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Augmenting geovisual analytics of social media data with heterogeneous information network mining—Cognitive plausibility assessment

This paper investigates the feasibility, from a user perspective, of integrating a heterogeneous information network mining (HINM) technique into SensePlace3 (SP3), a web-based geovisual analytics environment. The core contribution of this paper is a user study that determines whether an analyst wit...

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Autores principales: Savelyev, Alexander, MacEachren, Alan M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279051/
https://www.ncbi.nlm.nih.gov/pubmed/30513083
http://dx.doi.org/10.1371/journal.pone.0206906
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author Savelyev, Alexander
MacEachren, Alan M.
author_facet Savelyev, Alexander
MacEachren, Alan M.
author_sort Savelyev, Alexander
collection PubMed
description This paper investigates the feasibility, from a user perspective, of integrating a heterogeneous information network mining (HINM) technique into SensePlace3 (SP3), a web-based geovisual analytics environment. The core contribution of this paper is a user study that determines whether an analyst with minimal background can comprehend the network data modeling metaphors employed by the resulting system, whether they can employ said metaphors to explore spatial data, and whether they can interpret the results of such spatial analysis correctly. This study confirms that all of the above is, indeed, possible, and provides empirical evidence about the importance of a hands-on tutorial and a graphical approach to explaining data modeling metaphors in the successful adoption of advanced data mining techniques. Analysis of outcomes of data exploration by the study participants also demonstrates the kinds of insights that a visual interface to HINM can enable. A second contribution is a realistic case study that demonstrates that our HINM approach (made accessible through a visual interface that provides immediate visual feedback for user queries), produces a clear and a positive difference in the outcome of spatial analysis. Although this study does not aim to validate HINM as a data modeling approach (there is considerable evidence for this in existing literature), the results of the case study suggest that HINM holds promise in the (geo)visual analytics domain as well, particularly when integrated into geovisual analytics applications. A third contribution is a user study protocol that is based on and improves upon the current methodological state of the art. This protocol includes a hands-on tutorial and a set of realistic data analysis tasks. Detailed evaluation protocols are rare in geovisual analytics (and in visual analytics more broadly), with most studies reviewed in this paper failing to provide sufficient details for study replication or comparison work.
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spelling pubmed-62790512018-12-20 Augmenting geovisual analytics of social media data with heterogeneous information network mining—Cognitive plausibility assessment Savelyev, Alexander MacEachren, Alan M. PLoS One Research Article This paper investigates the feasibility, from a user perspective, of integrating a heterogeneous information network mining (HINM) technique into SensePlace3 (SP3), a web-based geovisual analytics environment. The core contribution of this paper is a user study that determines whether an analyst with minimal background can comprehend the network data modeling metaphors employed by the resulting system, whether they can employ said metaphors to explore spatial data, and whether they can interpret the results of such spatial analysis correctly. This study confirms that all of the above is, indeed, possible, and provides empirical evidence about the importance of a hands-on tutorial and a graphical approach to explaining data modeling metaphors in the successful adoption of advanced data mining techniques. Analysis of outcomes of data exploration by the study participants also demonstrates the kinds of insights that a visual interface to HINM can enable. A second contribution is a realistic case study that demonstrates that our HINM approach (made accessible through a visual interface that provides immediate visual feedback for user queries), produces a clear and a positive difference in the outcome of spatial analysis. Although this study does not aim to validate HINM as a data modeling approach (there is considerable evidence for this in existing literature), the results of the case study suggest that HINM holds promise in the (geo)visual analytics domain as well, particularly when integrated into geovisual analytics applications. A third contribution is a user study protocol that is based on and improves upon the current methodological state of the art. This protocol includes a hands-on tutorial and a set of realistic data analysis tasks. Detailed evaluation protocols are rare in geovisual analytics (and in visual analytics more broadly), with most studies reviewed in this paper failing to provide sufficient details for study replication or comparison work. Public Library of Science 2018-12-04 /pmc/articles/PMC6279051/ /pubmed/30513083 http://dx.doi.org/10.1371/journal.pone.0206906 Text en © 2018 Savelyev, MacEachren http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Savelyev, Alexander
MacEachren, Alan M.
Augmenting geovisual analytics of social media data with heterogeneous information network mining—Cognitive plausibility assessment
title Augmenting geovisual analytics of social media data with heterogeneous information network mining—Cognitive plausibility assessment
title_full Augmenting geovisual analytics of social media data with heterogeneous information network mining—Cognitive plausibility assessment
title_fullStr Augmenting geovisual analytics of social media data with heterogeneous information network mining—Cognitive plausibility assessment
title_full_unstemmed Augmenting geovisual analytics of social media data with heterogeneous information network mining—Cognitive plausibility assessment
title_short Augmenting geovisual analytics of social media data with heterogeneous information network mining—Cognitive plausibility assessment
title_sort augmenting geovisual analytics of social media data with heterogeneous information network mining—cognitive plausibility assessment
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279051/
https://www.ncbi.nlm.nih.gov/pubmed/30513083
http://dx.doi.org/10.1371/journal.pone.0206906
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