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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.

Power outages are a common outcome of hurricanes in the United States with potentially serious implications for community wellbeing. Understanding how power outage recovery is influenced by factors such as the magnitude of the outage, storm characteristics, and community demographics is key to building community resilience. Outage data is a valuable tool that can help to better understand how hurricanes affect built infrastructure and influence the management of short-term infrastructure recovery process. We conduct a spatial regression analysis on customers experiencing outages and the total power recovery time to investigate the factors influencing power outage recovery in Louisiana after Hurricane Isaac. Our interest was in whether infrastructure damage and recovery times resulting from a hurricane disproportionately affect socio-economically vulnerable populations and racial minorities. We find that median income is a significant predictor of 50%, 80%, and 95% recovery times, even after controlling for hurricane characteristics and total outages. Higher income geographies and higher income adjacent geographies experience faster recovery times. Our findings point to possible inequities associated with income in power outage recovery prioritization, which cannot be explained by exposure to outages, storm characteristics, or the presence of critical services such as hospitals and emergency response stations. These results should inform more equitable responses to power outages in the future helping to improve overall community resilience.

Place attachment is an important factor that may influence migration decisions, although how it relates to environmental change and mobility is poorly understood. This article uses survey data from 1,695 household heads in 13 villages in southwestern Bangladesh to examine how place attachment, demographics, perceptions of environmental change, and mobility interact. We begin by asking how place attachment and mobility are related in this context. We then ask what individual demographics are important for predicting place attachment; how trust in one's neighbors correlate with place attachment; and how perceptions about environmental change in communities influence place attachment. Results indicate that mobility and place attachment are significantly correlated, though more work is needed to understand the nature of the relationship. We find that education and religion are important predictors of place attachment at the individual level. At the community level, trust in one's neighbors is also a strong predictor. In this context, several perceived changes in environmental conditions are also significant, including groundwater salinity and riverbank erosion. In this way, the analysis draws empirical connections among individual perceptions of place, community dynamics, environmental change, and mobility with implications for policies to support communities impacted by environmental stress.What is the significance of this article for the general public?This study advances the understanding of how place attachment, demographics, environmental perceptions, and mobility are related in southwestern Bangladesh. We show that mobility and self-reported place attachment are significantly related, motivating a further investigation of place attachment. We find that education, religion, community-level trust, and select environmental perceptions are important predictors of place attachment. Results highlight the need for policies that consider the complexities of place and people connections when communities experience environmental stress.