Using Open Data for Information Support of Simulation Model of the Russian Federation Spatial Development

  • Aleksandra L. MashkovaEmail author
  • Olga A. Savina
  • Yuriy A. Banchuk
  • Evgeniy A. Mashkov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 947)


In this paper we present a model of spatial development of the Russian Federation and principles of integrating open data into it. Our study is interdisciplinary and combines methods of computer modeling, artificial intelligence, demographic, financial and economic analysis. The proposed approach has significant differences from currently used mathematical and computer models of the economy, as it allows to reflect the spatial aspect of economic dynamics, integrate large arrays of accumulated data, take into account structural interrelationships of economic agents, influence of administrative mechanisms and institutional environment. The model is agent-based and consists of several modules, representing demographic, economic, financial processes, employment and consumption, educational and administrative institutions. Acting subjects in the model are artificial agents capable of interaction with each other and social environment. For the information support of the model large amounts of data on economic interrelations and spatial structure of the Russian economy are formed, including Federal State Statistics Service yearbooks and official information on the websites of the ministries.


Computer model Open data Spatial development Computational Experiment Agent-based modeling Statistics 



The reported study was funded by RFBR according to the research project № 18-29-03049.


  1. 1.
    Barros, J.: Exploring urban dynamics in Latin American cities using an agent-based simulation approach. In: Heppenstall, A., Crooks, A., See, L., Batty, M. (eds.) Agent-Based Models of Geographical Systems, pp. 571–589. Springer, Dordrecht (2012). Scholar
  2. 2.
    Benenson, I., Omer, I., Hatna, E.: Entity-based modeling of urban residential dynamics: the case of Yaffo, Tel Aviv. Environ. Plan. B: Plan. Des. 29, 491–512 (2002)CrossRefGoogle Scholar
  3. 3.
    Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human systems. Proc. Nat. Acad. Sci. U.S.A. 99(Suppl 3), 7280–7287 (2002). Scholar
  4. 4.
    Combes, P.-P., Mayer, T., Thisse, J.-F.: Economic Geography. The Integration of Regions and Nations. Princeton University Press, Princeton (2008)Google Scholar
  5. 5.
    Conte, R., Castelfranchi, C.: Understanding the effects of norms in social groups through simulation. In: Gilbert, N., Conte, R. (eds.) Artificial Societies: the Computer Simulation of Social Life, pp. 213–226. UCL Press, London (1995)Google Scholar
  6. 6.
    Davis, D.R., Weinstein, D.E.: Bones, bombs, and break points: the geography of economic activity. Am. Econ. Rev. 92(5, Dec), 1269–1289 (2002). Scholar
  7. 7.
    Epstein, J.M., Axtell, R.: Growing Artificial Societies: Social science from the bottom up. Brookings Institution Press, Washington, DC (1996)CrossRefGoogle Scholar
  8. 8.
    Epstein, J.M.: Modeling civil violence: an agent-based computational approach. Proc. Nat. Acad. Sci. U.S.A. 99, 7243–7250 (2002)CrossRefGoogle Scholar
  9. 9.
    Feitosa, F.F., Le, Q.B., Vlek, P.L.G.: Multi-agent simulator for urban segregation (MASUS): a tool to explore alternatives for promoting inclusive cities. Comput. Environ. Urban Syst. 35(2), 104–115 (2011)CrossRefGoogle Scholar
  10. 10.
    Gilbert, N.: When does social simulation need cognitive models? In: Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, pp. 428–432. Cambridge University Press, Cambridge (2006)Google Scholar
  11. 11.
    Holland, J.H., Miller, J.H.: Artificial adaptive agents in economic theory. Am. Econ. Rev. Pap. Proc. 81, 365–370 (1991)Google Scholar
  12. 12.
    Krugman, P.: Development, Geography, and Economic Theory, 4th edn. The MIT Press, Cambridg (1998)Google Scholar
  13. 13.
    Lee, J.S., et al.: The complexities of agent-based modeling output analysis. J. Artif. Soc. Soc. Simul. 18(4), 1–4 (2015)CrossRefGoogle Scholar
  14. 14.
    Macy, M., Willer, R.: From factors to actors: computational sociology and agent-based modeling. Ann. Rev. Sociol. 28, 143–166 (2002)CrossRefGoogle Scholar
  15. 15.
    Mashkova, A.L., Demidov, A.V., Savina, O.A., Koskin, A.V., Mashkov, E.A.: Developing a complex model of experimental economy based on agent approach and open government data in distributed information-computational environment. In: Proceedings of International Conference Electronic Governance and Open Society: Challenges in Eurasia (Saint-Petersburg), pp. 27–31. ACM, New York (2017)Google Scholar
  16. 16.
    Mashkova, A.L., Savina, O.A., Lazarev, S.A.: Agent model for evaluating efficiency of socially oriented federal programs. In: Proceedings of the 11th IEEE International Conference on Application of Information and Communication Technologies (Moscow), vol. 2, pp. 217–221. V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow (2017)Google Scholar
  17. 17.
    Moss, S.: Alternative approaches to the empirical validation of agent-based models. J. Artif. Soc. Soc. Simul. 11(1), 1–5 (2008)Google Scholar
  18. 18.
    Ottaviano, G., Thisse, J.-F.: New economic geography: what about the N? Environ. Plan. A 37(10), 1707–1725 (2005)CrossRefGoogle Scholar
  19. 19.
    Redding, S.J.: The empirics of new economic geography. J. Reg. Sci. 50(1), 297–311 (2010)CrossRefGoogle Scholar
  20. 20.
    Russian Federation Federal State Statistics Service Homepage. Accessed 26 Mar 2018
  21. 21.
    Savina, A.L.: Algorithmic aspects of constructing an agent model of migration flows. In: Proceedings of the Fifth All-Russian Scientific and Practical Conference on Simulation Modeling and its Application in Science and Industry, vol. 1, pp. 260–264. CTCC, Saint-Petersburg (2011). (in Russian)Google Scholar
  22. 22.
    Semboloni, F., Assfalg, J., Armeni, S., Gianassi, R., Marsoni, F.: CityDev, an interactive multi-agents urban model on the web. Comput. Environ. Urban Syst. 28(1), 45–64 (2004)CrossRefGoogle Scholar
  23. 23.
    Sun, R., Naveh, I.: Social institution, cognition, and survival: a cognitive–social simulation. Mind Soc. 6, 115–142 (2007)CrossRefGoogle Scholar
  24. 24.
    Sun, R.: Prolegomena to integrating cognitive modeling and social simulation. In: Sun, R. (ed.) Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, pp. 3–28. Cambridge University Press, Cambridge (2006)Google Scholar
  25. 25.
    Sun, R.: The CLARION cognitive architecture: Extending cognitive modeling to social simulation. In: Sun, R. (ed.) Cognition and Multi-Agent Interaction, pp. 79–102. Cambridge University Press, New York (2006)Google Scholar
  26. 26.
    Tesfatsion, L.: Agent-based computational economics: growing economies from the bottom up. Artif. Life 8(1), 55–82 (2002)MathSciNetCrossRefGoogle Scholar
  27. 27.
    The Open Definition website. Accessed 26 Mar 2018
  28. 28.
    Thisse, J.F.: Economic geography. In: Handbook on the History of Economic Analysis, vol. III, pp. 133–147. Chapters, Edward Elgar Publishing, November 2016. Chap. 11Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Orel State University Named After I.S. TurgenevOrelRussian Federation
  2. 2.Central Economics and Mathematics Institute Russian Academy of SciencesMoscowRussian Federation
  3. 3.Belgorod National Research UniversityBelgorodRussian Federation

Personalised recommendations