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Study on city digital twin technologies for sustainable smart city design: A review and bibliometric analysis of geographic information system and building information modeling integration / Haishan Xia in Sustainable Cities and Society, vol 84 (September 2022)
[article]
Titre : Study on city digital twin technologies for sustainable smart city design: A review and bibliometric analysis of geographic information system and building information modeling integration Type de document : Article/Communication Auteurs : Haishan Xia, Auteur ; Zishuo Liu, Auteur ; Maria Efremochkina, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104009 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] bibliométrie
[Termes IGN] CityGML
[Termes IGN] format Industry foudation classes IFC
[Termes IGN] intégration de données
[Termes IGN] jumeau numérique
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] ontologie
[Termes IGN] planification urbaine
[Termes IGN] système d'information géographique
[Termes IGN] ville durable
[Termes IGN] ville intelligenteRésumé : (auteur) Geographic information system (GIS) data provide geospatial data on cities and spatial analysis functions that are essential for urban design. Building information modeling (BIM) includes a digital entity of construction, a passive presentation of micro-digital information on real entities, and an active application of models in the entire life cycle realization of the architecture, engineering, and construction industries. A combination of these technologies could provide a core technology for the urban digital twin to support sustainable smart city design. Through an insightful literature review, this paper summarizes the different disciplinary classifications of GIS and BIM functional integration, distills the value of data, and discusses the ontology-based data integration approach that GIS and BIM should take in the future to conduct research on integration applications in smart cities. To verify this view, keyword analysis, co-country analysis, and co-citation and coupling analyses are conducted using CiteSpace. GIS and BIM integration has attracted much attention. However, a professional disconnect and fragmented composition pose challenges in the field of GIS and BIM integration. Future research should focus on smart city planning, updating, management; ontology-based GIS and BIM data integration platform; and operation; and the collaborative management of urban rail transportation engineering. Numéro de notice : A2022-543 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104009 Date de publication en ligne : 18/06/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101118
in Sustainable Cities and Society > vol 84 (September 2022) . - n° 104009[article]Smart city data science: Towards data-driven smart cities with open research issues / Iqbal H. Sarker in Internet of Things, vol 19 (August 2022)
[article]
Titre : Smart city data science: Towards data-driven smart cities with open research issues Type de document : Article/Communication Auteurs : Iqbal H. Sarker, Auteur Année de publication : 2022 Article en page(s) : n° 100528 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] gestion urbaine
[Termes IGN] internet des objets
[Termes IGN] planification urbaine
[Termes IGN] science des données
[Termes IGN] sécurité
[Termes IGN] télédétection
[Termes IGN] ville intelligenteRésumé : (auteur) Cities are undergoing huge shifts in technology and operations in recent days, and ‘data science’ is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting useful knowledge or actionable insights from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the scientific study and analysis of actual happenings with historical data using a variety of scientific methodologies, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore “Smart City Data Science”, where city data collected from various sources such as sensors, Internet-connected devices, or other external sources, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, artificial intelligence, particularly, machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world city services. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, industry professionals, as well as policy-makers of a country, particularly, from the technological point of view. Numéro de notice : A2022-383 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1016/j.iot.2022.100528 Date de publication en ligne : 20/04/2022 En ligne : https://doi.org/10.1016/j.iot.2022.100528 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100660
in Internet of Things > vol 19 (August 2022) . - n° 100528[article]A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)
[article]
Titre : A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method Type de document : Article/Communication Auteurs : Yongyang Xu, Auteur ; Bo Zhou, Auteur ; Shuai Jin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] arbre aléatoire minimum
[Termes IGN] distribution spatiale
[Termes IGN] noeud
[Termes IGN] Pékin (Chine)
[Termes IGN] planification urbaine
[Termes IGN] point d'intérêt
[Termes IGN] réseau neuronal de graphes
[Termes IGN] taxinomie
[Termes IGN] trafic routier
[Termes IGN] triangulation de Delaunay
[Termes IGN] utilisation du sol
[Termes IGN] zone urbaineRésumé : (auteur) Land-use classification plays an important role in urban planning and resource allocation and had contributed to a wide range of urban studies and investigations. With the development of crowdsourcing technology and map services, points of interest (POIs) have been widely used for recognizing urban land-use types. However, current research methods for land-use classifications have been limited to extracting the spatial relationship of POIs in research units. To close this gap, this study uses a graph-based data structure to describe the POIs in research units, with graph convolutional networks (GCNs) being introduced to extract the spatial context and urban land-use classification. First, urban scenes are built by considering the spatial context of POIs. Second, a graph structure is used to express the scenes, where POIs are treated as graph nodes. The spatial distribution relationship of POIs is considered to be the graph's edges. Third, a GCN model is designed to extract the spatial context of the scene by aggregating the information of adjacent nodes within the graph and urban land-use classification. Thus, the land-use classification can be treated as a classification on a graphic level through deep learning. Moreover, the POI spatial context can be effectively extracted during classification. Experimental results and comparative experiments confirm the effectiveness of the proposed method. Numéro de notice : A2022-460 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101807 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101807 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100622
in Computers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101807[article]Detecting spatiotemporal traffic events using geosocial media data / Shishuo Xu in Computers, Environment and Urban Systems, vol 94 (June 2022)
[article]
Titre : Detecting spatiotemporal traffic events using geosocial media data Type de document : Article/Communication Auteurs : Shishuo Xu, Auteur ; Songnian Li, Auteur ; Wei Huang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101797 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse de groupement
[Termes IGN] base de données d'objets mobiles
[Termes IGN] base de données spatiotemporelles
[Termes IGN] détection d'événement
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données spatiotemporelles
[Termes IGN] planification urbaine
[Termes IGN] sécurité routière
[Termes IGN] Toronto
[Termes IGN] trafic routier
[Termes IGN] TwitterRésumé : (auteur) Social media platforms enable efficient traffic event detection by allowing users to produce geo-tagged content (e.g., tweets) known as geosocial media data. Geosocial media data improve road safety by providing timely updates for traffic flow and traffic control. Recent studies on traffic event detection with geosocial media data have been focused around keyword-based query approaches, where the event content was inferred by predetermined categories, to retrieve relevant traffic events. Spatiotemporal features associated with traffic-related posts have not been fully investigated. In this study, we filtered irrelevant posts with association rules. A spatiotemporal clustering-based method was then used to retrieve traffic events from these filtered posts, where the content of detected events was automatically inferred with a set of representative terms. For comparison, a typical text classification-based method was also used by classifying the posts filtered from association rules into different categories. By validating the detection results with vehicle travel speed data, we demonstrate that the former outperforms the latter in terms of the number of correctly detected traffic events from one-year of Twitter data in Toronto, Canada. Our proposed approach helps organizations and governments to be aware of when and where traffic events occur by identifying event hotspots and peak periods, which improves both traffic management and urban planning. Numéro de notice : A2022-264 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101797 Date de publication en ligne : 26/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101797 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100261
in Computers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101797[article]A geospatial workflow for the assessment of public transit system performance using near real-time data / Anastassios Dardas in Transactions in GIS, vol 26 n° 4 (June 2022)
[article]
Titre : A geospatial workflow for the assessment of public transit system performance using near real-time data Type de document : Article/Communication Auteurs : Anastassios Dardas, Auteur ; Brent Hall, Auteur ; Jon Salter, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1642 - 1664 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] ArcGIS
[Termes IGN] Calgary
[Termes IGN] collecte de données
[Termes IGN] données spatiotemporelles
[Termes IGN] itinéraire
[Termes IGN] planification urbaine
[Termes IGN] Python (langage de programmation)
[Termes IGN] stockage de données
[Termes IGN] temps réel
[Termes IGN] trafic routier
[Termes IGN] transport public
[Termes IGN] WebSIGRésumé : (auteur) This article presents the development of a Geographical Information Systems (GIS) workflow that harvests high-volume and high-frequency near real-time data from a public General Transit Feed Specification (GTFS) and calculates metrics for the assessment of on-time and route speed performance for a public transit system. The approach is applied to near real-time and static GTFS data collected over a 9-month period for the City of Calgary, Alberta, Canada. The workflow uses two Azure Virtual Machines (VMs), one to harvest the data and the other to process observations in parallel using Python and the ArcGIS API libraries. A Web GIS application is described that queries data from MongoDB to visualize the performance results in spatiotemporal form. The purpose of the workflow and Web GIS application is to provide actionable information to transit planners to improve public transportation systems. The data management and analysis workflow is transferable to similar GTFS data from other cities. Numéro de notice : A2022-531 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : sans Date de publication en ligne : 02/05/2022 En ligne : https://doi.org/10.1111/tgis.12942 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101078
in Transactions in GIS > vol 26 n° 4 (June 2022) . - pp 1642 - 1664[article]Multi-objective optimization of urban environmental system design using machine learning / Peiyuan Li in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkThe promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning / Elie Morin in Ecological indicators, vol 139 (June 2022)PermalinkGreen infrastructure planning through EO and GIS analysis: the canopy plan of Liège, Belgium, to mitigate its urban heat island / Benjamin Beaumont in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)PermalinkExploring the association between street built environment and street vitality using deep learning methods / Yunqin Li in Sustainable Cities and Society, vol 79 (April 2022)PermalinkSpatial modeling of migration using GIS-based multi-criteria decision analysis: A case study of Iran / Naeim Mijani in Transactions in GIS, vol 26 n° 2 (April 2022)PermalinkUnravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata / Aditya Tafta Nugraha in Computers, Environment and Urban Systems, vol 92 (March 2022)PermalinkUsing street view images to identify road noise barriers with ensemble classification model and geospatial analysis / Kai Zhang in Sustainable Cities and Society, vol 78 (March 2022)PermalinkSimulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: a case of Mumbai India / Vaibhav Kumar in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkEmerging technologies for smart cities’ transportation: Geo-information, data analytics and machine learning approaches / Li-Minn Ang in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)PermalinkRaw GIS to 3D road modeling for real-time traffic simulation / Yacine Amara in The Visual Computer, vol 38 n° 1 (January 2022)Permalink