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Recurrent origin–destination network for exploration of human periodic collective dynamics / Xiaojian Chen in Transactions in GIS, vol 26 n° 1 (February 2022)
[article]
Titre : Recurrent origin–destination network for exploration of human periodic collective dynamics Type de document : Article/Communication Auteurs : Xiaojian Chen, Auteur ; Jiayi Xie, Auteur ; Changjiang Xiao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 317 - 340 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données localisées dynamiques
[Termes IGN] flux
[Termes IGN] origine - destination
[Termes IGN] planification urbaine
[Termes IGN] réseau neuronal récurrent
[Termes IGN] série temporelle
[Termes IGN] taxi
[Termes IGN] Wuhan (Chine)Résumé : (auteur) While daily periodic movements of individuals have been widely studied, their collective dynamics are not understood. To capture periodic collective dynamics, this article represents individual daily movements as a time series of directed weighted origin–destination (OD) networks, and proposes an approach to identify a sub-network called the “recurrent OD network”, which contains frequent edges appearing in each day. Taxi trajectory data over a period of 6 months in Wuhan, China are used for the case study. Here, we extracted the recurrent OD networks for each 2-h period on a given day, and compared them with the corresponding “major OD network” defined by both frequent and infrequent edges. Results show that the recurrent OD networks coincidentally exhibit spatially localized community structures and distinctive patterns of inflow and outflow for each region within a day. Overall, both methodology and findings in this study might make significant contributions in a range of fields, such as urban planning, regional economic development, and infectious disease control. Numéro de notice : A2022-179 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12849 Date de publication en ligne : 05/10/2021 En ligne : https://doi.org/10.1111/tgis.12849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99838
in Transactions in GIS > vol 26 n° 1 (February 2022) . - pp 317 - 340[article]Les risques-réseaux : une matrice des défaillances des réseaux urbains interdépendants / Nabil Touili in Belgeo, vol 2022 n° 1 (2022)
[article]
Titre : Les risques-réseaux : une matrice des défaillances des réseaux urbains interdépendants Titre original : Network-risks: a matrix of interdependent urban networks' failures Type de document : Article/Communication Auteurs : Nabil Touili, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Français (fre) Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] infrastructure
[Termes IGN] matrice
[Termes IGN] réseau technique urbain
[Termes IGN] risque technologique
[Termes IGN] territoire
[Termes IGN] vulnérabilitéRésumé : (auteur) À partir d’une typologie théorique et de données empiriques tirées d’évènements catastrophiques, cet article fournit une matrice des défaillances des réseaux interdépendants d’énergie, de transport, de télécommunications, d’eau et d’assainissement, d’alimentation et de santé. La forte urbanisation a entraîné une expansion des réseaux d’infrastructures critiques, dont les défaillances sont notamment dues à leurs interdépendances multiples et de plus en plus complexes. L’identification préalable des relations d’interdépendances s’avère alors primordiale pour prévenir des risques de défaillances. Cet article identifie quatre types d’interdépendances (fonctionnelles, géo-spatiales, procédurales et sociétales) et examine leurs rôles respectifs dans les défaillances recensées lors des ouragans Irma et Maria en 2017, de l’attentat du WTC (World Trade Center) en 2001, de la catastrophe de Fukushima en 2011, de l’éruption du volcan islandais Eyjafjallajökull en 2010, de la canicule de 2003 et du black-out en Inde de 2012. Ce travail aboutit à une matrice inter-réseaux urbains qui apporte des réponses à la question suivante : par quel(s) type(s) d’interdépendance(s), la défaillance initiale d’un réseau urbain entraîne-t-elle la défaillance d’autres réseaux ? Cette matrice met en évidence des risques-réseaux en identifiant les types d’interdépendances par lesquels les réseaux génèrent et subissent des défaillances. Cette démarche déductive permet de nuancer des relations de dépendances unidirectionnelles, réciproques et mutuelles. Nos résultats argumentent la criticité des infrastructures par la criticité du type d’interdépendance qui les relie. Une réflexion est ainsi menée sur la résilience territoriale et celle des réseaux dans des perspectives d’accroissement des interdépendances étudiées. Numéro de notice : A2022-693 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.4000/belgeo.54349 En ligne : https://doi.org/10.4000/belgeo.54349 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101814
in Belgeo > vol 2022 n° 1 (2022)[article]SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows / Qiliang Liu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)
[article]
Titre : SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows Type de document : Article/Communication Auteurs : Qiliang Liu, Auteur ; Jie Yang, Auteur ; Min Deng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 253 - 279 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] classification barycentrique
[Termes IGN] flux
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] mobilité urbaine
[Termes IGN] noeud
[Termes IGN] origine - destination
[Termes IGN] Pékin (Chine)
[Termes IGN] réseau routier
[Termes IGN] taxi
[Termes IGN] trajet (mobilité)Résumé : (auteur) Identifying clusters from individual origin–destination (OD) flows is vital for investigating spatial interactions and flow mapping. However, detecting arbitrarily-shaped and non-uniform flow clusters from network-constrained OD flows continues to be a challenge. This study proposes a shared nearest-neighbor-based clustering method (SNN_flow) for inhomogeneous OD flows constrained by a road network. To reveal clusters of varying shapes and densities, a normalized density for each OD flow is defined based on the concept of shared nearest-neighbor, and flow clusters are constructed using the density-connectivity mechanism. To handle large amounts of disaggregated OD flows, an efficient method for searching the network-constrained k-nearest flows is developed based on a local road node distance matrix. The parameters of SNN_flow are statistically determined: the density threshold is modeled as a significance level of a significance test, and the number of nearest neighbors is estimated based on the variance of the kth nearest distance. SNN_flow is compared with three state-of-the-art methods using taxicab trip data in Beijing. The results show that SNN_flow outperforms existing methods in identifying flow clusters with irregular shapes and inhomogeneous distributions. The clusters identified by SNN_flow can reveal human mobility patterns in Beijing. Numéro de notice : A2022-163 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1899184 Date de publication en ligne : 16/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1899184 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99786
in International journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 253 - 279[article]An extended patch-based cellular automaton to simulate horizontal and vertical urban growth under the shared socioeconomic pathways / Yimin Chen in Computers, Environment and Urban Systems, vol 91 (January 2022)
[article]
Titre : An extended patch-based cellular automaton to simulate horizontal and vertical urban growth under the shared socioeconomic pathways Type de document : Article/Communication Auteurs : Yimin Chen, Auteur Année de publication : 2022 Article en page(s) : n° 101727 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] Canton (Kouangtoung)
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] construction
[Termes IGN] croissance urbaine
[Termes IGN] données socio-économiques
[Termes IGN] Kouangtoung (Chine)
[Termes IGN] modèle de simulation
[Termes IGN] urbanisationRésumé : (auteur) Most contemporary urban cellular automata (CA) models primarily focus on the simulation of urban land expansion, and cannot effectively simulate vertical urban growth. This study addresses this drawback by extending a patch-based urban CA model with a component that can predict the building volumes of an urban land expansion scenario. The proposed model is evaluated through a case study in the Guangzhou-Foshan metropolitan area, China. The horizontal urban growth simulations achieve a mean ‘Figure-of-merit’ value of 0.1406 at the cell level and an agreement of 97% at the pattern level. The building volume prediction made by the methods of random forest and k-nearest-neighbor has a testing R2 of 0.90 and a mean percentage absolute error of 22%. The proposed model is applied to the urban growth projections under the shared socioeconomic pathways (SSPs). The results successfully reflect the influences that different SSPs have on vertical urban developments. These results also complement related research of urbanization projections under the SSPs, because most existing studies consider the impacts of horizontal urban growth only. As building volumes and heights are fundamental parameters to urban climate modeling, the ability of the proposed model to project future change in vertical urban developments can support the mitigation of climate change effects on human settlements. Numéro de notice : A2022-008 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101727 Date de publication en ligne : 21/10/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101727 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99049
in Computers, Environment and Urban Systems > vol 91 (January 2022) . - n° 101727[article]Application of machine learning to predict transport modes from GPS, accelerometer, and heart rate data / Santosh Giri in International Journal of Health Geographics, vol 21 (2022)
[article]
Titre : Application of machine learning to predict transport modes from GPS, accelerometer, and heart rate data Type de document : Article/Communication Auteurs : Santosh Giri, Auteur ; Ruben Brondeel, Auteur ; Tarik El Aarbaoui, Auteur ; Basile Chaix, Auteur Année de publication : 2022 Article en page(s) : n° 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] accéléromètre
[Termes IGN] bicyclette
[Termes IGN] données GPS
[Termes IGN] données médicales
[Termes IGN] Ile-de-France
[Termes IGN] transport publicRésumé : (auteur) Background : There has been an increased focus on active transport, but the measurement of active transport is still difficult and error-prone. Sensor data have been used to predict active transport. While heart rate data have very rarely been considered before, this study used random forests (RF) to predict transport modes using Global Positioning System (GPS), accelerometer, and heart rate data and paid attention to methodological issues related to the prediction strategy and post-processing.
Methods : The RECORD MultiSensor study collected GPS, accelerometer, and heart rate data over seven days from 126 participants living in the Ile-de-France region. RF models were built to predict transport modes for every minute (ground truth information on modes is from a GPS-based mobility survey), splitting observations between a Training dataset and a Test dataset at the participant level instead at the minute level. Moreover, several window sizes were tested for the post-processing moving average of the predicted transport mode.
Results : The minute-level prediction rate of being on trips vs. at a visited location was 90%. Final prediction rates of transport modes ranged from 65% for public transport to 95% for biking. Using minute-level observations from the same participants in the Training and Test sets (as RF spontaneously does) upwardly biases prediction rates. The inclusion of heart rate data improved prediction rates only for biking. A 3 to 5-min bandwidth moving average was optimum for a posteriori homogenization.
Conclusion : Heart rate only very slightly contributed to better predictions for specific transport modes. Moreover, our study shows that Training and Test sets must be carefully defined in RF models and that post-processing with carefully chosen moving average windows can improve predictions.Numéro de notice : A2022-077 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1186/s12942-022-00319-y Date de publication en ligne : 16/11/2022 En ligne : https://doi.org/10.1186/s12942-022-00319-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102445
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Yair Grinberger in Transactions in GIS, Vol 25 n° 2 (April 2021)PermalinkUne analyse de la couverture 5G francilienne avec PostGIS et QGIS / Anonyme in Géomatique expert, n° 134 (avril 2021)PermalinkA BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)PermalinkTélédétection et analyse spatiale avec QGIS et PostGIS / Anonyme in Géomatique expert, n° 134 (avril 2021)PermalinkA trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks / Bozhao Li in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)PermalinkUrban heat island formation in greater Cairo: Spatio-temporal analysis of daytime and nighttime land surface temperatures along the urban–rural gradient / Darshana Athukorala in Remote sensing, vol 13 n° 7 (April-1 2021)PermalinkUtilizing urban geospatial data to understand heritage attractiveness in Amsterdam / Sevim Sezi Karayazi in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)PermalinkSpatial analysis of subway passenger traffic in Saint-Petersburg / Tatiana Baltyzhakova in Geodesy and cartography, vol 47 n° 1 (January 2021)PermalinkRépartitions spatiale et temporelle des feux à Madagascar / Solofo Rakotondraompiana in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkUrban growth analysis and simulations using cellular automata and geo-informatics: comparison between Almaty and Astana in Kazakhstan / Aigerim Ilyassova in Geocarto international, vol 36 n° 5 ([15/03/2021])PermalinkGeographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships / Sensen Wu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkGraph convolutional autoencoder model for the shape coding and cognition of buildings in maps / Xiongfeng Yan in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkLandslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the indian Himalayan region: Recent developments, gaps, and future directions / Amit Batar in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkMachine learning in ground motion prediction / Farid Khosravikia in Computers & geosciences, vol 148 (March 2021)PermalinkModelling the effect of landmarks on pedestrian dynamics in urban environments / Gabriele Filomena in Computers, Environment and Urban Systems, vol 86 (March 2021)PermalinkSpatial multi-criteria evaluation in 3D context: suitability analysis of urban vertical development / Kendra Munn in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)PermalinkSuitability assessment of urban land use in Dalian, China using PNN and GIS / Ziqian Kang in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkSusceptibilité aux glissements de terrain dans la ville d’Al Hoceima et sa périphérie : application de la méthode de la théorie de l’évidence / Taoufik Byou in Geomatica, vol 75 n° 1 (Mars 2021)PermalinkUrban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkIntegrating runoff map of a spatially distributed model and thematic layers for identifying potential rainwater harvesting suitability sites using GIS techniques / Hamid Karimi in Geocarto international, vol 36 n° 3 ([15/02/2021])PermalinkAgricultural land partitioning model based on irrigation efficiency using a multi‐objective artificial bee colony algorithm / Mehrdad Bijandi in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkAn improved rainfall-threshold approach for robust prediction and warning of flood and flash flood hazards / Geraldo Moura Ramos Filho in Natural Hazards, Vol 105 n° 3 (February 2021)PermalinkA comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping / Zhice Fang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)Permalink