Method to Comparison of Cities

  • Michal JerabekEmail author
  • Jan Kubat
  • Vit Fabera
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


The paper deals with the comparison of cities, which will allow to identify reserves in the management of public funds and in the provision of services to its residents. The first step in the comparison is to determine the parameters that can be used to compare a set of cities. An appropriate choice of parameters allows performance comparison across a wide range of areas. A very interesting method for comparison seems to be the DEA method, which allows a relative comparison of a set of cities. Relative comparisons are advantageous if the best values of the parameters that can be achieved are not known, but at the same time it is necessary to compare comparable cities. The paper describes the use of DEA method for comparing cities with each other. The DEA description includes an illustrative example showing the differences between VRS, CRS, and FDH models. The paper also describes the software application that is developed for the purpose to compare the efficiency of municipalities. The paper describes the architecture of the application, its functions, and print screens from the web interface.


Smart city DEA Data envelopment analysis Comparison of smart cities 



This work is solved within the project “Application of Nonparametric Methods (DEA) to Analyze and to Compare the Efficiency of Municipalities” that is supported by TAČR (Technology Agency of the Czech Republic), program Eta (project code TL01000463).


  1. 1.
    Definition - What Does Smart City mean? (2019). Available from
  2. 2.
    Přibyl, P., Přibyl, O.: Definition of a Smart Street as Smart City’s building element. In: Smart Cities Symposium Prague (2015). ISBN: 978-1-4673-6727-1CrossRefGoogle Scholar
  3. 3.
    Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Operat. Res. 2, 429–444 (1978)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Cooper, W.W., Park, K.S., Yu, G.: IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Manag. Sci. 45, 597–607 (1999)CrossRefGoogle Scholar
  5. 5.
    Kao, C., Liu, S.T.: Fuzzy efficiency measures in data envelopment analysis. Fuzzy Set. Syst. 119, 149–160 (2000)zbMATHGoogle Scholar
  6. 6.
    Jablonský, J., Dlouhý, M.: Modely hodnocení efektivnosti produkčních jednotek. Professional Publishing, Praha (2004)Google Scholar
  7. 7.
    Deprins, D., Simar, L., Tulkens, H.: Measuring labor inefficiency in post offices. In: Marchand, M., et al. (eds.) The Performance of Public Enterprises: Concepts and Measurement, pp. 243–267. Elsevier, Amsterdam (1984)Google Scholar
  8. 8.
    Package deaR (2019). Available from

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Czech Technical University in PraguePragueCzech Republic
  2. 2.AMBISPragueCzech Republic

Personalised recommendations