Sociological diagnostics of population dissatisfaction as a tool for managing territorial development
Relevance. The issues of municipal governance efficiency are becoming especially important in the context of increasing institutional and demographic polarization of Russian regions. Sociological surveys organized on a regular basis provide a unique empirical basis for monitoring and analyzing public assessment of the work of regional and local government bodies. The research problem is to develop and test tools that allow identifying stable vectors of population dissatisfaction as indicators of regional governance based on long-term sociological measurement data. Methods. The study is based on the author's methodology for vector assessment of dissatisfaction, which includes a dynamic analysis of a representative database of annual surveys of the population of the Chelyabinsk region for 2017-2024. The methods of data normalization, visualization using radar diagrams, hierarchical clustering of municipalities and analysis of changes in the vector of public priorities were applied. Analytical processing was carried out in the Python environment using the pandas, matplotlib, scikit-learn libraries. Research results. The dominant areas of population dissatisfaction were established. These areas had remained consistent over an eight-year period and included housing and communal services, healthcare and transport accessibility. Periods of aggravation of social expectations associated with specific management cycles and crises were also identified. As a result of cluster analysis, types of municipalities by the level and structure of dissatisfaction were identified, including persistently problematic territories and territories with positive dynamics of public assessment. Conclusions. The methodology presented in the article allows us to identify territorial and typological differences in the public assessment of the effectiveness of regional and local governance, as well as the dynamics of social expectations that are transformed under the influence of management decisions. Vectors of population dissatisfaction can be interpreted as sensitive indicators of local development applicable in the system of sociological support of regional governance.
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Sitkovskiy, A. M., Rostovskaya, T. K. (2025), “Sociological diagnostics of population dissatisfaction as a tool for managing territorial development”, Research Result. Sociology and Management, 11 (4),
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Bobylev, S. N., Zubarevich, N. V., & Solovyeva, S. V. (2015), “Crisis challenges: how to measure sustainable development?”, Voprosy ekonomiki, (1), 147-160, DOI: 10.32609/0042-8736-2015-1-147-160. (In Russian)
Gordeev, S. S., Zyryanov, S. G., & Sitkovskiy, A. M. (2019), “Assessment of quality of life and social priorities for territorial development”, Vestnik Chelyabinskogo gosudarstvennogo universiteta, 11(433), 38-47, DOI: 10.24411/1994-2796-2019-11105. (In Russian)
Zubarevich, N. V. (2018), Sotsialnoe razvitie regionov Rossii: problemy i tendentsii perekhodnogo perioda [Social development of Russian regions: problems and trends of the transition period], Editorial URSS, Moscow. (In Russian)
Lapin, N. I., & Belyaeva, L. A. (2020), Programma i tipovoy instrumentariy “Sotsiokulturny portret regiona Rossii” [Program and standard tools “Sociocultural portrait of the Russian region”], IFRAN, Moscow. (In Russian)
Petukhov, V. V. (2019), “Civic participation in Russia today: interaction of social and political practices”, Sotsiologicheskie issledovaniya, (12), 3-14, DOI: 10.31857/S013216250007743-0. (In Russian)
Rostovskaya, T. K., & Ryazantsev, S. V. (2015), “Socio-demographic characteristics of Russian youth”, Gosudarstvennyy sovetnik, 2(10), 66-75. (In Russian)
Sitkovskiy, A. M. (2021), “Modeling of multi-criteria assessment of socio-ecological-economic state and dynamics of territory”, Voprosy upravleniya, 2(69), 102-119, DOI: 10.22394/2304-3369-2021-2-102-119. (In Russian)
Sitkovskiy, A. M., & Kozlova, O. A. (2023), “The use of vector assessments and grapho-analytical methods in the study of demographic behavior of the population of the region”, Vestnik Nizhegorodskogo universiteta im. N. I. Lobachevskogo. Seriya: Sotsialnye nauki, (3), 44-59, DOI: 10.52452/18115942_2023_3_44. (In Russian)
Tikhonova, N. E. (2014), Sotsialnaya struktura Rossii: teorii i realnost’ [Social structure of Russia: theories and reality], Novyy khronograf, Moscow. (In Russian)
Burch, M., Bott, F., Beck, F., & Diehl, S. (2008), “Cartesian vs. Radial – A Comparative Evaluation of Two Visualization Tools”, Advances in Visual Computing – ISVC 2008. Lecture Notes in Computer Science, 5358, 151-160, DOI: 10.1007/978-3-540-89639-5_15.
Gordeev, S., Zyryanov, S., & Sitkovskiy, A. (2020), “Visualization in models of transformation of social space of the Eurasian macroregion: the example of the Urals”, E3S Web of Conferences, 217, 07021, DOI: 10.1051/e3sconf/202021707021.
Few, S. (2009), Now You See It: Simple Visualization Techniques for Quantitative Analysis, Analytics Press, Oakland, CA.
OECD (2020), How’s Life? 2020: Measuring Well-being, OECD Publishing, Paris, DOI: 10.1787/9870c393-en.
Stiglitz, J., Sen, A., & Fitoussi, J.-P. (2009), Report by the Commission on the Measurement of Economic Performance and Social Progress, Paris.
UNDP Georgia (2022), Citizen Satisfaction Survey with Public Services – Research report, UNDP, Tbilisi.
Waldner, M., Diehl, A., Gracanin, D., et al. (2020), “A Comparison of Radial and Linear Charts for Visualizing Daily Patterns”, IEEE Transactions on Visualization and Computer Graphics, 26(1), 1033-1042, DOI: 10.1109/TVCG.2019.2934784.
Ward, M., Grinstein, G., & Keim, D. (2015), Interactive Data Visualization: Foundations, Techniques, and Applications (2nd ed.), CRC Press, Boca Raton, FL. DOI: 10.1201/b18379.
The study was supported by the Russian Science Foundation Grant No. 25-78-30004 “Digital Demographic Observatory: Development of a System for Monitoring Demographic Processes in Russian Regions Using GIS Technologies and Big Data”, https://rscf.ru/project/25-78-30004/.