Browsing by Author "Wiehe, Sarah Elizabeth"

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    Determining trip and travel mode from GPS and accelerometer data
    (2016-03) Burgess, Aaron W.; Wilson, Jeffrey S.; Wiehe, Sarah Elizabeth; Filippelli, Gabriel M.
    The use of Global Positioning Systems (GPS) and/or accelerometers to identify trips and transportation modes such as walking, running, bicycling or motorized transportation has been an active goal in multiple disciplines such as Transportation Engineering, Computer Science, Informatics and Public Health. The purpose of this study was to review existing methods that determined trip and travel mode from raw Global Positioning System (GPS) and accelerometer data, and test a select group of these methods. The study had three specific aims: (1) Create a systematic review of existing literature that explored various methods for determining trip and travel mode from GPS and/or accelerometer data, (2) Collect a convenience sample of subjects who were assigned a GPS and accelerometer unit to wear while performing and logging travel bouts consisting of walking, running, bicycling and driving, (3) Replicate selected method designs extracted from the systematic review (aim 1) and use subject data (aim 2) to compare the methods. The results were be used to examine which methods are effective for various modes of travel.
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    Medical Imaging Centers in Central Indiana: Optimal Location Allocation Analyses
    (2016-01) Seger, Mandi J.; Banerjee, Aniruddha; Wilson, Jeffrey S.; Lulla, Vijay O.; Wiehe, Sarah Elizabeth
    While optimization techniques have been studied since 300 B.C. when Euclid first considered the minimal distance between a point and a line, it wasn’t until 1966 that location optimization was first applied to a problem in healthcare. Location optimization techniques are capable of increasing efficiency and equity in the placement of many types of services, including those within the healthcare industry, thus enhancing quality of life. Medical imaging is a healthcare service which helps to determine medical diagnoses in acute and preventive care settings. It provides physicians with information guiding treatment and returning a patient back to optimal health. In this study, a retrospective analysis of the locations of current medical imaging centers in central Indiana is performed, and alternate placement as determined using optimization techniques is considered and compared. This study focuses on reducing the drive time experienced by the population within the study area to their nearest imaging facility. Location optimization models such as the P-Median model, the Maximum Covering model, and Clustering and Partitioning are often used in the field of operations research to solve location problems, but are lesser known within the discipline of Geographic Information Science. This study was intended to demonstrate the capabilities of these powerful algorithms and to increase understanding of how they may be applied to problems within healthcare. While the P-Median model is effective at reducing the overall drive time for a given network set, individuals within the network may experience lengthy drive times. The results further indicate that while the Maximum Covering model is more equitable than the P-Median model, it produces large sets of assigned individuals overwhelming the capacity of one imaging center. Finally, the Clustering and Partitioning method is effective at limiting the number of individuals assigned to a given imaging center, but it does not provide information regarding average drive time for those individuals. In the end, it is determined that a capacitated Maximal Covering model would be the preferred method for solving this particular location problem.