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A New Quality Measure and Visualization of the Short-Quantified Sentences of Natural Language on Maps – A Case on COVID-19 Data
Volume 33, Issue 2 (2022), pp. 321–342
Pub. online: 20 June 2022      Type: Research Article      Open accessOpen Access
Received
1 July 2021
Accepted
1 June 2022
Published
20 June 2022

Abstract

Maps are a common tool for visualizing various statistical figures that describe development in our society. Domain experts, journalists, and general public can pose questions on how to emphasize regions where, for instance, most young patients have long stayed in hospitals. One of the visualization’s problems is expressing validities of short-quantified sentences for regions on maps. The truth value of a summary assigns a value from the unit interval, which makes it suitable for interpretation on maps by hues of a selected colour, but it does not reflect the data distribution among regions. To meet this goal, a new quality measure covering data distribution among districts and its aggregation by the ordinal sums of conjunctive and disjunctive functions with the truth value is proposed and documented on examples. The next proposal is a relative quantifier expressing significant proportion of entities. This model is applied to the interpretation of COVID-19 cases development in the Slovak Republic on real data from one health insurance company. Finally, this article discusses the applicability of the proposed approach in other areas where the interpretation of summarized sentences on maps is beneficial.

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Biographies

Hudec Miroslav
miroslav.hudec@vsb.cz

M. Hudec is an associate professor at the University of Economics in Bratislava, Faculty of Economic Informatics (Slovak Republic) and VSB – Technical University of Ostrava, Faculty of Economics (Czech Republic). He received the PhD degree from the University of Belgrade (Serbia). His work is mainly focused on fuzzy logic, knowledge discovery and business intelligence. He is a member of program committees of several international conferences and serves as an associate editor in Applied Soft Computing. He has published more than 50 articles including a monograph in Springer.

Malovcová Kristína
malovcova.kristina@dovera.sk

K. Malovcová received a master’s degree from the University of Economics in Bratislava, Faculty of Economic Informatics (Slovak Republic) in June 2021. In her final thesis she dealt with the flexible calculation of linguistic summaries and interpretation of results on a map. She works as a junior analyst at the health insurance company DÔVERA zdravotná poisťovňa, a.s., Slovak Republic.

Trumic Rijad
rijad.trumic@vsb.cz

R. Trumic holds a master in business administration from the University of Applied Sciences Munich, Germany. Currently, he is a PhD student at the Faculty of Economics, VSB – Technical University of Ostrava, Czech Republic. He has been working in supply chain management for BMW in Munich for many years and his research is mainly related to cost avoidance in change management.

Rakovská Eva
eva.rakovska@euba.sk

E. Rakovská received her PhD degree in applied informatics at the University of Economics in Bratislava (Slovak Republic) in 2010. She received a master’s degree in mathematics at Comenius University in 1986. She worked as a programmer and IT developer in various companies, including a hospital. She is an assistant professor at the University of Economics in Bratislava, Faculty of Economic Informatics since 2001. Her research interests are oriented toward applied informatics and knowledge engineering. The main impact is on the soft computing application and expert systems.


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Copyright
© 2022 Vilnius University
Open access article under the CC BY license.

Keywords
linguistic summary visualization on maps quality measure of summary aggregation function relative quantifier COVID-19

Funding
This work was partially supported by the Ministry of Education, Youth and Sports of the Czech Republic under Grant SGS No. SP2021/86; and partially supported by the Ministry of Education, Science, Research and Sport of the Slovak Republic under Grant 025EU-4/2021.

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