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The quality and representativeness of longitudinal datasets play a central role in historical migration research. In this study, we apply the child-ladder (CL) method to a population-scale family tree dataset to analyze U.S. interstate family migration from 1850 to 1920. The CL method infers moves from changes in birthplaces between successive children, allowing for more precise dating of migration events. However, it is limited to families with at least two children. To evaluate the representativeness and utility of family trees for migration research, we compare the CL data to the IPUMS Multigenerational Longitudinal Panel (MLP), which tracks household moves across census decades and serves as a proxy for broader population migration. The CL data reveal higher migration rates, suggesting a likely closer approximation to migration levels in the overall population. Also, by capturing intercensal and return migrations, the CL method provide a detailed view of migration patterns across space and time. Despite differences in migration rates, both datasets reveal similar regional migration structures, especially in the earlier periods. These findings show that population-scale family trees when analyzed using the CL method, offer a valuable complement to linked census data by enhancing our understanding of long-term U.S. migration patterns and regional divisions. 

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. 

ABSTRACT GIS and GIScience education have continually evolved over the past three decades, responding to technological advances and societal issues. Today, the content and context in which GIScience is taught continue to be impacted by these disruptions, notably from technology through artificial intelligence (AI) and society through the myriad environmental and social challenges facing the planet. These disruptions create a new landscape for training within the discipline that is affecting not onlywhatis taught in GIScience courses but alsowhois taught,whyit is being taught, andhowit is taught. The aim of this paper is to structure a direction for developing and delivering GIScience education that, amid these disruptions, can generate a capable workforce and the next generation of leaders for the discipline. We present a framework for understanding the various emphases of GIScience education and use it to discuss how the content, audience, and purpose are changing. We then discuss how pedagogical strategies and practices can change how GIScience concepts and skills are taught to train more creative, inclusive, and empathetic learners. Specifically, we focus on how GIScience pedagogy should (1) center on problem‐based learning, (2) be open and accelerate open science, and (3) cultivate ethical reasoning and practices. We conclude with remarks on how the principles of GIScience education can extend beyond disciplinary boundaries for holistic spatial training across academia. 

Abstract Susceptibility to infectious diseases such as COVID-19 depends on how those diseases spread. Many studies have examined the decrease in COVID-19 spread due to reduction in travel. However, less is known about how much functional geographic regions, which capture natural movements and social interactions, limit the spread of COVID-19. To determine boundaries between functional regions, we apply community-detection algorithms to large networks of mobility and social-media connections to construct geographic regions that reflect natural human movement and relationships at the county level in the coterminous United States. We measure COVID-19 case counts, case rates, and case-rate variations across adjacent counties and examine how often COVID-19 crosses the boundaries of these functional regions. We find that regions that we construct using GPS-trace networks and especially commute networks have the lowest COVID-19 case rates along the boundaries, so these regions may reflect natural partitions in COVID-19 transmission. Conversely, regions that we construct from geolocated Facebook friendships and Twitter connections yield less effective partitions. Our analysis reveals that regions that are derived from movement flows are more appropriate geographic units than states for making policy decisions about opening areas for activity, assessing vulnerability of populations, and allocating resources. Our insights are also relevant for policy decisions and public messaging in future emergency situations. 

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.