Analysis and Study on Spatial Matching between Governmental Affairs’ Service Resources and Population: Empirical Analysis Based on the Statistical Data of L City in Central China

Guo, Ya-Guang; Xu, Jun; Yin, Qian

doi:https://doi.org/10.1155/2022/4975208

Mathematical Problems in Engineering

On this page

Abstract Introduction Data and Methods Conclusions Data Availability Conflicts of Interest References Copyright Related Articles

Special Issue

Big Data Modelling of Engineering and Management 2022

View this Special Issue

Research Article | Open Access

Volume 2022 | Article ID 4975208 | https://doi.org/10.1155/2022/4975208

Analysis and Study on Spatial Matching between Governmental Affairs’ Service Resources and Population: Empirical Analysis Based on the Statistical Data of L City in Central China

Ya-Guang Guo,^1,2Jun Xu,^1,2and Qian Yin³

Academic Editor: Yiwen Zhang

Received08 Mar 2022

Accepted23 Apr 2022

Published05 May 2022

Abstract

Objective. To construct a coupling model between resources for governmental affairs services and population data and its evaluation system, this research investigates and surveys offline (site, hardware, and human resources) and online service resources of governmental affairs on the basis to the Seventh National Census. The study aims to provide countermeasures and suggestions to promote governmental affairs service resources for people in specific areas more effectively. Method. This study analyzes and examines spatial matching between governmental affairs’ service resources and the population by using principal component analysis. Using the coupling matching degree model for reference, this study constructs a matching model based on governmental affairs’ service resources and its evaluation system. Furthermore, the study analyzes and demonstrates the coupling matching degree between governmental affairs’ service resources and the population system. Conclusions. Over the past five years, the matching level between governmental affairs’ service resources and the population has been categorized as moderate coordinative matching on the whole. From the perspective of changing trends, which are affected by population fluctuations, the matching degree also fluctuates, thus indicating a close relationship. Therefore, some relevant governmental departments need to continue promoting the “One Net, One Access, One Time” reform further and encouraging the equal allocation of governmental affairs’ service resources.

1. Introduction

According to the Seventh National Population Census, the total number, structure, and distribution of China’s population are comprehensively investigated, which accurately reflects the population change and provides a large number of valuable information resources. To analyze and study the census data deeply and give full play to the social benefits of the data, the leading group of the seventh national census of the State Council has carried out research in the fields of population and education, housing, and medical treatment.

Previous research has mostly focused on the matching degree between the economy and the environment, between the economy and tourism, and between urbanization and ecological environment [1–5]. Meanwhile, few research studies have been conducted on governmental affairs’ service resources and population allocation. Under the Internet + governmental affairs’ service policy, the critical factor in the reform of administrative management system is to construct a better and more accurate service-oriented government. In addition, the spatial fairness of the distribution of governmental affairs’ service resources is the premise and material basis for achieving the equalization of basic public services. It is also the most important goal of the government.

The governmental affairs’ service resources and the population are two different systems. Their coordination relationship and degree are formed by the interaction of multiple factors. The analysis on matching and coordination between these two aspects is the basis of the One Net, One Access, One Time reform in governmental affairs’ service. Furthermore, it is of great practical significance for building a service-oriented government to satisfy people’s needs and overcome the last kilometer barriers in convenient services.

Under this guidance, the L municipal government in Central China has launched research on the population structure and education, elderly care, and the adaptation of governmental affairs service resources in combination with regional characteristics. The organization where the author is affiliated has made an in-depth study on the governmental affairs service resources of the city together with the Data Resource Management Bureau of L City. On the basis of population census data, they have investigated the spatial accessibility of governmental affairs service resources, established the matching calculation model using statistical methods, and calculated the matching degree. Furthermore, they have found the problems in the adaptation and provided some suggestions.

2. Data and Methods

2.1. Data Sources

The population data come from the seventh population census and statistical yearbook of L City in 2017–2020. The data on governmental affairs’ service resources come from the annual open report on the government information of the L City Data Resources Bureau and the statistical work. In addition, the relevant data have been placed on the GitHub open source data site. According to the characteristics of the population and regional structure, the data processing and description are as follows. (1) All data are collected and sorted according to the spatial partition of data. (2) In terms of spatial allocation of the population and governmental affairs’ service resources, resources have been unevenly allocated due to the registered residents. However, the resident population is taken as the main object of the optimal allocation of governmental affairs service resources in this study.

2.2. Establishment of Evaluation Index System

According to the overall evaluation goal in the spatial matching between governmental affairs’ service resources and the population, the target layer and branch indexes are set up on the basis of the model in the establishment of the evaluation index system [6–8]. The population data of L City from 2017 to 2021 are divided into three categories according to the population number and distribution. The data of governmental affairs service resources are divided into three categories: hardware resources, human resources, and network resources. A total of 23 indicators from six categories are selected to analyze the spatial matching between governmental affairs’ service resources and the population (Table 1).

Tables 2 and 3 present the population data and governmental affairs service resources, including hardware resources (service halls and number of service networks), human resources (number of service personnel and number of village-level agents), and network resources (number of “All-in-one Netcom” offices at the municipal level and the number of “Trans-provincial Netcom” offices).

3. Principal Component Analysis and Matching Degree Calculation Model

3.1. Principle Component Analysis

Principle component analysis [9–11] refers to a multivariate statistical method to convert multiple indicators into several comprehensive indicators. The premise of this method is the small information loss based on the idea of dimensionality reduction. Through principle component analysis, some main components can be determined from the complex relationship between things, which can then be simplified. Through effective quantitative analysis on a large number of statistical data, deep enlightenment on the characteristics of things and their development laws is obtained. The essential law of data is also found.

In the quantitative analysis model, the quantitative relationship between the classification cause index Y_i and original index X_i is as follows:

Therefore, when the research object has many indicators and if the contribution rate of the first t principal components in the cumulative variance is greater than or equal to 85%, the newly generated t variables can be used to replace the original indicator variables. These new variables can fully absorb the information on the original indicators and greatly improve the accuracy and reliability of the evaluation results through mathematical modeling.

3.2. Theoretical Analysis of System Matching

The matching process is shaped by a combination of resource development and demographic changes in government services. The “coordination” matching examines the degree of variation between systems in the dynamic evolution of time series. The lower the degree of difference between the two systems, the higher the degree of mutual optimization and cooperation between the systems. “Comprehensive development level” examines the comprehensive advantage state of system combination. The detailed analysis of the above ideas is as follows:

Assuming the existence of two interrelated systems with development levels X and Y, their linear equations are as follows:where is the deviation of , and is the comprehensive development level of and , and , , , and are the model parameters. Further derivation leads to

From (3), we can see that, according to the definition of coordinated matching, the smaller the deviation of and , the higher the degree of matching.

From (4), if the comprehensive development levels of and are constant, then there is a complete substitution relationship between and , which can form a linear systematic nondifference curve. If the general law of diminishing marginal rate of substitution of the nondifferentiable curve is followed, then the convex curve can be obtained. The key significance is that the outward shift to the right with the convex curve indicates an increasing level of comprehensive development level.

Applying the above theoretical resolution principles, the objective of this study is to determine and evaluate the “deviation” and “comprehensive development level” of the two systems of government service resources and population.

3.3. Matching Degree Calculation Model

The model is analyzed on the basis of [12–15]. In addition, the calculation formula of the coordination degree between the population and governmental affairs’ service resource system [12, 16–18] is as follows:

In this regard, the coordination degree can be derived from the deviation formula of the two systems, while the development index can be equated to the comprehensive development level of the two systems, where refers to the coupling degree between the population and governmental affairs service resource system and refers to the total development index of the population subsystem and the governmental affairs’ service subsystem. Their calculation formulas are as follows:where and are undetermined coefficients, and their values reflect the importance of the population and government service resources. Here, the population and government service resource system are equally important. These coefficients are set to = = 1/2 [19–21].

The matching degree between the population and governmental affairs’ service system is calculated according to the matching calculation model. The comprehensive evaluation is carried out according to the division standard of the system matching degree in Table 4 and [22–24].

4. Empirical Analysis

4.1. Data Standardization Processing

Tables 2 and 3 demonstrated that the original data are in different units and different orders of magnitude to reflect the characteristics of a group of data from various perspectives. Therefore, the original data must be standardized [25]. The standard conversion is conducted for the original data by 0–1 standardization method in Tables 2 and 3. Tables 5 and 6 are presented as follows.

4.2. Principle Component Analysis

Using the statistical software IBM SPSS Statistics 23, standardized data (Zp1–Zp10, Zq1–Zq13) in Tables 5 and 6 are selected to make principle component analysis. Based on KMO and Bartlett sphericity test, where the KMO values are more than 90%, two principle components are extracted in the population system to explain 99.240% of the original variable information. Moreover, three principle components are extracted in the governmental affairs’ service resource system to explain 93.756% of the original variable information. Thus, promising results are obtained. The component matrix of the population and governmental affairs’ service resources must be constructed (see Table 7).

4.3. Development Indicators of Population and Governmental Affairs’ Service Resources Based on Principle Component Analysis

According to the obtained principle component score matrix, the data in the principle component load matrix are divided by the eigenvalue corresponding to the principle component. Then, by taking the square root, the coefficient corresponded by each indicator in two principle components is determined. In addition, the expressions of the two principal components of the population system are obtained:

In the same way, the expressions of three principal components of governmental affairs’ service resource system are obtained:

Then, the variance proportion of eigenvalues corresponded by the two principal components of the population system and the three principal components of the governmental affairs’ service resource system are taken as the weight coefficients. The comprehensive function of the principal components is calculated as follows:

Substituting the standardized data of the population system in Table 5 into Formulas (7), (8), and (13), the development level score of the population system from 2017 to 2021 is obtained. Similarly, substituting the standardized data of governmental affairs’ service resources in Table 6 into formulas (10)–(13), the development level score of governmental affairs service resources from 2017 to 2021 is obtained, as shown in Figure 1:

Figure 1 demonstrates that the total population of the city is in a downward trend from 2017 to 2021, with a sharp decline from 2019 to 2020. In addition, the development index from 2020 to 2021 is negative, which is at an extremely low level.

In terms of the development index of governmental affairs’ service resources, the governmental affairs’ service resources of L City is always in a upward trend from 2017 to 2021, with a sharp rise from 2018 to 2019, a stable rise from 2019 to 2020, and large rise from 2020 to 2021.

4.4. Measurement and Analysis on Coupling Degree between Governmental Affairs’ Service Resources and Population System

In the previous section, the development level scores of the population system and the government service resource system in L City from 2017 to 2021 was determined. On this basis, the matching coefficient between population and governmental affairs’ service resources in L City is calculated using the matching calculation model (Table 8). The population development index and governmental affairs’ service development index and their matching degree D are shown in Figure 2.

4.4.1. Overall Evaluation of Data

The population data development index has been in a downward trend. With the increase of migrant workers from L City and the promotion of the Yangtze River Delta integration strategy, the population of L City will still be in a state in which more people go out for work. Moreover, the number of permanent residents is expected to have a declining trend. With the in-depth reform of Internet + governmental affairs’ service, the development level of governmental affairs’ services is in a steady rising state. It also reflects the achievements of the governmental affairs’ service reform in the past five years. According to the division of system matching degree in Table 1, the overall average matching between the population and governmental affairs’ service in L City is moderate, with the highest value of 0.6517 in 2019, thus reaching a good matching level.

4.4.2. Data Evolution Analysis

The population and governmental affairs’ service resources in L City experienced a significant changing trend in 2017–2021. In addition, the population development level has increased continuously. Meanwhile, the matched governmental affairs’ service resources have risen steadily, with a synchronous growth and coordinated change trend, thus reaching its best match in 2019. However, in 2019–2021, the increase of people going out for work led to a sharp decline of permanent residents and a negative value of the population development index. In contrast, with the promotion of the Internet + governmental affairs’ service and the implementation of the One Net, One Access, One Time reform plan, the development index of governmental affairs’ service resources has experienced an upward trend. On the one hand, the population decreased sharply in 2019–2020. On the other hand, the governmental affairs’ service level increased steadily. Hence, the matching degree experienced a downward trend in 2019–2020. However, in 2020–2021, the decrease in the population index slowed down, while the governmental affairs’ service resources retained a steady rise. As a result, the matching degree rose again in 2021.

5. Conclusions and Recommendations

5.1. Conclusions

The matching level between governmental affairs’ service resources and population is categorized as moderate coordinative matching. From the perspective of changing trends, which are affected by population fluctuations, the matching degree also fluctuates, thus indicating a close relationship between these two aspects.

The principle component analysis method used in this study not only evaluates the development level of the population system and the governmental affairs service resource system but also can effectively calculate and comprehensively evaluate the matching between population and governmental affairs’ service resources by using the matching degree model. The evaluation model can also be applied to the provincial or national data on the population and governmental affairs’ service resources. It provides a more scientific and effective research and evaluation means for the matching calculation and intelligent matching between governmental affairs’ service resources and the population. Thus, the model is of far-reaching significance.

This study finds the matching degree and trend of government service resources and population allocation from a macroperspective and fails to do in-depth research on more refined and personalized matching due to the complexity and diversity of population data; we intend to conduct the corresponding study in our future work.

5.2. Suggestions

The matching between governmental affairs’ service resources and population is moderate. Moreover, it still has great development potential. Hence, the government needs to promote the development of matching between governmental affairs’ service resources and population further. The following suggestions are proposed in this study. First, the service resources based on spatial accessibility are more accurately allocated. On the basis of the total population, the population characteristics are analyzed, and the needs of the masses are more accurately judged. For example, the day of a governmental affairs’ service is determined through the gathering places of the elderly or those with low education to provide door-to-door services. In the gathering places of middle-aged and young people or those with high education, intelligent means, such as the all-in-one machine for governmental affairs’ services, are promoted; for instance, a specific policy is implemented in one district to provide services more accurately. Second, service in the grassroots units needs to be enhanced. Moreover, itinerant services must be provided in villages, and the construction of village-level agents must be strengthened. The standardization of village-level services must be perfected, and the last kilometer of governmental affairs’ service must be covered in all aspects in detail. Third, the government must upgrade the One Netcom Office. On the basis of the One Netcom Office and the Anhui Governmental Affairs’ Netcom Office in the early stage, more One Netcom matters are explored according to regional characteristics to provide the public with more convenient governmental affairs’ services.

Data Availability

The data used to support the findings of the study are available at https://github.com/grcpeng/The-number-of-governmental-affair-service-resource-and-population-of-L-City-in-2017-2021.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

C. Oliveira and C. H. Antunes, “A multi-objective multi-sectoral economy-energy-environment model: application to Portugal,” Energy, vol. 36, no. 5, pp. 2856–2866, 2011.
View at: Publisher Site | Google Scholar
F. Ahmad, M. U. Draz, L. Su, and A. Rauf, “Taking the bad with the good: the nexus between tourism and environmental degradation in the lower middle-Income Southeast Asian economies,” Journal of Cleaner Production, vol. 233, pp. 1240–1249, 2019.
View at: Publisher Site | Google Scholar
A. K. Jorgenson and B. Clark, “Are the economy and the environment decoupling? A comparative international study,” American Journal of Sociology, vol. 118, no. 1, pp. 1960–2005, 2012.
View at: Publisher Site | Google Scholar
E. Sanyé-Mengual, M. Secchi, S. Corrado, A. Beylot, and S. Sala, “Assessing the decoupling of economic growth from environmental impacts in the European Union: a consumption-based approach,” Journal of Cleaner Production, vol. 236, Article ID 117535, 2019.
View at: Publisher Site | Google Scholar
Z. Zhang, X. Chen, P. Heck, B. Xue, and Y. Liu, “Empirical study on the environmental pressure versus economic growth in China during 1991-2012,” Resources, Conservation and Recycling, vol. 101, pp. 182–193, 2015.
View at: Publisher Site | Google Scholar
J. Chen, Z. Li, Y. Dong, M. Song, M. Shahbaz, and Q. Xie, “Coupling coordination between carbon emissions and the eco-environment in China,” Journal of Cleaner Production, vol. 276, Article ID 123848, 2020.
View at: Publisher Site | Google Scholar
Y. Li, Y. Li, Y. Zhou, Y. Shi, and X. Zhu, “Investigation of a coupling model of coordination between urbanization and the environment,” Journal of Environmental Management, vol. 98, pp. 127–133, 2012.
View at: Publisher Site | Google Scholar
C. Wang and N. Tang, “Spatio-temporal characteristics and evolution of rural production-living-ecological space function coupling coordination in Chongqing Municipality,” Geographical Research, vol. 6, pp. 1100–1114, 2018.
View at: Google Scholar
H. Abou-Ali and Y. M. Abdelfattah, “Integrated paradigm for sustainable development: a panel data study,” Economic Modelling, vol. 30, pp. 334–342, 2013.
View at: Publisher Site | Google Scholar
Q. Li and J. Niu, “Evaluation on integrated innovation capability of regions based on principal component analysis,” Canadian Society of Forensic Science Journal, vol. 6, pp. 12–19, 2010.
View at: Google Scholar
X. F. Sun, H. Y. Shao, X. Y. Xiang, and L. G. Yuan, “Natural resources research A coupling method for eco-geological environmental safety assessment in mining areas using pca and catastrophe theory,” Natural Resources Research, vol. 20, pp. 96–110, 2020.
View at: Google Scholar
J. He, S. Wang, Y. Liu, H. Ma, and Q. Liu, “Examining the relationship between urbanization and the eco-environment using a coupling analysis: case study of Shanghai, China,” Ecological Indicators, vol. 77, pp. 185–193, 2017.
View at: Publisher Site | Google Scholar
P. Xie, S. Gao, and F. Sun, “An analysis of the decoupling relationship between CO2 emission in power industry and GDP in China based on LMDI method,” Journal of Cleaner Production, vol. 211, pp. 598–606, 2019.
View at: Publisher Site | Google Scholar
F. Tan and Z. Lu, “Study on the interaction and relation of society, economy and environment based on PCA-VAR model: as a case study of the Bohai Rim region, China,” Ecological Indicators, vol. 48, pp. 31–40, 2015.
View at: Publisher Site | Google Scholar
X. Lu, M. G. Xue, and M. S. Hu, “Dynamic simulation and assessment of the coupling coordination degree of the economy–resource–environment system: case of Wuhan City in China,” Journal of Environmental Management, vol. 230, pp. 474–487, 2019.
View at: Google Scholar
Q. Song, N. Zhou, T. Liu, S. A. Siehr, and Y. Qi, “Investigation of a “coupling model” of coordination between low-carbon development and urbanization in China,” Energy Policy, vol. 121, pp. 346–354, 2018.
View at: Publisher Site | Google Scholar
Y. Fan, C. Fang, and Q. Zhang, “Coupling coordinated development between social economy and ecological environment in Chinese provincial capital cities-assessment and policy implications,” Journal of Cleaner Production, vol. 229, pp. 289–298, 2019.
View at: Publisher Site | Google Scholar
Y. Zhang, Z. Su, G. Li, Y. Zhuo, and Z. Xu, “Spatial-temporal evolution of sustainable urbanization development: a perspective of the coupling coordination development based on population, industry, and built-up land spatial agglomeration,” Sustainability, vol. 10, no. 6, p. 1766, 2018.
View at: Publisher Site | Google Scholar
A. Bellin, B. Majone, O. Cainelli, D. Alberici, and F. Villa, “A continuous coupled hydrological and water resources management model,” Environmental Modelling & Software, vol. 75, pp. 176–192, 2016.
View at: Publisher Site | Google Scholar
Z. Tang, “An integrated approach to evaluating the coupling coordination between tourism and the environment,” Tourism Management, vol. 46, pp. 11–19, 2015.
View at: Publisher Site | Google Scholar
D. Zhou, J. Xu, and Z. Lin, “Conflict or coordination? Assessing land use multi-functionalization using production-living-ecology analysis,” The Science of the Total Environment, vol. 577, pp. 136–147, 2017.
View at: Publisher Site | Google Scholar
Y. Yang, W. Bao, and Y. Liu, “Coupling coordination analysis of rural production-living-ecological space in the Beijing-Tianjin-Hebei region,” Ecological Indicators, vol. 117, Article ID 106512, 2020.
View at: Publisher Site | Google Scholar
T. Shi, S. Y. Yang, W. Zhang, and Q. Zhou, “Coupling coordination degree measurement and spatiotemporal heterogeneity between economic development and ecological environment --Empirical evidence from tropical and subtropical regions of China,” Journal of Cleaner Production, vol. 244, Article ID 118736, 2020.
View at: Publisher Site | Google Scholar
J. Wang, S. Wang, S. Li, and K. Feng, “Coupling analysis of urbanization and energy-environment efficiency: evidence from Guangdong province,” Applied Energy, vol. 254, Article ID 113650, 2019.
View at: Publisher Site | Google Scholar
N. Liu, C. Liu, Y. Xia, and B. Da, “Examining the coordination between urbanization and eco-environment using coupling and spatial analyses: a case study in China,” Ecological Indicators, vol. 93, pp. 1163–1175, 2018.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2022 Ya-Guang Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies