895 resultados para Geographical computer applications


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Available on demand as hard copy or computer file from Cornell University Library.

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Available on demand as hard copy or computer file from Cornell University Library.

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Available on demand as hard copy or computer file from Cornell University Library.

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Available on demand as hard copy or computer file from Cornell University Library.

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Page 134 incorrectly numbered 136.

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Available on demand as hard copy or computer file from Cornell University Library.

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Available on demand as hard copy or computer file from Cornell University Library.

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Available on demand as hard copy or computer file from Cornell University Library.

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Available on demand as hard copy or computer file from Cornell University Library.

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Thesis (Ph.D.)--University of Washington, 2016-06

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Thesis (Ph.D.)--University of Washington, 2016-06

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Online geographic information systems provide the means to extract a subset of desired spatial information from a larger remote repository. Data retrieved representing real-world geographic phenomena are then manipulated to suit the specific needs of an end-user. Often this extraction requires the derivation of representations of objects specific to a particular resolution or scale from a single original stored version. Currently standard spatial data handling techniques cannot support the multi-resolution representation of such features in a database. In this paper a methodology to store and retrieve versions of spatial objects at, different resolutions with respect to scale using standard database primitives and SQL is presented. The technique involves heavy fragmentation of spatial features that allows dynamic simplification into scale-specific object representations customised to the display resolution of the end-user's device. Experimental results comparing the new approach to traditional R-Tree indexing and external object simplification reveal the former performs notably better for mobile and WWW applications where client-side resources are limited and retrieved data loads are kept relatively small.