Alice B. Kasakoff, University of South Carolina
Caglar Koylu, University of Iowa
Yuan Huang, University of South Carolina
Diansheng Guo, University of South Carolina
Increasing availability of crowd-sourced family tree data provide a unique opportunity to study historical migration and kinship networks. In this paper we analyze and visualize the spatial and temporal patterns in the dispersion of kin in the United States using a crowd-sourced set of Big Data from user submitted family trees. We first assess the accuracy and representativeness of these data by comparing it with spatial patterns using data from the 1880 census. Our preliminary results highlight that the data on the native born white population are more complete as compared to the family networks of immigrant groups such as the Irish and the German. Second, starting with the individuals alive in 1880, we trace them back several generations to measure and map family (kin) connectedness in the United States between 1800 and 1880. We locate individuals at the county level, and propose to employ four measures of distance to evaluate the kin connectedness: father-son, father-daughter, mother-son and mother-daughter. We expect to identify the East to West migration and geographic expansion patterns captured by previous studies. Recently, Koplanis et.al. (2018), using family trees from another source, have found an increase in distances after 1850 but they did not map these patterns. A major contribution of our work will be to map family connectedness, which will allow us to “see” where the distances are greatest and to more clearly identify the reasons for the geographic variation in the kinship patterns.
No extended abstract or paper available
Presented in Session 201. Living Arrangements and Family Connections