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Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility / Qingqing Chen in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1425 - 1448 Titre : Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility Type de document : Article/Communication Auteurs : Qingqing Chen, Auteur ; Ate Poorthuis, Auteur Année de publication : 2021 Article en page(s) : pp 1425 - 1448 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] géopositionnement [Termes descripteurs IGN] logement [Termes descripteurs IGN] mobilité urbaine [Termes descripteurs IGN] R (langage) [Termes descripteurs IGN] service fondé sur la position [Termes descripteurs IGN] Singapour Résumé : (auteur) Identifying meaningful locations, such as home or work, from human mobility data has become an increasingly common prerequisite for geographic research. Although location-based services (LBS) and other mobile technology have rapidly grown in recent years, it can be challenging to infer meaningful places from such data, which – compared to conventional datasets – can be devoid of context. Existing approaches are often developed ad-hoc and can lack transparency and reproducibility. To address this, we introduce an R package for inferring home locations from LBS data. The package implements pre-existing algorithms and provides building blocks to make writing algorithmic ‘recipes’ more convenient. We evaluate this approach by analyzing a de-identified LBS dataset from Singapore that aims to balance ethics and privacy with the research goal of identifying meaningful locations. We show that ensemble approaches, combining multiple algorithms, can be especially valuable in this regard as the resulting patterns of inferred home locations closely correlate with the distribution of residential population. We hope this package, and others like it, will contribute to an increase in use and sharing of comparable algorithms, research code and data. This will increase transparency and reproducibility in mobility analyses and further the ongoing discourse around ethical big data research. Numéro de notice : A2021-449 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1887489 date de publication en ligne : 10/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1887489 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97861 [article]

Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes / Qingying Yu in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1346 - 1373 Titre : Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes Type de document : Article/Communication Auteurs : Qingying Yu, Auteur ; Yonglong Luo, Auteur ; Dongxia Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1346 - 1373 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] classification par Perceptron multicouche [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] direction [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] entropie [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] mobilité urbaine [Termes descripteurs IGN] Pékin (Chine) [Termes descripteurs IGN] plan de déplacement urbain [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] trajectoire [Termes descripteurs IGN] vitesse de déplacement Résumé : (auteur) Residents’ trajectory data denote their instantaneous locations along their movements. Mobility research that applies trajectory mining techniques to identify the transportation modes of these movements can inform urban transportation planning. Herein, we propose a five-step approach with information entropy and a multi-layer neural network to identify transportation modes from trajectory data. First, this approach extracts the motion features at each time-stamped location based on foundation geospatial data and spatiotemporal trajectory data, including the speed, acceleration, change of direction, rate of change in direction, and distance from each basic transportation facility. The second step uses information entropy to identify the features that play key roles in identifying transportation modes. The third step weighs each attribute in the feature vector consisting of the selected features and normalizes it to prepare it as input data. The fourth step constructs, trains, and tests a multi-layer neural network with seven-fold cross-validation. The final step includes a post-processing method to optimize the identification result. We use F-measure metric to evaluate the performance. Experimental results on a real trajectory dataset show that the proposed approach can identify the transportation mode at each time-stamped location and outperforms existing transportation-mode identification methods in terms of accuracy and stability. Numéro de notice : A2021-448 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1901904 date de publication en ligne : 15/04/2021 En ligne : https://doi.org/10.1080/13658816.2021.1901904 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97860 [article]

Understanding collective human movement dynamics during large-scale events using big geosocial data analytics / Junchuan Fan in Computers, Environment and Urban Systems, vol 87 (May 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101605 Titre : Understanding collective human movement dynamics during large-scale events using big geosocial data analytics Type de document : Article/Communication Auteurs : Junchuan Fan, Auteur ; Kathleen Stewart, Auteur Année de publication : 2021 Article en page(s) : n° 101605 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données massives [Termes descripteurs IGN] dynamique spatiale [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] éclipse solaire [Termes descripteurs IGN] estimation par noyau [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] événement [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] migration humaine [Termes descripteurs IGN] mobilité territoriale [Termes descripteurs IGN] téléphonie mobile Résumé : (auteur) Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. However, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes. Numéro de notice : A2021-275 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101605 date de publication en ligne : 05/02/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101605 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97358 [article]

Incorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks / Hang Zhang in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 1 (January 2021) . - n° 10 Titre : Incorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks Type de document : Article/Communication Auteurs : Hang Zhang, Auteur ; Mingxin Gan, Auteur ; Xi Sun, Auteur Année de publication : 2021 Article en page(s) : n° 10 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] comportement [Termes descripteurs IGN] filtrage d'information [Termes descripteurs IGN] interprétation (psychologie) [Termes descripteurs IGN] mémoire [Termes descripteurs IGN] mobilité [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] réseau social géodépendant [Termes descripteurs IGN] tourisme Résumé : (auteur) In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods. Numéro de notice : A2021-148 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10010036 date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.3390/ijgi10010036 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97056 [article]

Urban Informatics / Wenzhong Shi (2021)  

Public Titre : Urban Informatics Type de document : Monographie Auteurs : Wenzhong Shi, Editeur scientifique ; Michael F. Goodchild, Editeur scientifique ; Michael Batty, Editeur scientifique ; et al., Auteur Editeur : Springer Nature Année de publication : 2021 Collection : The Urban Book Series Importance : 941 p. ISBN/ISSN/EAN : 978-981-1589836-- Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Urbanisme [Termes descripteurs IGN] données massives [Termes descripteurs IGN] infrastructure urbaine de données localisées [Termes descripteurs IGN] mobilité urbaine [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] pollution [Termes descripteurs IGN] sécurité publique [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] ville durable [Termes descripteurs IGN] ville intelligente Résumé : (éditeur) This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity. Note de contenu : 1- Introduction 2- Dimensions of Urban Science 3- Urban Systems and Applications Numéro de notice : 28559 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Monographie DOI : 10.1007/978-981-15-8983-6 En ligne : https://link.springer.com/book/10.1007/978-981-15-8983-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97592

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)  

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Using multi-agent simulation to predict natural crossing points for pedestrians and choose locations for mid-block crosswalks / Egor Smirrnov in Geo-spatial Information Science, vol 23 n° 4 (December 2020)  

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Modalflow: cross-origin flow data visualization for urban mobility / Ignacio Pérez-Messina in Algorithms, vol 13 n° 11 (November 2020)  

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Unfolding spatial-temporal patterns of taxi trip based on an improved network kernel density estimation / Boxi Shen in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

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Urban Wi-Fi fingerprinting along a public transport route / Guenther Retscher in Journal of applied geodesy, vol 14 n° 4 (October 2020)  

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Impact of extreme weather events on urban human flow: A perspective from location-based service data / Zhenhua Chen in Computers, Environment and Urban Systems, vol 83 (September 2020)  

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Objets connectés et mobilité urbaine : visualiser les déplacements des usagers de Twitter avec des graphes dynamiques / Françoise Lucchini in Mappemonde [en ligne], n° 128 (juillet 2020)  

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An empirical study on the intra-urban goods movement patterns using logistics big data / Pengxiang Zhao in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

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Estimating and interpreting fine-scale gridded population using random forest regression and multisource data / Yun Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC / Andreas Keler in Geo-spatial Information Science, vol 23 n° 2 (June 2020)  

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