Gravity Model for Flow Migration Within Wireless Communication Networks

Authors

  • Manhal K. Alqaysi Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0003-3091-4762
  • Suhad Faisal Behadili Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq

DOI:

https://doi.org/10.24996/ijs.2022.63.10.32

Keywords:

Gravity Model, Call Details Recorded, COVID-19, Migration, Iraq

Abstract

     This paper investigated an Iraqi dataset from Korek Telecom Company as Call Detail Recorded (CDRs) for six months falling between Sep. 2020-Feb. 2021. This data covers 18 governorates, and it falls within the period of COVID-19. The Gravity algorithm was applied into two levels of abstraction in deriving the results as the macroscopic and mesoscopic levels respectively. The goal of this study was to reveal the strength and weakness of people migration in-between the Iraqi cities. Thus, it has been clear that the relationship between each city with the others is based on   and  of mobile people. However, the COVID-19 effects on the people’s migration needed to be explored. Whereas the main function of the gravity model is to clarify the migration flows through modeling spatial interaction. This was implemented using Python scripting language. It is concluded that the gravity model has a powerful ability to analyze the movement of people between cities. According to the mean of result between governorates, showing that the highest attraction was between Babil and Anbar governorates amounted to , while the lowest attraction was between Wasit and Thi-Qar governorates with , and the others ranged between .

Downloads

Published

2022-10-30

Issue

Section

Computer Science

How to Cite

Gravity Model for Flow Migration Within Wireless Communication Networks. (2022). Iraqi Journal of Science, 63(10), 4474-4487. https://doi.org/10.24996/ijs.2022.63.10.32

Similar Articles

1-10 of 4293

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)