Survey Assessment for Decision Support Using Self-Organizing Maps Profile Characterization with an Odds and Cluster Heat Map: Application to Children’s Perception of Urban School Environments
| Autor: | Francisco Sergio Campos-Sánchez, Francisco Javier Abarca-Álvarez, Rubén Mora-Esteban |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Self-organizing map
Decision support system decision support system Computer science 0211 other engineering and technologies General Physics and Astronomy self-organizing maps lcsh:Astrophysics 02 engineering and technology Machine learning computer.software_genre Article Odds lcsh:QB460-466 0202 electrical engineering electronic engineering information engineering odds ratio odds and cluster heat map Cluster analysis lcsh:Science Categorical variable Self-Organizing Maps opinion surveys business.industry Contrast (statistics) 021107 urban & regional planning Odds ratio lcsh:QC1-999 Identification (information) Opinion surveys Unsupervised learning 020201 artificial intelligence & image processing lcsh:Q Artificial intelligence business ANN computer lcsh:Physics |
| Zdroj: | Digibug. Repositorio Institucional de la Universidad de Granada instname Entropy, Vol 21, Iss 9, p 916 (2019) Entropy Volume 21 Issue 9 |
| Popis: | The interpretation of opinion and satisfaction surveys based exclusively on statistical analysis often faces difficulties due to the nature of the information and the requirements of the available statistical methods. These difficulties include the concurrence of categorical information with answers based on Likert scales with only a few levels, or the distancing of the necessary heuristic approach of the decision support system (DSS). The artificial neural network used for data analysis, called Kohonen or self-organizing maps (SOM), although rarely used for survey analysis, has been applied in many fields, facilitating the graphical representation and the simple interpretation of high-dimensionality data. This clustering method, based on unsupervised learning, also allows obtaining profiles of respondents without the need to provide additional information for the creation of these clusters. In this work, we propose the identification of profiles using SOM for evaluating opinion surveys. Subsequently, non-parametric chi-square tests were first conducted to contrast whether answer was independent of each profile found, and in the case of statistical significance (p ≤ 0.05), the odds ratio was evaluated as an indicator of the effect size of such dependence. Finally, all results were displayed in an odds and cluster heat map so that they could be easily interpreted and used to make decisions regarding the survey results. The methodology was applied to the analysis of a survey based on forms administered to children (N = 459) about their perception of the urban environment close to their school, obtaining relevant results, facilitating results interpretation, and providing support to the decision-process. This research was funded by Campus de Excelencia Internacional BIOTIC Granada, University of Granada, grant number V1.2015 and the APC was funded by University of Granada. |
| Databáze: | OpenAIRE |
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