Mapping Temporal Trends of Parent-Child Migration from Population-Scale Family Trees

TL;DR

This study analyzes 150 years of U.S. migration patterns using family trees, evaluating temporal partitioning strategies and applying a gravity model to reveal long-term population mobility trends.

Contribution

It introduces a novel approach to extract and analyze intergenerational migration flows from family trees over a century and a half, assessing temporal partitioning effects.

Findings

Identified significant longitudinal population mobility trends.

Compared different temporal partitioning strategies for migration analysis.

Applied gravity model to normalize flow data considering proximity and volume.

Abstract

User-generated family trees are invaluable for constructing population-scale family networks and studying population dynamics over many generations and far into the past. Family trees contain information on individuals such as birth and death places and years, and kinship ties, e.g., parent-child, spouse, and sibling relationships. Such information about individuals in family trees makes it possible to extract migration networks over time. Despite the recent advances, existing spatial and temporal abstraction techniques for time-variant flow data have limitations due to the lack of knowledge on the effect of temporal partitioning on flow patterns. In this study, we extracted state-to-state migration patterns over a period of 150 years between 1776 and 1926 from a cleaned, geocoded and connected family trees from Rootsweb.com. We used birthplaces and birthyears of parents and children to extract intergenerational migration flows between states. To reveal the temporal trends of migration patterns, we evaluated three temporal partitioning strategies: (1) predefined periods in American history, (2) overlapping time periods with fixed length, and (3) time periods with variable length, which have approximately equal volume of moves per time period. To account for the effect of geographic proximity and flow volumes in migration flows, we transformed the raw flows into modularity flows using a double-constrained a gravity model. Our preliminary results revealed longitudinal population mobility in the U.S. on such a large spatial and temporal scale.