Browsing by Author "Ali, Reem Y."

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    Spatiotemporal Change Footprint Pattern Discovery: An Interdisciplinary Survey
    (2014-10-01) Zhou, Xun; Shekhar, Shashi; Ali, Reem Y.
    Given a definition of change and a dataset about spatiotemporal (ST) phenomena, ST change footprint discovery is the process of identifying the location and/or time of such changes from the dataset. Change footprint discovery is fundamentally important for the study of climate change, the tracking of disease, and many other applications. Methods for detecting change footprints have emerged from a diverse set of research areas, ranging from time series analysis and remote sensing to spatial statistics. Researchers have much to learn from one another, but are stymied by inconsistent use of terminology and varied definitions of change across disciplines. Existing reviews focus on discovery methods for only one or a few types of change footprints (e.g., point change in a time series). To facilitate sharing of insights across disciplines, we conducted a multi-disciplinary review of ST change patterns and their respective discovery methods. We developed a taxonomy of possible ST change footprints and classified our review findings accordingly. This exercise allowed us to identify gaps in the research that we consider ripe for exploration, most notably change pattern discovery in vector ST datasets. In addition, we illustrate how such pattern discovery might proceed using two case studies from historical GIS.
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    Supply-Demand Ratio and On-Demand Spatial Service Brokers
    (2016-09-08) Ali, Reem Y.; Eftelioglu, Emre; Shekhar, Shashi; Athavale, Shounak; Marsman, Eric
    This paper investigates an on-demand spatial service broker for suggesting service provider propositions and the corresponding estimated waiting times to mobile consumers while meeting the consumer’s maximum travel distance and waiting time constraints. The goal of the broker is to maximize the number of matched requests. In addition, the broker has to keep the “eco-system” functioning not only by meeting consumer requirements, but also by engaging many service providers and balancing their assigned requests to provide them with incentives to stay in the system. This problem is important because of its many related societal applications in the on-demand and sharing economy (e.g. on-demand ride hailing services, on-demand food delivery, etc). Challenges of this problem include the need to satisfy many conflicting requirements for the broker, consumers and service providers in addition to the problem’s computational complexity which is shown to be NP-hard. Related work has mainly focused on maximizing the number of matched requests (or tasks) and minimizing travel cost, but did not consider the importance of engaging more service providers and balancing their assignments, which could become a priority when the available supply highly exceeds the demand. In this work, we propose several matching heuristics for meeting these conflicting requirements, including a new category of service provider centric heuristics. We employed a discrete-event simulation framework and evaluated our algorithms using synthetic datasets with real-world characteristics. Experimental results show that the proposed heuristics can help engage more service providers and balance their assignments while achieving a similar or better number of matched requests. We also show that the matching heuristics have different dominance zones that vary with the supply-demand ratio and that a supply-demand ratio aware broker is needed to select the best matching policy.
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    Transdisciplinary Foundations of Geospatial Data Science
    (2017-12-05) Xie, Yiqun; Eftelioglu, Emre; Ali, Reem Y.; Tang, Xun; Li, Yan; Doshi, Ruhi; Shekhar, Shashi
    Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understanding of the scientific reasoning behind data mining and machine learning approaches. This requires taking a transdisciplinary view of data science and recognizing its foundations in mathematics, statistics, and computer science. Focusing on the geospatial domain, we apply this crucial transdisciplinary perspective to five common geospatial techniques (hotspot detection, colocation detection, prediction, outlier detection and teleconnection detection). We also describe challenges and opportunities for future advancement.