With the increasing population and proportion of older adults, the U.S. is facing some challenges and opportunities of an aging society. To provide better insights and accommodations to older adults, the overarching research question of this dissertation is what older adult migration choices and living environments, including vulnerability and accessibility, are in the aging U.S. using GIS and statistical methods. First, this dissertation uses both linear regression and decision tree models to understand the relationships between the number of older migrants and destination characteristics, including long-term care facilities, affordable housing, and geriatrician availability, which are rarely considered in previous research. The results show that relationships are different for older migrant subgroups of intrastate, interstate, younger, and aged migrants. Second, this study introduces a new Vulnerability Index of Older adults (VIO) to consider both environmental and social factors that make older adults more vulnerable than other age groups. The index locates some areas that need more attention for older adults, especially in potential natural hazards or emergencies. Lastly, accessibility to emergency services is vital for older adults to receive timely treatment. This study applies time series concepts and methods to decompose real-time travel time data collected from Google for over five months. The embedded trend and seasonality (daily and weekly) can be detected and used for future travel time prediction. With time series tools, accessibility components can be expressed as functions of time to capture the changing dynamics of travel distance, demand, and supply.
