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A conceptual framework for developing dashboards for big mobility data / Lindsey Conrow in Cartography and Geographic Information Science, Vol 50 n° 5 (June 2023)  

Public [article]  inCartography and Geographic Information Science > Vol 50 n° 5 (June 2023) . - pp 495 - 514 Titre : A conceptual framework for developing dashboards for big mobility data Type de document : Article/Communication Auteurs : Lindsey Conrow, Auteur ; Cheng Fu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 495 - 514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cadre conceptuel [Termes IGN] données massives [Termes IGN] mobilité humaine [Termes IGN] tableau de bord Résumé : (auteur) Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets. Numéro de notice : A2023-236 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2023.2190164 Date de publication en ligne : 11/04/2023 En ligne : https://doi.org/10.1080/15230406.2023.2190164 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103595 [article]

A spatiotemporal data model and an index structure for computational time geography / Bi Yu Chen in International journal of geographical information science IJGIS, vol 37 n° 3 (March 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 3 (March 2023) . - pp 550 - 583 Titre : A spatiotemporal data model and an index structure for computational time geography Type de document : Article/Communication Auteurs : Bi Yu Chen, Auteur ; Yu-Bo Luo, Auteur ; Tao Jia, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 550 - 583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] approche hiérarchique [Termes IGN] données massives [Termes IGN] données spatiotemporelles [Termes IGN] modèle conceptuel de données spatio-temporelles [Termes IGN] requête spatiotemporelle [Termes IGN] stockage de données [Termes IGN] Time-geography Résumé : (auteur) The availability of Spatiotemporal Big Data has provided a golden opportunity for time geographical studies that have long been constrained by the lack of individual-level data. However, how to store, manage, and query a huge number of time geographic entities effectively and efficiently with complex spatiotemporal characteristics and relationships poses a significant challenge to contemporary GIS platforms. In this article, a hierarchical compressed linear reference (CLR) model is proposed to transform network-constrained time geographic entities from three-dimensional (3D) (x, y, t) space into two-dimensional (2D) space. Accordingly, time geographic entities can be represented as 2D spatial entities and stored in a classical spatial database. The proposed CLR model supports a hierarchical linear reference system (LRS) including not only underlying a link-based LRS but also multiple higher-level route-based LRSs. In addition, an LRS-based spatiotemporal index structure is developed to index both time geographic entities and the corresponding hierarchical network. The results of computational experiments on large datasets of space–time paths and prisms show that the proposed hierarchical CLR model is effective at storing and managing time geographic entities in road networks. The developed index structure achieves satisfactory query performance in milliseconds on large datasets of time geographic entities. Numéro de notice : A2023-153 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2128192 Date de publication en ligne : 03/10/2023 En ligne : https://doi.org/10.1080/13658816.2022.2128192 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102836 [article]

Understanding public perspectives on fracking in the United States using social media big data / Xi Gong in Annals of GIS, vol 29 n° 1 (January 2023)  

Public [article]  inAnnals of GIS > vol 29 n° 1 (January 2023) . - pp 21 - 35 Titre : Understanding public perspectives on fracking in the United States using social media big data Type de document : Article/Communication Auteurs : Xi Gong, Auteur ; Yujian Lu, Auteur ; Daniel Beene, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 21 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse socio-économique [Termes IGN] données issues des réseaux sociaux [Termes IGN] données massives [Termes IGN] enquête sociologique [Termes IGN] Etats-Unis [Termes IGN] fracturation [Termes IGN] hétérogénéité spatiale [Termes IGN] régression géographiquement pondérée [Termes IGN] Twitter Résumé : (auteur) People’s attitudes towards hydraulic fracturing (fracking) can be shaped by socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information. Existing research typically conducts surveys and interviews to study public attitudes towards fracking among a small group of individuals in a specific geographic area, where limited samples may introduce bias. Here, we compiled geo-referenced social media big data from Twitter during 2018–2019 for the entire United States to present a more holistic picture of people’s attitudes towards fracking. We used a multiscale geographically weighted regression (MGWR) to investigate county-level relationships between the aforementioned factors and percentages of negative tweets concerning fracking. Results indicate spatial heterogeneity and varying scales of those associations. Counties with higher median household income, larger African American populations, and/or lower educational level are less likely to oppose fracking, and these associations show global stationarity in all contiguous US counties. Eastern and Central US counties with higher unemployment rates, counties east of the Great Plains with less fracking sites nearby, and Western and Gulf Coast region counties with higher health insurance enrolments are more likely to oppose fracking activities. These three variables show clear East-West geographical divides in influencing public perspective on fracking. In counties across the southern Great Plains, negative attitudes towards fracking are less often vocalized on Twitter as the share of Republican voters increases. These findings have implications for both predicting public perspectives and needed policy adjustments. The methodology can also be conveniently applied to investigate public perspectives on other controversial topics. Numéro de notice : A2023-160 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2022.2121856 Date de publication en ligne : 10/09/2022 En ligne : https://doi.org/10.1080/19475683.2022.2121856 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102862 [article]

Identification of urban agglomeration spatial range based on social and remote-sensing data - For evaluating development level of urban agglomerations / Shuai Zhang in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 456 Titre : Identification of urban agglomeration spatial range based on social and remote-sensing data - For evaluating development level of urban agglomerations Type de document : Article/Communication Auteurs : Shuai Zhang, Auteur ; Hua Wei, Auteur Année de publication : 2022 Article en page(s) : n° 456 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] agglomération [Termes IGN] analyse spatiale [Termes IGN] Chine [Termes IGN] croissance urbaine [Termes IGN] données massives [Termes IGN] données socio-économiques [Termes IGN] éclairage public [Termes IGN] fusion de données [Termes IGN] image NPP-VIIRS [Termes IGN] point d'intérêt [Termes IGN] prise de vue nocturne [Termes IGN] segmentation d'image [Termes IGN] transformation en ondelettes Résumé : (auteur) The accurate identification of urban agglomeration spatial area is helpful in understanding the internal spatial relationship under urban expansion and in evaluating the development level of urban agglomeration. Previous studies on the identification of spatial areas often ignore the functional distribution and development of urban agglomerations by only using nighttime light data (NTL). In this study, a new method is firstly proposed to identify the accurate spatial area of urban agglomerations by fusing night light data (NTL) and point of interest data (POI); then an object-oriented method is used by this study to identify the spatial area, finally the identification results obtained by different data are verified. The results show that the accuracy identified by NTL data is 82.90% with the Kappa coefficient of 0.6563, the accuracy identified by POI data is 81.90% with the Kappa coefficient of 0.6441, and the accuracy after data fusion is 90.70%, with the Kappa coefficient of 0.8123. The fusion of these two kinds of data has higher accuracy in identifying the spatial area of urban agglomeration, which can play a more important role in evaluating the development level of urban agglomeration; this study proposes a feasible method and path for urban agglomeration spatial area identification, which is not only helpful to optimize the spatial structure of urban agglomeration, but also to formulate the spatial development policy of urban agglomeration. Numéro de notice : A2022-645 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080456 Date de publication en ligne : 21/08/2022 En ligne : https://doi.org/10.3390/ijgi11080456 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101461 [article]

Interactive visual analytics of moving passenger flocks using massive smart card data / Tong Zhang in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)  

Public [article]  inCartography and Geographic Information Science > Vol 49 n° 4 (July 2022) . - pp 354 - 369 Titre : Interactive visual analytics of moving passenger flocks using massive smart card data Type de document : Article/Communication Auteurs : Tong Zhang, Auteur ; Wei He, Auteur ; Jing Huang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 354 - 369 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatiale [Termes IGN] analyse visuelle [Termes IGN] carte à puce [Termes IGN] données massives [Termes IGN] mobilité urbaine [Termes IGN] objet mobile [Termes IGN] Shenzhen [Termes IGN] trajet (mobilité) [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Understanding urban mobility patterns is constrained by our limited capabilities to extract and visualize spatio-temporal regularities from large amounts of mobility data. Moving flocks, defined as groups of people traveling along over a pre-defined time duration, can reveal collective moving patterns at aggregated spatio-temporal scales, thereby facilitating the discovery of urban mobility structure and travel demand patterns. In this study, we extend classical trajectory-oriented flock mining algorithms to discover moving flocks of transit passengers, accounting for the constraints of multi-modal transit networks. We develop a map-centered visual analytics approach by integrating the flock mining algorithm with interactive visualization designs of discovered flocks. Novel interactive visualizations are designed and implemented to support the exploration and analyses of discovered moving flocks at different spatial and temporal scales. The visual analytics approach is evaluated using a real-world smart card dataset collected in Shenzhen City, China, validating its applicability in capturing and mapping dynamic mobility patterns over a large metropolitan area. Numéro de notice : A2022-480 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2022.2039775 Date de publication en ligne : 09/03/2022 En ligne : https://doi.org/10.1080/15230406.2022.2039775 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100886 [article]

Analysis of massive imports of open data in Openstreetmap database: a study case for France / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

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Emerging 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)  

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Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning / Jiayu Wu in Computers, Environment and Urban Systems, vol 91 (January 2022)  

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Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

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Learning multi-view aggregation in the wild for large-scale 3D semantic segmentation / Damien Robert (2022)  

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Révision de la chaîne de valorisation des données en système d’information décisionnel / Quentin Courtiade (2022) 

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La 3D dans tous ses états [à Cergy-Pontoise] / Marielle Mayo in Géomètre, n° 2197 (décembre 2021)

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Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions / Haosheng Huang in Computers, Environment and Urban Systems, vol 90 (November 2021)  

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Mobile mapping et PCRS / Clément Benoît in Géomatique expert, n° 136 (novembre - décembre 2021)

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The geography of social media data in urban areas: Representativeness and complementarity / Alvaro Bernabeu-Bautista in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)  

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