Author(s)

Liping Du ¹, Jiayu Zhang ¹, Sa Li ¹

Affiliation(s)

¹ College of Science, North China University of Science and Technology, Tangshan, Hebei, 063000, China

Corresponding Author

Liping Du

ABSTRACT

The K-means clustering algorithm is applied to perform cluster analysis of 11 prefecture level cities in Hebei Province under six economic indicators such as analysis regional GDP, annual average salary, fiscal revenue, disposable income of rural residents, disposable income of urban residents, total volume of imports and exports. After standardizing the data from different regions in 2014 and 2019, the optimal number of clusters obtained through elbow method is four, which means that the 11 prefecture level cities are divided into four classes: economically developed areas, relatively developed areas, moderately developed areas, and underdeveloped areas. It is shown that Tangshan, Langfang, and Baoding have risen from the second, third, and fourth class to the first, second, and third class, respectively. Finally, an analysis of economic differences is conducted for different classes of regions, and relevant suggestions is provided based on the classification of economic development characteristics in each region.

KEYWORDS

K-means clustering; Economic differences; Regional economy

CITE THIS PAPER

Liping Du, Jiayu Zhang, Sa Li, The Analysis of Economic Differences among Prefecture-level Cities in Hebei Province Based on K-means Clustering. Information Systems and Economics (2023) Vol. 4: 60-65. DOI: http://dx.doi.org/10.23977/infse.2023.041009.

REFERENCES

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[4] Zhang Zengchen, Wang Pengzhen. The Study on the Economic Strength of Various Districts and Cities in Hebei Province Based on Factor Analysis and Cluster Analysis [J]. Statistics and Management, 2019(11):105-108.
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