Total: 126
Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh
Global Environmental Change May.
Author(s): Xin Lu,David J. Wrathall,Pål Roe Sundsøy,Md. Nadiruzzaman,Erik Wetter,Asif Iqbal,Taimur Qureshi,Andrew Tatem,Geoffrey Canright,Kenth Engø-Monsen,Linus Bengtsson
Type: method. Year: 2016
DOI: 10.1016/j.gloenvcha.2016.02.002.

Abstract: Climate change is likely to drive migration from environmentally stressed areas. However quantifying short and long-term movements across large areas is challenging due to difficulties in the collection of highly spatially and temporally resolved human mobility data. In this study we use two datasets of individual mobility trajectories from six million de-identified mobile phone users in Bangladesh over three months and two years respectively. Using data collected during Cyclone Mahasen, which struck Bangladesh in May 2013, we show first how analyses based on mobile network data can describe important short-term features (hours–weeks) of human mobility during and after extreme weather events, which are extremely hard to quantify using standard survey based research. We then demonstrate how mobile data for the first time allow us to study the relationship between fundamental parameters of migration patterns on a national scale. We concurrently quantify incidence, direction, duration and seasonality of migration episodes in Bangladesh. While we show that changes in the incidence of migration episodes are highly correlated with changes in the duration of migration episodes, the correlation between in- and out-migration between areas is unexpectedly weak. The methodological framework described here provides an important addition to current methods in studies of human migration and climate change.
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patiotemporal patterns of population in mainland China, 1990 to 2010
Scientific Data 3, Article number: 160005 (2016.
Author(s): Andrea E. Gaughan, Forrest R. Stevens, Zhuojie Huang, Jeremiah J. Nieves, Alessandro Sorichetta, Shengjie Lai, Xinyue Ye, Catherine Linard, Graeme M. Hornby, Simon I. Hay, Hongjie Yu & Andrew J. Tatem.
Type: method. Year: 2016
DOI: 10.1038/sdata.2016.5.

Abstract: According to UN forecasts, global population will increase to over 8 billion by 2025, with much of this anticipated population growth expected in urban areas. In China, the scale of urbanization has, and continues to be, unprecedented in terms of magnitude and rate of change. Since the late 1970s, the percentage of Chinese living in urban areas increased from ~18% to over 50%. To quantify these patterns spatially we use time-invariant or temporally-explicit data, including census data for 1990, 2000, and 2010 in an ensemble prediction model. Resulting multi-temporal, gridded population datasets are unique in terms of granularity and extent, providing fine-scale (~100 m) patterns of population distribution for mainland China. For consistency purposes, the Tibet Autonomous Region, Taiwan, and the islands in the South China Sea were excluded. The statistical model and considerations for temporally comparable maps are described, along with the resulting datasets. Final, mainland China population maps for 1990, 2000, and 2010 are freely available as products from the WorldPop Project website and the WorldPop Dataverse Repository.
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Census-derived migration data as a tool for informing malaria elimination policy
Malaria Journal201615:273 .
Author(s): Nick W. Ruktanonchai, Darlene Bhavnani, Alessandro Sorichetta, Linus Bengtsson, Keith H. Carter, Roberto C. Córdoba, Arnaud Le Menach, Xin Lu, Erik Wetter, Elisabeth zu Erbach-Schoenberg and Andrew J. Tatem
Type: method. Year: 2016
DOI: 10.1186/s12936-016-1315-5.

Abstract: Numerous countries around the world are approaching malaria elimination. Until global eradication is achieved, countries that successfully eliminate the disease will contend with parasite reintroduction through international movement of infected people. Human-mediated parasite mobility is also important within countries near elimination, as it drives parasite flows that affect disease transmission on a subnational scale.
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Improving Large Area Population Mapping Using Geotweet Densities
Trans. in GIS. (2016)..
Author(s): Nirav N. Patel, Forrest R. Stevens, Zhuojie Huang, Andrea E. Gaughan, Iqbal Elyazar, Andrew J. Tatem
Type: method. Year: 2016
DOI: 10.1111/tgis.12214.

Abstract: Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo-located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo-located tweets in 1x1 km grid cells over a 2-month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests-based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media-derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.
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