This is a preview and has not been published.

Spatiotemporal Modeling in Wireless Communication Networks

Authors

DOI:

https://doi.org/10.21123/bsj.2022.6848

Keywords:

Call detail records, COVID-19, Flow Migration, Gravity model, Iraqi CDRs, Radiation model.

Abstract

This study aims to analyze the flow migration of individuals between Iraqi governorates using real anonymized data from Korek Telecom company in Iraq. The purpose of this analysis is to understand the connection structure and the attractiveness of these governorates through examining the flow migration and population densities. Hence, they are classified based on the human migration at a particular period. The mobile phone data of type Call Detailed Records (CDRs) have been observed, which fall in a 6-month period during COVID-19 in the year 2020-2021. So, according to the CDRs nature, the well-known spatiotemporal algorithms: the radiation model and the gravity model were applied to analyze these data, and they are turned out to be complementary to each other. However, the results explore the flows of each governorate at two levels of abstraction: The Macroscopic and Mesoscopic. These results found that the spatiotemporal interaction models are complementary to the other, as the determined flows based on the radiation model have been used in the gravitational model. Furthermore, flows summary among all the governorates as well as for each of them has been obtained separately. Thus, based on the total number of flows, the highest attraction rate was between Nineveh and Dhi Qar governorates which reached , while the lowest attraction was between Wasit and Karbala governorates which reached . In addition, the extracted geographical maps showed each governorate ratio. Regarding the color of each governorate that degraded from light to dark, which indicated the low to high attraction respectively. In the future, it is possible to obtain more detailed data, and to use complex network algorithms for analyzing this data.

Downloads

Download data is not yet available.

References

Matin MA. Introduction to wireless networks. Dev Wirel Netw Prototyping, Des Deploy Future Gener. 2012;(June 2012): 1–9. DOI:10.4018/978-1-4666-1797-1.ch001

Abdel-Aal MMM. Calibrating a trip distribution gravity model stratified by the trip purposes for the city of Alexandria. Alexandria Eng J. 2014; 53(3): 677–89. Available from: http://dx.doi.org/10.1016/j.aej.2014.04.006

Robinson C, Dilkina B. A machine learning approach to modeling human migration. Proc 1st Acm Sigcas Conf Comput Sustain Soc Compass 2018; 30: 1-8 . https://doi.org/10.1145/3209811.3209868

Varani N, Bernardini E. Globalisation, migration flows and sustainability. Geopolit Soc Secur Free J. 2019; 2(2): 108–26.

Vermeulen WRJ, Roy D, Quax R. Modelling the Influence of Regional Identity on Human Migration. Urban Sci. 2019; 3(3): 78.

Hankaew S, Phithakkitnukoon S, Demissie MG, Kattan L, Smoreda Z, Ratti C. Inferring and Modeling Migration Flows Using Mobile Phone Network Data. IEEE Access. 2019; 7: 164746–58.

Skeldon R. International Migration, Internal Migration, Mobility and Urbanization:Towards more integrated approaches. Migr Res Ser No53 IOM. 2018;(August): 1–15. Available from: http://www.un-ilibrary.org/migration/international-migration-internal-migration-mobility-and-urbanization_a97468ba-en

Oshan T. A primer for working with the spatial interaction modeling (SpInt) module in the python spatial analysis library (PySAL). Region. 2016; 3(2): R11–23.

Behadili SF. Adaptive modeling of urban dynamics with mobile Suhad Faisal Behadili to cite this version: HAL Id: tel-01668513 Adaptive Modeling of Urban Dynamics with Mobile Phone Database . (PhD dissertation) Normandie Universite 2017; (November 2016). https://tel.archives-ouvertes.fr/tel-01668513

Simini F, González MC, Maritan A, Barabási AL. A universal model for mobility and migration patterns. Nature. 2012; 484(7392): 96–100.

Wesolowski A, O’Meara WP, Eagle N, Tatem AJ, Buckee CO. Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa. PLoS Comput Biol. 2015; 11(7): 1–16.

Ciavarella C, Ferguson NM. Deriving fine-scale models of human mobility from aggregated origin-destination flow data. PLoS Comput Biol. 2021; 17(2): 1–18. Available from: http://dx.doi.org/10.1371/journal.pcbi.1008588

Chen M, Li M, Hao Y, Liu Z, Hu L, Wang L. The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy. Inf Fusion. 2020; 64(June): 252–8.

Bonaccorsi G, Pierri F, Cinelli M, Flori A, Galeazzi A, Porcelli F, et al. Economic and social consequences of human mobility restrictions under COVID-19. Proc Natl Acad Sci U S A. 2020; 117(27): 15530–5.

Hughes C, Zagheni E, Abel GJ, Wisniowski A, Sorichetta4 A, Weber I, et al. Inferring Migrations: Traditional Methods and New Approaches based on Mobile Phone, Social Media, and other Big Data: Feasibility study on Inferring (labour) mobility and migration in the European Union from big data and social media data. Report for the European Commission. 2016. 41 p. Available from: https://eprints.soton.ac.uk/408499/1/KE0216632ENN_002.pdf

Shibasak R. Call detail record (CDR) analysis: Republic of Liberia. On-line. 2017. 52 p. Available from:https://www.itu.int/en/ITU-D/EmergencyTelecommunications/Documents/2017/Reports/LB/D012A0000C93301PDFE.pdf

Isaacman S, Frias-Martinez V, Frias-Martinez E. Modeling human migration paerns during drought conditions in La Guajira, Colombia. Proc 1st Acm Sigcas Conf Comput Sustain Soc Compass 2018; 18. DOI: https://doi.org/10.1145/3209811.3209861

Takahiro Yabe, Kota Tsubouchi, Naoya Fujiwara, Takayuki Wada, Yoshihide Sekimoto & Satish V. Ukkusuri. Non‑compulsory measures sufficiently reduced human mobility inTokyo during the COVID‑19 epidemic. Sci Rep. 2020; 10: 18053.

Piovani D, Arcaute E, Uchoa G, Wilson A, Batty M. Measuring accessibility using gravity and radiation models. arXiv. 2018; 1–12.

De Lellis P, Ruiz Marín M, Porfiri M. Modeling Human Migration Under Environmental Change: A Case Study of the Effect of Sea Level Rise in Bangladesh. Earth’s Futur. 2021; 9(4): 1–14.

Martínez-zarzoso I, Márquez-ramos L. International trade, technological innovation and income: a gravity model approach. IVIE Work Pap. 2005;(June).

Pourebrahim N, Sultana S, Niakanlahiji A, Thill JC. Trip distribution modeling with Twitter data. Comput Environ Urban Syst. 2019; 77(July).

Keramat Jahromi K, Zignani M, Gaito S, Rossi GP. Simulating human mobility patterns in urban areas. Simul Model Pract Theory. 2016; 62: 137–56.

Lai S, Erbach-Schoenberg E zu, Pezzulo C, Ruktanonchai NW, Sorichetta A, Steele J, et al. Exploring the use of mobile phone data for national migration statistics. Palgrave Commun. 2019; 5(1). Available from: http://dx.doi.org/10.1057/s41599-019-0242-9

Zeki AbdAlsamad SM. Advanced GIS-based multi-function support system for identifying the best route. Baghdad Sci J. 2022; 19(3): 631–41.

Aldeen YAAS, Qureshi KN. Solutions and recent challenges related to energy in wireless body area networks with integrated technologies: Applications and perspectives. Baghdad Sci J. 2020; 17(1): 378–84.

Aldeen YAAS, Qureshi KN. Solutions and recent challenges related to energy in wireless body area networks with integrated technologies: Applications and perspectives. Baghdad Sci J. 2020;17(1):378–84.

Pickthall A, Enders A, Nicoletti L, Cullinan C. COVID-19 and Mobility. 2020; 44(December). DOI:10.13140/RG.2.2.20412.16000

Dash M, Koo KK, Decraene J, Yap GE, Krishnaswamy SP, Wu W, et al. CDR-To-MoVis: Developing a Mobility Visualization System from CDR data. Proc - Int Conf Data Eng. 2015; 2015-May: 1452–5.

Masucci AP, Serras J, Johansson A, Batty M. Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows. Phys Rev E - Stat Nonlinear, Soft Matter Phys. 2013; 88(2).

Downloads

Issue

Section

article