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    Enabling geovisual analytics of health data using a server-side approach

    Access Status
    Fulltext not available
    Authors
    Turdukulov, Ulanbek
    Moncrieff, Simon
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Turdukulov, U. and Moncrieff, S. 2016. Enabling geovisual analytics of health data using a server-side approach. Cartography and Geographic Information Science. 43 (1): pp. 16-29.
    Source Title
    Cartography and Geographic Information Science
    DOI
    10.1080/15230406.2015.1065762
    ISSN
    1523-0406
    School
    Department of Spatial Sciences
    URI
    http://hdl.handle.net/20.500.11937/26366
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 Cartography and Geographic Information Society. Geovisual analytics can enable a user to explore multivariate spatio-temporal health datasets and to understand spatial distribution of diseases especially in relation to external factors that may influence outbreaks. External data are presently distributed using geo web services. Web services are used in health mainly to present results leading to a supplier-driven service model limiting the exploration of health data. In this paper, we illustrate server-side approach of designing a geovisual analytics environment that allows user-driven geovisual analytics. The server-side combines a data query, processing technique, and styling methodology to rapidly visually summarize properties of a dataset. We illustrate this functionality on a typical workflow used by a health researcher and demonstrate analytical functionality in cases where a consistent classification and styling scheme is needed across dynamically aggregated multivariate spatio-temporal datasets. Since the framework builds on the existing Open Geospatial Consortium web mapping standards, it integrates the existing geo web services as well as stand-alone health data repositories into an infrastructure that allows combination and interactive exploration of these heterogeneous datasets in a visual environment.

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