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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCI LTD  

A new exploratory method for analyzing time series data is proposed. A computational algorithm detects partial similarities between simultaneously occurring time series data and clusters the data into groups based on their similarities. A graphical representation that visualizes the data clustering process helps us understand similarity between time series data and classifies them into smaller subgroups. Numerical measures evaluate the effectiveness of clusters and provide a means for testing their statistical significance. The proposed method was applied to an analysis of the change of population distribution during a day in Salt Lake County, Utah, USA. This application supports the technical soundness of the method and provides empirical findings. (C) 2014 Elsevier Ltd. All rights reserved.

DOI： 10.1016/j.compenvurbsys.2014.02.001

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：WILEY-BLACKWELL  

Development in techniques of spatial data acquisition enables us to easily record the trajectories of moving objects. Movement of human beings, animals, and birds can be captured by GPS loggers. The obtained data are analyzed by visualization, clustering, and classification to detect patterns frequently or rarely found in trajectories. To extract a wider variety of patterns in analysis, this article proposes a new method for analyzing trajectories on a network space. The method first extracts primary routes as subparts of trajectories. The topological relations among primary routes and trajectories are visualized as both a map and a graph-based diagram. They permit us to understand the spatial and topological relations among the primary routes and trajectories at both global and local scales. The graph-based diagram also permits us to classify trajectories. The representativeness of primary routes is evaluated by two numerical measures. The method is applied to the analysis of daily travel behavior of one of the authors. Technical soundness of the method is discussed as well as empirical findings.

DOI： 10.1111/j.1467-9671.2012.01330.x

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD  

There is an increasing need for a quick, simple method to represent diurnal population change in metropolitan areas for effective emergency management and risk analysis. Many geographic studies rely on decennial U.S. Census data that assume that urban populations are static in space and time. This has obvious limitations in the context of dynamic geographic problems. The U.S. Department of Transportation publishes population data at the transportation analysis zone level in fifteen-minute increments. This level of spatial and temporal detail allows for improved dynamic population modeling. This article presents a methodology for visualizing and analyzing diurnal population change for metropolitan areas based on this readily available data. Areal interpolation within a geographic information system is used to create twenty-four (one per hour) population surfaces for the larger metropolitan area of Salt Lake County, Utah. The resulting surfaces represent diurnal population change for an average workday and are easily combined to produce an animation that illustrates population dynamics throughout the day. A case study of using the method to visualize population distributions in an emergency management context is provided using two scenarios: a chemical release and a dirty bomb in Salt Lake County. This methodology can be used to address a wide variety of problems in emergency management.

DOI： 10.1080/00330124.2010.533565

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER HEIDELBERG  

The space-time prism demarcates all locations in space-time that a mobile object or person can occupy during an episode of potential or unobserved movement. The prism is central to time geography as a measure of potential mobility and to mobile object databases as a measure of location possibilities given sampling error. This paper develops an analytical approach to assessing error propagation in space-time prisms and prism-prism intersections. We analyze the geometry of the prisms to derive a core set of geometric problems involving the intersection of circles and ellipses. Analytical error propagation techniques such as the Taylor linearization method based on the first-order partial derivatives are not available since explicit functions describing the intersections and their derivatives are unwieldy. However, since we have implicit functions describing prism geometry, we modify this approach using an implicit function theorem that provides the required first-order partials without the explicit expressions. We describe the general method as well as details for the two spatial dimensions case and provide example calculations.

DOI： 10.1007/s10109-010-0139-z

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Total pages：177  

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