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Exploring 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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2475 -2 496 Titre : Exploring the heterogeneity of human urban movements using geo-tagged tweets Type de document : Article/Communication Auteurs : Ding Ma, Auteur ; Toshihiro Osaragi, Auteur ; Takuya Oki, Auteur ; Bin Jiang, Auteur Année de publication : 2020 Article en page(s) : pp 2475 -2 496 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] flux de données [Termes descripteurs IGN] géoétiquetage [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] hétérogénéité [Termes descripteurs IGN] Londres [Termes descripteurs IGN] migration humaine [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle orienté agent [Termes descripteurs IGN] population urbaine [Termes descripteurs IGN] Tokyo (Japon) [Termes descripteurs IGN] Twitter Résumé : (auteur) The availability of vast amounts of location-based data from social media platforms such as Twitter has enabled us to look deeply into the dynamics of human movement. The aim of this paper is to leverage a large collection of geo-tagged tweets and the street networks of two major metropolitan areas—London and Tokyo—to explore the underlying mechanism that determines the heterogeneity of human mobility patterns. For the two target cities, hundreds of thousands of tweet locations and road segments were processed to generate city hotspots and natural streets. User movement trajectories and city hotspots were then used to build a hotspot network capable of quantitatively characterizing the heterogeneous movement patterns of people within the cities. To emulate observed movement patterns, the study conducts a two-level agent-based simulation that includes random walks through the hotspot networks and movements in the street networks using each of three distance types—metric, angular and combined. Comparisons of the simulated and observed movement flows at the segment and street levels show that the heterogeneity of human urban movements at the collective level is mainly shaped by the scaling structure of the urban space. Numéro de notice : A2020-692 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1718153 date de publication en ligne : 24/01/2020 En ligne : https://doi.org/10.1080/13658816.2020.1718153 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96233 [article]

Streets 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)  

Public [article]  inComputers, Environment and Urban Systems > vol 84 (November 2020) . - n° 101524 Titre : Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city Type de document : Article/Communication Auteurs : Azam Raha Bahrehdar, Auteur ; Benjamin Adams, Auteur ; Ross S. Purves, Auteur Année de publication : 2020 Article en page(s) : n° 101524 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] image Flickr [Termes descripteurs IGN] interactivité [Termes descripteurs IGN] Londres [Termes descripteurs IGN] métadonnées [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] orthoimage géoréférencée [Termes descripteurs IGN] perception [Termes descripteurs IGN] segmentation sémantique Résumé : (auteur) In his classic book “The Image of the City” Kevin Lynch used empirical work to show how different elements of the city were perceived: such as paths, landmarks, districts, edges, and nodes. Streets, by providing paths from which cities can be experienced, were argued to be one of the key elements of cities. Despite this long standing empirical basis, and the importance of Lynch's model in policy associated areas such as planning, work with user generated content has largely ignored these ideas. In this paper, we address this gap, using streets to aggregate filtered user generated content related to more than 1 million images and 60,000 individuals and explore similarity between more than 3000 streets in London across three dimensions: user behaviour, time and semantics. To perform our study we used two different sources of user generated content: (1) a collection of metadata attached to Flickr images and (2) street network of London from OpenStreetMap. We first explore global patterns in the distinctiveness and spatial autocorrelation of similarity using our three dimensions, establishing that the semantic and user dimensions in particular allow us to explore the city in different ways. We then used a Processing tool to interactively explore individual patterns of similarity across these four dimensions simultaneously, presenting results here for four selected and contrasting locations in London. Before drilling into the data to interpret in more detail, the identified patterns demonstrate that streets are natural units capturing perception of cities not only as paths but also through the emergence of other elements of the city proposed by Lynch including districts, landmarks and edges. Our approach also demonstrates how user generated content can be captured, allowing bottom-up perception from citizens to flow into a representation. Numéro de notice : A2020-710 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101524 date de publication en ligne : 05/08/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101524 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96255 [article]

Combined InSAR and terrestrial structural monitoring of bridges / Sivasakthy Selvakumaran in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 7141 - 7153 Titre : Combined InSAR and terrestrial structural monitoring of bridges Type de document : Article/Communication Auteurs : Sivasakthy Selvakumaran, Auteur ; Cristian Rossi, Auteur ; Andrea Marinoni, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7141 - 7153 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] coin réflecteur [Termes descripteurs IGN] données multisources [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] interféromètrie par radar à antenne synthétique [Termes descripteurs IGN] Londres [Termes descripteurs IGN] pont [Termes descripteurs IGN] surveillance d'ouvrage [Termes descripteurs IGN] tachéomètre électronique Résumé : (auteur) This article examines advances in interferometric synthetic aperture radar (InSAR) satellite measurement technologies to understand their relevance, utilization, and limitations for bridge monitoring. Waterloo Bridge is presented as a case study to explore how InSAR data sets can be combined with traditional measurement techniques including sensors installed on the bridge and automated total stations. A novel approach to InSAR bridge monitoring was adopted by the installation of physical reflectors at key points of structural interest on the bridge, in order to supplement the bridge’s own reflection characteristics and ensure that the InSAR measurements could be directly compared and combined with in situ measurements. The interpretation and integration of InSAR data sets with civil infrastructure data are more than a trivial task, and a discussion of uncertainty of measurement data is presented. Finally, a strategy for combining and interpreting varied data from multiple sources to provide useful insights into each of these methods is presented, outlining the practical applications of this data analysis to support wider monitoring strategies. Numéro de notice : A2020-588 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2979961 date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2979961 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95916 [article]

A name‐led approach to profile urban places based on geotagged Twitter data / Juntao Lai in Transactions in GIS, Vol 24 n° 4 (August 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 4 (August 2020) . - 22 p. Titre : A name‐led approach to profile urban places based on geotagged Twitter data Type de document : Article/Communication Auteurs : Juntao Lai, Auteur ; Guy Lansley, Auteur ; James Haworth, Auteur ; Tao Cheng, Auteur Année de publication : 2020 Article en page(s) : 22 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] Foursquare [Termes descripteurs IGN] Londres [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] réseau social géodépendant [Termes descripteurs IGN] site urbain [Termes descripteurs IGN] toponyme [Termes descripteurs IGN] Twitter Résumé : (auteur) Place is a concept that is fundamental to how we orientate and communicate space in our everyday lives. Crowdsourced social media data present a valuable opportunity to develop bottom‐up inferences of places that are integral to social activities and settings. Conventional location‐led approaches use a predefined spatial unit to associate data and space with places, which cannot capture the richness of urban places (i.e., spatial extents and their dynamic functions). This article develops a name‐led framework to overcome these limitations in using social media data to study urban places. The framework first derives place names from georeferenced Twitter data combining text mining and spatial point pattern analysis, then estimates the spatial extents by spatial clustering, and further extracts their dynamic functions with time, which makes up a complete place profile. The framework is tested on a case study in Camden Borough, London and the results are evaluated through comparisons to the Foursquare point of interest data. This name‐led approach enables the shift from space‐based analysis to place‐based analysis of urban space. Numéro de notice : A2020-670 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12599 date de publication en ligne : 05/12/2019 En ligne : https://doi.org/10.1111/tgis.12599 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96155 [article]

Estimating 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)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 88 (June 2020) . - 10 p. Titre : Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data Type de document : Article/Communication Auteurs : Rochelle Schneider dos Santos, Auteur Année de publication : 2020 Article en page(s) : 10 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme du gradient [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] chaleur [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] Londres [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] mortalité [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] politique publique [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] santé [Termes descripteurs IGN] station météorologique [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] température de l'air [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Urbanisation generates greater population densities and an increase in anthropogenic heat generation. These factors elevate the urban–rural air temperature (Ta) difference, thus generating the Urban Heat Island (UHI) phenomenon. Ta is used in the fields of public health and epidemiology to quantify deaths attributable to heat in cities around the world: the presence of UHI can exacerbate exposure to high temperatures during summer periods, thereby increasing the risk of heat-related mortality. Measuring and monitoring the spatial patterns of Ta in urban contexts is challenging due to the lack of a good network of weather stations. This study aims to produce a parsimonious model to retrieve maximum Ta (Tmax) at high spatio-temporal resolution using Earth Observation (EO) satellite data. The novelty of this work is twofold: (i) it will produce daily estimations of Tmax for London at 1 km2 during the summertime between 2006 and 2017 using advanced statistical techniques and satellite-derived predictors, and (ii) it will investigate for the first time the predictive power of the gradient boosting algorithm to estimate Tmax for an urban area. In this work, 6 regression models were calibrated with 6 satellite products, 3 geospatial features, and 29 meteorological stations. Stepwise linear regression was applied to create 9 groups of predictors, which were trained and tested on each regression method. This study demonstrates the potential of machine learning algorithms to predict Tmax: the gradient boosting model with a group of five predictors (land surface temperature, Julian day, normalised difference vegetation index, digital elevation model, solar zenith angle) was the regression model with the best performance (R² = 0.68, MAE = 1.60 °C, and RMSE = 2.03 °C). This methodological approach is capable of being replicated in other UK cities, benefiting national heat-related mortality assessments since the data (provided by NASA and the UK Met Office) and programming languages (Python) sources are free and open. This study provides a framework to produce a high spatio-temporal resolution of Tmax, assisting public health researchers to improve the estimation of mortality attributable to high temperatures. In addition, the research contributes to practice and policy-making by enhancing the understanding of the locations where mortality rates may increase due to heat. Therefore, it enables a more informed decision-making process towards the prioritisation of actions to mitigate heat-related mortality amongst the vulnerable population. Numéro de notice : A2020-448 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2020.102066 date de publication en ligne : 10/02/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102066 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95524 [article]

Street-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)  

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Graph-based matching of points-of-interest from collaborative geo-datasets / Tessio Novack in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)  

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Extraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos / Yu Feng in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)  

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Into the woods / Sandrine Paradis-Grenouillet (2018)  

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A GIS approach to exploring monetary value on enclosure era property-related maps / Christopher Macdonald Hewitt in Cartographic journal (the), Vol 54 n° 2 (May 2017)  

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Etude de l'impact d'un projet de développement sur les propriétés avoisinantes / Sylvain Jourdan (2017) 

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Geo-temporal Twitter demographics / Paul A. Longley in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)  

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Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing / Jerome O’Connell in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)  

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The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns / Nick Malleson in Cartography and Geographic Information Science, Vol 42 n° 2 (April 2015)  

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Environmental, spatial and temporal drivers of plant community composition in British forest habitat / Adam Robert Kimberley (2015)  

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