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Digital
Library of the European Council for Modelling
and Simulation |
Title: |
Fuzzy Logic Modelling Of The Russian Demographic Space |
Authors: |
Anna Bagirova, Oksana Shubat, Alexander Akishev |
Published in: |
(2018). ECMS 2018
Proceedings Edited by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens
Werner, Jens Wellhausen European Council for Modeling and Simulation. doi: 10.7148/2018-0005 ISSN:
2522-2422 (ONLINE) ISSN:
2522-2414 (PRINT) ISSN:
2522-2430 (CD-ROM) 32nd European Conference on Modelling and
Simulation, Wilhelmshaven, Germany, May 22nd
– May 265h, 2018 |
Citation
format: |
Anna
Bagirova, Oksana Shubat, Alexander Akishev (2018). Fuzzy Logic Modelling Of
The Russian Demographic Space, ECMS 2018
Proceedings Edited by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens
Werner, Jens Wellhausen European Council for Modeling and Simulation. doi: 10.7148/2018-0035 |
DOI: |
https://doi.org/10.7148/2018-0035 |
Abstract: |
Demographic
processes are extremely difficult to manage and require the different methods
of their research. Our studies aimed at modelling the demographic space of
Russian regions by using fuzzy clustering. Our analysis is based on the
indicators of the regions’ demographic potential. We used our own original
methodology combining the statistical procedures of fuzzy clustering and
expert survey data. We considered indicators characterizing the reproduction
potential and variables characterizing the potential quality of the future
population. As a result of fuzzy clustering, five clusters were formed. Our
experts evaluated the reproduction potential and the quality of the future
population for each cluster. The data for each region were used to calculate
their reproduction potential and the quality of their future population. In
comparison to hard clustering, fuzzy clustering enhances the flexibility of
evaluation: our assesments of each region do not depend exclusively on the
potential of the nearest cluster, as we also take into consideration the
region’s possible similarities with other neighbouring clusters with
different potential. Such modelling allows us to identify those Russian
regions that could be considered as ‘growth points’ in the implementation of
demographic policy. |
Full
text: |