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From data to narratives: Scrutinising the spatial dimensions of social and cultural phenomena through lenses of interactive web mapping / Tian Lan in Journal of Geovisualization and Spatial Analysis, vol 6 n° 2 (December 2022)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 6 n° 2 (December 2022) . - n° 22 Titre : From data to narratives: Scrutinising the spatial dimensions of social and cultural phenomena through lenses of interactive web mapping Type de document : Article/Communication Auteurs : Tian Lan, Auteur ; Oliver O'Brien, Auteur ; James Cheshire, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] carte interactive [Termes IGN] cartographie sensible [Termes IGN] culture [Termes IGN] données démographiques [Termes IGN] données socio-économiques [Termes IGN] impact social [Termes IGN] récit [Termes IGN] Royaume-Uni [Termes IGN] sciences sociales [Termes IGN] web mapping Mots-clés libres : narrative mapping Résumé : (auteur) Modern web mapping techniques have enhanced the storytelling capability of cartography. In this paper, we present our recent development of a web mapping facility that can be used to extract interesting stories and unique insights from a diverse range of socio-economic and demographic variables and indicators, derived from a variety of datasets. We then use three curated narratives to show that online maps are effective ways of interactive storytelling and visualisation, which allow users to tailor their own story maps. We discuss the reasons for the revival of the recent attention to narrative mapping and conclude that our interactive web mapping facility powered by data assets can be employed as an accessible and powerful toolkit, to identify geographic patterns of various social and economic phenomena by social scientists, journalists, policymakers, and the public. Numéro de notice : A2022-541 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s41651-022-00117-x Date de publication en ligne : 16/06/2022 En ligne : https://doi.org/10.1007/s41651-022-00117-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101105 [article]

Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators / Luis Izquierdo-Horna in Computers, Environment and Urban Systems, vol 96 (September 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 96 (September 2022) . - n° 101834 Titre : Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators Type de document : Article/Communication Auteurs : Luis Izquierdo-Horna, Auteur ; Miker Damazo, Auteur ; Deyvis Yanayaco, Auteur Année de publication : 2022 Article en page(s) : n° 101834 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] déchet [Termes IGN] densité de population [Termes IGN] données socio-économiques [Termes IGN] Pérou [Termes IGN] régression logistique [Termes IGN] zone urbaine Résumé : (auteur) In the last decades, the accumulation of municipal solid waste in urban areas has become a latent concern in our society due to its implications for the exposed population and the possible health and environmental issues it may cause. In this sense, this research study contributes to the timely identification of these sectors according to the anthropogenic characteristics of their residents as dictated by 10 social indicators (i.e., age, education, income, among others) sorted into three assessment categories (sociodemographic, sociocultural, and socioeconomic). Then, the data collected was processed and analyzed using two machine learning algorithms (random forest (RF) and logistic regression (LR)). The primary information that fed the machine learning model was collected through field visits and local/national reports. For this research, the Puente Piedra and Chaclacayo districts, both located in the province of Lima, Peru, were selected as case studies. Results suggest that the most relevant social indicators that help identifying these sectors are monthly income, consumption patterns, age, and household population density. The experiments showed that the RF algorithm has the best performance, since it efficiently identified 63% of the possible solid waste accumulation zones. In addition, both models were capable of determining different classes (AUC – RF = 0.65, AUC – LR = 0.71). Finally, the proposed approach is applicable and reproducible in different sectors of the national Peruvian territory. Numéro de notice : A2022-512 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101834 Date de publication en ligne : 10/06/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101834 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101052 [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]

Measuring COVID-19 vulnerability for Northeast Brazilian municipalities: Social, economic, and demographic factors based on multiple criteria and spatial analysis / Ciro José Jardim De Figueiredo 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° 449 Titre : Measuring COVID-19 vulnerability for Northeast Brazilian municipalities: Social, economic, and demographic factors based on multiple criteria and spatial analysis Type de document : Article/Communication Auteurs : Ciro José Jardim De Figueiredo, Auteur ; Caroline Maria de Miranda Mota, Auteur ; Kaliane Gabriele Dias de Araújo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 449 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse multicritère [Termes IGN] autocorrélation spatiale [Termes IGN] Brésil [Termes IGN] densité de population [Termes IGN] données socio-économiques [Termes IGN] épidémie [Termes IGN] maladie virale [Termes IGN] vulnérabilité Résumé : (auteur) COVID-19 has brought several harmful consequences to the world from many perspectives, including social, economic, and well-being in addition to health issues. However, these harmful consequences vary in intensity in different regions. Identifying which cities are most vulnerable to COVID-19 and understanding which variables could be associated with the advance of registered cases is a challenge. Therefore, this study explores and builds a spatial decision model to identify the characteristics of the cities that are most vulnerable to COVID-19, taking into account social, economic, demographic, and territorial aspects. Hence, 18 features were separated into the four groups mentioned. We employed a model joining the dominance-based rough set approach to aggregate the features (multiple criteria) and spatial analysis (Moran index, and Getis and Ord) to obtain final results. The results show that the most vulnerable places have characteristics with high population density and poor economic conditions. In addition, we conducted subsequent analysis to validate the results. The case was developed in the northeast region of Brazil. Numéro de notice : A2022-646 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080449 Date de publication en ligne : 16/08/2022 En ligne : https://doi.org/10.3390/ijgi11080449 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101462 [article]

Exploring the spatial disparity of home-dwelling time patterns in the USA during the COVID-19 pandemic via Bayesian inference / Xiao Huang in Transactions in GIS, vol 26 n° 4 (June 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 4 (June 2022) . - pp 1939 - 1961 Titre : Exploring the spatial disparity of home-dwelling time patterns in the USA during the COVID-19 pandemic via Bayesian inference Type de document : Article/Communication Auteurs : Xiao Huang, Auteur ; Yang Xu, Auteur ; Rui Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1939 - 1961 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse multiéchelle [Termes IGN] disparité [Termes IGN] distribution spatiale [Termes IGN] données socio-économiques [Termes IGN] épidémie [Termes IGN] estimation bayesienne [Termes IGN] hétérogénéité spatiale [Termes IGN] inférence statistique [Termes IGN] logement [Termes IGN] maladie virale [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] méthode robuste Résumé : (auteur) In this study, we aim to reveal hidden patterns and confounders associated with policy implementation and adherence by investigating the home-dwelling stages from a data-driven perspective via Bayesian inference with weakly informative priors and by examining how home-dwelling stages in the USA varied geographically, using fine-grained, spatial-explicit home-dwelling time records from a multi-scale perspective. At the U.S. national level, two changepoints are identified, with the former corresponding to March 22, 2020 (9 days after the White House declared the National Emergency on March 13) and the latter corresponding to May 17, 2020. Inspections at U.S. state and county level reveal notable spatial disparity in home-dwelling stage-related variables. A pilot study in the Atlanta Metropolitan area at the Census Tract level reveals that the self-quarantine duration and increase in home-dwelling time are strongly correlated with the median household income, echoing existing efforts that document the economic inequity exposed by the U.S. stay-at-home orders. To our best knowledge, our work marks a pioneering effort to explore multi-scale home-dwelling patterns in the USA from a purely data-driven perspective and in a statistically robust manner. Numéro de notice : A2022-533 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.1111/tgis.12918 Date de publication en ligne : 17/03/2022 En ligne : https://doi.org/10.1111/tgis.12918 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101081 [article]

The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events / Sidgley Camargo de Andrade in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)  

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Discovering co-location patterns in multivariate spatial flow data / Jiannan Cai in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

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Human movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation? / Meiliu Wu in Annals of GIS, vol 28 n° 2 (April 2022)  

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Spatial modeling of migration using GIS-based multi-criteria decision analysis: A case study of Iran / Naeim Mijani in Transactions in GIS, vol 26 n° 2 (April 2022)  

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GIS-based employment availabilities by mode of transport in Kuwait / S. Alkheder in Applied geomatics, vol 14 n° 1 (March 2022)  

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An extended patch-based cellular automaton to simulate horizontal and vertical urban growth under the shared socioeconomic pathways / Yimin Chen in Computers, Environment and Urban Systems, vol 91 (January 2022)  

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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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Development and analysis of land-use/land-cover spatio-temporal metrics in urban environments: Exploring urban growth patterns and linkages to socio-economic factors / Marta Sapena Moll (2021)  

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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)  

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Using OpenStreetMap data and machine learning to generate socio-economic indicators / Daniel Feldmeyer in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)  

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