Détail de l'auteur
Auteur Krishnachandran Balakrishnan |
Documents disponibles écrits par cet auteur (1)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
![Tris disponibles](./images/orderby_az.gif)
A method for urban population density prediction at 30m resolution / Krishnachandran Balakrishnan in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)
![]()
[article]
Titre : A method for urban population density prediction at 30m resolution Type de document : Article/Communication Auteurs : Krishnachandran Balakrishnan, Auteur Année de publication : 2020 Article en page(s) : pp 193 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] densité de population
[Termes IGN] gestion urbaine
[Termes IGN] hauteur du bâti
[Termes IGN] image Cartosat-1
[Termes IGN] Inde
[Termes IGN] logiciel de traitement d'image
[Termes IGN] modèle de simulation
[Termes IGN] modélisation du bâti
[Termes IGN] système d'information géographique
[Termes IGN] véhicule automobileRésumé : (auteur) This paper proposes a new method for urban population density prediction at 30 m resolution. Using data for Bangalore, the paper demonstrates that population within each 30 m residential built-up cell can be modeled as a function of cell-level data on street density and building heights and ward-level data on car ownership. Building-height data were generated from Cartosat-1 stereo imagery using an open-source satellite stereo image processing software. Using this building-height data in conjunction with the other datasets, the paper demonstrates that a 30 m resolution population density surface can be generated such that, when summed to the ward level, the median absolute percentage error between predicted population and known census population at the ward level is 8.29%. The paper also shows that the relationship between population density, street density, building height, and ward level car ownership is spatially non-stationary. A fine-grained understanding of urban population densities, as enabled by the proposed method, can be beneficial to research, policy, and practice related to cities. Numéro de notice : A2020-168 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1687014 Date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1080/15230406.2019.1687014 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94839
in Cartography and Geographic Information Science > vol 47 n° 3 (May 2020) . - pp 193 - 213[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2020031 RAB Revue Centre de documentation En réserve L003 Disponible