Using the spatial RIMER+ approach to estimate negative self-rated health and its causes across Northern Ireland

Alberto Calzada, Jun Liu, Chris Nugent, Hui Wang, Luis Martinez

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    1 Citation (Scopus)

    Abstract

    Self-rated health is a commonly-used survey technique that helps collecting information about the public health in an area. It is widely recognized that self-rated health has a strong correlation with key public-health variables such as deprivation, poverty, fear of crime or mortality. Therefore, it is a useful tool when assessing the public health situation of a neighborhood or town. This paper utilizes a recently-developed decision framework, named, Spatial RIMER+, to model a decision problem using real data where self-rated health is unknown in certain areas of Northern Ireland and needs to be estimated. The results retrieved in the study demonstrate the high accuracy of the methodology as well as its the flexibility and applicability to model a wide range of spatial decision scenarios.

    Original languageEnglish
    Title of host publicationUbiquitous Computing and Ambient Intelligence
    Subtitle of host publicationPersonalisation and User Adapted Services - 8th International Conference, UCAmI 2014, Proceedings
    EditorsRamón Hervás, José Bravo, Sungyoung Lee, Chris Nugent
    PublisherSpringer Verlag
    Pages312-319
    Number of pages8
    ISBN (Electronic)9783319131016
    DOIs
    Publication statusPublished - 2014

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume8867
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Keywords

    • Knowledge representation
    • Self-Rated Health
    • Spatial Decision Support Rule-Based Systems Knowledge-based approach

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