1. Introduction

2. Materials and Methods

2.1. The research area

The Grand Canal is the oldest and largest canal in the world. With a total length of 1,794 kilometres, it is often called the second ‘Golden Waterway’ of China, the first being the Yangtze River. Its value is comparable to the Great Wall. The Grand Canal flows through six provinces and 21 cities from north to south, connecting five major water systems, namely, the Hai River, the Yellow River, the Huai River, the Yangtze River, and the Qiantang River (Figure 1). It also possesses one of the richest agricultural regions in China, with developed industries. This region has a high population density, intensive agriculture, and huge production potential. It also impacts the economic and cultural development of the Canal, exchanges between the northern and southern regions of China, and the development of industrial and agricultural economies along the Canal. Due to the natural environment, development policies, and land development intensity, there are significant differences in socioeconomic development between the regions. Likewise, there are significant differences in rural land use. Land is an important factor driving urban development. Rapid land urbanisation has transformed a large amount of agricultural land into urban construction land, promoting industrial development and attracting a large population to increase the land value.

Figure 1. The location of the Grand Canal

Figure 1. The location of the Grand Canal

2.2. Data Processing

This paper uses the 100 m ×100 m grid data provided by the Resource and Environmental Science Data Centre of the Chinese Academy of Sciences to analyse the spatiotemporal distribution of rural residential land in 21 cities along the Grand Canal from 1990 to 2020. It amassed DEM data from the National Geographic Information Centre in China and analysed it through image correction and slope analysis to obtain the elevation and slope of the study area, which represent the regional driving factors of the natural environment. Using the spatial neighbourhood analysis method in ArcGIS 10.2, the study acquired the distances from each grid to major railways, rivers, motorways, or existing urban, county, and town centres. Major rivers (the watershed area exceeds 100 km2 and the river length exceeds 50 km), motorways, and railways are typical transportation arteries in each city centre. The distance from the spatial grid to each transportation artery represents the spatial axis and the driving factor of transportation accessibility. The distance to the city, county, or town centre is measured from the internal grid to the centre of the city/county/town, which represents the spatial point and a driving socioeconomic factor. As a result, this study constructed the point-axis-region three-dimensional (3D) spatial driving factor system of the Grand Canal (Table 1).

Table 1. The point-axis-region 3D spatial driving factor system.

Table 1. The point-axis-region 3D spatial driving factor system.

Target layer	Factor layer	Element layer
Point	Market location (ML)	Distance to the city centre ($x_1$)
		Distance to the county centre ($x_2$)
		Distance to the town centre ($x_3$)
Axis	Traffic accessibility (TA)	Distance to main railways ($x_4$)
		Distance to main motorways ($x_5$)
		Distance to main rivers ($x_6$)
Region	Natural environment (NE)	Elevation ($x_7$)
		Slope ($x_8$)

Using the spatial aggregation function of ArcGIS 10.2, the study converted the land use data from 100 m to 1000 m grid data. Then, it measured the percentage of the change of rural residential land area of each grid unit using grid operation. This way it analysed the spatial differentiation of rural residential land in the cities on the Grand Canal. Converting raster data into point data, the study observed the spatial relationship between rural residential land and the driving factors. Furthermore, it used the Euclidean distance and buffer analysis to obtain the distance distribution of the driving factors, such as the urban centres and major motorways, railways, and rivers. Lastly, the study obtained a data table of rural residential land and point-axis-region 3D driving factors for geographic detector data analysis using spatial neighbourhood analysis and spatial correspondence [28,29].

2.3. The geographic detector model

Wang Jinfeng et al. (2012) introduced the geographic detector model through the factor force index, combining GIS spatial superposition technology and set theory [30] They used it to identify the interaction between multiple factors. The theory of spatial differentiation provides a means to obtain the correlation between factor variables and outcome variables. By applying different discrete classification methods to various factors, different types of variables are normalized and analysed at the same spatial scale. In this study, the dependent variable is the proportion of the change of rural residential land, whereas the independent variables are the driving factors. This study explored and analysed the impact of point-axis-region 3D driving factors on rural residential land in reference to the decisive force q of spatial differentiation of rural residential land. The formula for q is:

$$q=1-\frac{1}{n{\sigma }^2}\sum _{i=1}^m{n}_i{q}_i^2$$

(1)

${\mathit{q}}_{\mathit{i}}^2$ represents the discrete variance of the dependent variable $\mathit{y}$; $\mathit{i}$ represents the number of the independent variable ${\mathit{x}}_{\mathit{i}}$; ${\mathit{n}}_{\mathit{i}}$ represents the number of samples; ${\mathit{n}}_{}$ represents the total number of samples in the study area; ${\mathit{\sigma }}^2$ is the total variance within the study area.

This paper selected 8 driving factors as independent variables, including elevation, slope, distance to major rivers, distance to major motorways, distance to major railways, distance to urban centres, distance to county centres, and distance to town centres. It analysed the spatial differentiation of the driving factors of the rural residential land and classified the driving factors based on their data characteristics.

Table 2. Classification of driving factors of the rural residential land.

Table 2. Classification of driving factors of the rural residential land.

Factor layer	Element layer	Extremum distribution	Classification interval	Number of categories
Market location (ML)	Distance to the city centre ($x_1$)	(0.15-111.77 km)	5 km	23
	Distance to the county centre ($x_2$)	(0.01-65.88 km)	5 km	14
	Distance to the town centre ($x_3$)	(0.02-19.02 km)	1 km	19
Traffic accessibility (TA)	Distance to main railways ($x_4$)	(0-92.6 km)	5 km	19
	Distance to main motorways ($x_5$)	(0-75.51 km)	5 km	15
	Distance to main rivers ($x_6$)	(0-126.04 km)	5 km	26
Natural environment (NE)	Elevation ($x_7$)	(-0.12-1881 m)	100 m	19
	Slope ($x_8$)	(0-49.49°)	5°	10

3. Results

3.1. The characteristics of overall changes in land use

The land use types of the Grand Canal mainly include agricultural land, forest land, rural residential land, water area land, grassland, and urban construction land. In 1990, the area proportions of the six land use types were 64.6%, 12.6%, 8.8%, 8.0%, 3.1%, and 1.7%, respectively. In 2020, however, the area proportions of the six land use types were 56.6%, 12.4%, 11.3%, 8.8%, 2.8%, and 6.5%, respectively. Agricultural land, urban construction land, and rural residential land were evenly distributed. In the north, the main expansion areas of urban construction land were Beijing and Tianjin, while in the south, they were Suzhou and Wuxi. The water area land was found in Tianjin, Huai’an, Yangzhou, and Suzhou, while forest land was found in Beijing, Jining, Zaozhuang, Chuzhou, and Hangzhou.

Figure 2. The spatial distribution of land use types in the Grand Canal (1990-2020).

Figure 2. The spatial distribution of land use types in the Grand Canal (1990-2020).

From 1990 to 2000, agricultural land, grassland, and other land decreased by 6.553 million mu, 353,000 mu, and 909,000 mu, respectively. Forest land, water area land, urban construction land, industrial and mining land, and rural residential land increased by 208,000 mu, 1.672 million mu, 2.663 million mu, 642,000 mu, and 2.649 million mu, respectively. At this stage, agricultural land and unused land were converted to water area land, urban construction land, and rural residential land. From 2000 to 2010, agricultural land, forest land, grassland, and other land decreased by 5.998 million mu, 170,000 mu, 110,000 mu, and 31,000 mu, respectively. In contrast, water area land, urban construction land, industrial and mining land, and rural residential land increased by 259,000 mu, 3.549 million mu, 1.009 million mu, and 1.492 million mu, respectively. This stage was characterized by the conversion of agricultural land to urban land and industrial and mining land. From 2010 to 2020, agricultural land, forest land, and grassland decreased by 10.502 million mu, 636,000 mu, and 544,000 mu, respectively, while water area land, urban construction land, industrial and mining land, and rural residential land increased by 232,000 mu, 7.51 million mu, 883,000 mu, and 3,011,000 mu, respectively. At this stage, agricultural land, forest land, and grassland were converted to urban industrial and mining land. From 1990 to 2020, the main areas that experienced significant changes were agricultural land, urban construction land, and rural residential land. In particular, agricultural land decreased by 23.053 million mu, accounting for 8.1% of the area. Urban construction land increased by 13.722 million mu, accounting for 4.8% of the area, while rural residential land increased by 5.6981 million mu, accounting for 2.5% of the area. It can be seen that the reduced agricultural land, forest land, and grassland on the Grand Canal was converted to urban construction land, rural residential land, or industrial and mining land.

Figure 3. Changes in land use types and their area size on the Grand Canal during different periods.

Figure 3. Changes in land use types and their area size on the Grand Canal during different periods.

3.2. Characteristics of changes in rural residential land

From 1990 to 2020, the distribution of changes in rural residential land was relatively uniform, mainly concentrated in villages around the central urban areas of the cities on the Grand Canal. As time passed and new policies were introduced, there had been significant changes in the spatial layout of rural residential land, with some villages experiencing a decrease or an increase in area size. Within the 1 km ×1 km grid range, the proportion of changes in rural residential land in most cities ranged from -10% to 10%. Based on the spatial distribution of rural residential land, it was found that the extremely dense rural residential land in northern cities was mainly concentrated around Beijing, where rural residential land increased over 50%. The areas with significantly sparse rural residential land were mainly distributed in Tianjin and Dezhou, where rural residential land decreased over 50%. The significant increase in rural residential land in southern cities was mainly found in Suzhou, Wuxi, Changzhou, etc. The proportion of changes in rural residential land was between 20-50%.

Figure 4. The change in density of rural residential land (1990-2020).

Figure 4. The change in density of rural residential land (1990-2020).

According to the changes in rural residential land area on the Grand Canal from 1990 to 2020, the total area increased by 16.6521 million mu and decreased by 10.954 million mu, resulting in an actual increase of 5.6981 million mu. Cities that have added over 1 million mu include Beijing, Cangzhou, Liaocheng, Tianjin, and Jining. Cities with an area of less than 500,000 mu include Changzhou, Hangzhou, Zhenjiang, Zaozhuang, and Wuxi. In contrast, cities with a decrease in rural residential land area of over 1 million mu include Dezhou, Cangzhou, and Tianjin, while cities with less than 300,000 mu include Chuzhou, Suzhou, Hangzhou, Zhenjiang, Changzhou, and Wuxi. Thus, it was found that the spatial changes of rural residential land in the northern cities of the Grand Canal were significant, while those in the southern cities were relatively small.

Based on the actual changes in rural residential land, it was found that only Beijing’s rural residential land increased by over 1 million mu, followed by Suzhou, with a rural residential land area of 629,000 mu. The size of rural residential land in Dezhou and Tianjin significantly decreased, with 449,500 mu and 85,100 mu, respectively. The change in size of rural residential land in 12 cities ranges from 200000 to 400000 mu, while that in Huai’an, Tai’an, Hangzhou, Cangzhou, and Zaozhuang was less than 200,000 mu. Cities with per capita rural residential land exceeding 5 mu include Chuzhou, Yangzhou, Zhenjiang, and Hangzhou. However, cities with less than 2 mu include Liaocheng, Langfang, Zaozhuang, and Jiaxing. Due to the impact of the rural population in different regions, the per capita rural residential land area in each region varied to some extent. For example, the per capita rural residential land area in the southern region of the Grand Canal was higher than that in the northern region. Thus, it can be inferred that Beijing and Suzhou, as the central cities of the northern and southern regions, respectively, had significantly higher levels of socioeconomic development and urbanisation compared to other regions. This had a strong effect on the changes in the size of rural residential land. However, as the main cities in Shandong Province began implementing the ‘Merging Villages and Towns’ policy, Dezhou significantly reduced the size of rural residential land.

Figure 5. The change in area size of rural residential land on the Grand Canal (1990-2020).

Figure 5. The change in area size of rural residential land on the Grand Canal (1990-2020).

3.3. Driving factors of rural residential land

According to the distribution characteristics of the driving factors that influence the spatial differentiation of rural residential land, it was found that the distance to the market location, such as city centres, county centres, and town centres, was distributed in a clear concentric circle. The maximum distance to city or county centres was distributed in the peripheral areas of the Grand Canal, while the maximum distance to town centres was distributed around cities, such as Cangzhou, Hengshui, and Suzhou. Furthermore, the spatial distribution of the distance to major motorways, railways, and rivers was zonal. The maximum distance to major railways was distributed in a line, from Suqian, then to Huai’an, and finally to Yangzhou. The maximum distance to major motorways was found west of Liaocheng, while the maximum distance to rivers was located east of Tai’an. Lastly, the distribution of the factors of the natural environment, such as elevation and slope, were similar, with their maximum values distributed in the northern and southern peripheral areas of the study area.

Figure 6. The distribution of driving factors that influence the spatial differentiation of rural residential land on the Grand Canal.

Figure 6. The distribution of driving factors that influence the spatial differentiation of rural residential land on the Grand Canal.

Using geographic detectors, this study found that all 21 cities were influenced by socioeconomic driving factors. Except for Xuzhou City, 20 cities were affected by the driving factors of transportation accessibility. Only five cities, including Beijing, Tai’an, Zaozhuang, Zhenjiang, and Hangzhou, were affected by the driving factors of the natural environment. The distance to the city centre had the greatest impact on the differentiation of rural residential land, with 20 cities being affected and an average decision-making power of 2.9. Next is the distance to the main rivers, which affected 19 cities with an average decision-making power of 1.9. The impact of elevation and slope was minimal. Elevation affected 4 cities, while slope affected 3 cities, with an average decision-making power of only 0.3 and 0.29, respectively.

To further explore the differentiation types of rural residential land in 21 cities, the study divided all cities into three categories based on the dimensions of the driving factors. Xuzhou, the only one-dimensional city, was influenced by the distance to the city and county centre. Thus, it represents a point-driven development city. Next, two-dimensional cities, including 15 cities, among which are Tianjin, Langfang, and Cangzhou, were influenced by the distance to the city, county, or town centre and the distance to the main railways, motorways, or rivers. These cities represent the main types of rural residential land, which can be termed point-axis-driven development cities. Finally, three-dimensional cities, including Beijing, Tai’an, Zaozhuang, Zhenjiang, and Hangzhou, were driven by a combination of socioeconomic factors, transportation accessibility, and the natural environment. These cities represent the ‘point-axis-region-driven development cities.’

Table 3. Results of driving factor detection for rural residential land on the Grand Canal.

Table 3. Results of driving factor detection for rural residential land on the Grand Canal.

City	X1	X2	X3	X4	X5	X6	X7	X8	Driver dimension
Beijing	2.03	0.34	0.52	0.51	0.42	1.33	0.97	0.90	three-dimensional
Tianjin	3.34	5.31	1.56	0.58	0.54	1.99	0.34	0.13	two-dimensional
Langfang	1.47	1.21	0.21	0.22	0.29	2.68	0.12	0.24	two-dimensional
Cangzhou	19.12	9.94	0.77	0.79	0.65	4.40	0.13	0.00	two-dimensional
Hengshui	1.19	0.16	0.65	0.15	0.07	0.75	0.00	0.00	two-dimensional
Dezhou	4.16	0.25	0.51	1.34	0.72	3.15	0.00	0.00	two-dimensional
Liaocheng	1.69	1.12	0.35	1.23	0.99	1.14	0.00	0.00	two-dimensional
Tai’an	1.11	0.40	0.29	1.20	0.70	2.98	1.33	0.11	three-dimensional
Jining	0.97	0.60	0.40	0.20	0.55	0.37	0.09	0.06	two-dimensional
Zaozhuang	1.83	3.12	0.52	1.26	0.18	1.12	0.77	0.08	three-dimensional
Xuzhou	0.73	0.60	0.30	0.40	0.29	0.29	0.02	0.11	one-dimensional
Suqian	0.48	0.80	0.22	1.87	0.06	1.12	0.00	0.24	two-dimensional
Huai’an	0.89	0.37	0.30	2.43	1.71	0.99	0.00	0.01	two-dimensional
Chuzhou	1.89	0.55	0.79	2.13	2.53	5.66	0.06	0.10	two-dimensional
Yangzhou	3.00	0.40	0.05	1.28	0.31	0.80	0.09	0.00	two-dimensional
Zhenjiang	1.63	1.27	1.34	0.25	0.72	1.76	0.42	1.40	three-dimensional
Changzhou	3.67	1.06	1.16	1.58	1.21	2.30	0.27	0.33	two-dimensional
Wuxi	4.54	3.25	1.52	0.65	0.38	1.70	0.30	0.30	two-dimensional
Suzhou	3.93	0.50	0.25	1.41	0.73	2.09	0.07	0.24	two-dimensional
Jiaxing	1.60	0.18	0.80	0.57	0.18	3.14	0.07	0.28	two-dimensional
Hangzhou	1.78	1.32	0.57	1.46	0.39	1.19	1.32	1.62	three-dimensional

4. Discussion

4.1. The conversion of rural residential land

Through spatial correction and matching analysis, it was found that agricultural land was the main source of increase in rural residential land from 1990 to 2020, accounting for 93.09%, followed by forest land, accounting for 2.17%. The main sources of decrease in rural residential land were farmland, urban construction land, water area land, and industrial and mining land, with a proportion of 68.11%, 19.0%, 5.41%, and 5.03%, respectively. To further analyse the driving factors of the change in rural residential land in various cities, this study chose Beijing, Cangzhou, Dezhou, Chuzhou, Huai’an, Suzhou, Jiaxing, and Hangzhou cities as cases where the change in rural residential land was most prominent. It based the analysis on the driving factors and change in area size using an equal proportion amplification method.

4.1.1. The conversion of rural residential land in the one-dimensional city

The only one-dimensional city in this study was Xuzhou. From 1990 to 2020, the area of rural residential land increased by 949,000 mu and decreased by 612,000 mu, thus actually increasing by 337,000 mu. The main sources of increase in rural residential land were agricultural land and forest land, with an area of 884,100 mu and 32,100 mu, respectively, accounting for 93.2% and 3.39%. However, the main source of decrease was also agricultural land, followed by urban construction land, with an area of 407,500 mu and 174,900 mu, respectively, accounting for 66.58% and 28.59%. This indicates that the source of changes in rural residential land in Xuzhou was agricultural land. The change was mainly influenced by socioeconomic driving factors, such as the distance to the city and county centre, with a decision-making power of 0.73 and 0.60, respectively. Due to the fact that Xuzhou is located in the North China Plain and is flat, it was less affected by the driving factors of the natural environment. Moreover, Xuzhou is an important transportation hub that connects the Grand Canal to the eastern and western regions of China. Its main motorway and railway network density were relatively high, so it had little impact on the spatial differentiation of rural residential land.

4.1.2. The conversion of rural residential land in two-dimensional cities

To examine the changes in rural residential land in two-dimensional cities, the study selected Cangzhou, Dezhou, Suqian, and Suzhou as representatives. It found differences in the number of the driving factors affecting each city. There were also differences in the changes in rural residential land. Due to the large number of two-dimensional cities, they were mainly influenced by the distance to the city, county, or town centre and the distance to major railways, motorways, or rivers. In contrast, they were less affected by the driving factors of the natural environment.

The actual changes in rural residential land from 1990 to 2020 were 141,000 mu in Cangzhou, -449,500 mu in Dezhou, 222,400 mu in Suqian, and 629,000 mu in Suzhou. Suzhou is the economic centre of the southern part of the Grand Canal. In Suzhou, the actual increase in rural residential land area was second only to Beijing. The main sources of increase in rural residential land were agricultural land, water area land, and urban construction land, with an area proportion of 92.14%, 3.27%, and 2.66%, respectively. The main sources of decrease in rural residential land were urban construction land, agricultural land, and water area land, with an area proportion of 59.29%, 33.83%, and 4.07%, respectively. This city was mainly influenced by four driving factors, including the distance to the city centre, the distance to main railways, the distance to main motorways, and the distance to main rivers, with a decision-making power of 3.93, 1.41, 0.73, and 2.09, respectively. Furthermore, Dezhou is one of the central cities of the Grand Canal. It experienced a negative growth in rural residential land from 1990 to 2020. The main sources of increase in rural residential land were agricultural land and other land, with an area proportion of 93.84% and 3.73%, respectively. However, the main source of decrease in rural residential land was also agricultural land, followed by urban construction land, with an area proportion of 89.0% and 8.97%, respectively. This city was influenced by five driving factors, including the distance to the city centre, the distance to the town centre, the distance to main railways, the distance to main motorways, and the distance to main rivers. Each of these factors had a decision-making power of 4.16, 0.51, 1.34, 0.74, and 3.15, respectively. Next, Cangzhou is a city in the northern part of the Grand Canal. From 1990 to 2020, the increase and decrease in rural residential land exceeded 1 million mu. The main source of increase in rural residential land was agricultural land, accounting for 97.01% of the total area. The main sources of decrease in rural residential land were agricultural land, water area land, industrial and mining land, and urban construction land, with an area proportion of 65.06%, 18.31%, 12.46%, and 3.62%, respectively. This city was influenced by six driving factors, including the distance to the city centre, the distance to the county centre, the distance to the town centre, the distance to main railways, the distance to main motorway, and the distance to main rivers. These factors had a decision-making power of 19.12, 9.94, 0.77, 0.79, 0.65, and 4.40, respectively. Like Dezhou, Suqian is also one of the medial cities of the Grand Canal. It experienced a change in rural residential land area of less than 300,000 mu. The main source of increase in rural residential land was agricultural land, accounting for 96.98% of the total area. The main sources of decrease in rural residential land were agricultural land and urban construction land, with an area proportion of 72.43% and 25.34%, respectively. This city was mainly influenced by the distance to the county centre, the distance to main railways, and the distance to main rivers, each of which had a decision-making power of 0.80, 1.87, and 1.12, respectively.

4.1.3. The conversion of rural residential land in three-dimensional cities

To show the change of rural residential land in three-dimensional cities, this study selected Beijing and Hangzhou as representatives. From 1990 to 2020, the area of rural residential land increased by 1.736 million mu in Beijing and 430,000 mu in Hangzhou. It decreased by 616,000 mu and 248,000 mu, respectively. The actual increase in Beijing was 1.12 million mu and 182,000 mu in Hangzhou. The main sources of increase in rural residential land in Beijing were agricultural land, forest land, and grassland, with an area proportion of 85.46%, 7.57%, and 2.6%, respectively. The main sources of decrease in rural residential land were urban construction land, agricultural land, industrial and mining land, and forest land, with an area proportion of 51.83%, 23.59%, 13.35%, and 5.67%, respectively. The driving factors of rural residential land in Beijing were the distance to the city centre, the distance to the town centre, the distance to main railways, the distance to main rivers, elevation, and slope. Their decision-making power was 2.03, 0.52, 0.51, 1.33, 0.97, and 0.90, respectively. In Hangzhou, the main sources of increase in rural residential land were agricultural land and forest land, with an area proportion of 86.76% and 10.39%, respectively. The main sources of decrease in rural residential land were agricultural land, urban construction land, industrial and mining land, and forest land, with an area proportion of 49.52%, 23.21%, 11.42%, and 11.27%, respectively. Similar to Beijing, the driving factors for rural residential land in Hangzhou include the distance to the city centre, the distance to the county centre, the distance to the town centre, the distance to main railways, the distance to main rivers, elevation, and slope. Their decision-making power was 1.78, 1.32, 0.57, 1.46, 1.19, 1.32, and 1.62, respectively.

Beijing and Hangzhou are both economically developed cities in the northern and southern parts of the Grand Canal, respectively. As the capital of China, Beijing is a political, economic, and cultural centre, with the largest area size in rural residential land and the greatest scale of converting rural residential land to urban construction land. Due to socioeconomic development, the central urban area represents the main area where people live and work. Urban construction land continues to expand to the periphery, occupying a large amount of rural residential land and agricultural land. To meet the production and living demands of rural residents, a large amount of agricultural land has been transformed into new rural communities. The transportation network and various terrain types in Beijing have a significant impact on the selection of the space where rural residential land can be built, especially in the northern mountainous areas of Beijing, where the density of rural residential land is low, and the proportion of its change is small.

As the capital of Zhejiang Province, Hangzhou is an important city in the Yangtze River Delta Economic Zone. The density of transportation arteries, such as railways and motorways, is high. Likewise, the speed at which urban construction land is expanding is high. Due to the influence of urban terrain and landforms, especially in the mountainous areas in the southern part of the city, the proportion of rural residential land converted to urban construction land is relatively low and the proportion of the increase in rural residential land is relatively small.

Figure 7. Main types of changes in rural residential land in the Grand Canal.

Figure 7. Main types of changes in rural residential land in the Grand Canal.

5. Conclusions

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