Predictability of population displacement after the 2010 Haiti earthquake

By Xin Lu, Linus Bengtsson, Petter Holme. Proceedings of the National Academy of Sciences of the United States of America, 2012

626 citations recorded by the source corpus.

Subfields: Ocean Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/b3f6df4e71c542902946ec31323232612e367d24

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Citations in the corpus

Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, background): “In particular, several works tried to infer the human patterns of mobility during emergencies and special events [Bagrow et al. 2011; Calabrese et al. 2010; Ferrari et al. 2012; Lu et al. 2012; Traag et al. 2011].”
Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China (2020, neutral): “This study is also related to disaster-induced migration, which has often occurred during flooding (Gray and Mueller, 2012), drought (Munshi, 2003), earthquake (Lu et al., 2012), and other destructive climatic phenomena.”
On the Use of Human Mobility Proxies for Modeling Epidemics (2013, background): “They were indeed used to shed light on malaria diffusion and identify hotspot areas [24,26,27], to monitor human displacements in case of natural disasters [25,28] and to study disease containment strategies in Ivory Coast [29].”
A review of urban computing for mobile phone traces: current methods, challenges and opportunities (2013, background): “A particular challenge in this paper is how to reconcile previous findings observed at more aggregate spatial scales, such as use of subway stations [19] or mobile phone towers, with triangulated mobile phone data sets containing thousands of noisy coordinates per individual user [42, 29].”
Big Data for Development: A Review of Promises and Challenges (2016, positive): “…phone call records allow to approach the limit of extractable predictability in human mobility, being able to predict up to 95 % of people’s movements in more stable situations (Song et al., 2010; Lu et al., 2013) and even 85 % in chaotic situations, such after an earthquake (Lu et al., 2012).”
The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, background): “The characterization of individuals and entire populations’ mobility is of paramount importance for public health [57]: for example, it is key to predict the spatial and temporal risk of diseases [35, 82, 94], to quantify exposure to air pollution [48], to understand human migrations after natural d”
Dynamic population mapping using mobile phone data (2014, background): “Population movement analyses based on MP data are particularly promising for improving responses to disasters (47, 48) and for planning malaria elimination strategies (49 – 51).”
Global Burden of Sickle Cell Anaemia in Children under Five, 2010–2050: Modelling Based on Demographics, Excess Mortality, and Interventions (2013, methodology): “Accessing immigration data [63] and using mobile phone network data [64,65] for international migrations would be possible, but this is currently beyond the scope of this project.”
Mobile phone location data for disasters: A review from natural hazards and epidemics (2021, positive): “(Lu, Bengtsson, & Holme, 2012) Using data collected from 1.9 million mobile phone users during the period from 42 days before to 341 days after the shock, the study estimated that 23% of the population in Port-au-Prince had been displaced due to the earthquake.”
Approaching the Limit of Predictability in Human Mobility (2013, result): “The finding of high predictability is consistent with two previous studies which investigated the mobility patterns of mobile phone users invery different settings, one in a high-income country with stable social conditions 17 and one in a low-income country following an extreme natural disaster 1 .”
The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, result): “Similar results are found by other researchers (Lu et al., 2012, 2013).”