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1. Bangladesh Environment and Migration Survey (BEMS), 2019 :Version 1

   https://doi.org/10.3886/ICPSR38846.v1  

   Carrico, Amanda ; Donato, Katharine M. ( January 2023 , ICPSR - Interuniversity Consortium for Political and Social Research)

   The Bangladesh Environment and Migration Survey (BEMS) collects detailed retrospective information about migration trips in southwest Bangladesh, including the first, last, and second-to-last to internal destinations, India, and other international destinations. BEMS collects information about the year, origin, destination, and duration of all trips. Furthermore, BEMS includes information on migration and livelihood histories, socioeconomic conditions, agricultural resources and practices, disasters and perceptions about environment, and self-reported health.

   Dataset 1 is a household-level file with information about household composition, economic and migratory activity of household members, land ownership/usage, business ownership, household environmental perceptions, environmental conditions, agricultural activities, and physical and psychological health/well-being of household members. Dataset 2 is an individual-level file containing details of internal and international migration trips, as well as measures of economic and social activity during those trips. It also contains information provided by household heads, spouses, and other migrants in the household. Dataset 3 is an individual-level data file that provides general demographic information and brief migration history for each member of a surveyed household. It also includes health information for the head of household and spouse.

   The purpose of the Bangladesh Environment and Migration Survey (BEMS) is to understand patterns and processes of contemporary internal and international migration in Bangladesh. The project derives from a multi-disciplinary research effort that will generate data on the characteristics and behavior of Bangladeshi migrants and non-migrants and the communities in which they live, and examine whether and how environmental stressors (e.g., salinity, riverbank erosion) affect patterns of migration in this region. The household ethnosurvey is administered to self-identified household heads and spouses in randomly selected households. After gathering social, demographic, and economic information on households and their members, interviewers will collect basic information on each person's first, 2nd to last, and last (or most recent) internal and international migration trips. From household heads and spouses, they will compile migration histories and administer a detailed series of questions about a selection of these trips, focusing on economic livelihoods, methods of moving, connections to other migrants, and use of health and school services. In addition to detailed migration histories, the BEMS will collect information about household wealth, physical conditions of households and communities, and perceptions of environmental conditions. It will also gather some self-reported health information about household members, such as recent illnesses, use of health services, height and weight, and diet. The BEMS is closely modeled on the sampling design and ethnosurvey used in the Mexican Migration Project. The BEMS data were collected in 20 research sites from a random sample of 200 households in each site in 2019. BEMS data include a total of 4,000 households in communities broadly covering the southwest region of Bangladesh. Households in southwest Bangladesh. Smallest Geographic Unit: Administrative region

   For more information about this study, please visit the ISEE Bangladesh project website.

    

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2. Applying machine learning to social datasets: a study of migration in southwestern Bangladesh using random forests

   https://doi.org/10.1007/s10113-022-01915-1  

   Best, Kelsea ; Gilligan, Jonathan ; Baroud, Hiba ; Carrico, Amanda ; Donato, Katharine ; Mallick, Bishawjit ( March 2022 , Regional Environmental Change)

   As researchers collect large amounts of data in the social sciences through household surveys, challenges may arise in how best to analyze such datasets, especially where motivating theories are unclear or conflicting. New analytical methods may be necessary to extract information from these datasets. Machine learning techniques are promising methods for identifying patterns in large datasets, but have not yet been widely used to identify important variables in social surveys with many questions. To demonstrate the potential of machine learning to analyze large social datasets, we apply machine learning techniques to the study of migration in Bangladesh. The complexity of migration decisions makes them suitable for analysis with machine learning techniques, which enable pattern identification in large datasets with many covariates. In this paper, we apply random forest methods to analyzing a large survey which captures approximately 2000 variables from approximately 1700 households in southwestern Bangladesh. Our analysis ranked the covariates in the dataset in terms of their predictive power for migration decisions. The results identified the most important covariates, but there exists a tradeoff between predictive ability and interpretability. To address this tradeoff, random forests and other machine learning algorithms may be especially useful in combination with more traditional regression methods. To develop insights into how the important variables identified by the random forest algorithm impact migration, we performed a survival analysis of household time to first migration. With this combined analysis, we found that variables related to wealth and household composition are important predictors of migration. Such multi-methods approaches may help to shed light on factors contributing to migration and non-migration.

    

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3. A novel framework to study community-level social and physical impacts of hurricane-induced winds through synthetic scenario analysis

   https://doi.org/10.3389/fbuil.2023.1005264  

   Mazumder, Ram Krishna ; Enderami, S. Amin ; Sutley, Elaina J. ( February 2023 , Frontiers in Built Environment)

   Strong hurricane winds often cause severe infrastructure damage and pose social and economic consequences in coastal communities. In the context of community resilience planning, estimating such impacts can facilitate developing more risk-informed mitigation plans in the community of interest. This study presents a new framework for synthetically simulating scenario-hurricane winds using a parametric wind field model for predicting community-level building damage, direct economic loss, and social consequences. The proposed synthetic scenario approach uses historical hurricane data and adjusts its original trajectory to create synthetic change scenarios and estimates peak gust wind speed at the location of each building. In this research, a stochastic damage simulation algorithm is applied to assess the buildings’ physical damage. The algorithm assigns a damage level to each building using the corresponding damage-based fragility functions, predicted maximum gust speed at the building’s location, and a randomly generated number. The monetary loss to the building inventory due to its physical damage is determined using FEMA’s direct loss ratios and buildings’ replacement costs considering uncertainty. To assess the social impacts of the physical damage exposure, three likely post-disaster social disruptions are measured, including household dislocation, employment disruption, and school closures. The framework is demonstrated by its application to the hurricane-prone community of Onslow County, North Carolina. The novel contribution of the developed framework, aside from the introduced approach for spatial predicting hurricane-induced wind hazards, is its ability to illuminate some aspects of the social consequences of substantial physical damages to the building inventory in a coastal community due to the hurricane-induced winds. These advancements enable community planners and decision-makers to make more risk-informed decisions for improving coastal community resilience.

    

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4. Modeling Multi-Level Patterns of Environmental Migration in Bangladesh: An Agent-Based Approach

   https://doi.org/10.1109/WSC52266.2021.9715380  

   Best, Kelsea ; Gilligan, Jonathan ; Qu, Ao ( January 2021 , Proceedings of the 2021 Winter Simulation Conference)

   S. Kim ; B. Feng ; K. Smith ; S. Masoud ; Z. Zheng ; C. Szabo ; M. Loper (Ed.)

   Environmental change interacts with population migration in complex ways that depend on interactions between impacts on individual households and on communities. These coupled individual-collective dynamics make agent-based simulations useful for studying environmental migration. We present an original agent-based model that simulates environment-migration dynamics in terms of the impacts of natural hazards on labor markets in rural communities, with households deciding whether to migrate based on maximizing their expected income. We use a pattern-oriented approach that seeks to reproduce observed patterns of environmentally-driven migration in Bangladesh. The model is parameterized with empirical data and unknown parameters are calibrated to reproduce the observed patterns. This model can reproduce these patterns, but only for a narrow range of parameters. Future work will compare income-maximizing decisions to psychologically complex decision heuristics that include non-economic considerations. 

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5. Evaluation of predicted loss of different land use and land cover (LULC) due to coastal erosion in Bangladesh

   https://doi.org/10.3389/fenvs.2023.1144686  

   Islam, Md Sariful ; Crawford, Thomas W. ; Shao, Yang ( April 2023 , Frontiers in Environmental Science)

   Coastal erosion is one of the most significant environmental threats to coastal communities globally. In Bangladesh, coastal erosion is a regularly occurring and major destructive process, impacting both human and ecological systems at sea level. The Lower Meghna estuary, located in southern Bangladesh, is among the most vulnerable landscapes in the world to the impacts of coastal erosion. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. With an aim to assess the impacts of historical and predicted shoreline change on different land use and land cover, this study estimated historical shoreline movement, predicted shoreline positions based on historical data, and quantified and assessed past land use and land cover change. Multi-temporal Landsat images from 1988–2021 were used to quantify historical shoreline movement and past land use and land cover. A time-series classification of historical land use and land cover (LULC) were produced to both quantify LULC change and to evaluate the utility of the future shoreline predictions for calculating amounts of lost or newly added land resources by LULC type. Our results suggest that the agricultural land is the most dominant land cover/use (76.04% of the total land loss) lost over the studied period. Our results concluded that the best performed model for predicting land loss was the 10-year time depth and 20-year time horizon model. The 10-year time depth and 20-year time horizon model was also most accurate for agricultural, forested, and inland waterbody land use/covers loss prediction. We strongly believe that our results will build a foundation for future research studying the dynamics of coastal and deltaic environments. 

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