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The study area includes 36 prefecture-level cities in Liaoning, Jilin, and Heilongjiang (from south to north) (Fig. 1). The basic analysis unit in this study is the prefecture-level city; its central city and peripheral region are used as the urban and rural areas, respectively. Since no data were available on the Daxinganling Prefecture in Heilongjiang Province and the Yanbian Prefecture in Jilin Province, 34 prefecture-level cities were used in this study. In addition, since Daqing, Heihe, Suihua, Baicheng, and Songyuan were established after 1990, they were not included due to the lack of data before its establishment.
The research period is from 1990 to 2018, and the original data are from the China City Statistical Yearbook (1991–2019) (Urban Socioeconomic Investigation Department, National Bureau of Statistics of China, 1991–2019), the Liaoning Statistical Yearbook (1991–2019) (Urban Socioeconomic Investigation Department, National Bureau of Statistics of Liaoning, 1991–2019), the Jilin Statistical Yearbook (1991–2019) (Urban Socioeconomic Investigation Department, National Bureau of Statistics of Jilin, 1991–2019), the Heilongjiang Statistical Yearbook (1991–2019) (Urban Socioeconomic Investigation Department, National Bureau of Statistics of Heilongjiang, 1991–2019), the China County Statistical Yearbook (1991–2019) (Urban Socioeconomic Investigation Department, National Bureau of Statistics of China, 1991–2019). Missing data were interpolated using the data of adjacent years.
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It is essential to evaluate the level of comprehensive development of both urban and rural systems in order to use the coupling coordination degree model to measure the urban-rural coordination. The urban-rural coordination is a four-dimensional integration process that is based on the economy, population, space, and society. The economy is the driving force; population is a subject of behavior; space is the carrier, and society is the main embodiment. To show the connotation of these four dimensions, this study established a preliminary database of urban and rural comprehensive development in Northeast China by consulting the literature on urban-rural relationship that used quantitative analysis from 2003 (Zhou et al., 2011). The primary data consisted of 20 indicators for the urban system and 20 for the rural system. Due to the many indicators and the obvious multicollinearity of indicators in the same field, it was essential to use a quantitative method to screen the primary indicators. Cluster analysis, variance analysis, and selection of representative indicators were used to quantitatively select the evaluation indicators. Finally, experts were consulted. Then, the evaluation indicator system of the comprehensive development of urban and rural areas in Northeast China was determined. It consisted of 23 indicators in 4 dimensions, 12 for the urban system and 11 for the rural system (Table 1).
Table 1. Comprehensive development indicators of urban and rural systems in Northeast China
Target Factor Indicator Target Factor Indicator Urbansystem Economy GDP per capita / yuan (RMB) Rural system Economy Primary sector output value per capita / yuan (RMB) Secondary sector output per capita / yuan (RMB) Secondary sector output per capita / yuan (RMB) Tertiary sector output per capita / yuan (RMB) Tertiary sector output per capita / yuan (RMB) Fixed asset investment / yuan (RMB) Total power of agricultural machinery / W Population Total urban population / person Population Total rural population / person) Number of employees in the city / person Proportion of rural non- agricultural labor force / % Space Built-up urban area / km2 Space Fixed asset investment / yuan (RMB) Urban green space per capita / m2 Density of the road network / km / km2 Society Average wage of urban on-the-job
workers / yuan (RMB)Society Number of doctors per 10000 residents / person Number of doctors per 10 000 residents / person Number of primary and secondary school teachers
per 10000 residents / personNumber of primary and secondary school teachers
per 10000 residents / personNumber of books in public libraries per
100 residents / volumeNumber of books in public libraries
per 100 residents / volume -
The GTWR model was used to discuss the main factors influencing the evolution of urban-rural coordination in Northeast China. The degree of urban-rural coupling coordination was used as the dependent variable, and the independent variables were selected because of the following reasons (Sun et al., 2013). The evolution dynamics of urban-rural coordination mainly include influences from the urban system, rural system, and government capacity. Regarding the urban system, the size of the central city, the speed of economic development, and the superiority of living environment are the basic prerequisites for the inflow of rural factors. Improving the rural production efficiency will lead to a crowding-out effect, resulting in a surplus of factors, which promote the flow of factors into cities. Moreover, improving rural construction conditions and developing non-agricultural industries will strengthen the connection between urban and rural industries, attract urban elements, and form the reverse thrust of the central cities. In addition, the government’s urban-rural policies and functions can accelerate or hinder urban-rural linkages. Therefore, 14 indicators were selected from the three dimensions used as the independent variables (Table 2).
Table 2. Variables of the causes of urban-rural coordination evolution in Northeast China
Source Independent variable Definition Unit Urban system Population of the central city Census registered population of the urban area (CRPU) Person Urban economic strength Total GDP of the urban area (UGDP) yuan (RMB) Urban employment income level Average wage of urban on-the-job workers (AWUW) yuan (RMB) Scale of urban space construction Urban built-up area (UBA) m2 Urban health service Doctors per 10000 residents of the urban area (UD) Person Urban education service Teachers per 10000 residents of the urban area (UT) Person Cash cost of rural population transfer Consumption level per capita in the urban area (UCL) yuan (RMB) Rural system GDP per capita in rural areas GDP per capita in the rural area (RGDP) yuan (RMB) Rural non-agricultural industrial base Proportion of rural non-agricultural labor force (RNAP) % Modernization level of rural agriculture Total power of agricultural machinery (AMPT) W Preference for investment in supporting agriculture Rural proportion of local fixed asset investment (RPFAI) % Density of the road network Level of rural transportation facilities km / km2 Governmentcapacity Regional construction level Regional fixed assets investment per capita (RFAI) yuan (RMB) Regional economic level Regional GDP per capita (RGDP) yuan (RMB) -
Urban-rural interaction is mainly through the process of allocating capital, population, land, and facilities between urban and rural systems (Tacoli, 1998b; Chen et al., 2016). In physics, the coupling coordination degree model is often used to measure the degree that two or more systems or forms of motion influence each other during an interaction (Liu et al., 2005; Wu, 2006). This study uses the coupling coordination degree model to measure the urban-rural coordination in Northeast China. The calculation formula is as follows:
$$ {C = {{\left[ {{U_1}{U_2}/{{\left({{U_1} + {U_2}} \right)}^2}} \right]}^{1/2}}} $$ (1) $$ {D = {{(CT)}^{1/2}}} $$ (2) $$ {T = \alpha f\left({{U_1}} \right) + \beta g\left({{U_2}} \right)} $$ (3) where C represents the coupling degree, which measures the degree of urban-rural interaction. Due to the limitation of the coupling degree, to thoroughly evaluate the urban-rural coordination, it is essential to calculate the coupling coordination degree, which is expressed as D in Equation (2) and indicates the harmony of urban and rural areas in the process of development (Wan et al., 2020). The values of both C and D range from 0 to 1; when the value is close to 1, it indicates a better effect. T represents the comprehensive evaluation index of the two systems; it reflects their overall efficiency or level. U1 and U2 represent the scores of the comprehensive development of urban and rural systems, respectively. α and β are undetermined parameters, which are necessary to satisfy α + β = 1. Since urban and rural systems contribute differently to the coordinated development of the urban-rural relationship, following Sun Dongqi’s prediction of urbanization in Northeast China (Sun et al., 2016), this study set the following values: α = 0.65, β = 0.35. U1 and U2 were calculated as follows:
$$ U_{1}=U_{2}=\sum \lambda_{i j} u_{i j} $$ (4) where uij represents the standardized indictor value of index j in urban system (rural system) i, and λij represents the weight of index j in urban system (rural system) i, which are necessary to satisfy
$\displaystyle\sum $ λij = 1. -
The range, standard deviation, and coefficient of variation were used to measure the internal differences in the degree of urban-rural coupling coordination in Northeast China. The range represents the absolute developmental difference, whereas standard deviation and coefficient of variation reflect the relative developmental difference.
The relative development rate index (Nich index) measures the development rate of individual regions relative to that of the entire study area over a period. It was used to analyze the evolution characteristics of the growth in the degree of urban-rural coupling coordination in Northeast China; the formula used for the calculation is as follows:
$$ { Nich }=\left(Y_{2 i}-Y_{1 i}\right) /\left(Y_{2}-Y_{1}\right) $$ (5) where Y1i and Y2i represent the degree of urban-rural coupling coordination at the end and beginning of a period in region i, respectively, whereas Y1 and Y2 represent the degree of urban-rural coupling coordination at the end and beginning of a period in the entire study area, respectively.
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The GTWR is an extension of the geographically weighted regression (GWR), which is a spatial-temporal non-stationary regression model. However, in the GTWR, a time factor is added to the GWR. Space and time coordinates are required to calculate the space-time weight matrix in the model, whereas in the traditional GWR analysis, there is no temporal dimension. The GTWR extends the traditional GWR analysis by constructing three-dimensional coordinates from space position and time-series coordinates, and it considers the effect of space and time on the regression coefficient of each explanatory variable simultaneously. In the space-time coordinate system, the coordinates of the space-time position i are (ui, vi, ti). Therefore, the GTWR model may be expressed as follows (Gelfand et al., 2003; Huang et al., 2009; 2010):
$$ Y_{i}=\alpha_{0}\left(u_{i}+v_{i}+t_{i}\right)+\sum\limits_{j=1}^{m} \alpha_{j}\left(u_{i}+v_{i}+t_{i}\right) X_{i j}+\xi_{i} $$ (6) where Yi represents the value of the interpreted variable at sample point i (i = 1, 2, 3, …, n); m represents the number of explanatory variables; ti represents the time coordinate of the sample point i; α0 (ui,vi,ti) represents the spatial-temporal intercept term of sample point i; Xij represents the value of explanatory variable j at sample point i; αj (ui,vi,ti) represents the regression coefficient of variable j at sample point i, which is a function of space-time coordinates, and ξi represents residuals. By introducing spatial-temporal coordinates in the model, the GTWR improves the accuracy of the model fitting, and it makes it possible to analyze the effect of each explanatory variable on the dependent variable from a spatial-temporal three-dimensional perspective. Thus, it has an efficient explanatory power than previous models.
Spatial-temporal Evolution of the Urban-rural Coordination Relationship in Northeast China in 1990–2018
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Abstract: To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China, this study uses the coupling coordination degree model and geographically and temporally weighted regression (GTWR) model to analyze the spatial-temporal patterns and the corresponding driving mechanisms of its urban-rural coordination since 1990. The results are as follows. First, the urban-rural coupling coordination degree in Northeast China was very low and improved slowly, but its stages of evolution is a good interpretation of the strategic arrangements of China’s urbanization. Second, the urban-rural coupling coordination degree in Northeast China had spatial differences and was characterized by central polarization, converging on urban agglomeration, which was high in the south and low in the north. Moreover, the gap between the north and south weakened. Third, the spatial-temporal evolution of the urban-rural coordination relationship in Northeast China was influenced by pulling from the central cities, pushing from rural transformation, and government regulations. The influence intensity of the three mechanisms was weak, but the pulling from the central cities was stronger than that of the other two mechanisms. Furthermore, the spatial difference between the three mechanisms determines the spatial pattern and its evolution of the urban-rural coordination relationship in Northeast China. Fourth, to promote the development of urban-rural coordination in Northeast China, it is essential to advance urban-rural economic correlation, enhance the government’s role in regulating and guiding, and adopt different policies for each region in Northeast China.
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Table 1. Comprehensive development indicators of urban and rural systems in Northeast China
Target Factor Indicator Target Factor Indicator Urbansystem Economy GDP per capita / yuan (RMB) Rural system Economy Primary sector output value per capita / yuan (RMB) Secondary sector output per capita / yuan (RMB) Secondary sector output per capita / yuan (RMB) Tertiary sector output per capita / yuan (RMB) Tertiary sector output per capita / yuan (RMB) Fixed asset investment / yuan (RMB) Total power of agricultural machinery / W Population Total urban population / person Population Total rural population / person) Number of employees in the city / person Proportion of rural non- agricultural labor force / % Space Built-up urban area / km2 Space Fixed asset investment / yuan (RMB) Urban green space per capita / m2 Density of the road network / km / km2 Society Average wage of urban on-the-job
workers / yuan (RMB)Society Number of doctors per 10000 residents / person Number of doctors per 10 000 residents / person Number of primary and secondary school teachers
per 10000 residents / personNumber of primary and secondary school teachers
per 10000 residents / personNumber of books in public libraries per
100 residents / volumeNumber of books in public libraries
per 100 residents / volumeTable 2. Variables of the causes of urban-rural coordination evolution in Northeast China
Source Independent variable Definition Unit Urban system Population of the central city Census registered population of the urban area (CRPU) Person Urban economic strength Total GDP of the urban area (UGDP) yuan (RMB) Urban employment income level Average wage of urban on-the-job workers (AWUW) yuan (RMB) Scale of urban space construction Urban built-up area (UBA) m2 Urban health service Doctors per 10000 residents of the urban area (UD) Person Urban education service Teachers per 10000 residents of the urban area (UT) Person Cash cost of rural population transfer Consumption level per capita in the urban area (UCL) yuan (RMB) Rural system GDP per capita in rural areas GDP per capita in the rural area (RGDP) yuan (RMB) Rural non-agricultural industrial base Proportion of rural non-agricultural labor force (RNAP) % Modernization level of rural agriculture Total power of agricultural machinery (AMPT) W Preference for investment in supporting agriculture Rural proportion of local fixed asset investment (RPFAI) % Density of the road network Level of rural transportation facilities km / km2 Governmentcapacity Regional construction level Regional fixed assets investment per capita (RFAI) yuan (RMB) Regional economic level Regional GDP per capita (RGDP) yuan (RMB) -
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