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Forecasting the Directions of the State Policy of the Region on the Basis of Cluster Analysis of Indicators of Socio-Economic Development of Its Municipalities
Abstract
Assessment of the prospects for the development of the region is an important task for both economic science and public authorities.
The purpose of this article is to develop an algorithm for assessing the prospects for the development of the region, which would allow identifying the most problematic areas and competitive advantages of territorial development in order to determine the most important areas of budget expenditures in the context of municipalities in the region.
The research uses the concept of sustainable development and the theory of clusters as a methodological basis. Cluster analysis by the Ward method and k-means were used as research methods. As a forecasting method, the most effective among the methods was used: least squares, exponential smoothing, moving average for 3 and 5 years. Separately, the demographic forecast for each municipality separately was used to predict the population. As a result of the conducted research, groups of municipalities of the region differentiated by the level of development have been identified, for which the main problem areas have been formulated, the leveling of which should be addressed by the state policy of the region. The advantage of the proposed approach is that it identifies and predicts the problems of socio-economic development of the region, which may be hidden in the medium-term forecast of the socio-economic region. In this research, such problems were identified in many municipalities of the Leningrad region in the field of housing construction, demography and economics. The article may be useful to public authorities when forming a strategy for socio-economic development.
... Another article S. N. Bordin [2] in 2023 create an calculation for evaluating the prospects for the advancement of the locale, which would permit distinguishing the foremost risky regions and competitive points of interest of regional improvement in order to decide the foremost imperative ranges of budget uses within the setting of regions within the locale. The investigate employments the concept of economical improvement and the hypothesis of clusters as a methodological premise. ...
This thesis aims to analyze the current socioeconomic situation and forecast the impact of various factors on economic indicators, with a focus on living standards. The study utilizes a dataset comprising multiple socioeconomic indicators, including household consumption, gross domestic production(GDP), wages, pensions, population demographics, and minimum wages. Initial exploratory data analysis (EDA) reveals insights into the relationships between these indicators and household consumption. Subsequently, predictive modeling techniques, including Random Forest Regression and Neural Networks, are employed to forecast various factors based on the selected features. Furthermore, we implemented XGboost and LSTM for deep learning the model which helps us to compare the models and Feature importance analysis. Forward/backward feature selection techniques are utilized to identify the most influential factors affecting household consumption. The study contributes to a deeper understanding of the socioeconomic landscape and provides valuable insights for policymakers, economists, and stakeholders in making informed decisions to address economic challenges and foster sustainable growth.
... По мнению А. Р. Карапетяна, государственная социально-экономическая политика представляет собой комплекс мер, которые взаимосвязаны и реализуются органами власти в таких сферах, как законодательная, административная и экономическая [1]. В работе С. Н. Бородина под региональной экономикой социальноэкономического развития региона понимается деятельность, направленная на предвидение будущего состояния экономики и социальной сферы, которые являются составной частью государственного регулирования экономики, призванных определять направления развития регионального комплекса и его структурных составляющих [2]. Социальный эффект влияния строительного рынка на развитие социально-экономической системы региона подробно раскрывают в своих трудах многие ученые-экономисты. ...
The article describes the features of building and assessment of spatial development scenarios in long-term forecasts. The author used the scenario approach for qualitative and quantitative assessment of alternative strategies for regional development within the framework of the macroeconomic forecast for the development of the Russian economy. Further, the author analyzed the experience of developing spatial scenarios for the EU countries and Russia. Next, the longterm regional trends are presented, which, due to the high inertia of space, will determine spatial development in the future. The author also describes modern problems that significantly impact the choice of strategies for the regions. Prospects for spatial development the author assessed in the framework of three forecast scenarios. For two options of the macroeconomic forecast, the author calculated quantitative estimates of the spatial development parameters characterizing the scenarios. Relevant calculations the author performed using macroeconomic and interregional forecasting and analytical models. Finally, the author showed the advantages of the scenario of balanced growth from the standpoint of implementing national goals of social and economic development.
One of the urgent tasks of the state policy of spatial development is the introduction of a permanent system for monitoring the socio-economic development of municipalities (at least at the level of municipal and districts), for which, as stated in the article, it is necessary to develop typologies of municipalities. It is advisable to generalize and analyze statistical information on municipalities on the basis of its automatic processing for different types of territories. The article analyzes the existing experience of the EU and the OECD in the typology of regions comparable to Russian municipal districts, and also summarizes the experience accumulated in Russia in the typologization of territories – municipalities in general, cities. It is shown that the basic typology can and should be based on an assessment of the settlement system – the ratio of urban and rural population, population density, the presence of a large urban center or proximity to it. Additionally, it is also important to take into account natural and climatic conditions, geographical location, sustainable economic specialization, and administrative status.
In Russia, the issue of improving socio-economic sustainability of municipalities in the region through the assessment of its indicators are particularly relevant. The aim of this work is to assess the sustainability of socio-economic development of municipalities in the Voronezh region. The paper uses a systematic approach for a comprehensive, structured and dynamic study of the state of socio-economic sustainability of municipalities in the region. This approach uses the methods of classification and comparative analysis. The authors propose a methodology for assessing the sustainability of socio-economic development of the region’s municipalities. The results of calculating the integral sustainability index indicate the presence of five groups of municipalities, characterized by a particular degree of socio-economic sustainability, ranging from high to crisis. The proposed methodology for assessing the sustainability of socio-economic development of municipalities allows us to highlight the problematic parameters of socio-economic development within the municipalities themselves and to identify the main strategic objectives on the way to a single strategic goal: improving the level and quality of life of the population. Regional policy measures to equalize the level of socio-economic development should be designed, taking into account the identified features of the territorial development of municipal districts.
Research background: Previous studies on the economic and social development of urban agglomerations mostly focus on a single primacy comparative analysis and efficiency evaluation. Spatial structure differentiation is an important feature of urban agglomeration. The lack of economic and social analysis on the spatial structure makes it impossible to determine the development positioning of each city in the urban agglomeration, which affects the sustainable economic devel-opment ability of these areas. Purpose of the article: The objective of the article is to analyze the spatial development law and experience of urban agglomeration, this study explores the practice of economic and population spatial structure of city areas in China. For this purpose, CPUA and its central city Zhengzhou was taken as an example, the spatial gradient structure of example was analyzed. Methods: Using economic and population data of 32 cities in this region, growth pole theory, and pole-axis theory, the economic and population spatial structure of urban agglomeration, the spatial gradient structure of central cities in urban agglomerations were analyzed with the method of cluster about radiation index. Findings & value added: (1) In the process of the formation of CPUA, the geo-graphical spatial pattern plays a decisive role in economic and social development. This is an experience from developing countries. (2) CPUA presents a gradient development pattern with Zhengzhou as the center, and economic and social development gradually radiates to the metropolitan area, the core development area, and the character development demonstration area. (3) The economic and social gradients of Zhengzhou, the central city, present the hierarchy rules and characteristics which are driven by the Beijing-Guangzhou-Railway axis and the Longhai-Railway axis. (4) The central city of Zhengzhou still presents insufficient primacy in regional development, which shows that Zhengzhou accounts for 6% of the population of the Central Plains Economic Zone and 14% of GDP, and insufficient agglomeration. Different countries at different stages of economic development have different urban agglomeration development models. The conclusions from China provide new decision-making ideas and methods for spatial structure research and development strategy analysis of urban agglomerations.
The socio-economic development of municipalities is defined by a set of indicators in a period of interest and can be analyzed as a multivariate time series. It is important to know which municipalities have similar socio-economic development trends when recommendations for policy makers are provided or datasets for real estate and insurance price evaluations are expanded. Usually, key indicators are derived from expert experience, however this publication implements a statistical approach to identify key trends. Unsupervised machine learning was performed by employing K-means clusterization and principal component analysis for a dataset of multivariate time series. After 100 runs, the result with minimal summing error was analyzed as the final clusterization. The dataset represented various socio-economic indicators in municipalities of Lithuania in the period from 2006 to 2018. The significant differences were noticed for the indicators of municipalities in the cluster which contained the 4 largest cities of Lithuania, and another one containing 3 districts of the 3 largest cities. A robust approach is proposed in this article, when identifying socio-economic differences between regions where real estate is allocated. For example, the evaluated distance matrix can be used for adjustment coefficients when applying the comparative method for real estate valuation.
This paper's top-level goal is to provide an overview of research conducted in the many academic domains concerned with forecasting. By providing a summary encompassing these domains, this survey connects them, establishing a common ground for future discussions. To this end, we survey literature on human judgement and quantitative forecasting as well as hybrid methods that involve both humans and algorithmic approaches. The survey starts with key search terms that identified more than 280 publications in the fields of computer science, operations research, risk analysis, decision science, psychology and forecasting. Results show an almost 10-fold increase in the application-focused forecasting literature between the 1990s and the current decade, with a clear rise of quantitative, data-driven forecasting models. Comparative studies of quantitative methods and human judgement show that (1) neither method is universally superior, and (2) the better method varies as a function of factors such as availability, quality, extent and format of data, suggesting that (3) the two approaches can complement each other to yield more accurate and resilient models. We also identify four research thrusts in the human/machine-forecasting literature: (i) the choice of the appropriate quantitative model, (ii) the nature of the interaction between quantitative models and human judgement, (iii) the training and incentivization of human forecasters, and (iv) the combination of multiple forecasts (both algorithmic and human) into one. This review surveys current research in all four areas and argues that future research in the field of human/machine forecasting needs to consider all of them when investigating predictive performance. We also address some of the ethical dilemmas that might arise due to the combination of quantitative models with human judgement.
This paper analyzes the smart strategies of European cities through the dynamic capabilities approach. We develop a clustering of smart cities based on the activities implemented by the cities. Our methodology considers three steps. First, we establish an empirical assessment of the smart dimensions for 40 European cities. Then, we categorize and interpret core capabilities via a principal component analysis. Finally, we highlight a hierarchical ascending classification identifying three relevant groups of cities. As a result, the first cluster represents cities with emerging smart strategies. The second cluster regroups international metropolises, which have technology-oriented strategies to deal with specific challenges. The third cluster stands for middle-sized European cities with a good quality of life. Our outcomes show that there is not just one smart city but several smart cities emerging according to the cities’ environment. These findings enrich the analysis of smart cities’ dynamic capabilities and point out how these strategies make cities sustainable.
The spatial structure of urban areas plays a major role in the daily life of dwellers. The current policy framework to ensure the quality of life of inhabitants leaving no one behind, leads decision-makers to seek better-informed choices for the sustainable planning of urban areas. Thus, a better understanding between the spatial structure of cities and their socioeconomic level is of crucial relevance. Accordingly, the purpose of this paper is to quantify this two-way relationship. Therefore, we measured spatial patterns of 31 cities in North Rhine-Westphalia, Germany. We rely on spatial pattern metrics derived from a Local Climate Zone classification obtained by fusing remote sensing and open GIS data with a machine learning approach. Based upon the data, we quantified the relationship between spatial pattern metrics and socioeconomic variables related to 'education', 'health', 'living conditions', 'labor', and 'transport' by means of multiple linear regression models, explaining the variability of the socioeconomic variables from 43% up to 82%. Additionally, we grouped cities according to their level of 'quality of life' using the socioeconomic variables, and found that the spatial pattern of low-dense built-up types was different among socioeconomic groups. The proposed methodology described in this paper is transferable to other datasets, levels, and regions. This is of great potential, due to the growing availability of open statistical and satellite data and derived products. Moreover, we discuss the limitations and needed considerations when conducting such studies.
Problem
How to help practitioners, academics, and decision makers use experimental research findings to substantially reduce forecast errors for all types of forecasting problems.
Methods
Findings from our review of forecasting experiments were used to identify methods and principles that lead to accurate forecasts. Cited authors were contacted to verify that summaries of their research were correct. Checklists to help forecasters and their clients undertake and commission studies that adhere to principles and use valid methods were developed. Leading researchers were asked to identify errors of omission or commission in the analyses and summaries of research findings.
Findings
Forecast accuracy can be improved by using one of 15 relatively simple evidence-based forecasting methods. One of those methods, knowledge models, provides substantial improvements in accuracy when causal knowledge is good. On the other hand, data models – developed using multiple regression, data mining, neural nets, and “big data analytics” – are unsuited for forecasting.
Originality
Three new checklists for choosing validated methods, developing knowledge models, and assessing uncertainty are presented. A fourth checklist, based on the Golden Rule of Forecasting, was improved.
Usefulness
Combining forecasts within individual methods and across different methods can reduce forecast errors by as much as 50%. Forecasts errors from currently used methods can be reduced by increasing their compliance with the principles of conservatism (Golden Rule of Forecasting) and simplicity (Occam’s Razor). Clients and other interested parties can use the checklists to determine whether forecasts were derived using evidence-based procedures and can, therefore, be trusted for making decisions. Scientists can use the checklists to devise tests of the predictive validity of their findings.
The article deals with the modern problems of forecasting methods classification. This work defines the basic concepts of the subject domain, forecasts' classifications, forecasting objects and methods, and describes the main forecasting methods. This work also performs a comparative analysis and identifies the groups of forecasting methods, corresponding to the various types of forecasts and objects. It can be used as a study guide of mathematical forecasting and system analysis in the higher school for such directions of training as "Mathematics", "Applied Mathematics and Computer Science," "Business Informatics".
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