County Town Comprehensive Service Functions in China: Measurement, Spatio-Temporal Divergence Evolution, and Heterogeneity of Influencing Factors

Jian Zhang, Liuqing Wei, Ying Wang (), Xiaohong Chen and Wei Pan
Additional contact information
Jian Zhang: College of Geographical Sciences, Harbin Normal University, Harbin 150025, China
Liuqing Wei: College of Geographical Sciences, Harbin Normal University, Harbin 150025, China
Ying Wang: College of Geographical Sciences, Harbin Normal University, Harbin 150025, China
Xiaohong Chen: College of Geographical Sciences, Harbin Normal University, Harbin 150025, China
Wei Pan: Harbin Urban and Rural Planning and Design Research Institute, Harbin 150010, China

Sustainability, 2024, vol. 16, issue 7, 1-22

Abstract: Strengthening the service function of small towns, using its fundamental role in the urban system to drive rural development, is the main issue that needs to be addressed urgently in numerous developing countries. County towns are unique types of small towns in China. Analyzing the spatial-temporal patterns and differentiation mechanisms of comprehensive service functions of county towns in China from a geographic point of view can not only provide a basis for the macro-control of county towns but also provide typical regional research results for the study of urban systems and urban–rural coordination in developing countries. Based on Point of Interest (POI) data of 1788 county towns in China, this study analyzes the evolution of spatial and temporal differentiation of comprehensive service functions and influencing factors by using modeling methods such as Getis-Ord Gi* analysis, the random forest model, and Multiscale Geographically Weighted Regression (MGWR). The obtained results show that (1) from 2012 to 2021, the average value of the comprehensive service function index (CSFI) of county towns in China shows a significant increase, and the proportion of county towns with medium–high service levels and above increases from 3.41% to 54.50%; (2) spatially, the comprehensive service function of county towns is characterized by the basic pattern of “high east, low west; high south, low north”, which keeps getting stronger. During the study period, eastern China has always been a high-level region, northwestern and southwestern China have always been low-level regions, and northeastern China has been a stagnant region, while central, northern, and southern China have been fast-growing regions; (3) county general public budget revenues, value added of secondary industry, GDP per capita, county town resident population, altitude, and GDP per capita of affiliated prefecture-level cities to which it belongs are the key factors influencing the comprehensive service function of county towns in China. The county general public budget revenue indicator, which represents the governmental capacity, has the strongest influence; and (4) the results of the MGWR analysis indicate that there is spatial and temporal heterogeneity in the intensity of the above-mentioned key influencing factors on the development of comprehensive service functions of county towns in China. Based on this finding, differentiated strategies should be proposed to policy makers and urban planners in different regions in order to effectively enhance the level of comprehensive service functions of county towns in China.

Keywords: county town; comprehensive service function; random forest model; MGWR; China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/7/2869/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/7/2869/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:7:p:2869-:d:1366751

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().