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Connecting family trees to construct a population-scale and longitudinal geo-social network for the U.S / Caglar Koylu in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 12 (December 2021) . - pp 2380 - 2423 Titre : Connecting family trees to construct a population-scale and longitudinal geo-social network for the U.S Type de document : Article/Communication Auteurs : Caglar Koylu, Auteur ; Diansheng Guo, Auteur ; Yuan Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2380 - 2423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] appariement sémantique [Termes IGN] collecte de données [Termes IGN] démographie [Termes IGN] dix-neuvième siècle [Termes IGN] données localisées des bénévoles [Termes IGN] données publiques [Termes IGN] Etats-Unis [Termes IGN] géocodage [Termes IGN] historique des données [Termes IGN] itération [Termes IGN] migration humaine [Termes IGN] mobilité [Termes IGN] réseau social géodépendant [Termes IGN] système d'information historique Résumé : (auteur) We collected 92,832 user-contributed and publicly available family trees from rootsweb.com, including 250 million individuals who were born in North America and Europe between 1630 and 1930. We cleaned and connected the family trees to create a population-scale and longitudinal family tree dataset using a workflow of data collection and cleaning, geocoding, fuzzy record linkage and a relation-based iterative search for connecting trees and deduplication of records. Given the largest connected component of nearly 40 million individuals, and a total of 80 million individuals, we generated, to date, the largest population-scale and longitudinal geo-social network over centuries. We evaluated the representativeness of the family tree dataset for historical population demography and mobility by comparing the data to the 1880 Census. Our results showed that the family trees were biased towards males, the elderly, farmers, and native-born white segments of the population. Individuals were highly mobile – in our 1880 sample of parent-child pairs where both were born in the U.S., 47% were born in different states. Our findings agreed with prior studies that people migrated from East to West in horizontal bands, and the trend was reflected in the dialects and regional structure of the U.S. Numéro de notice : A2021-876 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1821885 Date de publication en ligne : 30/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1821885 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99139 [article]

Impact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources / Yan Lin in Cartography and Geographic Information Science, vol 48 n° 6 (October 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 6 (October 2021) . - pp 471 - 490 Titre : Impact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources Type de document : Article/Communication Auteurs : Yan Lin, Auteur ; Christopher Lippitt, Auteur ; Daniel Beene, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 471 - 490 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] accessibilité [Termes IGN] analyse de sensibilité [Termes IGN] ArcMap [Termes IGN] données localisées des bénévoles [Termes IGN] Google Maps [Termes IGN] incertitude des données [Termes IGN] indexation spatiale [Termes IGN] navigation automobile [Termes IGN] Nouveau-Mexique (Etats-Unis) [Termes IGN] OpenStreetMap [Termes IGN] plan de déplacement urbain [Termes IGN] réseau routier [Termes IGN] système d'information géographique [Termes IGN] temps de trajet [Termes IGN] transport public Résumé : (auteur) GIS-based spatial access measures have been used extensively to monitor social equity and to help develop policy. However, inherent uncertainties in the road datasets used in spatial access estimates remain largely underreported. These uncertainties might result in unrecognized biases within visualization products and decision-making outcomes that strive to improve social equity based on seemingly egalitarian accessibility metrics. To better understand and address these uncertainties, we evaluated variations in travel impedance for car and bus transportation using proprietary, volunteer-information-based, and free (non-volunteer-information-based) street networks. We then interpreted the measured variations through the lens of street data uncertainty and its propagation in a common E2SFCA model of spatial accessibility. Results indicated that travel impedance disagreement propagates through the modeling process to effect agreement of spatial access index (SPAI) estimates among different street sources, with larger uncertainties observed for bus travel than car travel. Higher impedance coefficients (β), a model parameter, reduced the impact of street-source variations on estimates. Less urbanized regions were found to experience higher street-source variations when compared with the core-metropolitan area. We also demonstrated that a relative spatial access measure – the spatial access ratio (SPAR) – reduced uncertainties introduced by the choice of street datasets. Careful selection of reliable street sources and model parameters (e.g. higher β), as well as consideration of the potential for bias, particularly for less urbanized areas and areas reliant on public transportation, is warranted when leveraging SPAI to inform policy. When reliable/accurate road network data are not accessible or data quality information is not available, the SPAR is a suitable alternative or supplement to SPAI for visualization and analyses. Numéro de notice : A2021-712 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1960609 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.1080/15230406.2021.1960609 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98617 [article]

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Spatial interpolation of mobile positioning data for population statistics / Anto Aasa in Journal of location-based services, vol 15 n° inconnu ([01/10/2021])  

Public [article]  inJournal of location-based services > vol 15 n° inconnu [01/10/2021] Titre : Spatial interpolation of mobile positioning data for population statistics Type de document : Article/Communication Auteurs : Anto Aasa, Auteur ; Pilleriine Kamenjuk, Auteur ; Erki Saluveer, Auteur ; et al., Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse comparative [Termes IGN] classification par forêts aléatoires [Termes IGN] données démographiques [Termes IGN] données GNSS [Termes IGN] interpolation spatiale [Termes IGN] mobilité [Termes IGN] traitement de données localisées Résumé : (auteur) Mobile positioning is recognised to be one of the most promising new sources of data for the production of fast and cost-effective statistics regarding population and mobility. Considerable interest has been shown by government institutions in their search for a way to use mobile positioning data to produce official statistics, although to date there are only few examples of successful projects. Apart from data access and sampling, the main challenges relate to the spatial interpolation of mobile positioning data and extrapolation of recorded data to the level of the entire population. This area of work has to date received relatively little attention in the academic discussion. In the current study, we compare five different methods of spatial interpolation of mobile positioning data. The best methods of describing population distribution and size in comparison with Census data are the adaptive Morton grid and the Random forest model (R2 > 0.9), while the more widely used point-in-polygon and areal-weighted methods produce results that are far less satisfactory (R2 = 0.42; R2 = 0.35). Careful selection of spatial interpolation methods is therefore of the utmost importance for producing reliable population statistics from mobile positioning data. Numéro de notice : A2021-727 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17489725.2021.1917710 Date de publication en ligne : 10/05/2021 En ligne : https://doi.org/10.1080/17489725.2021.1917710 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98658 [article]

Understanding the modifiable areal unit problem in dockless bike sharing usage and exploring the interactive effects of built environment factors / Feng Gao in International journal of geographical information science IJGIS, vol 35 n° 9 (September 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 9 (September 2021) . - pp 1905 - 1925 Titre : Understanding the modifiable areal unit problem in dockless bike sharing usage and exploring the interactive effects of built environment factors Type de document : Article/Communication Auteurs : Feng Gao, Auteur ; Shaoying Li, Auteur ; Zhangzhi Tan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1905 - 1925 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aménagement du territoire [Termes IGN] bicyclette [Termes IGN] données issues des réseaux sociaux [Termes IGN] mobilité urbaine [Termes IGN] origine - destination [Termes IGN] planification urbaine [Termes IGN] problème d'unité zonale modifiable [Termes IGN] Shenzhen Résumé : (auteur) Understanding the influence mechanisms of dockless bike-sharing usage is essential for land use planning and bike scheduling strategy implementation. Although various studies have been carried out to explore the impact of built environment (BE) factors on bike-sharing usage, few studies have examined the modifiable areal unit problem (MAUP). Moreover, previous studies mainly focused on the separate effect of each factor but neglected the interactions between these factors. Taking Shenzhen, China as the case, this study fills these two gaps by employing the geographical detector method to examine the MAUP in dockless bike-sharing usage as well as the interactive effects of BE factors. The results revealed that the influences of most BE variables are sensitive to the spatial areal units, which have informed urban planners what built-environment factors should be paid more attention to at certain spatial scales. Additionally, through the comparisons between single effect and interactive effect, this study revealed some interesting findings that can provide scientific basis for temporal rebalance strategy for the innovative and high-density metropolis in China. Numéro de notice : A2021-593 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1863410 Date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.1080/13658816.2020.1863410 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98214 [article]

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Predicting user activity intensity using geographic interactions based on social media check-in data / Jing Li in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 8 (August 2021) . - n° 555 Titre : Predicting user activity intensity using geographic interactions based on social media check-in data Type de document : Article/Communication Auteurs : Jing Li, Auteur ; Wenyue Guo, Auteur ; Haiyan Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 555 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] données issues des réseaux sociaux [Termes IGN] interaction spatiale [Termes IGN] mobilité [Termes IGN] modèle non linéaire [Termes IGN] relation spatiale [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau neuronal récurrent [Termes IGN] utilisateur Résumé : (auteur) Predicting user activity intensity is crucial for various applications. However, existing studies have two main problems. First, as user activity intensity is nonstationary and nonlinear, traditional methods can hardly fit the nonlinear spatio-temporal relationships that characterize user mobility. Second, user movements between different areas are valuable, but have not been utilized for the construction of spatial relationships. Therefore, we propose a deep learning model, the geographical interactions-weighted graph convolutional network-gated recurrent unit (GGCN-GRU), which is good at fitting nonlinear spatio-temporal relationships and incorporates users’ geographic interactions to construct spatial relationships in the form of graphs as the input. The model consists of a graph convolutional network (GCN) and a gated recurrent unit (GRU). The GCN, which is efficient at processing graphs, extracts spatial features. These features are then input into the GRU, which extracts their temporal features. Finally, the GRU output is passed through a fully connected layer to obtain the predictions. We validated this model using a social media check-in dataset and found that the geographical interactions graph construction method performs better than the baselines. This indicates that our model is appropriate for fitting the complex nonlinear spatio-temporal relationships that characterize user mobility and helps improve prediction accuracy when considering geographic flows. Numéro de notice : A2021-588 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080555 Date de publication en ligne : 17/08/2021 En ligne : https://doi.org/10.3390/ijgi10080555 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98206 [article]

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)  

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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)  

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Reconsidering movement and exposure: Towards a more dynamic health geography / Malcolm Campbell in Geography compass, vol 15 n° 6 (June 2021)  

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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)  

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Action Cœur de Ville de Saumur, le SIG comme outil de pilotage / Amandine Loil (2021)

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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)  

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Recueil des contributions, Colloque international Tous (im)mobiles, tous cartographes ? Approches cartographiques des mobilités, des circulations, des flux et des déplacements : Méthodes, outils, représentations, pratiques et usages / Françoise Bahoken (2021)  

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Urban Informatics / Wenzhong Shi (2021)  

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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)  

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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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