The goal of the article is to determine which components of sustainable and smart development of urban areas are the most important for the economy of a city. For this, regression, cluster and discriminant analysis are applied, using the data of the ranking positions of 180 cities of the world according to the Cities in Motion Index (CIMI) and its components for 2022. The Stata and Statgraphics 19 software packages are used for the calculations. The statistical significance of the input data is confirmed using descriptive statistics, and the normality of the data distribution was determined according to the Shapiro-Wilk test. A regression analysis (based on the least squares method) of the influence of the integral value of CIMI and its components (Human capital, Social cohesion, Environment, Governance, Urban planning, International profile, Technology, Mobility and Transportation) on its first component – Economy, is carried out. It testifies that only four indicators have a statistically significant impact: Cities in Motion, Environment, Urban planning, and International profile. Multiple regression, constructed using the strict screening procedure, confirms these findings; and discriminant analysis proves that the regression equation coefficients is used to predict the Economy variable. Analysis of Spearman’s and Kendall’s correlation matrices prove a close relationship between the Economy, Human capital, Governance, and Cities in motion; direct dependence between Cities in motion and such indicators as Technology, Urban planning, and International profile; average direct connection between Economy, Social cohesion and Mobility and transportation. Cluster analysis using the k-means method in the R Studio software environment made it possible to distinguish eight clusters of cities according to their ranking positions in relation to various parameters of the CIMI index (their number was calculated according to the Sturgess formula, and the optimality of their number is confirmed by the agglomeration scheme according to the Ward method). For the cities of the first cluster (17 cities, 9.44% of the total number analyzed, mostly world capitals), Cities in motion has the greatest impact on the Economy component, while Mobility and Transportation has a lesser impact; for the cities of the second cluster (23 cities, 12.78%, mostly large cities of the United States and China) it is Technology that has the greatest impact; for cities of the third cluster (35 cities, 19.44%, primarily powerful regional centers)it is Cities in motion, International profile, Mobility and transportation, Social cohesion, and Urban planning; for clusters four (9 cities, 5%) and five (6 cities, 3.33%), the regressions are not significant, so these clusters require further study for each city separately; for the cities of the sixth cluster (33 cities, 18, 33%, mostly developed European cities) the most important are Cities In motion, Environment, Governance, Mobility and transportation, Social cohesion, and Urban planning; for the cities of the seventh cluster (10 cities, 5.56%) – Human capital, Social cohesion, and Technology; for cities in the eighth cluster (47 cities, 26.11%, mostly cities facing economic obstacles to their development) – Cities in motion, Environment, Technology, and Urban planning. The discriminant analysis shows that the Environment indicator has the greatest impact on the division of clusters into groups.
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