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Mapping population distribution from open address data: application to mainland Portugal / Nelson Mileu in Journal of maps, vol 18 n° 3 (March 2023)  

Public [article]  inJournal of maps > vol 18 n° 3 (March 2023) . - pp 585 - 593 Titre : Mapping population distribution from open address data: application to mainland Portugal Type de document : Article/Communication Auteurs : Nelson Mileu, Auteur ; Margarida Queirós, Auteur ; Paolo Morgado, Auteur Année de publication : 2023 Article en page(s) : pp 585 - 593 Note générale : bilbliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] base de données d'adresses [Termes IGN] carte thématique [Termes IGN] distribution spatiale [Termes IGN] grille [Termes IGN] planification urbaine [Termes IGN] population [Termes IGN] Portugal [Termes IGN] QGIS Résumé : (auteur) Mapping population distribution remains a common need in various fields of studies. Several approaches and methodologies have been adopted to obtain high-resolution population distribution grids. The use of addresses data to obtain gridded population distribution maps emerges as one of the more recent and accurate approaches. The increasing dissemination and availability of geo-data and more specifically address data allow us to obtain updated, granular and high spatial resolution population distribution maps. This paper describes a bottom-up open addresses data mapping-based approach of gridded population distribution with a fine spatial resolution. Through a QGIS plugin, an adaptation of the housing unit methodology was implemented to obtain 500 m × 500 and 250 m × 250 m population grids for mainland Portugal. The results showed that the use of reliable addresses databases can generate gridded population distribution maps with a high degree of adjustment to reality. Numéro de notice : A2023-154 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17445647.2022.2114862 Date de publication en ligne : 07/09/2022 En ligne : https://doi.org/10.1080/17445647.2022.2114862 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102839 [article]

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]

Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand / Kiatkulchai Jitt-Aer in Natural Hazards, vol 113 n° 1 (August 2022)  

Public [article]  inNatural Hazards > vol 113 n° 1 (August 2022) . - pp 185 - 211 Titre : Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand Type de document : Article/Communication Auteurs : Kiatkulchai Jitt-Aer, Auteur ; Graham Wall, Auteur ; Dylan Jones, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 185 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse spatiale [Termes IGN] ArcGIS [Termes IGN] figuration de la densité [Termes IGN] gestion de crise [Termes IGN] interpolation spatiale [Termes IGN] planification côtière [Termes IGN] population [Termes IGN] prévention des risques [Termes IGN] Thaïlande [Termes IGN] tsunami Résumé : (auteur) The 2004 Indian Ocean tsunami led to improvements in Thailand’s early warning systems and evacuation procedures. However, there was no consideration of better aid delivery, which critically depends on estimates of the affected population. With the widespread use of geographical information systems (GIS), there has been renewed interest in spatial population estimation. This study has developed an application to determine the number of disaster-impacted people in a given district, by integrating GIS and population estimation algorithms, to facilitate humanitarian relief logistics. A multi-stage spatial interpolation is used for estimating the affected populations using ArcGIS software. We present a dasymetric mapping approach using a population-weighted technique coupled with remote sensing data. The results in each target area show the coordinates of each shelter location for evacuees, with the minimum and maximum numbers of people affected by the tsunami inundation. This innovative tool produces not only numerical solutions for decision makers, but also a variety of maps that improve visualisation of disaster severity across neighbourhoods. A case study in Patong, a town of Phuket, illustrates the application of this GIS-based approach. The outcomes can be used as key decision-making factors in planning and managing humanitarian relief logistics in the preparedness and response phases to improve performance with future tsunami occurrences, or with other types of flood disaster. Numéro de notice : A2022-703 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-022-05295-x Date de publication en ligne : 09/03/2022 En ligne : https://doi.org/10.1007/s11069-022-05295-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101566 [article]

Temporal transitions of demographic dot maps / Jeff Allen in International journal of cartography, vol 8 n° 2 (July 2022)  

Public [article]  inInternational journal of cartography > vol 8 n° 2 (July 2022) . - pp 208 - 222 Titre : Temporal transitions of demographic dot maps Type de document : Article/Communication Auteurs : Jeff Allen, Auteur Année de publication : 2022 Article en page(s) : pp 208 - 222 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatio-temporelle [Termes IGN] carte de répartition par points [Termes IGN] carte interactive [Termes IGN] distribution spatiale [Termes IGN] données démographiques [Termes IGN] données localisées historiques [Termes IGN] interpolation linéaire [Termes IGN] pauvreté [Termes IGN] population [Termes IGN] répartition géographique [Termes IGN] représentation cartographique [Termes IGN] représentation du changement [Termes IGN] Toronto [Termes IGN] visualisation cartographique [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Dot maps are often used to display the distributions of populations over space. This paper details a method for extending dot maps in order to visualize changes in spatial patterns over time. Specifically, we outline a selective linear interpolation procedure to encode the time range in which dots are visible on a map, which then allows for temporal queries and animation. This methodology is exemplified first by animating population growth across the United States, and second, through an interactive application showing changing poverty distributions in Toronto, Canada. Numéro de notice : A2022-920 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2021.1910184 Date de publication en ligne : 18/05/2021 En ligne : https://doi.org/10.1080/23729333.2021.1910184 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102460 [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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 6 (June 2022) . - pp 1166 - 1184 Titre : Mapping monthly population distribution and variation at 1-km resolution across China Type de document : Article/Communication Auteurs : Zhifeng Cheng, Auteur ; Jianghao Wang, Auteur ; Yong Ge, Auteur Année de publication : 2022 Article en page(s) : pp 1166 - 1184 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse spatiale [Termes IGN] autocorrélation spatiale [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] densité de population [Termes IGN] distribution spatiale [Termes IGN] figuration de la densité [Termes IGN] krigeage [Termes IGN] population [Termes IGN] série temporelle [Termes IGN] téléphonie mobile Résumé : (auteur) Fine-grained inner-annual population data are instrumental in climate change response, resource allocation, and epidemic control. However, such data are currently scarce due to the lack of human-related indicators with both high temporal resolution and long-term coverage that can be used in the process of population spatialization. Here, we estimate monthly 1-km gridded population distribution across China in 2015 using time-series mobile phone positioning data. We construct a hybrid downscaling model to map the gridded population by incorporating random forest and area-to-point kriging. The estimated monthly population products appear to capture inner-annual population variations, especially during special periods, such as the festival, holiday, and short-term labor flow period, which are characterized by large-scale population movements. Additionally, compared with census data, the hybrid model-based results obtained exhibit higher consistency than popular global population products across all spatial extents. Our monthly 1-km data products for the population distribution across China in 2015 provide a credible dataset that can be employed in studies aimed at accurate population-dependent decisions. Numéro de notice : A2022-407 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1854767 Date de publication en ligne : 07/12/2020 En ligne : https://doi.org/10.1080/13658816.2020.1854767 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100724 [article]

Atlas of global change risk of population and economic systems / Peijun Shi (2022)  

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A square-grid sampling support to reconcile systematicity and adaptivity in the periodic spatial survey of natural resources / Olivier Bouriaud (2022)  

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Des pixels et des peuples / Laurent Polidori in Géomètre, n° 2190 (avril 2021)

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Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates / Franz Schug in Plos one, vol 16 n° 3 (March 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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Population dynamics and natural hazard risk management: conceptual and practical linkages for the case of Austrian policy making / Christoph Clar in Natural Hazards, Vol 105 n° 2 (January 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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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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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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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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