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BIM et enjeux climatiques, ch. City Information Modelling pour des aménagements sobres et durables : potentiel du CIM pour calculer l’intensité urbaine / Adeline Deprêtre (2023)  

Public Titre de série : BIM et enjeux climatiques, ch Titre : City Information Modelling pour des aménagements sobres et durables : potentiel du CIM pour calculer l’intensité urbaine Type de document : Chapitre/Contribution Auteurs : Adeline Deprêtre, Auteur ; Florence Jacquinod , Auteur ; Bruno Barroca, Auteur ; Vincent Becue, Auteur Editeur : Paris : Eyrolles Année de publication : 2023 Conférence : EduBIM 2022, 8e édition des Journées de l'enseignement et de la recherche autour du BIM et de la maquette numérique 29/11/2022 30/11/2022 Champs-sur-Marne France programme Importance : pp ISBN/ISSN/EAN : 978-2-416-00841-2 Note générale : bibliographie Projet E3S Langues : Français (fre) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] densité de population [Termes IGN] format Industry foudation classes IFC [Termes IGN] impact sur l'environnement [Termes IGN] planification urbaine [Termes IGN] urbanisme Résumé : (auteur) Les analyses urbaines font partie des méthodes déployées afin de tendre vers un urbanisme durable, diminuant l’impact de la construction et de la planification sur les ressources. Dans cet article nous proposons d’explorer le potentiel des City Information Models (CIM) pour étudier l’intensité urbaine à l’échelle d’un quartier, prenant en considération plusieurs paramètres contribuant à ses impacts environnementaux. Nous utilisons pour cela les premières versions du CIM du quartier La Vallée, actuellement en cours de construction. Nous exposons notre méthode expérimentale compatible avec le format ouvert IFC afin de pouvoir reproduire la démarche sur d’autres quartiers. Nous présentons ensuite une partie de nos résultats sur l’exploitation du CIM pour l’évaluation de l’intensité urbaine en phase conception. Enfin, nous proposons diverses préconisations afin de faciliter la constitution de CIM aisément mobilisables pour les analyses urbaines, mais également pour d’autres types d’analyses pouvant contribuer à la conception ou au réaménagement de quartiers en limitant leurs impacts environnementaux. Numéro de notice : H2023-001 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/URBANISME Nature : Chapître / contribution En ligne : https://hal.science/hal-04061004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102988

A comparative assessment of the statistical methods based on urban population density estimation / Merve Yılmaz in Geocarto international, vol 38 n° 1 ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° 1 [01/01/2023] . - n° 2152494 Titre : A comparative assessment of the statistical methods based on urban population density estimation Type de document : Article/Communication Auteurs : Merve Yılmaz, Auteur Année de publication : 2023 Article en page(s) : n° 2152494 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] densité de population [Termes IGN] planification urbaine [Termes IGN] population urbaine [Termes IGN] régression géographiquement pondérée [Termes IGN] régression multiple [Termes IGN] Turquie Résumé : (auteur) Population density is important spatial information for addressing the use and access to land resources in cities under the Sustainable Development Goals. This is because the spatial data support appropriate spatial policies at the spatial scale and predicts how much land will be consumed in the future. The study aims to compare and evaluate the regression tools in the context of estimating the population density difference. The three analysis tools used are Random Forest-Based Classification, Multiple Linear Regression, and Geographically Weighted Regression. The sampling area covers cities around Türkiye. Comparative results showed that the two most important descriptive variables in the Random Forest-Based Classification model are the density difference of the new developed area and the connectivity. The three main explanatory variables of the Multiple Linear Regression model are centrality, vehicle ownership, and accessibility. The results of the Multiple Linear Regression model (a non-spatial model) and the Geographically Weighted Regression model (a spatial model), were found to be quite similar. The importance of accessibility and connectivity is more evident in the Multiple Linear Regression model when the Random Forest-Based Classification model highlights the density values in the new development areas. Numéro de notice : A2023-055 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2152494 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.1080/10106049.2022.2152494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102388 [article]

Landscape metrics regularly outperform other traditionally-used ancillary datasets in dasymetric mapping of population / Heng Wan in Computers, Environment and Urban Systems, vol 99 (January 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 99 (January 2023) . - n° 101899 Titre : Landscape metrics regularly outperform other traditionally-used ancillary datasets in dasymetric mapping of population Type de document : Article/Communication Auteurs : Heng Wan, Auteur ; Jim Yoon, Auteur ; Vivek Srikrishnan, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101899 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] carte thématique [Termes IGN] densité de population [Termes IGN] distribution spatiale [Termes IGN] Etats-Unis [Termes IGN] indicateur paysager [Termes IGN] interpolation [Termes IGN] occupation du sol [Termes IGN] paysage [Termes IGN] planification urbaine [Termes IGN] réduction d'échelle Résumé : (auteur) Population downscaling and interpolation methods are required to produce data which correspond to spatial units used in urban planning, demography, and environmental modeling. Population data are typically aggregated at census enumeration units, which can have arbitrary, temporally-evolving boundaries. Previous approaches to imperviousness-based dasymetric mapping ignore cell-level patterning of imperviousness within a spatial unit of prediction, which potentially serve as a strong indicator of population. Landscape metrics derived from imperviousness data offer a promising approach to capture these patterns. In this study, we incorporate landscape metrics derived from impervious cover percentage maps into intelligent dasymetric mapping to downscale population from census tracts to block groups in four states with varying population densities: Connecticut, South Carolina, West Virginia, and New Mexico. We compare the performance of the landscape metrics-based models against two baseline models in all four states across three different time periods. The results show that intelligent dasymetric mapping using landscape metrics generally outperforms the two baseline models. We further compare the performance of landscape metrics as an ancillary source of information for dasymetric mapping against other traditionally-used datasets (e.g., land use, roads, nighttime lights data) in three states (Connecticut, South Carolina, and New Mexico) in 2000. We find that class area, landscape shape index, and number of patches consistently achieve lower error rates than other ancillary datasets in all the three states. Numéro de notice : A2023-013 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101899 Date de publication en ligne : 02/11/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102130 [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]

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]

Mapping monthly population distribution and variation at 1-km resolution across China / Zhifeng Cheng in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)  

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Dynamic linkage between urbanization, electrical power consumption, and suitability analysis using remote sensing and GIS techniques / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)  

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Development of a GIS-based alert system to mitigate flash flood impacts in Asyut governorate, Egypt / Soha A. Mohamed in Natural Hazards, vol 108 n° 3 (September 2021)  

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GIS-based logic scoring of preference method for urban densification suitability analysis / Shuoge Shen in Computers, Environment and Urban Systems, vol 89 (September 2021)  

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Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling / Stefanos Georganos in Geocarto international, vol 36 n° 2 ([01/02/2021])  

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Spatiotemporal patterns of urbanization during the last four decades in Switzerland and their impacts on urban heat islands / Marti Bosch Padros (2021)  

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Landuse and land cover identification and disaggregating socio-economic data with convolutional neural network / Jingtao Yao in Geocarto international, vol 35 n° 10 ([01/08/2020])  

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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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Fine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method / Zhenzhong Peng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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A method for urban population density prediction at 30m resolution / Krishnachandran Balakrishnan in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

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