Dynamic population mapping using mobile phone data

By Pierre Deville, Catherine Linard, S. Martin, M. Gilbert, Forrest R. Stevens, Andrea E. Gaughan, Vincent D. Blondel, Andrew J. Tatem. Proceedings of the National Academy of Sciences of the United States of America, 2014

881 citations recorded by the source corpus.

Subfields: Epidemiology, Global and Planetary Change, Transportation

View Paper: https://www.semanticscholar.org/paper/1d2f34e85ff39d601227dc7d98ab11b92b3e74b7

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In Epidemiology

In Global and Planetary Change

Andrew J. Tatem

In Transportation

Citations in the corpus

Mobile phone location data for disasters: A review from natural hazards and epidemics (2021, neutral): “Therefore, using such data requires a rigorous analysis of checking the representativeness of the mobile phone location dataset.”
A Survey on Deep Learning for Human Mobility (2020, background): “, predicting the purpose of movement) [153], home location detection [136, 195], and population inference [49].”
Demographic research with non-representative internet data (2015, methodology): “Cellphone data have also been used to evaluate patterns and regularities of internal mobility, for a country, or movements within a relatively small region (Bayir et al., 2009; Gonzalez et al., 2008; Candia et al., 2008; Blumenstock, 2012; Deville et al., 2014).”
A multi-source dataset of urban life in the city of Milan and the Province of Trentino (2015, background): “This dataset [Data citations 8,9] provides the directional interaction strengths between different areas of Milan and the Province of Trento.”
The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use (2019, background): “…climate change impacts (Rigaud et al., 2018), near-real-time population modeling (Bharti et al., 2015), mobility mapping, population dynamics (Deville et al., 2014), increased temporal resolution (Batista e Silva et al., 2018) and working directly with national statistical offices to…”
A Deep Gravity model for mobility flows generation (2020, background): “…from rural to urban areas 18,19 , the study and modeling of mobility patterns in urban environments 15,20 – 24 , the estimation of city population 25 – 27 , the migration induced by natural disasters, climate change, and con ﬂ icts 28 – 32 , the prediction of traf ﬁ c and crowd ﬂ ows 14,33 – 37…”