Une méthode pour l’estimation désagrégée de données de population à l’aide de données ouvertes

Luciano Gervasoni 1 Serge Fenet 2, 1 Peter Sturm 1
1 STEEP - Sustainability transition, environment, economy and local policy
LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
2 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this article we present a method to perform dissagregated population estimation at building level using open data. Our goal is to estimate the number of people living at the fine level of individual households by using open urban data and coarse-scaled population data. First, a fine scale description of residential land use per building is built using OpenStreetMap. Then, using coarse-scale gridded population data, we perform the down-scaling for each household given their containing area for residential usage. We rely solely on open data in order to ensure replicability, and to be able to apply our method to any city in the world, as long as sufficient data exists. The evaluation is carried out using fine-grained census block data for cities in France as ground-truth.
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Conference papers
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https://hal.inria.fr/hal-01667975
Contributor : Peter Sturm <>
Submitted on : Tuesday, December 19, 2017 - 4:56:34 PM
Last modification on : Wednesday, April 3, 2019 - 1:08:50 AM

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Luciano Gervasoni, Serge Fenet, Peter Sturm. Une méthode pour l’estimation désagrégée de données de population à l’aide de données ouvertes. EGC 2018 - 18ème Conférence Internationale sur l'Extraction et la Gestion des Connaissances, Jan 2018, Paris, France. pp.83-94. ⟨hal-01667975⟩

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