic flow characteristics of a highway network. It is composed of three components, including 1) detection of rapid weather change events in a highway network using the streaming weather information from a sensor network of weather stations; 2) detection of rapid traffic change events on the traffic flow characteristics (e.g., travel time) of the highway network; and 3) analysis of correlations between the detected weather and traffic change events in space and time. The proposed approach was applied to a weather dataset provided by New York State Mesonet and a traffic flow dataset the National Performance Management Research Data Set (NPMRDS) provided by NYSDOT. The empirical results provide potential evidence about the significant impacts of rapid weather change events on traffic flow characteristics of the Interstate 90 (I-90) Highway in the state of New York. We show the quantitative performance evaluation of our change event detection algorithm and three baseline methods on manually labeled the weather dataset and our method outperforms baselines in terms of precision, recall and F-score. We present the analysis of Top K detected change events as case studies and also provide the spatio-temporal correlation statistics of top k weather and traffic change events. The limitations of the proposed approach and the empirical study are also discussed. Numéro de notice : A2020-358 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00395-x Date de publication en ligne : 12/02/2020 En ligne : https://doi.org/10.1007/s10707-020-00395-x Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95263 [article]

Automatic targeted-domain spatiotemporal event detection in twitter / Ting Hua in Geoinformatica, vol 20 n° 4 (October - December 2016)  

Public [article]  inGeoinformatica > vol 20 n° 4 (October - December 2016) . - pp 765 - 795 Titre : Automatic targeted-domain spatiotemporal event detection in twitter Type de document : Article/Communication Auteurs : Ting Hua, Auteur ; Feng Chen, Auteur ; Liang Zhao, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 765 - 795 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse spatio-temporelle [Termes IGN] données issues des réseaux sociaux [Termes IGN] positionnement automatique [Termes IGN] Twitter Résumé : (Auteur) Twitter has become an important data source for detecting events, especially tracking detailed information for events of a specific domain. Previous studies on targeted-domain Twitter information extraction have used supervised learning techniques to identify domain-related tweets, however, the need for extensive manual labeling makes these supervised systems extremely expensive to build and maintain. What’s more, most of these existing work fail to consider spatiotemporal factors, which are essential attributes of target-domain events. In this paper, we propose a semi-supervised method for Automatical Targeted-domain Spatiotemporal Event Detection (ATSED) in Twitter. Given a targeted domain, ATSED first learns tweet labels from historical data, and then detects on-going events from real-time Twitter data streams. Specifically, an efficient label generation algorithm is proposed to automatically recognize tweet labels from domain-related news articles, a customized classifier is created for Twitter data analysis by utilizing tweets’ distinguishing features, and a novel multinomial spatial-scan model is provided to identify geographical locations for detected events. Experiments on 305 million tweets demonstrated the effectiveness of this new approach. Numéro de notice : A2016-815 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1007/s10707-016-0263-0 En ligne : http://dx.doi.org/10.1007/s10707-016-0263-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82616 [article]

On detecting spatial categorical outliers / X. Liu in Geoinformatica, vol 18 n° 3 (July 2014)  

Public [article]  inGeoinformatica > vol 18 n° 3 (July 2014) . - pp 501 - 536 Titre : On detecting spatial categorical outliers Type de document : Article/Communication Auteurs : X. Liu, Auteur ; Feng Chen, Auteur ; Tien Lu, Auteur Année de publication : 2014 Article en page(s) : pp 501 - 536 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] détection d'anomalie [Termes IGN] valeur aberrante Résumé : (Auteur)Spatial outlier detection is an important research problem that has received much attentions in recent years. Most existing approaches are designed for numerical attributes, but are not applicable to categorical ones (e.g., binary, ordinal, and nominal) that are popular in many applications. The main challenges are the modeling of spatial categorical dependency as well as the computational efficiency. This paper presents the first outlier detection framework for spatial categorical data. Specifically, a new metric, named as Pair Correlation Ratio (PCR), is measured for each pair of category sets based on their co-occurrence frequencies at specific spatial distance ranges. The relevances among spatial objects are then calculated using PCR values with regard to their spatial distances. The outlierness for each object is defined as the inverse of the average relevance between an object and its spatial neighbors. Those objects with the highest outlier scores are returned as spatial categorical outliers. A set of algorithms are further designed for single-attribute and multi-attribute spatial categorical datasets. Extensive experimental evaluations on both simulated and real datasets demonstrated the effectiveness and efficiency of our proposed approaches. Numéro de notice : A2014-499 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-013-0188-9 Date de publication en ligne : 28/09/2013 En ligne : https://doi.org/10.1007/s10707-013-0188-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74091 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-2014031	RAB	Revue	Centre de documentation	En réserve L003	Disponible