Pattern Identification by Factor Analysis for Regions with Similar Economic Activity Based on Mobile Communication Data

  • Irina ArhipovaEmail author
  • Gundars Berzins
  • Edgars Brekis
  • Martins Opmanis
  • Juris Binde
  • Jevgenija Kravcova
  • Inna Steinbuka
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)


The study analyses the regions’ economic activity in Latvia using Latvia Mobile Telephone (LMT) mobile communication data from July 2015 to January 2017. The call activity and a number of unique phone users by 119 Latvia counties and biggest cities were analysed in two steps: at first method of principal components was used to explain the variance in the data and then exploratory factor analysis was applied. Three factors were identified that describe 87.5% of the total variance of the aggregated daily data. The first factor is related more to the regions with higher economic activity, the second and third factors capture, respectively, lowers call activity during weekdays and are related to the regions with lower economic activity in total. When to look at the same data but aggregated not only by day but also by daytime, then there are two new factors that describe 94.8% of the total variance. One of the factors is related to higher economic activity in regions as has higher values during normal working hours and lower values after working time, the second is related to the regions with lower economic activity. So it was concluded that during normal working hours the economic activity is higher in the regions with higher call activity. As the result considered indicators of call activities and unique phone users can be used for the identification of the regions with similar economic activity patterns.


Indicators of economic activity Mobile data Regional economic activity 



The research leading to these results has received funding from the research project “Large-scale data processing mathematical model for “Updatable Latvian regional business index” using limited data asset”, Contract No. AAP2016/B089 signed between University of Latvia and LMT Ltd.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Irina Arhipova
    • 1
    Email author
  • Gundars Berzins
    • 1
  • Edgars Brekis
    • 1
  • Martins Opmanis
    • 2
  • Juris Binde
    • 3
  • Jevgenija Kravcova
    • 3
  • Inna Steinbuka
    • 1
  1. 1.Faculty of Business, Management and EconomicsUniversity of LatviaRigaLatvia
  2. 2.Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLatvia
  3. 3.Latvian Mobile TelephoneRigaLatvia

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