Data sets composed of sequences of curves sampled at high frequencies in time are increasingly common in practice, but they can exhibit complicated dependence structures that cannot be modelled using common methods in functional data analysis. We detail a hierarchical approach that treats the curves as observations from a hidden Markov model. The distribution of each curve is then defined by another fine-scale model that may involve autoregression and require data transformations using moving-window summary statistics or Fourier analysis. This approach is broadly applicable to sequences of curves exhibiting intricate dependence structures. As a case study, we use this framework to model the fine-scale kinematic movements of a northern resident killer whale (Orcinus orca) off the western coast of Canada. Through simulations, we show that our model produces more interpretable state estimation and more accurate parameter estimates compared to existing methods.
Safe and affordable transportation has a positive impact on the health and well-being of older adults. What is less understood are which factors influence these outcomes. To examine the impact of trip characteristics on the mood of older adults, residents in three neighborhoods in Franklin County, Ohio (n = 32) were provided tablets and used an app (MyAmble) to document their travel. During a 14-day period, 1,190 trips were recorded; 71% of which were completed by car. Participants reported 72% of the trips improved their mood. Perceived importance of the trip, challenges associated with the trip, and trip destinations to social activities and to employment/education explained 33% of the variance in mood. Challenges associated with the trip was the strongest predictor of impact on mood. Identifying trip characteristics that impact mood provides new insights for the design and implementation of travel interventions for older persons.
Aging is linked to an increased risk of disability. Disabilities that limit major life activities such as seeing, walking, and motor skills impact a person’s ability to drive a car. Low utilization of alternative transportation by older adults may put them at risk for social isolation. The purpose of this paper is to illustrate how community-based participatory research (CBPR) was used to engage older residents in the development of alternative transportation options in a metropolitan county in the Midwestern U.S. Older residents worked as co-investigators to develop, use and evaluate alternative transportation options including walking, biking, fixed route busing, senior circulator, ride sharing, and transit training. Data were collected through mapping the built environment, an electronic daily transportation diary app called “MyAmble” on tablets, walk audits and focus groups. CBPR approaches led by interdisciplinary teams resulted in community engagement and more equitable strategies for transportation planning and utilization.
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