Fluctuations in the socioeconomic development of Russian cities: Methodology and results of calculating the rank vector of dynamics

Жихаревич, Б. С.; Rusetskaya, Olga

doi:10.1134/s2079970515010128

Cited by 6 publications

(8 citation statements)

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“…We use nine socioeconomic attributes to evaluate fluctations in dynamics of development of cities, suggested in [6]. As in [6], data are collected from Statisctical Russian proceedings in period of 10 years (from 2002 to 2011). Clustering techniques we use are Minimum sum of squares clustering (MSSC) and minimum harmonic means clustering (KHMC).…”

Section: Discussionmentioning

confidence: 99%

“…We next compare clustering results with those obtained with ranking method from [6], where 5 groups of citeies are recognised. Since the vector of dynamic data are very sensitive, direct comparison among approaches is almost impossible.…”

Section: Clustering Approachmentioning

confidence: 98%

“…In section 3 we briefly explain steps of the recent method for ranking of 120 Russian cities. Since full details are only available in Russian lanquage (i.e., in [6]), Section 2 and 3 does not contain new results. In section 4 we provide results obtained by two well known clustering paradigms: Minimum sum of squares and Harmonic means.…”

Section: Introductionmentioning

confidence: 97%

“…Regional centers that have strategic planning documents known to us are included in sampling. The main source of information for the calculation of "dynamics index" was statistical data from "Regions of Russia: main social and economic indicators of Cities", years 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012. In the recent paper [6] we propose ranking approach for grouping 120 Russian cities that use the dynamic data set, or "vector of dynamics". In fact, such data are obtained when a cumulative attribute for each year is considered as a substitute for nine other attributes.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Clustering cities based on their development dynamics and Variable neigborhood search

Жихаревич

Rusetskay

Mladenović

2015

Electronic Notes in Discrete Mathematics

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Section: Discussionmentioning

confidence: 99%

Section: Clustering Approachmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Clustering cities based on their development dynamics and Variable neigborhood search

Жихаревич

Rusetskay

Mladenović

2015

Electronic Notes in Discrete Mathematics

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“…The main methodological objective of the research is to test the authors' improved methodology, which makes it possible to define and classify the trajectories of change in the relative position of a city in the considered set in each year of the considered period. The method was first tested by us on a sampling of 120 large Russian cities [4,23,24]. In this version, the cost parameters have been adjusted.…”

Section: Introductionmentioning

confidence: 99%

Fluctuations in the Relative Dynamics of Development of Russia’s Regional Centers in 2003–2013

Жихаревич¹,

Rusetskaya

2018

Reg. Res. Russ.

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The paper considers the dynamics of the socioeconomic development of 75 cities, administrative centers of Russian regions. The main objective of the study is to test the authors' methodology, which makes it possible to determine and classify the trajectories of change in the relative position of a city in the investigated set in each year of the period. Unlike the common analysis scheme, when situations at the beginning and end of the period are compared, the authors investigate changes over 11 years on an annual basis, i.e., the trajectory of change in the relative position of an object in the selected set. The study and comparison of the trajectories makes it possible to (a) see inflection points, i.e., the years in which the development of a city was relatively accelerated or decelerated in order to further reveal the factors of the change in trajectory and (b) to suggest a basis for grouping cities by trajectory type. The methodology calculates the city development dynamics index by integrating the nine indicators and constructing a rank vector. In addition, the characteristics of a sampling, such as the minimum and maximum values and variation coefficients, are calculated for each of the nine primary indicators for each year. Cities are grouped by the rank vectors that describe the trajectory of the dynamics. Groups of cities that are steadily growing faster than others, stably lagging behind, and with changing trajectories are identified. Hypotheses explaining the acceleration or deceleration of the development of individual cities are put forward. No significant relationship between acceleration of development and development of official strategies has been established.

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Geoheritage Resource of a Small Town: Evidence from Southwestern Russia

et al. 2021

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Fluctuations in the socioeconomic development of Russian cities: Methodology and results of calculating the rank vector of dynamics

Cited by 6 publications

References 1 publication

Clustering cities based on their development dynamics and Variable neigborhood search

Clustering cities based on their development dynamics and Variable neigborhood search

Fluctuations in the Relative Dynamics of Development of Russia’s Regional Centers in 2003–2013

Geoheritage Resource of a Small Town: Evidence from Southwestern Russia

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