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BY 4.0 license Open Access Published by De Gruyter Open Access December 22, 2022

Slope characteristics of urban construction land and its correlation with ground slope in China

  • Junhao Duan , Qiuzhi Peng EMAIL logo and Peiyi Huang
From the journal Open Geosciences


Since the 21st century, China’s urban construction land has been growing rapidly, piquing academic interest. However, mountainous counties account for the majority in China, previous studies have concentrated on the horizontal expansion characteristics of construction land, leaving a gap in the vertical expansion. This study used datasets for urban construction land and digital elevation model to analyze the spatiotemporal evolution characteristics of construction land slope in 2670 China’s counties, by exploratory spatial data analysis. Furtherly, we explored the slope relationship between ground and construction land using regression analysis. The findings indicate what follows: (1) The average slope of construction land had a spatial pattern of “high in the south and low in the north,” with significant spatial agglomeration characteristics. And it had increased with the urban expansion, shifting the slope-weighted mean center toward the southeast while enhancing spatial agglomeration. (2) There was a significant and steadily rising linear association between the urban construction land slope and ground slope, due to spatial heterogeneity; the most affected counties were primarily located near the Sichuan basin. According to the results, we provided suggestions for the rational use and sustainable development of land in cities, especially for mountainous regions in a period of rapid urbanization.

1 Introduction

The scientific understanding, interpretation, and regulation of the urbanization process are made possible by the analysis of spatiotemporal characteristics of construction land, which is extremely valuable to areas during a period of fast urbanization. Between 1985 and 2015, the urban land increased from 362,747 to 653,354 km², with 69% of this growth occurring in Asia and North America, mainly China, India, and the United States. At the same time, urban expansion is anticipated to continue in the decades to come as the world’s urban population is predicted to grow significantly [1]. Since the reform and openness in 1978, China has made enormous strides in economic and social development, with the level of urbanization of population rising quickly from 20% to approximately 65% by the end of 2021, keeping an average annual growth rate of 1% and dominating the world [2], while driving the continuous expansion of the size of urban construction land [3,4]. Protecting China’s industrialization, urbanization, and modernization has benefited from the continual expansion of construction land for more than 40 years. China’s urbanization process has clear topographical distinctions, with the growth rate slowing down following a period of rapid urbanization in the plains [5]. However, the mountainous regions are currently experiencing a period of rapid urbanization, with the south-western mountainous region’s urbanization expanding more quickly than the national average level [6], and there is still a high demand for new urban construction land. It is this difference that determines the phenomenon of land expansion for construction in China still deserves to be studied and explored, notably in terms of how to meet the massive demand for land resulting from the urbanization of mountainous regions.

Many academics have carried out in-depth research on the expansion of construction land during the urbanization process. Early research on construction land expansion mostly concentrated on the horizontal direction, examining the spatiotemporal characteristics of change in construction land at various spatial scales [7,8,9], influencing factors [10,11,12], and modal projections [13,14]. Additionally, as cities have grown, land use patterns have changed drastically and continuously, contributing to several troubling problems, including an increase in human-land conflict [15], the loss of arable land [16,17,18], the degradation of ecological services [19,20,21], geological disaster [22], and others. However, as a three-dimensional space [23], horizontal and vertical growth continue to cooperate and alternatively function to encourage the development of construction land [24]. Vertical urban development has particularly caught the attention of academics as a way to increase urban compactness as the need for intense land use rises and building technology continues to advance [25]. As for the vertical direction, several researchers have explored the pattern of three-dimensional urban spatial expansion using metrics like floor area ratio and building height [26,27,28], as well as the underlying driving forces [29,30]. However, it is important to know that the three-dimensional expansion is indicated not only in the building’s height but also in the gradient of the ground slope.

Mountains occupy 24% of the global land surface and about 12% of the population lives in mountainous areas [31], but the current urbanization rate in mountainous areas is still much lower than that in plain areas [32]. About 70% of Chinese land is mountain, and 70% of counties and cities are located in mountainous areas where cluster nearly 45% of the population [33]. Mountain area is being used for urban construction to an increasing extent [34,35]. In China, the Ministry of Land and Resources issued pilot projects to develop idle land on low hills and slowly sloping wastelands in 2011, and as a result, investigations on the suitability of the land have been conducted [36]. The southwest of China, Zhejiang, and Fujian are the majority of studies on the evaluation of the viability of low hills, and gentle slopes for land conversion to construction land are currently centered. Because of the significant differences in natural habitats between mountains and plains [37], the assessment of the suitability of land resources on low hills and gentle slopes tends to focus more on ecological and geological criteria [38,39]. In terms of study scale, these evaluative investigations frequently concentrate on small-scale research units at the urban [40], county [41], and even village levels [42]. From the perspective of terrain factor selection, slope, elevation, and terrain relief are mostly used as quantitative terrain gradient factors to explore the limiting effect of natural conditions on economic and social development [43]. But Slope is better suited for small-scale microtopographic descriptions than others, and it is frequently considered in models of urban expansion and assessments of a land’s appropriateness for urban construction. In order to deeper understand the mechanism of the slope factor to improve the interpretability of the simulation model or evaluation results, research findings on the distribution and variation of construction land on the slope gradient are gradually increasing; for example, it has been found that cities such as Shenzhen, Chongqing, and Guiyang have experienced persistent construction land slope climbing in the last decade or so, and the slope climbing phenomenon often occurs during the infill expansion of construction land [44,45,46]. These studies, however, primarily concentrate on typical cities and do not yet give information on spatiotemporal patterns at the regional scale, which cannot provide reliable support for China’s macro decision-making as well. In addition, the General Office of the CPC Central Committee and the State Council issued the Pilot Plan for Provincial-level Spatial Planning in 2017, covering 9 provinces, including Guizhou, Guangxi, and Zhejiang. And in the process of delineating the “urban development boundary,” the standard of per capita planned construction land stipulated by the current standard is obviously low in some cities distributed in mountainous and hilly areas, which is far from the requirements of local government for urban expansion land. Considering the current construction land standard for urban planning in China, there is a lack of differentiated standards for cities, especially in mountainous and hilly areas. A few studies have also explored the spatiotemporal relationship between construction land expansion and slope at the economic zone, province, or city scale across China [47], finding that relatively more pronounced construction land slop climbing exists in central and western regions, areas populated by ethnic minorities, and areas where the development policy is focused on promoting low hills and gentle slopes. Such national studies, however, continue to only offer broad-brush fundamental information and have not yet honed the analysis’s unit of analysis to the county level, which is the most fundamental and comprehensive administrative subdivision in China. Additionally, the ground slope invariably affects how slopes distribute and evolve on construction land, but this link is currently understudied.

To sum up, taking 2,670 county units across China as the research objects, we investigated the spatiotemporal patterns of urban construction land slope and its relationship with ground slope using three periods of construction land data in 2000, 2010, and 2020 as well as digital elevation model (DEM). This work provides a scientific reference for the guiding and regulation of construction land expansion in various topographic contexts, which contributes to a deeper understanding of the pattern of construction land expansion in China from the standpoint of the slope.

2 Materials and methods

In this article, we used urban construction land and DEM data set to explore the slope characteristics of urban construction land by exploratory spatial data analysis. And we further used regression analysis to study the relationship between ground slope and urban construction land slope. The technical flow chart is shown in Figure 1.

Figure 1 
               Technical flow chart.
Figure 1

Technical flow chart.

2.1 Data sources

2.1.1 County construction land

First, we used UrbanChina [48] and GLC_FCS30 data set [49] to extract urban construction land for the year 2000 through 2015 and 2020, respectively. Both data sets were mapped using the Google Earth Engine platform with multivariate remote sensing data, with an overall accuracy of over 90% and a horizontal spatial resolution of 1” (about 30 m). Second, because the 2020 data set was produced with a uniform raster rejection for slopes greater than 15°, which would affect the accuracy of this study, we fused GLC FCS data set with UrbanChina data set to obtain the 2020 data based on the assumption of irreversible urban development construction [50]. Finally, we gathered the county-level boundary data via the Gaode Map API interface.

2.1.2 Slope data

We downloaded the SRTMGL1v3.0 DEM data set from, and it was used to produce data on the slope (Figure 2). On the one hand, the STRTMGL1 DEM data set offers significant global land coverage from 60°N to 56°S with the same horizontal spatial resolution of 1” (about 30 m) as the ASTER GDEM data set [51], but with higher elevation accuracy. Moreover, the original SRTMGL1 data, which was gathered by the American space shuttle Endeavour in mid-February 2000, is better suited to documenting changes in the original ground slope gradient of urban construction land after 2000.

Figure 2 
                     Spatial distribution of the ground slope in the study area.
Figure 2

Spatial distribution of the ground slope in the study area.

2.1.3 Data preprocessing

Considering the impact of the vertical error on this study, and the modifiable areal unit problem caused by the administrative boundary of the county, we removed the counties with little statistical significance from the following two perspectives. First of all, the western part of China is a vast area with few people. In order to avoid the problem of small construction land but large county units, the threshold of construction land area was set as 0.5 km² according to research experience, and too small county units were removed. Then, SRTM data are affected by slope, and the error increases with the increase in slope, so we took 1° as the slope interval to calculate the frequency of urban construction land area on each slope section and draw a broken line statistical map called slope spectrum. When the sample size of construction land is large enough and the slope data accuracy is high enough, the slope spectrum line is relatively smooth and the sample has statistical significance. However, when the line is fluctuant, it has no research significance. Based on above-mentioned factors, we calculated the fluctuation of slope spectrum line in slope intervals greater than 5°. Its formula is as follows:

(1) F 5 = i = 5 89 | y i + 1 y i | ,

where F 5 represents the fluctuation of the slope spectrum line, the larger value means more fluctuation of the slope spectrum line; i is the slope interval; y is the frequency of construction land area. Taking the actual situation and research experience, the threshold of F 5 was set as 0.2 to remove counties without statistical significance. Finally, we selected 2,670 counties as research samples based on construction land area and fluctuation of slope spectrum.

3 Methods

3.1 Mean center

Mean center and weighted mean center characterize the central position of a spatial data set and are used to measure trends in spatial concentration. The mean center is comparable to the geographic center of the event distribution, this article used the geographic coordinates of hot spot counties with a high slope to calculate it. Its formula is as follows:

(2) ( x ̅ , y ̅ ) = i = 1 n x i , i = 1 n y i ,

where x i and y i are the geographical coordinates of county i ; n is the number of counties.

In contrast to the mean center, the weighted mean center uses a specific feature from the dataset as a weight to determine the dataset’s center. In this study, the weighted mean center for all counties was determined using the slope of the construction land. The following is its formula:

(3) ( x w ̅ , y w ̅ ) = i = 1 n w i x i i = 1 n w i , i = 1 n w i y i i = 1 n w i ,

where x i and y i are the geographical coordinates of county i ; n is the number of counties; w i is the weight of county i .

3.1.1 Standard deviational ellipse (SDE)

The SDE is a spatial statistical technique for precisely describing the distribution of geographical features, which reflects the overall dominating direction of distribution and the degree of dispersion in each direction. The long axis’ direction corresponds to the spatial distribution trend, while the short axis depicts the size of that trend distribution. The spatial distribution pattern is reflected in the ellipse’s oblateness, with the greater value indicating a more concentrated distribution and vice versa.

3.1.2 Global spatial autocorrelation analysis

Due to spatial interactions and spatial diffusion, object property values are geographically dependent on each other and appear agglomerated, discrete or randomly distributed, generally measured by the Mora n s I index. Its formula is as follows:

(4) Mora n s I = n i = 1 n j = 1 n w ij ( x i x ̅ ) ( x j x ̅ ) i = 1 n j = 1 n w ij i = 1 n ( x i x ̅ ) 2 ,

where n is the number of counties; x i , x j are the average slope of construction land in county i and county j , respectively; x ̅ is the average slope of construction land for all counties; w ij is the spatial weight matrix representing the proximity of the two counties. Mora n s I [ 1 , 1 ] , less than 0, indicates negative spatial correlation and discrete distribution of slopes in adjacent counties. Greater than 0 indicates positive spatial correlation and spatial agglomeration of slopes in adjacent counties, and the higher the value, the more significant the spatial correlation between two counties. A value of 0 indicates a random distribution.

3.1.3 Hot spot analysis

G e tis Ord G i * index is used in hot spot analysis to reveal the spatial agglomeration characteristics of local areas and identify spatial aggregations with statistically significant high values (hot spots) and low values (cold spots). Its formula is as follows:

(5) G i * ( d ) = j = 1 n w ij ( d ) x j j = 1 n x j ,

where w ij ( d ) is the matrix of weight within distance d ; G i * ( d ) is positive indicating that the county is also surrounded by counties with high slopes and is a hotspot; G i * ( d ) is negative indicating that the county is a cold spot area.

3.1.4 Geographically weighted regression (GWR)

To break the limitations of traditional linear regression models in terms of spatial characteristics and “global” estimation of the independent variables, we used a combination of the ordinary least squares (OLS) and the GWR models to measure the influence of ground slope on the construction land slope, in order to visualize its spatial heterogeneity and its trend. OLS formula is as follows:

(6) y = α 0 + i = 1 n α 1 x i + ε ,

where y is the construction land slope; x i is the average value of ground slope; α 0 is the estimated value of the constant; α 1 is the estimated value of the coefficient; ε is the error. GWR formula is as follows:

(7) y i = β 0 ( u i , v i ) + i = 1 n β i ( u i , v i ) x i + ε i ,

where ( u i , v i ) is the geographical coordinates of the county i ; y i is the average slope of construction land in the county i ; x i is the ground slope in the county i ; β 0 ( u i , v i ) is the estimate of the constant for county i ; β i ( u i , v i ) is the coefficient estimate for the county i ; ε i is the error for the county i .

4 Results

4.1 Horizontal expansion characteristics of construction land

We used the urban construction land density (UCLD) as an index to explore the horizontal expansion characteristics of China’s construction land (Figure 3). From 2000 to 2020, the horizontal expansion of construction land showed significant spatiotemporal differences. In 2000, most of the counties with high UCLD were concentrated in the flat and economically developed regions of the Northeast Plain, North China Plain, the Middle and Lower Yangtze River Plain as well as the southeast coast. By 2010, construction land had been further expanded in the four regions mentioned earlier, and there was also a significant expansion in the southwest. In 2020, China’s southern provinces expanded rapidly and were fully developed, while the southwest region showed a trend of further development. China has undergone a rapid evolutionary process over the past 20 years, with the growth of construction land in the eastern plains now slowing down, while cities in the southern mountainous regions, especially in the southwest, will continue to grow rapidly.

Figure 3 
                  Spatial distribution pattern of UCLD in China’s counties from 2000 to 2020.
Figure 3

Spatial distribution pattern of UCLD in China’s counties from 2000 to 2020.

4.2 Characteristics of construction land slope

In order to determine the average slope of construction land in China’s counties at each time point and to draw the standard error bars, the years 2000, 2010, and 2020 were chosen as the time points (Figure 4a). From 2000 to 2020, the average slope increased at an accelerated rate, rising from 3.07° in 2000 to 3.42° in 2020. The growth was 0.11° in the first decade and increased to 0.24° in the second. To examine the quantitative characteristics of both the construction land and the new section, we separated the average slope of counties into six intervals: 0–2°, 2–4°, 6–8°, 8–10°, and greater than 10° (Figure 4b). The distribution of the number of counties in each interval basically maintained the characteristic of a long tail on the right; that is, 2–4° was the most, 0–2° was the second, and the rest decreased with the increase in the slope interval. And the number of counties with slopes from 0° to 4° gradually decreased, while the counties with slope greater than 4° gradually increased.

Figure 4 
                  (a) Changes in the average slope of construction land in China’s counties; (b) the number of counties in each slope interval.
Figure 4

(a) Changes in the average slope of construction land in China’s counties; (b) the number of counties in each slope interval.

The slope of construction land in China’s counties typically displayed a spatial pattern of “high in the south and low in the north” over the past 20 years (Figure 5). In 2000, there was a small number of counties with slopes larger than 4°, most of which centered in the southwest, Fujian, as well as Shaanxi and Shanxi on the Loess Plateau. Among these, counties with slopes greater than 10° were mostly found in Chongqing. The slope of construction land in 0–2° and 2–4° counties was widely distributed, and most counties in the southern region belonged to 2–4° except for a small number of counties in southern Hubei and northern Hunan with 0–2°, while in the north most counties showed a staggered distribution of slopes from 0–2° and 2–4°. In 2010, counties between 4 and 6° increased noticeably in central Hunan and near the Hebei–Beijing border. In 2020, counties larger than 10° can remain stable and the overall increase in slope in southwest China was evident, with greater than 6–8° counties increasing significantly in Sichuan and central Guizhou, and 8–10° counties in south-central Yunnan.

Figure 5 
                  Spatial distribution pattern of construction land slope in China’s counties from 2000 to 2020.
Figure 5

Spatial distribution pattern of construction land slope in China’s counties from 2000 to 2020.

The slope of the new section largely follows the rule of “the higher the higher, the lower the lower” (Figure 5). Besides the southwest region and the Loess Plateau, where the slope of construction land was higher, the slope of new section increased more noticeably in central Fujian between 2000 and 2010. From 2010 to 2020, there was an obvious increase in the number of counties with new construction land that had a slope of 6–8°, particularly in Sichuan and central Guizhou. In addition, Yunnan had the highest concentration of counties with new construction land with a slope greater than 8°.

From 2000 to 2020, the construction land slope’s SDE steadily gets smaller and gets closer to that of the ground. When combined with the slope-weighted mean center of county construction land (Figure 6), it is located to the east of the center of the ground and was positioned between 31.5°N–32.5°N and 111°E–112°E, showing that the slope distribution of construction land was generally more southeastward than the ground slope. From 2000 to 2020, the mean center was moving to the southwest, with an overall offset distance of about 45 km. During the first decade, there was a slight shift of 8 km to the south, which corresponded to the intense development of the southeast coast. Between 2010 and 2020, there was a 37 km movement in a southwest direction, which correlated to a faster follow-through in the southwest over that time frame.

Figure 6 
                  SDE and mean center of construction land slope and ground slope in China.
Figure 6

SDE and mean center of construction land slope and ground slope in China.

We calculated the global Moran’s I based on the slope of construction land in 2000, 2010, and 2020 to examine changes in the spatial agglomeration (Table 1). It can be shown that Moran’s I index for all years was greater than zero and passed the significance test of p < 0.01, showing that the slope of construction land in China’s counties exhibited significant characteristics of spatial agglomeration that gradually became stronger, with the 2010–2020 time period seeing the fastest increase.

Table 1

Moran’s I value of global spatial auto-correlation analysis of construction land slope at county level in China

Year Moran’s I Z p-Value
2000 0.373 144.3 0.00
2010 0.385 149.2 0.00
2020 0.443 171.2 0.00

Furthermore, we used Getis-OrdGi* to visualize the distribution pattern of hot and cold spots. With significant north–south differences, the spatial distribution of cold and hot spots in 2000, 2010, and 2020 exhibited characteristics of stability and continuity (Figure 7). In 2000, the hot spot areas were mainly distributed in the southwestern provinces, the Loess Plateau, and southeast coast side four regions, cold spot areas were mainly distributed in two regions, one was located in northwestern Xinjiang, the other range was larger from the northeastern provinces spread south to Anhui, Jiangsu area. In 2010, the southern Hunan region had a significant increase in the number of hot spots, while the extent of hot spots in Qinghai and Gansu Provinces shrank. In 2020, hot spots were expanding further in the southwest, and cold spots continued to spread in northwest Xinjiang. Hot spot distribution became more concentrated, mostly in the southwest, the Loess Plateau, and the southeast coast. In terms of the slope of the new section, it was comparable to the distribution of cold and hot areas at each time point.

Figure 7 
                  Hot and cold spots on the slope of construction land in China’s counties.
Figure 7

Hot and cold spots on the slope of construction land in China’s counties.

Combining the SDE and the average center to explore the Spatiotemporal evolution trend of hotspots (Figure 8). The area of the SDE has gradually shrunk over the past 20 years and shifted toward the southwest, indicating that the clustering of hot spots has been gradually increasing and the number of them in the southwest increased significantly. In terms of the change of the mean center, it moved about 35 km to the southeast in 2010 compared to 2000, and another 35 km to the southwest between 2010 and 2020, showing that urban construction land in southern China, particularly in the southeast and southwest, has increased to higher slopes during the past 20 years.

Figure 8 
                  SDE and mean center of hot spots.
Figure 8

SDE and mean center of hot spots.

4.3 Correlation between construction land slope ground slope

Topography and natural resource circumstances have a significant impact on the expansion of construction land [52,53,54]. The OLS model was used to investigate the relationship between the ground slope as the independent variable and the construction land slope as the dependent variable. There was a significant linear correlation between them, according to the model operation findings (Table 2), which all passed the 1% significance test. In general, the regression coefficients and R-Squared of various types of counties continued to rise year after year, showing that the ground slope’s influence on the construction land slope was gradually growing. In spite of the high ground slope, construction land remained “flat building” in the hotspot counties in 2000 and 2010, where smaller coefficients were detected, indicating that the ground slope had less of an impact. However, the higher value of the coefficient appeared in the hot spots. In 2020, the construction land in the hotspot counties was greatly affected by the ground conditions, it was speculated that during the period from 2010 to 2020, and the phenomenon of construction land slope climbing was obvious. For cold spots, although the impact of ground slope on construction land was gradually becoming stronger, the impact intensity was weaker than the overall level. Finally, compared with each time node points, the slope of new construction land was greatly affected by the ground.

Table 2

Results of the OLS model

Type Year C R-Squared P Koenker (BP) AICc
All counties 2000 0.147 0.399 0.000 174 8,949
2010 0.159 0.423 0.000 213 9,082
2020 0.199 0.527 0.000 400 9,173
2000–2010 0.187 0.419 0.000 252 9,899
2010–2020 0.213 0.525 0.000 379 9,573
Hot spots 2000 0.106 0.098 0.000 19 3,217
2010 0.119 0.126 0.000 23 3,288
2020 0.173 0.270 0.000 44 3,237
Cold spots 2000 0.089 0.265 0.000 107 2,166
2010 0.101 0.311 0.000 103 2,358
2020 0.103 0.321 0.000 98 2,499

The Koenker (BP) statistics all passed the significance test, showing that the OLS model varied across the research region, there was spatial heterogeneity between the construction land slope and the ground slope, and the GWR model must be take into account. According to the result of the GWR model operation (Table 3), the R-Squared was greater than that of the OLS model in the same year, meaning the goodness of fit of the model significantly improved, and the AICc value was lower, which suggested fitting performance of the GWR model improved. As for the median regression coefficient, there was a yearly increase in the positive association between the slope of the ground and the slope of the construction land.

Table 3

Results of the GWR model

Year C ave C med C max R-Squared AICc
2000 0.118 0.115 0.218 0.520 8,411
2010 0.129 0.128 0.232 0.545 8,510
2020 0.160 0.161 0.350 0.683 8,169
2000–2010 0.159 0.164 0.259 0.532 9,389
2010–2020 0.173 0.177 0.381 0.680 8,581

We performed a spatial visualization of the coefficients of the explanatory variables in the results of the GWR model (Figure 9). The south-west and central China were the main concentrations of regions where the ground slope had a greater impact on the construction land in 2000, while the northeast region, Xinjiang, and the southeast coast were the locations of the lower areas. Compared to 2000, the north-east and the area around Zhejiang have seen an increase in the influence of the ground slope in 2010. It was noteworthy that the coefficients expanded greatly around the Southeast of the Sichuan Basin in 2020, with the most significant influence centered in the southwestern section of Yunnan. In 2020, the coefficients of the ground slope also increased in Guangdong, as well as the northeast. Looking at the new construction land, there were the Loess Plateau, southwest region, and southern Hubei that ware heavily influenced by the ground from 2000 to 2010. The coefficients of ground slope from 2010 to 2020 for the new section were similar to those in 2020, but the range of high coefficients in central China was wider.

Figure 9 
                  Spatial distribution of regression coefficients for ground slope.
Figure 9

Spatial distribution of regression coefficients for ground slope.

5 Discussion

The most basic county unit in China’s administrative system was used in this article to examine the slope characteristics of construction land. It revealed a spatial pattern of “high in the south and low in the north,” with the higher slope counties being primarily located in the southwest. The average slope of China’s construction land has been rising over time, and the phenomenon of agglomeration and north–south difference have intensified. Although fewer studies have comprehensively examined the slope characteristics of construction land, some of their findings appear to be consistent with those of our study. For example, Liu et al. showed that the intensity of construction land expansion in China has gradually become stronger in hilly and mountainous areas [5], but they lack an in-depth study of the characteristics of spatial patterns; Zhou et al. studied the construction land slope spectrum curve from city units and similarly concluded that construction land slope climbing exists in the southwest [47]; however, our article then went further and found that the effect of construction land slope was more significant in the southwest compared to other regions (Figure 7); Jiang et al. suggested that the economic development of the southwest region was also lagging due to the influence of the topography, where the level of urbanization was still somewhat different from the rest of China, but there was a lack of quantitative description of the influence of topographic factors [55].

5.1 Policy implications

Over the past two decades, the slope of construction land in the southwest has been much higher than that of other regions (Figure 5), and this trend may be maintained in the long term due to the stable presence of natural background conditions. In western China, mountain urbanization is a crucial step toward modernization, and urbanization in southwest China is likewise gaining speed [56]. Wang and Lu discovered that the development of mountain cities in China was significantly influenced by policy guidance [57]. The efficient and intensive use of land resources, the elimination of land resource bottlenecks, as well as the effective protection of high-quality arable land resources can all be advanced by encouraging the sensible development of low hills and gentle slope resources[58]. Since the southwest region is rich in mountainous hills and is undergoing a time of accelerated urbanization, it is significant to encourage the development and utilization of land resources on low hills and gentle slopes. In the process of implementing the policy, there should also be an emphasis on averting the geological and ecological issues that it may cause.

5.2 Geological disasters

The expansion of urban construction land to mountains and hills is inevitably accompanied by the terracing and transformation of sloping land, a process that is very likely to destroy the soil structure and increase the potential for geological disasters [39,59]. In this research, the counties with the higher average slope of construction land are also, in general, counties with the most sensitivity to geological risks including landslides and ground subsidence in China. In order to lessen the impact of geological hazards caused by the expansion of construction land, first and foremost, the risk of geological hazards needs to be completely taken into consideration in the evaluation of the suitability of construction land, and a comprehensive survey and analysis should be carried out to avoid places with a high risk of catastrophes, ensuring the protection of people’s lives and property. Second, a faultless monitoring and management system is created during construction in accordance with the site’s requirements to raise safety standards, and by ensuring the caliber of engineering construction, the likelihood of geological disasters and subsequent disasters is significantly lowered. The geological stability concerns must then be reevaluated, and precautionary reinforcement must be carried out in accordance with the defenses after the development. Last but not least, a routine monitoring, early warning, inspection, and disposal mechanism will be implemented during the daily management and operation of the built-up regions to guarantee sustainable operation.

5.3 Ecological environment

The process of urban land expansion frequently has unfavorable consequences, including the fragmentation of biological landscapes, a loss of connectedness, and heterogeneity across cities' landscape [60]. The majority of the study’s slope climbing hotspots, in particular, are found in the south’s mountainous region and important Yangtze River ecological zone, where the natural environment is better and home to a greater variety of species, actively contributing to ecological security. Irrational land use will aggravate both the pressure of land shortage and environmental pollution, which would cause huge economic losses [61,62]. Especially in the context of increasingly extreme global climate, it could induce more geological hazards. Mountain cities, unlike plains cities, were initially dispersed because of topographic relief and slope. As a result, urban development tended to concentrate in flat areas, which not only increased travel costs between urban areas and lowered operational efficiency but also fragmented the natural landscape and impeded integration into a rather stable ecological spatial system. Additionally, Dai et al. have demonstrated that China’s compact urban shape is the only viable option [63]. Therefore, the expansion of mountain cities should maintain continuity with the main urban areas, fill in the development within the cities or develop gradually and compactly around the cities. So as to reduce excessive embedding and fragmentation of the natural landscape, forests, and fields, ensuring the integrity of their ecosystems, promoting the efficient and intensive use of land, and achieving a harmonious coexistence and long-term improvement of the human living environment and the natural ecological environment on a more macro scale.

5.4 Limitations

In comparison to the current literature, this work is better equipped to examine the slope characteristics of construction land throughout China from a county viewpoint and to analyze the influence of ground circumstances on them, thus enhancing the study on the expansion of construction land. However, this study still has room for improvement. First, the administrative boundaries of the county have an impact on the study, and although this impact has been significantly reduced in the data processing section, it cannot be completely eliminated. So we need to further improve the research unit. Second, the precision of the data adopted in this article has met the requirements of the research at the national level and can reveal a large range of objective laws. But the spatial resolution of the data will limit and affect the research if it sinks to the city and county levels. Finally, the expansion of land for construction is influenced by both social development and environmental factors, which are interconnected with the growth of urbanization. So, the motivation and mechanism for the construction land slope need to be further explored.

6 Conclusion

This article aims to investigate the slope characteristics of construction land in China’s counties and their correlation with the ground slope. Key findings are as follows:

  1. China’s counties saw an accelerated upward trend in the average slope of construction land, which increased from 3.07° in 2000 to 3.42° in 2020, and there was a steady rise in counties with a slope larger than 4°. The spatial aggregation of the construction land slope is remarkable, forming a spatial pattern of “high in the south and low in the north.” With the dramatical increase of construction land slope in the south, the gap between north and south became increasingly apparent, leading to a more concentrated distribution of hotspots, as well as a progressive shift of the mean center towards the southwest.

  2. When viewed from the relationship between the ground slope and construction land, they exhibited a significant linear relationship, with the ground’s impact gradually growing. Before 2010, the hotspot counties were defined by “flat building”; however, after 2010, the ground conditions had a significant impact on the hotspot counties. Furtherly, the GWR results demonstrate that there was spatial heterogeneity in the influence of ground conditions on construction land, with counties surrounding the Sichuan basin being more influenced by the ground slope, particularly in the southwest of Yunnan Province.


The authors would like to thank the reviewers and editors for valuable comments and suggestions.

  1. Funding information: This work is supported by the project of the National Natural Science Foundation of China entitled “Distribution and change characteristics of construction land on slope gradient in mountainous cities of southern China” (No. 41961039).

  2. Author contributions: Conceptualization, Q.P. and J.D.; methodology, Q.P., J.D., and P.H.; validation, P.Q. and H.P.; investigation, P.Q.; resources, P.Q.; data curation, H.P.; writing – original draft preparation, D.J.; writing – review and editing, P.Q.; visualization, D.J. and P.H.; supervision, P.Q.; project administration, P.Q.; funding acquisition, P.Q. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: All authors declare no conflict of interest in this article.

  4. Data availability statement: The data are available on request from the corresponding author.


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Received: 2022-09-06
Revised: 2022-11-01
Accepted: 2022-11-20
Published Online: 2022-12-22

© 2022 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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