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When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.COSIT.2017.10
URN: urn:nbn:de:0030-drops-77543
URL: http://drops.dagstuhl.de/opus/volltexte/2017/7754/
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Kattenbeck, Markus

How Subdimensions of Salience Influence Each Other. Comparing Models Based on Empirical Data

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LIPIcs-COSIT-2017-10.pdf (0.7 MB)


Abstract

Theories about salience of landmarks in GIScience have been evolving for about 15 years. This paper empirically analyses hypotheses about the way different subdimensions (visual, structural, and cognitive aspects, as well as prototypicality and visibility in advance) of salience have an impact on each other. The analysis is based on empirical data acquired by means of an in-situ survey (360 objects, 112 participants). It consists of two parts: First, a theory-based structural model is assessed using variance-based Structural Equation Modeling. The results achieved are, second, corroborated by a data-driven approach, i.e. a tree-augmented naive Bayesian network is learned. This network is used as a structural model input for further analyses. The results clearly indicate that the subdimensions of salience influence each other.

BibTeX - Entry

@InProceedings{kattenbeck:LIPIcs:2017:7754,
  author =	{Markus Kattenbeck},
  title =	{{How Subdimensions of Salience Influence Each Other. Comparing Models Based on Empirical Data}},
  booktitle =	{13th International Conference on Spatial Information Theory (COSIT 2017)},
  pages =	{10:1--10:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-043-9},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{86},
  editor =	{Eliseo Clementini and Maureen Donnelly and May Yuan and Christian Kray and Paolo Fogliaroni and Andrea Ballatore},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7754},
  URN =		{urn:nbn:de:0030-drops-77543},
  doi =		{10.4230/LIPIcs.COSIT.2017.10},
  annote =	{Keywords: Salience models, consistent PLS-SEM Analysis, Bayesian Networks}
}

Keywords: Salience models, consistent PLS-SEM Analysis, Bayesian Networks
Seminar: 13th International Conference on Spatial Information Theory (COSIT 2017)
Issue Date: 2017
Date of publication: 24.08.2017


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