4.1. Spatial Patterns of the DI and TI
Based on AHP method, we calculate the urban-rural development index (DI) of study area in 1994 and 2010, and then we classify the area into four types of regions, the low value (0–0.04), relatively low value (0.04–0.12), relatively high value (0.12–0.2) and high value (0.2–0.64) regions, in ArcGIS. The result of DI patterns in two years is as follows (Figure 2
In 1994, the municipalities of Beijing, Tianjin, Shanghai and Chongqing; provincial capitals such as Guangzhou and Wuhan; and big cities in northeast China such as Heihe and Chifeng had a relative high DI values. Jiangxi, east Henan, Guangxi and south Shanxi Provinces had a relatively low DI values.
In 2010, the DI in the study area experienced a significant increase. The high value of DI was still located in the municipalities of Beijing, Shanghai, and Chongqing and provincial capitals such as Guangzhou, Shenzhen, Zhengzhou and Nanjing. Cities in northeast China, west Fujian, Anhui Province, southwest China and regions along the Hu Huanyong line received low values. The accumulation of high DI areas in Bohai Rim Region, Yangtze River Delta Region and Pearl River Delta Region became more and more significant.
In order to investigate the dominant factors for the spatial distribution pattern of DI, we chose the individual development indexes (PDI
) in 2010 to detect the high value and low value regions based on the hotspot analysis module in ArcGIS. We found that the hot spot areas of DI were mainly located in the cities of Bohai Rim Region and Yangtze River Delta Region (Figure 3
Among the five individual DIs, the population DI (PDI) showed that Henan, north Anhui, Guangxi, and Shandong Provinces were in high value clusters. Through observation of data sources, we realized that the huge amount of total population drove the high DIs of Henan and Shandong Provinces, while that in Guangxi Province derived from its rapid population growth. The cold spots of the population index were around northeast China owing to the small population size and, more importantly, low rate of population growth. The land DI (LDI) results showed that northeast China and municipalities such as Beijing and Tianjin were in the high value range because of large land areas and the expansion of built-up areas, respectively. The cold spots of the land DIs were around central China, such as Henan, Shandong and Anhui Provinces, mostly because of their small average areas and limited built-up area growth rates.
The industry DI (IDI) in the Bohai Rim, Yangtze River Delta, and Pearl River Delta regions had high values. It is worth noting that these regions also owned the fastest economic growth rates in China as well as the highest degree of human capital accumulation and creative power. The cold spots were around southwest China such as Yunnan and Guizhou Provinces, which are economically backward areas.
The distribution of society DI (SDI) was similar to that of industry DI: the hot spots were mainly located in the Bohai Rim and Yangtze River Delta regions such as Hebei, Jiangsu, Zhejiang, and Shandong Provinces, while the cold spots were around southwest China. Such similar distribution possibly owns to the fact that industrialization and economic growth will significantly promote the development of society.
The environment DI (DI) in 2010 for the cities in Yangtze River Delta Region of China were relatively high. These regions also showed high economic development and social welfare. The assumption is that a city with higher socioeconomic development would prefer to pay more money to improve its environment and living conditions.
Using the Pearson correlation coefficient method, we tested the relationship between different DIs in 2010 (Table 2
). The highest correlation was between the industry DI and society DI, with the coefficient reaching 0.849, indicating high connection between industrialization and social development; the correlation between the society DI and environment DI was relatively high (0.740), so was the industry DI and environment DI (0.718). The lowest correlation was between the population DI and environment DI. The high correlation between IDI and SDI/IDI indicated that, along with the advance of regional GDP and non-agricultural products, the government revenue increased correspondingly. As a result, the government paid more money to raise the level of regional public services, improve the living standard of local residents and increase the green land area with the purpose of improving those regional environments.
Based on the calculation of TI (urban-rural transformation index), we classified the area into four types of regions as well, the low value (0–0.4), relatively low value (0.4–0.6), relatively high value (0.6–0.7) and high value (0.7–1) regions in ArcGIS. The result of the TI is shown in Figure 4
. The figure shows that in 1994, the municipalities of Beijing, Tianjin, Shanghai and big cities like Shenzhen had high TI values. Chongqing, provincial capitals including Guangzhou, Hangzhou, and Kunming and cities in the northeast China had relative high TI values. On the contrary, the cities in southwest China, west Shandong and east Henan had low TI values.
The results of 2010 showed that the overall TI east of the Hu Huanyong line increased significantly. The values in the municipalities such as Beijing, Tianjin and Shanghai, provincial capitals and big cities in the study area increased significantly, while cities in southwest China and northeast China were mostly in low value range, especially in Yunnan and Heilongjiang Provinces. In addition, the further increases in the TIs in the Yangtze River Delta and Pearl River Delta regions along the east coast of China were a significant feature in this period.
The hotspot analysis result for the TI in 2010 showed that the high value cluster was in southeast China including Zhejiang, Fujian, Jiangxi Provinces, and north China including Hebei, Beijing and Liaoning Province (Figure 5
). Correspondingly, the low value cluster was in southwest China including Guizhou, Sichuan and southwest Hubei. Population TI showed that northeast China was in a high value cluster region, while low value areas were mainly located in southwest China, indicating a higher population quality in the northeast and a lower population quality in the west. South China including Yunnan and Sichuan Provinces had high land TIs, while central and east China such as Henan, Jiangsu, Shandong, and Zhejiang Provinces had low values. These regions are major grain producing areas, with a higher percentage of cultivated land. The hot spots of the industry TI were in Shandong, Shanxi, Anhui, Zhejiang, Jiangsu, and Guangdong Provinces, mostly in the Pearl River Delta region and Yangtze River Delta region, while the cold spots were in west China and northeast China such as Yunnan, Heilongjiang, and Guizhou Provinces. The society TI showed that Yangtze River Delta region, Bohai Rim region, and Pearl River Delta region were the three highest value clusters in study area, while the cold spots were in western regions such as Yunnan and Shaanxi Provinces. The distribution of environment TI showed a high value cluster in central and east China, containing Huang-Huai-Hai Plain and Beijing-Tianjin-Hebei region. These areas also had the most serious environmental problems, including toxic haze and water pollution.
Relying on the calculation results of TI, we tested the correlation between different individual TIs (Table 3
). The Pearson correlation coefficient test showed the highest positive correlation between the population TI and society TI, with the coefficient reaching 0.527, indicating a potential high connection between population quality and social economic development. The correlation coefficient between population TI and industry TI was also high (0.526). The high correlation between PTI and ITI/STI reflected the fact that the optimization of population structure would play a great role in upgrading the regional level of social economic development. According to Table 2
, PTI comprehensively reflected the age structure, employment structure and educational structure of the population. The upgrading and improvement of non-agricultural industry would create more employment opportunities for the labor force, which would attract more young adults with good educational background to cities. Thus, the comprehensive quality of local population would improve, and the industry system and society system would experience a new round of rapid development.
In the next step, we explored the correlation between the DIs and TIs. Using the Bivariate Local Moran’s I index method, we characterized the correlation types for two indexes in 1994 and 2010. The results showed that the “high-high” correlation type was mostly located in Zhejiang, Jiangxi and Jiangsu Provinces, indicating a significant development advantage in society and economy compared with other regions. By contrast, the “low-low” correlation type was mostly located in Henan, Anhui, and Sichuan Provinces, implying that the development and transformation processes in these regions were relatively slow, High-Low and Low-High types represent regions with unsynchronized development and transformation speeds (i.e., the development rate is higher than the transformation rate).
4.2. Spatio-Temporal Characteristics of Changes in the DI and TI Over Time
Based on the per-period analysis presented above, we then explored the spatio-temporal changes in each index for the 16-year study period. Figure 6
shows the result for the change in DIs, with red representing the fastest-growing areas and green the slowest-growing areas (note that, owing to the lack of sewage treatment rate data in 1994, we used the data in 2002 instead).
The fastest-growing areas of the population DI were mainly located in southwest China (Guangxi and Chongqing) and central China (Henan and south Hebei). By contrast, the slowest-growing areas of the population DI were in northeast China. The fastest-growing areas of the land DI were in municipalities including Chongqing and Shanghai; provincial capitals such as Guangzhou and Changchun; and provinces including Guangxi, south Jiangxi and east Hubei, whereas the slowest-growing areas were in northeast and southwest China such as Yunnan and Sichuan Provinces. The fastest-growing areas of the industry DIs were in four types of regions, the municipalities, the provincial capitals, three growth poles in China (the Yangtze River Delta region, Bohai Rim region, and Pearl River Delta region) and big cities in east coast China, while the slowest-growing areas were north of Heilongjiang Province and west of Yunnan and Guizhou Provinces. The fastest-growing areas for both the society DI and the environment DI were in the three growth pole regions and provincial capitals of China, whereas the slowest-growing areas were in southeast China including Yunnan, Guizhou, and Guangxi Provinces. In summary, the DIs showed a fast increase in the three growth pole regions as well as in provincial capitals, south Guangxi, south Jiangxi, east Hubei and east Henan provinces, but a slow increase in the northeast and west of the country.
The result of TI change during the 16 years showed that the population TI increased the fastest on the east coast, south Yunnan, south Sichuan, and Shanxi Province, while the slowest regions were in east and southwest China such as Guangxi, Guizhou, Sichuan, and south Jiangxi Province (Figure 7
). The land TI increased the fastest in west China along the Hu Huanyong line and slowest in the northeast China. The industry TI increased the fastest in Sichuan Province along the Hu Huanyong line as well as in Yunnan Province, south and west Shandong, east and south Hunan Province, north Guangdong and south Zhejiang Province, most of which are located in relatively undeveloped regions, whereas the slowest change occurred in northeast China, the abovementioned four municipalities, east Henan and south Jiangxi Province. The society TI showed that the four municipalities, most provincial capitals, the Yangtze River Delta and Pearl River Delta regions, and east Guizhou Province were the fastest transformation areas, whereas regions in west China along Hu line were the slowest. Finally, the environment TI showed that the four municipalities, central China including Henan, Jiangxi and Anhui Provinces, and Heilongjiang Province owned the fastest variation rates, while the major cities in west China owned the slowest. In summary, in some of the regions, the speed of environment TI variation showed a positive correlation with the industry TI change, both because the treatment rate of domestic sewage was limited by pollution control technology and because of the pollution control cost. The cities with faster land use transition and stronger economies were more likely to import advanced technology and incur higher costs to solve the pollution problem.
Besides, we also found that northeast China (including Heilongjiang, Jilin, Liaoning and east Inner Mongolia) was experiencing an economic recession during study period. The absolute value of DI in northeast China increased while the relative value of DI decreased during the 16 years. In the year 2010, the DI of northeast China fell behind all other regions east of the Hu Huanyong line. Correspondingly, the relative value of TI in northeast China also decreased significantly, representing the fact that both the quality and quantity of development in northeast China were in decline. The possible explanation was that the implementation of the priority development strategy for heavy industry had led northeast China to become an economically developed region in China before the 1990s. However, along with thoroughly carrying out reform and the opening-up policy, the northeast gradually fell behind the southeast coast of China and slipped into economic recession. Although the central government had implemented the revitalization strategy for the old industrial base in northeast China in 2003, the recession trend has continued according to our research.
We used scatter plots of DIs and TIs to analyze the correlation between the two groups of variables. The result showed that in 2010 the DI and TI was in positive correlation, with an R value of 0.42, reflecting that along with the increase of population quantity, economic quantity, built-up areas and social consumption quantity, the comprehensive quality of population, industrialization level and urbanization level improved synchronously, as well as the environmental governance enhancement. Among all the index change plots, SDI change showed positive correlation with STI change, with an R-value of 0.47. PDI change showed negative correlation with PTI change, with an R-value of 0.39, representing the opposite variation trend between population scale and population quality. Take Guangxi and Jilin Provinces as an example, during the study period, the PDI of Guangxi increased faster than other provinces, while the PTI increased relatively slowly. Jilin was had the opposite experience, with a slower PDI change and a faster PTI change. The possible explanation for such phenomenon was that China experienced a “demographic dividend” during the study period. The decline of fertility rates and population quantity would significantly promote social economic development process, which would improve the age structure, educational structure and employment structure of population, according to previous research (the PTI showed high correlation with ITI and STI), thus, leading to an unsynchronized variation trend between PDI change and PTI change (Figure 8
). Other indexes did not show significant correlations.
4.3. Spatio-Temporal Patterns of the DIs and TIs by Region
Based on the analysis of the spatio-temporal patterns derived from the DIs and TIs as well as their change over time, we divided the study area into six regions termed A to F based on Self-organization Mapping (SOM) Neural Network using the software platforms ArcGIS and MATLAB (Figure 9
). The distributions of the cities and their transformation characteristics are shown in Figure 9
and Figure 10
Region A, which comprised the municipalities of Beijing, Tianjin and Shanghai, most of the provincial capitals, south Jiangsu, and east Shandong Province, the socioeconomic development and transformation levels were the highest. During the 16-year study period, the average DIs and TIs increased the fastest, indicating rapid socioeconomic development in this region. Thus, we named the A region “multi-factor driven rapid development and transformation region”. Region B, which contained the second most cities, mostly distributed in the Huang-Huai-Hai Plain including Hebei, Shandong, Henan, Anhui, and Jiangsu Provinces as well as south Guangxi Province and northeast China. It was close to Region A in terms of space, and its increase in DIs and TIs over time was relatively fast. We named this region “industry and population driven rapid development and transformation region”. Region C mainly comprised the middle and west of Heilongjiang, middle Shaanxi, south Henan, north Shanxi, east Jiangsu, east Anhui, east Hunan and south Guangzhou Provinces, showing a steady development and transformation level, with its overall DIs and TIs increasing smoothly. We named this region “multi-factor driven smooth development and transformation region”. Region D contained the most cities, mainly distributed in Inner Mongolia along the Hu Huanyong line, northeast China (e.g., north Liaoning and north Heilongjiang), east China (e.g., north Jiangxi, Fujian, east Guangzhou, Guangxi, and central Hubei), and southwest China (e.g., Chongqing). Here, the development and transformation levels were relatively high. We named it “smooth development and transformation region driven by industrialization and land use transition”. Region E mainly located in southwest China (including Yunnan, Guizhou and Sichuan Provinces) and northeast China including cities in Heilongjiang and Jilin Provinces, which are relatively backward economic development areas. We named it “slow development and transformation region”. Region F (e.g., north and east Heilongjiang, west Liaoning, west Shanxi, south Hunan, south Jiangxi, and west China) showed lagging development and transformation levels, with a slower overall index change over time, representing relatively low socioeconomic development. We named it “industry and land driven slow development and transformation region”.