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1. Mapping migration regions and their evolution from population-scale family trees: what can they tell us about cultural identities and regions today?

   https://doi.org/10.1145/3557919.3565814  

   Koylu, Caglar; Torkashvand, Maryam; Kwon, Hoeyun; Kasakoff, Alice Bee (November 2022, Proceedings of the 6th ACM SIGSPATIAL International Workshop on Geospatial Humanities)

   Moncla, Ludovic; Martins, Bruno; McDonough, Katherine (Ed.)

   Using a population-scale family tree dataset, this paper proposes a study of migration regions and their evolution in the U.S. between 1789 and 1924. To extract migration events, we use the child ladder approach, which traces family moves based on changes in birthplaces of consecutive children in each individual family. We calculate a time series measure of migration rate and partition the time into optimal periods so that each period has a distinct migration network. We apply community detection to derive migration regions from each network of different periods. We map these regions and use a pair-counting measure to statistically compare the similarity of regions in consecutive time periods. Migration regions reveal the extent to which the strong regional identities we see today, and, in the past, which were rooted in migration. The North/South divide was pervasive not only in the early periods but throughout U.S. history. Migration regions are important for understanding the development of regional and national cultural forms such as music, literature, foodways, and dialects, as well as political divisions and events. 

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2. Ethical Challenges in Analyzing and Mapping Historical Demographic Changes and Migration Using Population-Scale Family Trees

   https://doi.org/10.14714/CP105.1945  

   Koylu, Caglar; Kasakoff, Alice Bee (September 2024, Cartographic perspectives)

   Despite the progress made toward generating and utilizing population-scale family trees to study historical population dynamics, little is known about their representativeness for the entire population. In this article, we confront the inherent complexities and biases in historical data collection and shed light on the extensive areas of history that remain unknown, unrecorded, or inaccurately portrayed. Although we do not provide definitive solutions for these data gaps, we aim to initiate a dialogue on these critical issues, contributing to the discourse on ethical data collection and representation in historical research. We first report on the preliminary results of a record linkage experiment between family tree records and a historical census, emphasizing the need for methods that integrate historical data from multiple sources to systematically evaluate representativeness. The experiment reveals significant underrepresentation of certain groups in the United States, notably Native American, Black, and Mexican persons, as well as those from eastern Europe, southern Europe, and Ireland. These findings underscore the ethical responsibilities that should guide historical research, including the need to address underrepresentation and improve methodologies to better reflect the diversity of population dynamics and migration patterns. To complement these efforts, we advocate for the use of interactive story maps to amplify the qualitative narratives of underrepresented populations and integrate them into the broader historical narrative. Our endeavor to map migration and demographic changes is not just about tracing the past; it’s about shaping a more equitable and comprehensive understanding of history that honors the diversity of all its participants. 

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3. Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections

   https://doi.org/10.1145/3651595  

   Bun, Mark; Gaboardi, Marco; Neunhoeffer, Marcel; Zhang, Wanrong (May 2024, Proceedings of the ACM on Management of Data)

   Motivated by privacy concerns in long-term longitudinal studies in medical and social science research, we study the problem of continually releasing differentially private synthetic data from longitudinal data collections. We introduce a model where, in every time step, each individual reports a new data element, and the goal of the synthesizer is to incrementally update a synthetic dataset in a consistent way to capture a rich class of statistical properties. We give continual synthetic data generation algorithms that preserve two basic types of queries: fixed time window queries and cumulative time queries. We show nearly tight upper bounds on the error rates of these algorithms and demonstrate their empirical performance on realistically sized datasets from the U.S. Census Bureau's Survey of Income and Program Participation. 

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4. Network evolution of regional brain volumes in young children reflects neurocognitive scores and mother’s education

   https://doi.org/10.1038/s41598-023-29797-1  

   Zhou, Yidong; Müller, Hans-Georg; Zhu, Changbo; Chen, Yaqing; Wang, Jane-Ling; O’Muircheartaigh, Jonathan; Bruchhage, Muriel; Deoni, Sean; Braun, Joseph; Bruchhage, Muriel; et al (December 2023, Scientific Reports)

   Abstract The maturation of regional brain volumes from birth to preadolescence is a critical developmental process that underlies emerging brain structural connectivity and function. Regulated by genes and environment, the coordinated growth of different brain regions plays an important role in cognitive development. Current knowledge about structural network evolution is limited, partly due to the sparse and irregular nature of most longitudinal neuroimaging data. In particular, it is unknown how factors such as mother’s education or sex of the child impact the structural network evolution. To address this issue, we propose a method to construct evolving structural networks and study how the evolving connections among brain regions as reflected at the network level are related to maternal education and biological sex of the child and also how they are associated with cognitive development. Our methodology is based on applying local Fréchet regression to longitudinal neuroimaging data acquired from the RESONANCE cohort, a cohort of healthy children (245 females and 309 males) ranging in age from 9 weeks to 10 years. Our findings reveal that sustained highly coordinated volume growth across brain regions is associated with lower maternal education and lower cognitive development. This suggests that higher neurocognitive performance levels in children are associated with increased variability of regional growth patterns as children age. 

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5. Explaining Income Disparities in Young Children’s Development: The Role of Community Contexts and Family Processes

   Coley, R. L. (January 2019, Early childhood research quarterly)

   null (Ed.)

   Growing economic disparities and the increased sorting of families into economically segregated communities have heightened the need to clearly delineate pathways through which family income promotes children’s development. Combining hypotheses from investment and stress theories, we developed and tested a multi-context and cross-domain conceptual model assessing how community and family contexts mediate links between family income and children’s cognitive and behavioral skills at kindergarten entry. We drew data on family income, parenting processes, and child functioning from the Early Childhood Longitudinal Study– Birth Cohort (ECLS-B; N ≈ 10,650), following children from infancy through age 5. We used Geographic Information Systems technology to create and validate community measures using administrative data from the Economic Census, Decennial Census, National Center of Education Statistics, Federal Bureau of Investigations, and Environmental Protection Agency, which were then linked to each child in the ECLS-B. Using structural equation modeling, our analyses revealed three primary lessons. First, lower-income children have limited access to community educational and cultural resources and heightened exposure to community stressors including concentrated disadvantage and violent crime. Second, these community features are associated with parenting processes, such that parent-child interactions tend to be less stimulating and supportive and more punitive in communities with fewer resources and heightened stressors. And third, community and family contexts together mediate connections between family income and children’s cognitive and behavioral functioning. Results, albeit showing small effect sizes, provide a more complex, multi-contextual view than prior research, delineating the role of both resources and stressors at community and family levels in explaining income disparities in young children’s developmental success. 

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