Browsing by Subject "Data visualization"

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    Data-driven knowledge discovery of intervention patterns for older adults with and without end-of-life care interventions using visualization techniques
    (2022-08) Moore, Derek
    The majority of hospice care in the United States (also known as end-of-life care [EOLC]) is home-based, provided by public health and home care agencies. Worldwide, palliative and EOLC care are often combined and can be provided years before death. In the United States, however, as most reimbursement for EOLC is limited to six months before death, palliative care services are often separate from EOLC. A systematic review of home-based palliative care outcomes in the United States found strong evidence for lower hospitalization rates and lower costs and limited evidence for high patient satisfaction, increased dying at home, and quality of life improvement. To study home-based EOLC, data from 1167 clients with and EOLC intervention were matched 1:1 with older adult health care clients by gender and age using the Omaha System. Those with an EOLC intervention had 41.6% more total interventions, 59.0% more total visits, and 25.6% fewer problems than those without an EOLC intervention. Data visualization techniques from exploratory data analysis were then used to compare this data to standardized guidelines. Some overlap between guidelines and data was found, but granularity increased when terms were combined, showing the ability of the Omaha System terminology to adapt to the level of granularity needed, making it ideal for intervention dataset analysis. This study leveraged Omaha System data from practice settings to discover novel EOLC intervention patterns for older adults. These methods may be used to generate new practice-based evidence for other populations, settings, and practices.
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    A Performance visualization and fine-tuning tool for arterial traffic signal systems
    (2014-04) Zheng, Jianfeng
    Maintaining an efficient traffic signal operation is a challenging task for many traffic management agencies. Due to the intensive labor cost required, most of the traffic signals in the US are retimed once every 2-5 years. However, it has been shown in the past that traffic delay increases 3-5% per year simply because the timing plans are not kept up to date. For many resource-constrained agencies, it would be desirable to reduce the signal re-timing costs by automating all or portion of the manual process. The research makes one-step forward towards this direction. In this research, we developed a performance monitoring and visualization tool for arterial traffic signal systems, aiming at reducing the labor cost for signal retiming, and helping to identify signal parameter adjustment opportunities. Specifically, an automated data collection unit (DCU) was developed to collect high-resolution event-based data from signal controller cabinets. Using the high-resolution data, two parameter fine-tuning algorithms were proposed, one for offset and another for green splits. To fine-tune signal offsets, a practical procedure to construct the time space diagram (TS-Diagram) to visualize the progression quality on arterials was proposed. The TS-Diagram was calibrated and validated using the field data collected from the DCUs and the probe vehicle runs. Reasonable agreements between the field observations and the generated TS-Diagrams were found. A field experiment was then carried out, to illustrate how decisions of changes could be made by intuitively evaluating the TS-Diagram. For green splits, an adjusted measure of effectiveness (MOE), the utilized green time (UGT), was proposed for performance evaluation. The information was further tabulated in the form of ring-and-barrier diagram to facilitate evaluation. Field examples were also illustrated to demonstrate implementation potentials for green split evaluation and fine-tuning.