A spatial autocorrelation for modelling the spread of coronavirus infections

Наумов, И. В.; Krasnykh, Sergey; Otmakhova, Yulia

doi:10.1051/shsconf/202110601001

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Данный методический подход показал высокую эффективность в определении полюсов роста, что подтверждается как отечественными, так и зарубежными исследованиями. В частности, данный метод использовался для определения потенциальных полюсов роста заболеваемости COVID-19 в Российской Федерации [8], для выявления полюсов развития в рамках регионального планирования [9], для оценки развития экономического роста регионов [10]…”

Section: теория и методыunclassified

Growth Poles of Neo-Industrial Exports of Russian Regions

Krasnykh¹

2022

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The development of high-tech (neo-industrial) exports is one of the most important tasks that the Russian economy is facing. The solution of these tasks is enshrined in presidential decrees, national projects, which confirms the high priority in the implementation of these decrees. The unprecedented sanctions imposed by the EU and the US significantly restrict the import of high-tech products into our country, as well as the export of raw materials. In these restrictive realities, and for the development of our country's economy, a transition to an import substitution policy is necessary, and it is important to look for growth poles for the formation of neo-industrial exports of the regions of the Russian Federation. In this connection, the purpose of this study is to determine the growth poles of high-tech exports of Russian regions. To solve this problem, spatial modeling methods were used, namely, the analysis of local and global Moran's indices. The study identified growth poles, clusters of high and low values, as well as potential territories that in the future may become new growth poles for high-tech exports of the Russian regions.

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Section: теория и методыunclassified

Growth Poles of Neo-Industrial Exports of Russian Regions

Krasnykh¹

2022

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“…Additionally, some researchers have utilized non-standard matrices such as inverse geodesic distances (Ivanova, 2014) and squares of inverse geodesic distances (Kholodilin et al, 2012), as well as matrices representing regional potentials (Zverev and Kolomak, 2010;Rusanovsky & Markov, 2016), trade and migration ows (Naumov et al, 2020). Naumov et al (2021) employed a comprehensive set of spatial weight matrices, including their standardized versions, in their spatial autocorrelation analysis and modeling. e utilization of multiple matrices is crucial to obtain reliable conclusions supported by various measures of spatial mutual in uence.…”

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confidence: 99%

Exploring the Innovative Development of Russian Regions: A Spatial Regression Analysis Using the Cobb-Douglas Model

Наумов¹,

Никулина²

2023

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Relevance. Extensive research has focused on evaluating and modeling innovative processes in territorial systems. However, an underexplored aspect is the assessment of spatial effects resulting from neighboring territories and the modeling of inter-territorial interactions in enterprise innovation. The existing regression models have limitations in accounting for spatial effects, indicating the presence of unaccounted factors. Research objective. This study aims to develop a methodological approach to evaluate the influence of factors on the dynamics of shipped innovative goods in Russian regions, taking into account spatial effects. Additionally, it aims to test the hypothesis that territories located near innovatively developing regions exhibit faster progress. Data and methods. The study utilizes regression analysis of panel data, employing combined least squares, fixed effects, and random effects methods to evaluate the influence of enterprise costs on innovation, the number of research personnel (researchers and technicians), advanced production technologies developed and used, the number of research organizations, as well as the internal costs of fundamental and applied research and development on the volume of shipped innovative goods in Russian regions from 2000 to 2021. To account for spatial effects, spatial econometrics techniques such as Spatial Autoregressive (SAR) models considering spatial lag and Spatial Autoregressive Conditional Heteroscedasticity (SAC) models considering both spatial lag and spatial error are employed. The Generalized Method of Moments (GMM) with the White period weight matrix is used to address heteroscedasticity, and data transformation techniques including orthogonal deviations and the inclusion of dummy variables for each spatial unit and time period are applied. Results. The study reveals deepening spatial heterogeneity in innovation processes during economic downturns, which smooth out during economic recovery. Regions with high and low concentrations of shipped innovative goods are identified. Regression analysis establishes the impact of various factors on shipped innovative goods. Spatial models utilizing the Cobb-Douglas SAR and SAC frameworks demonstrate positive spatial effects, wherein neighboring regions exert influence on innovative development. Regions with high enterprise innovation activity, including Moscow, St. Petersburg, and others, exhibit the highest spatial effects. Conclusions. The innovative development of a single region depends not only on its own production factors but also on the innovative activity of enterprises in the surrounding regions. These findings highlight the importance of considering spatial effects in assessing and modeling regional innovation dynamics.

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A spatial autocorrelation for modelling the spread of coronavirus infections

Cited by 2 publications

References 7 publications

Growth Poles of Neo-Industrial Exports of Russian Regions

Growth Poles of Neo-Industrial Exports of Russian Regions

Exploring the Innovative Development of Russian Regions: A Spatial Regression Analysis Using the Cobb-Douglas Model

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