Abstract
A challenging problem for human societies that affects human life is the spread of diseases such as COVID-19. Travel, especially air transportation, plays a significant role in the spread of such diseases. Analysis of air traffic data and its interaction with the disease outbreak is one of the applied and research topics. Flights from infected areas to less infected regions increase the outbreak. On the other hand, by following warnings and policies, air travel declines in such periods. The effects are reflected in the collected data with a time gap that requires efficient methods for rich analysis. This aspect is overlooked in existing research. In this paper, a new approach is proposed for spatio-temporal analysis that is based on granular computing and fuses the air traffic data with COVID-19 statistics. Data in diverse sources may be at different levels of granularity. Therefore, analyzing the interaction of two phenomena extracted from different sources is a challenging issue. Moreover, there may not be an exact match in other data sources for some data items, but applying a fuzzy inference system and constructing enriched data granules by using geographical knowledge bases eliminates this shortcoming. The proposed method fills the gaps and adjusts the differences to discover new patterns that other methods cannot detect.
| Original language | English |
|---|---|
| Title of host publication | Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference, held August 24–26, 2021. Volume 1 |
| Editors | Cengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari |
| Place of Publication | Switzerland |
| Publisher | Springer |
| Pages | 937-944 |
| Number of pages | 8 |
| ISBN (Electronic) | 9783030856267 |
| ISBN (Print) | 9783030856250 |
| DOIs | |
| Publication status | Published - 2022 |
