1. title and keywords
the Title:
Unveiling hidden Migration and Mobility Patterns in a stressed of all Climate Regions: A Longitudinal Study of the Users Six Million 101 - 500 Anonymous Mobile Phone in Bangladesh
reveal hidden areas of tension climate migrants and mobile modes: Bangladesh six million anonymous mobile phone users Longitudinal research
Keywords:
Climate change; Climate tension;
Adaptation;
Disaster;
Mobile data;
Migration;
Bangladesh.
2. Summary
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.
Climate change may prompt the migration of residents in environmentally stressed areas. However, quantifying short-term and long-term flows in a large area is a challenge due to the difficulty of collecting human movement data in altitude, time and space. In this study, we used two sets of personal movement trajectory data sets from 6 million unidentified mobile phone users in Bangladesh in 3 months and 2 years, respectively. Using data collected during Hurricane Mahasen, which hit Bangladesh in May 2013, we first showed how analysis based on mobile network data can describe important short-term characteristics (hour-week) of human activities during and after extreme weather events, using standards-based It is very difficult to quantify survey research. Then, we showed how mobile data allows us to study the relationship between the basic parameters of migration patterns across the country. We also quantify the incidence, direction, duration and seasonality of immigration events in Bangladesh. Although we found that changes in the rate of migration events are highly correlated with changes in migration duration, the correlation between inflow and outflow between regions is unexpectedly weak. The methodological framework described in this article provides an important complement to existing methods for studying human migration and climate change.
3. Innovation and academic value
Mobile network data is a very promising data source that can supplement current survey-based methods to monitor, interpret and respond to migration caused by climate change, including extreme weather and slowly emerging climate stressors.
4. Understanding of conclusions and enlightenment for learning work.
In this study, we demonstrated at the national level in Bangladesh how mobile data enables us to simultaneously quantify the incidence, direction and duration of migration events, so as to be able to describe Undocumented characteristics of long-term migration patterns in climate-stressed areas. Specifically, mobile network data provides a new tool to quantify the directionality and seasonality of migration patterns locally and nationwide.
Future work:
Although current research shows that mobile network data can well reflect the characteristics of population mobility, this method needs to be further developed in different socio-economic contexts. The key contribution of mobile data may come from combining the huge space, time, and population coverage of mobile network data with targeted phone-based and family group surveys. This is crucial in order to describe the representation of vulnerable groups such as women, children and the poorest in mobile data. With the further development of methods and the continuous increase in mobile penetration, a large-scale stratified sample based on country-specific mobile usage patterns may provide more accurate results. It can access many years of operator data, and combine operator data with longitudinal climate and remote sensing data to better simulate human adaptive response to climate change, and it also has considerable potential.