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Modelling the effect of landmarks on pedestrian dynamics in urban environments / Gabriele Filomena in Computers, Environment and Urban Systems, vol 86 (March 2021)
[article]
Titre : Modelling the effect of landmarks on pedestrian dynamics in urban environments Type de document : Article/Communication Auteurs : Gabriele Filomena, Auteur ; Judith A. Verstegen, Auteur Année de publication : 2021 Article en page(s) : n° 101573 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] carte cognitive
[Termes IGN] itinéraire piétionnier
[Termes IGN] Londres
[Termes IGN] milieu urbain
[Termes IGN] modèle de simulation
[Termes IGN] modèle orienté agent
[Termes IGN] navigation pédestre
[Termes IGN] point de repèreRésumé : (auteur) Landmarks have been identified as relevant and prominent urban elements, explicitly involved in human navigation processes. Despite the understanding accumulated around their functions, landmarks have not been included in simulation models of pedestrian movement in urban environments. In this paper, we describe an Agent-Based Model (ABM) for pedestrian movement simulation that incorporates the role of on-route and distant landmarks in agents' route choice behaviour. Route choice models with and without landmarks were compared by using four scenarios: road distance minimisation, least cumulative angular change, road distance minimisation and landmarks, least cumulative angular change and landmarks. The city centre of London was used as a case study and a set of GPS trajectories was employed to evaluate the model. The introduction of landmarks led to more heterogeneous patterns that diverge from the minimisation models. Landmark-based navigation brought about high pedestrian volumes along the river (up to 13% of agents) and the boundaries of the parks (around 8% of the agents). Moreover, the model evaluation showed that the results of the landmark-based scenarios were not significantly different from the GPS trajectories in terms of cumulative landmarkness, whereas the other scenarios were. This implies that our proposed landmark-based route choice approach was better able to reproduce human navigation. At the street-segment level, the pedestrian volumes emerging from the scenarios were comparable to the trajectories' volumes in most of the case study area; yet, under- and over-estimation were observed along the banks of the rivers and across green areas (up to +7%, −11% of volumes) in the landmark-based scenarios, and along major roads (up to +11% of volumes) in the least cumulative angular change scenario. While our model could be expanded in relation to the agents' cognitive representation of the environment, e.g. by considering other relevant urban elements and accounting for individual spatial knowledge differences, the inclusion of landmarks in route choice models results in more plausible agents that make use of relevant urban information. Numéro de notice : A2021-118 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2020.101573 Date de publication en ligne : 13/01/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101573 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96943
in Computers, Environment and Urban Systems > vol 86 (March 2021) . - n° 101573[article]Topological integration of BIM and geospatial water utility networks across the building envelope / Thomas Gilbert in Computers, Environment and Urban Systems, vol 86 (March 2021)
[article]
Titre : Topological integration of BIM and geospatial water utility networks across the building envelope Type de document : Article/Communication Auteurs : Thomas Gilbert, Auteur ; Philip James, Auteur ; Luke Smith, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101570 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] analyse multicritère
[Termes IGN] connexité (topologie)
[Termes IGN] empreinte
[Termes IGN] format Industry foudation classes IFC
[Termes IGN] infrastructure urbaine de données localisées
[Termes IGN] intégration de données
[Termes IGN] méthode heuristique
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] réseau de distribution d'eau
[Termes IGN] Royaume-UniRésumé : (auteur) Utility networks comprise a fundamental part of our complex urban systems and the integration of digital representations of these networks across multiple spatial scales can be used to help address priority challenges. Deteriorating water utility infrastructure and low routing redundancy result in network fragility and thus supply outages when assets fail. Water distribution network configurations can be optimised for higher resilience but digital representations of the networks used for simulations and analyses are not integrated with the finer scale networks inside buildings. This integration is hindered by differences in conceptualisation and semantics employed by the relevant data standards. We suggest that the geospatial and geometric data contained in Building Information Modelling (BIM) and water distribution network (WDN) models can be used for their integration; and that this supports the use cases of optimising dynamic network partitioning, reducing the risk of underground utility strikes and planning for future network configurations with higher topological redundancy. In this study, we develop and demonstrate the application of a weight-based spatial algorithm for inferring water network connections between urban-scale WDNs and BIM models, showing that spatial data can be used in the absence of complete or consistent semantic representations. We suggest that the method has potential for transferability to infrastructure for other utility resources (such as waste water, electricity and gas) and make recommendations such as standardising the representation of connection points between disjoint utility network models and extending the normal practical spatial remit of BIM MEP modelling to encompass the space between buildings and WDNs. Numéro de notice : A2021-119 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2020.101570 Date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101570 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96944
in Computers, Environment and Urban Systems > vol 86 (March 2021) . - n° 101570[article]Change detection of land use and land cover, using landsat-8 and sentinel-2A images / Mohammed Abdulmohsen Alhedyan (2021)
Titre : Change detection of land use and land cover, using landsat-8 and sentinel-2A images Type de document : Thèse/HDR Auteurs : Mohammed Abdulmohsen Alhedyan, Auteur Editeur : Leicester [Royaume-Uni] : University of Leicester Année de publication : 2021 Importance : 228 p. Format : 21 x 30 cm Note générale : bibliographie
Thesis submitted for the degree of PhD at the University of LeicesterLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse vectorielle
[Termes IGN] Arabie Saoudite
[Termes IGN] Corine (base de données)
[Termes IGN] détection de changement
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] Royaume-Uni
[Termes IGN] utilisation du solRésumé : (auteur) The main theme of this research is the development of a new hybrid method for change detection of land use and land cover (LULC). LULC change detection is one of most widely used applications of remote sensing. This study used data from two different optical sensors, Landsat-8 images and Sentinel-2A images. Given the newly developed capabilities of these remote sensing satellites, it was necessary to devise appropriate techniques to realise the benefits that they offer. Therefore, three effective change detection methods have been tested, comprehensively analysed, and used to inform the design and development of a new hybrid method of change detection. The studied change detection methods were change vector analysis (CVA), multi-index integrated change analysis (MIICA), and the comprehensive change detection method (CCDM). Case studies were conducted in two regions, Bristol (United Kingdom) and Hail (Saudi Arabia), to provide sufficient variety of inputs to enable the response of more LULC varieties to be recorded. Finally, the Coordination of Information on the Environment (Corine) land cover scheme was used to identify land cover types and LULC changes. In the study area of Bristol, the new hybrid change detection method achieved an overall accuracy of 90% and 0.81 kappa, while the results for the study area of Hail were 74% overall accuracy and 0.40 kappa. The change detection results obtained by the new hybrid method constitute a significant improvement over the implementation of the existing CVA, MIICA and CCDM methods at the two study areas while using Landsat-8 and Sentinel-2A images. Note de contenu : 1- Introduction
2- Literature review
3- Classification system, study areas, data sources and data preparation process
4- Evaluation of existing change detection
5- The hybrid change detection method
6- Discussion
7- ConclusionNuméro de notice : 28466 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : Leicester : Geography, Geology, and Environment : 2021 DOI : 10.25392/leicester.data.16988440.v1 En ligne : https://doi.org/10.25392/leicester.data.16988440.v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99094
Titre : Learning digital geographies through geographical artificial intelligence Type de document : Thèse/HDR Auteurs : Pengyuan Liu, Auteur ; Stefano de Sabbata, Directeur de thèse ; Yu-Dong Zhang, Directeur de thèse Editeur : Leicester [Royaume-Uni] : University of Leicester Année de publication : 2021 Importance : 199 p. Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy, Geology and EnvironmentLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse socio-économique
[Termes IGN] apprentissage profond
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] croissance urbaine
[Termes IGN] détection de changement
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] données spatiotemporelles
[Termes IGN] géomatique web
[Termes IGN] intelligence artificielle
[Termes IGN] Londres
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau sémantique
[Termes IGN] système d'information urbain
[Termes IGN] zone urbaineIndex. décimale : THESE Thèses et HDR Résumé : (auteur) As the distinction between online and physical spaces rapidly degrades, digital platforms have become an integral component of how people’s everyday experiences are mediated. User-generated content (UGC) shared on such platforms provides insights into how users want to represent their everyday lives, which augments and reinforces our understanding of local communities through time and layers dynamic information across and over the geographic space. Inspired by the development of the newly arisen scientific disciplines within geography: geographical artificial intelligence (GeoAI), this thesis adopts deep learning approaches on graph representations of human dynamics illustrated through geotagged UGC to explore how place representations are augmented and reinforced through users’ spatial experiences by classifying their multimedia activities and identifying the spatial clusters of UGC at the urban scale. Having the place representations described through UGC, this thesis explores how these representations can be used in conjunction with various official spatial statistics to understand and predict the dynamic changes of the socio-economic characteristics of places. The principal contributions of this thesis are: (1) to provide frameworks with higher classification and prediction accuracy but requiring fewer sample data; thus, contributing to an advanced framework to summarise spatial characteristics of places; (2) to show that multimedia content provides rich information regarding places, the use of space, and people’s experience of the landscape; thus, benefiting a better understanding of place representations; (3) to illustrate that the spatial patterns of UGC can be adopted as a valuable proxy to understand urban development and neighbourhood change; (4) to reinforce the concept that Spatial is Special. Spatial processes are commonly spatially autocorrelated. The mainstream of machine learning methods do not explicitly incorporate the spatial or spatio-temporal component to address such a speciality of spatial data. This thesis highlights the importance of explicitly incorporating spatial or spatio-temporal components in geographical analysis models. Note de contenu : 1- Introduction
2- Towards quantitative digital geographies: Concepts, research and implications
3- Data and methods
4- Classification learning through a graph-based semi-supervised approach
5- Location estimation of social media content through a graph-based linkPrediction
6- Urban change modelling with spatial knowledge graphs
7- DiscussionNuméro de notice : 28629 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD Thesis: Geology and Environment: Leicester : 2021 DOI : sans En ligne : https://leicester.figshare.com/articles/thesis/Learning_Digital_Geographies_thro [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99618 Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London / Nilufer Sari Aslam in Annals of GIS, vol 27 n° 1 (January 2021)
[article]
Titre : Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London Type de document : Article/Communication Auteurs : Nilufer Sari Aslam, Auteur ; Di Zhu, Auteur ; Tao Cheng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 29 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] carte à puce
[Termes IGN] collecte de données
[Termes IGN] données socio-économiques
[Termes IGN] données spatiotemporelles
[Termes IGN] enrichissement sémantique
[Termes IGN] loisir
[Termes IGN] Londres
[Termes IGN] méthode heuristique
[Termes IGN] planification urbaine
[Termes IGN] point d'intérêt
[Termes IGN] transport urbainRésumé : (auteur) The large volume of data automatically collected by smart card fare systems offers a rich source of information regarding daily human activities with a high resolution of spatial and temporal representation. This provides an opportunity for aiding transport planners and policy-makers to plan transport systems and cities more responsively. However, there are currently limitations when it comes to understanding the secondary activities of individual commuters. Accordingly, in this paper, we propose a framework to detect and infer secondary activities from individuals’ daily travel patterns from the smart card data and reduce the use of conventional surveys. First, we proposed a ‘heuristic secondary activity identification algorithm’, which uses commuters’ primary locations (home & work) and the direction (from & to) information to identify secondary activities for individuals. The algorithm provides a high-level classification of the activity types as before-work, midday and after-work activity patterns of individuals. Second, this classification is semantically enriched using Points of Interests to provide meaningful insights into individuals’ travel purposes and mobility in an urban environment. Lastly, using the transit data of London as a case study, the model is compared with a volunteer survey to demonstrate its effectiveness and offering a cost-effective method to travel demand research. Numéro de notice : A2021-319 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2020.1783359 Date de publication en ligne : 01/08/2020 En ligne : https://doi.org/10.1080/19475683.2020.1783359 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97550
in Annals of GIS > vol 27 n° 1 (January 2021) . - pp 29 - 41[article]The potential of LiDAR and UAV-photogrammetric data analysis to interpret archaeological sites: A case study of Chun Castle in South-West England / Israa Kadhim in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)PermalinkExploring the heterogeneity of human urban movements using geo-tagged tweets / Ding Ma in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)PermalinkDecentralized markets and the emergence of housing wealth inequality / Omar A. Guerrero in Computers, Environment and Urban Systems, vol 84 (November 2020)PermalinkStreets of London: Using Flickr and OpenStreetMap to build an interactive image of the city / Azam Raha Bahrehdar in Computers, Environment and Urban Systems, vol 84 (November 2020)PermalinkCombined InSAR and terrestrial structural monitoring of bridges / Sivasakthy Selvakumaran in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)PermalinkUrban flooding in Britain: an approach to comparing ancient and contemporary flood exposure / T.E. O'Shea in Natural Hazards, Vol 104 n° 1 (October 2020)PermalinkA name‐led approach to profile urban places based on geotagged Twitter data / Juntao Lai in Transactions in GIS, Vol 24 n° 4 (August 2020)PermalinkWhat influences the long-term development of mixtures in British forests? / William L. Mason in Forestry, an international journal of forest research, vol 93 n° 4 (July 2020)PermalinkEstimating 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)PermalinkStreet-Frontage-Net: urban image classification using deep convolutional neural networks / Stephen Law in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)Permalink