Preview

Public Administration. E-journal (Russia)

Advanced search

Methodological Approaches to Positioning Russian Regions in Spatial Development: A Conceptual Review

https://doi.org/10.55959/MSU2070-1381-117-2026-51-59

Abstract

The article examines methodological foundations for positioning constituent entities of the Russian Federation within spatial development. The starting point is the gap between the strategic description of a region’s role, the statistically observed structure of its economy and its spatial position. Based on a conceptual review of 2019–2025 publications selected through Crossref, OpenAlex, eLibrary, Cyberleninka and relevant journals, the paper systematizes Russian and international approaches to the analysis of regional strategies, indicators of regional socio-economic profile and sectoral specialization and spatial relations. It proposes a framework in which regional positioning is treated as a comparison of three sources: strategy text, observable indicators and spatial context. The aim is to systematize approaches to this problem, identify methodological gaps and outline an agenda for empirical testing; the contribution lies not in a new substantive classification of regions but in a scheme that distinguishes methods by their dominant source of measurement. As a result, four groups of approaches are distinguished — descriptive and text-based, indicator-based, spatial-network and integrated latent-role — and a “source type — level of analysis” matrix is built. Russian literature discusses institutional constraints of strategizing and the heterogeneity of regional documents in detail but rarely links textual, statistical and spatial data in a reproducible procedure, whereas international NLP and spatial ML methods are not adapted to Russian regions. An illustrative micro case of Sverdlovsk Oblast demonstrates the limits of a simple lexical count and the need to test the declaration– observation gap on a regional corpus. A five-direction research agenda is formulated, turning the critique of declarativeness into a testable empirical task.

About the Authors

M. A. Saprykin
School of Public Administration, Lomonosov Moscow State University
Russian Federation

Matvey A. Saprykin, Postgraduate Student

Moscow



L. S. Leontieva
School of Public Administration, Lomonosov Moscow State University
Russian Federation

Lidia S. Leontieva, DSc (Economics), Professor

Moscow



References

1. Антипин И.А., Власова Н.Ю., Иванова О.Ю. Стратегическое планирование в регионах России: вопросы пространственного развития // Управленец. 2023. Т. 14. № 6. С. 50–62. DOI: 10.29141/2218-5003-2023-14-6-4

2. Бухвальд Е.М. Институциональные проблемы стратегирования пространственного развития // Федерализм. 2023. Т. 28. № 1(109). С. 80–98. DOI: 10.21686/2073-1051-2023-1-80-98

3. Жихаревич Б.С., Прибышин Т.К. Стратегия пространственного развития России как результат взаимодействия науки и власти // Регион: экономика и социология. 2021. № 4(112). С. 3–26. DOI: 10.15372/REG20210401

4. Зубаревич Н.В. Стратегия пространственного развития: приоритеты и инструменты // Вопросы экономики. 2019. № 1. С. 135–145. DOI: 10.32609/0042-8736-2019-1-135-145

5. Козырь Н.С. Перспективные экономические специализации макрорегионов как ключевая недоработка Стратегии пространственного развития России // Ars Administrandi. 2023. Т. 15. № 1. С. 103–124. DOI: 10.17072/2218-9173-2023-1-103-124

6. Кузнецова О.В. Стратегия пространственного развития РФ: иллюзия решений и реальность проблем // Пространственная экономика. 2019. Т. 15. № 4. С. 107–125. DOI: 10.14530/se.2019.4.107-125

7. Трифонова П.С. Основные стратегические документы субъектов РФ: анализ, актуализация, индивидуализация // Вестник университета (ГУУ). 2021. № 6. С. 31–43. DOI: 10.26425/1816-4277-2021-6-31-43

8. Chen Y., Zhao P., Lin Y., Sun Y., Chen R., Yu L., Liu Y. Semantic-Enhanced Graph Convolutional Neural Networks for Multi-Scale Urban Functional-Feature Identification Based on Human Mobility // ISPRS International Journal of Geo-Information. 2024. Vol. 13. Is. 1. DOI: 10.3390/ijgi13010027

9. Jeong H., Shin Y., Lee S., Han S., An K. Identifying Local Characteristics for Customized Policy Application within Rural Areas Using LDA Topic Modelling // Sustainability. 2025. Vol. 17. Is. 12. DOI: 10.3390/su17125332

10. Kaczmarek I., Iwaniak A., Swietlicka A., Piwowarczyk M., Nadolny A. A Machine Learning Approach for Integration of Spatial Development Plans Based on Natural Language Processing // Sustainable Cities and Society. 2022. Vol. 76. DOI: 10.1016/j.scs.2021.103479

11. Kopczewska K. Spatial Machine Learning: New Opportunities for Regional Science // Annals of Regional Science. 2022. Vol. 68. P. 713–755. DOI: 10.1007/s00168-021-01101-x

12. Morandell T., Wicki M., Kaufmann D. The Planning of Urban–Rural Linkages: An Automated Content Analysis of Spatial Plans Adopted by European Intermediate Cities // Landscape and Urban Planning. 2025. Vol. 255. DOI: 10.1016/j.landurbplan.2024.105258

13. Seliverstov V.E. The “Five-Year Plan” of Spatial Development and Regional Policy of Russia: Running in Place or Readiness for a Sprint? // Regional Research of Russia. Vol. 12. No. 2. P. 177–191. DOI: 10.1134/s2079970522020228


Review

For citations:


Saprykin M.A., Leontieva L.S. Methodological Approaches to Positioning Russian Regions in Spatial Development: A Conceptual Review. Public Administration. E-journal (Russia). 2026;1(117):51-59. (In Russ.) https://doi.org/10.55959/MSU2070-1381-117-2026-51-59

Views: 106

JATS XML

ISSN 2070-1381 (Online)