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 proceedingChapter

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
EditorsRamón Hervás, José Bravo, Sungyoung Lee, Chris Nugent
Place of PublicationGermany
PublisherSpringer Verlag
Pages312-319
Number of pages8
DOIs
Publication statusPublished - 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag

Bibliographical note

Publisher Copyright: © Springer International Publishing Switzerland 2014.

Keywords

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

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