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    The Effects of Spatial Aggregation on Spatial Time Series Modeling and Forecasting

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    Genre
    Thesis/Dissertation
    Date
    2016
    Author
    Gehman, Andrew J.
    Advisor
    Wei, William W. S.
    Committee member
    Krafty, Robert T.
    Dong, Yuexiao
    Chervoneva, Inna
    Department
    Statistics
    Subject
    Statistics
    Economics
    Geography
    Forecasting
    Spatial Aggregation
    Spatial Scale
    Spatio-temporal
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/1282
    
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    DOI
    http://dx.doi.org/10.34944/dspace/1264
    Abstract
    Spatio-temporal data analysis involves modeling a variable observed at different locations over time. A key component of space-time modeling is determining the spatial scale of the data. This dissertation addresses the following three questions: 1) How does spatial aggregation impact the properties of the variable and its model? 2) What spatial scale of the data produces more accurate forecasts of the aggregate variable? 3) What properties lead to the smallest information loss due to spatial aggregation? Answers to these questions involve a thorough examination of two common space-time models: the STARMA and GSTARMA models. These results are helpful to researchers seeking to understand the impact of spatial aggregation on temporal and spatial correlation as well as to modelers interested in determining a spatial scale for the data. Two data examples are included to illustrate the findings, and they concern states' annual labor force totals and monthly burglary counts for police districts in the city of Philadelphia.
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