Abstract

Rural communities in the Upper-Midwest are essential for contributions to agriculture, oil and development of economic networks to larger towns and cities. Concerning rural population stability and transitions, this research study aims to discover complex migration flows by constructing specified groups of Upper-Midwest regions (i.e., Bakken oil, Taconite iron, high agriculture, developing area of rural depopulation and Interstate 94). Research questions on migrant distributions will be answered by investigating (in-) and (out-) flow data by demographic characteristics (e.g., age, gender and ethnicity) on a county-to-county level. By weighing total demographic populations, a more accurate representation of migration trends called Crude Net-Migration Rates (CNMR) are utilized as the primary variables with desired spatial statistical methods (Global and Local Moran’s I index). Global and Local Moran’s I Index detects the strength of spatial patterns (i.e., cluster, dispersed or random) and reveals areas of statistical significance related to human mobility all within a Geographic Information System (GIS) context. Results show a connection of pertinent demographic attributes with certain regional migrations: (a) young adults and males generally move to the Taconite iron region or agricultural areas to attain jobs in demand, and (b) higher percentages of females, college-aged and Asian migrants, frequently move to counties with a sizeable university or larger metropolitan. This examination of regional migration flows in a geographical perception identifies types of current migration events and leads to speculation of causes and effects. This research can be further applied to investigate additional findings by limiting the scope to a smaller area with defined spatial units and correlation of new or past time-series data to indicate potential migration flows.

Advisor

Woo Jang

First Committee Member

Fei Yuan

Second Committee Member

Forrest Wilkerson

Date of Degree

2015

Language

english

Document Type

Thesis

Degree

Master of Science (MS)

Department

Geography

College

Social and Behavioral Sciences

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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