Analysis of Urban Expansion and Human–Land Coordination of Oasis Town Groups in the Core Area of Silk Road Economic Belt, China

Abstract

Under economic globalization, synergy among cities has been actively promoted. Establishing inter–city networks and joint regional development could catalyze economic growth. The mode and pace of urban growth could be gauged by construction land expansion and human–land coordination. This study adopted the dynamic change, the center of gravity, and coordination analyses to comprehensively portray spatial patterns and changes amongst 13 oasis town groups in Xinjiang, China, from 2000 to 2018. The results identified that 2010 was the turning point of acceleration in construction land expansion, demonstrating notable spatial differentiations among town groups. Northern Xinjiang experienced faster urban growth than southern Xinjiang. The Urumqi–Changji–Shihezi (UCS) town group on the northern slope of the Tianshan Mountains constituted the crucial urban core with the fastest construction land expansion. Although the towns in southern Xinjiang were small and beset by inherent limitations in the early period, some town groups acquired new impetus and vitality and became the fastest–developing areas in Xinjiang in recent years. The growth was driven by China’s western development program, economic assistance, and Silk Road Economic Belt. Eastern Xinjiang had convenient transportation, but its small urban entities needed population supplementation to invigorate urban expansion. In the far north, the Altay and Tacheng–Emin (TE) town groups were situated too far from development cores. They lacked the collateral benefits of nearby strong–growth loci, resulting in sluggish growth. A north–south dual–hub strategy was proposed to spearhead the dissemination of urban growth by fostering core–periphery linkages pump–primed by improved road connections.

1. Introduction

In the context of globalization and integrated regional development, the cultivations of synergy and networking among cities are common features of regional economic evolution in the new era [1]. Forming urban grouping has been regarded as a key feature of China’s new urbanization initiative [2,3,4]. The inter–city connections and dependence become stronger, inducing an increase in the complexity of spatial–structure evolution [5]. As the main component of urban space, construction land can directly reflect the scale and direction of urban development with reference to spatial morphology. Moreover, construction land furnishes important sites for commercial activities, reflecting the changing economic intensity [6]. Therefore, it is essential to explore the pattern and construction of the land expansion and to analyze the underlying driving mechanisms to maintain sustainable development of the region [7].

Associated with urban expansion, the environmental and economic effects of land expansion have become a hot research topic [8,9]. The research focus has gradually shifted towards “smart growth” and “compact city” to improve land–use efficiency [10,11]. Recent research on construction land expansion has shifted to quantitative analysis. Scholars have established mathematical models to measure the changes in spatial patterns from various perspectives, such as expansion rate, expansion intensity, and elasticity coefficient [12,13,14]. Meanwhile, the research orientation has moved from construction land per se to the environmental effects caused by conversion to construction land. Specifically, construction land studies have been linked to pivotal externalities, such as ecological impacts, macro planning, and population efficiency [15,16,17]. The literature has built a rich repertoire, but most studies are conducted on the micro–scale. Coupled interaction studies of construction land from the perspectives of the spatial and temporal evolution of patterns are still relatively scarce. Research on the important relationship between construction land expansion and economic growth has yielded mixed results.

In May 2014, the second Central Working Symposium on Xinjiang officially identified the province as the “Silk Road construction core area” (http://ydyl.people.com.cn/n1/2017/0425/c411837–29235511.html, accessed on 19 July 2022). Meanwhile, this initiative has been included in the national “Belt and Road” strategy and the “Thirteenth Five–Year Plan.” Since the 1950s, Xinjiang has been supported by major national policies such as “Western Development”, “counterpart assistance to Xinjiang,” and “Silk Road Core Area.” Xinjiang’s town groups have become increasingly crucial in meeting urbanization policies [18]. They can use the strategic policy window to bolster economic and social development. Therefore, the aims of this paper are: (1) to explore the spatial and temporal changes of construction land in Xinjiang; (2) to analyze the coordination of population growth and construction land expansion in the development of town groups; and (3) to evaluate the expansion pattern of town groups and provide practical recommendations for improvement, including theoretical support to clarify the urban–economic structure of Xinjiang and formulating corresponding development plans.

2. Study Area and Data Sources

2.1. Overview of the Study Area

The study was conducted in the Xinjiang Uygur Autonomous Region (hereinafter referred to as “Xinjiang”) in northwest China, bordering eight central Asian countries, including Kyrgyzstan, Uzbekistan, Kazakhstan, etc. [19]. The region is considered a crucial passage of the New Asia–Europe Continental Bridge and the core area for the Silk Road Economic Belt development (http://www.scio.gov.cn/xwfbh/gssxwfbh/xwfbh/xinjiang/Document/1658084/1658084.htm, accessed on 23 July 2022). The study area included the province’s division of 13 oasis town groups plotted in Figure 1. The spatial classification and boundary demarcations were divided based on “The Urban System Plan of Xinjiang Uygur Autonomous Region (2014–2030)” [20].

The study area covers a vast region of 1.665 million km², China’s largest province–level administrative unit. It has a complex topography dominated by an arid and stressful ecological environment. With a low carrying capacity, cities and towns are sparsely scattered, mainly concentrated in oases, with development stifled by natural resource deficiency and difficult terrain. At the end of 2017, the regional GDP was 1090 billion Yuan, and 12.1 million out of 24.4 million of the total population are living in urban areas, attaining a 49.38% urbanization rate. With a per capita GDP of only 0.0451 million Yuan, the region ranked 21st out of 31 in the country [21,22]. Overall, the study area’s economic development lags significantly behind China’s most prosperous central and eastern parts.

Figure 1. Landform map and locations of town groups in Xinjiang, China. Notes: (a) Altay: Altay City, Burqin County, Fuyun County, Fuhai County, Habahe County, Qinghe County, Jeminay County; (b) TE:Tacheng City, Emin County, Toli County, Yumin County, Hoboksar Mongolian Autonomous County; (c) BAJ:Bole City, Alashankou City, Wenquan County, Jinghe County; (d) KKU:Karamay City, Kuitun City, Wusu City; (e) UCS:Urumqi City, Changji City, Fukang City, Hutubi County, Manas County, Qitai County, Jimsar County, Mulei Kazakh Autonomous County, Shihezi City, Shawan County; (f) Hami:Yizhou District, Balikun Kazakh Autonomous County, Yiwu County; (g) Ili Valley:Yining City, Yining County, Qapqal Xibe Autonomous County, Huocheng County, Gongliu County, Xinyuan County, Zhaosu County, Tekes County, Nilka County; (h) Turpan:Gaochang District, Shanshan County, Tuokexun County; (i) Aksu:Aksu City, Wensu County, Wushi County, Awati County, Keping County; (j) Kuqa:Kuqa County, Shaya County, Xinhe County, Baicheng County; (k) Korla:Korla City, Bugur County, Yuli County, Ruoqiang County, Qiemo County, Yanqi Hui Autonomous County, Hejing County, Heshuo County, Bohu County; (l) Kashgar:Atushi City, Aketao County, Aheqi County, Wuqia County, Kashgar City, Shufu County, Shule County, Yengisar County, Poskam County, Yarkant County, Yecheng County, Makit County, Yopurga County, Payzawat County, Marabishi County, Taxkorgan Tajik Autonomous County; (m) HML: Hotan City, Hotan County, Moyu County, Pishan County, Lop County, Qira County, Yutian County, Minfeng County. In addition, the meaning of the abbreviated town group names: BAJ (Bole–Alashankou–Jinghe); HML (Hotan–Moyu–Lop); KKU (Karamay–Kuitun–Wusu); TE (Tacheng–Emin); UCS (Urumqi–Changji–Shihezi). The remaining town groups are named after the principal city or geographical area. Some of the Xinjiang Production and Construction Corps (XPCC) [23] cities represent an emerging category in China’s urban system. The XPCC, established in 1954, draws from the traditional Chinese Tuntian system, a policy of settling military units in frontier areas so that they become self–sufficient in food [24]. In this study, XPCC excluded here due to missing data.

Figure 1. Landform map and locations of town groups in Xinjiang, China. Notes: (a) Altay: Altay City, Burqin County, Fuyun County, Fuhai County, Habahe County, Qinghe County, Jeminay County; (b) TE:Tacheng City, Emin County, Toli County, Yumin County, Hoboksar Mongolian Autonomous County; (c) BAJ:Bole City, Alashankou City, Wenquan County, Jinghe County; (d) KKU:Karamay City, Kuitun City, Wusu City; (e) UCS:Urumqi City, Changji City, Fukang City, Hutubi County, Manas County, Qitai County, Jimsar County, Mulei Kazakh Autonomous County, Shihezi City, Shawan County; (f) Hami:Yizhou District, Balikun Kazakh Autonomous County, Yiwu County; (g) Ili Valley:Yining City, Yining County, Qapqal Xibe Autonomous County, Huocheng County, Gongliu County, Xinyuan County, Zhaosu County, Tekes County, Nilka County; (h) Turpan:Gaochang District, Shanshan County, Tuokexun County; (i) Aksu:Aksu City, Wensu County, Wushi County, Awati County, Keping County; (j) Kuqa:Kuqa County, Shaya County, Xinhe County, Baicheng County; (k) Korla:Korla City, Bugur County, Yuli County, Ruoqiang County, Qiemo County, Yanqi Hui Autonomous County, Hejing County, Heshuo County, Bohu County; (l) Kashgar:Atushi City, Aketao County, Aheqi County, Wuqia County, Kashgar City, Shufu County, Shule County, Yengisar County, Poskam County, Yarkant County, Yecheng County, Makit County, Yopurga County, Payzawat County, Marabishi County, Taxkorgan Tajik Autonomous County; (m) HML: Hotan City, Hotan County, Moyu County, Pishan County, Lop County, Qira County, Yutian County, Minfeng County. In addition, the meaning of the abbreviated town group names: BAJ (Bole–Alashankou–Jinghe); HML (Hotan–Moyu–Lop); KKU (Karamay–Kuitun–Wusu); TE (Tacheng–Emin); UCS (Urumqi–Changji–Shihezi). The remaining town groups are named after the principal city or geographical area. Some of the Xinjiang Production and Construction Corps (XPCC) [23] cities represent an emerging category in China’s urban system. The XPCC, established in 1954, draws from the traditional Chinese Tuntian system, a policy of settling military units in frontier areas so that they become self–sufficient in food [24]. In this study, XPCC excluded here due to missing data.

2.2. Data Sources and Processing

The basic data used in this study include statistical data, vector data, and spatial data. Considering the availability and completeness of the data, the study was carried out with a 5–year time interval, adopting 2000, 2005, 2010, 2015, and 2017 as the time nodes. The building area data are derived from the historical land classification data of China’s Multi–period land use/land cover remote sensing monitoring database from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences. In this paper, ArcGIS was used to progressively reclassify land use data and obtain historical raster data of the construction land in Xinjiang town groups. The sources and applications of the collected data are summarized in

Table 1. Data types and sources.

Data Type	Time Node (Year)	Data Accuracy	Data Source	Application
Historical construction land	2000, 2005, 2010, 2015, 2018	County	The historical land classification data of China Multi–period land use/land cover remote sensing monitoring database	Classification by reclassification
Statistics	2000, 2005, 2010, 2015, 2017	County	Xinjiang Statistical Yearbook, China City Statistical Yearbook, China County Statistical Yearbook (https://www.cnki.net/, accessed on 24 July 2021) a	Accounting for statistical indicators
Vector boundary data	2017	County	National Geomatics Center of China (http://www.ngcc.cn, accessed on 24 July 2021)	Study area scope
Road vector data	2010, 2015, 2017	Grade 3 b	Open Street Map (https://www.openstreetmap.org, accessed on 24 July 2021)	Model construction
City center coordinates	2017	–	Baidu API (http://lbsyun.baidu.com/, accessed on 24 July 2021)	Distance measurement

^a Some missing data from the yearbooks were supplemented by linear interpolation; ^b It contains the district boundary of province, city, and county.

.

3. Methodology

3.1. Analyzing Changes in the Expansion of Construction Land

The expansion of construction land results from the joint action of social and economic development and a natural environment background. The study of construction land expansion and its landscape effect enables quantitative evaluations of the development profile of the town groups. The magnitude of annual expansion can reflect the absolute expansion rate of construction areas. Comparing the average annual expansion at a certain stage with the whole study period can indicate the rate of regional development. This study used the average annual expansion of town groups to tell their expansion rate. An index of the relative expansion rate for time series comparison is calculated by Equation (1) [25,26]:

$$\mathrm{H}=\frac{{\mathrm{C}}_{\mathrm{b}}-{\mathrm{C}}_{\mathrm{a}}}{\mathrm{b}-\mathrm{a}}; \mathrm{X}=\frac{\left({\mathrm{C}}_{\mathrm{b}}-{\mathrm{C}}_{\mathrm{a}}\right)/\left(\mathrm{b}-\mathrm{a}\right)}{\left({\mathrm{T}}_{\mathrm{B}}-{\mathrm{T}}_{\mathrm{A}}\right)/\left(\mathrm{B}-\mathrm{A}\right)}$$

(1)

where $\mathrm{H}$ is the expansion rate, X is the relative expansion rate, ${\mathrm{C}}_{\mathrm{b}}$ and ${\mathrm{C}}_{\mathrm{a}}$ are the construction land area in the starting year a and the ending year b, respectively. ${\mathrm{T}}_{\mathrm{B}}$ and ${\mathrm{T}}_{\mathrm{A}}$ are the construction land area at the beginning of study period A and the end of study period B, respectively. A larger value of $\mathrm{H}$ represents a faster expansion rate. The scenario of 0 < X < 1 denotes that the expansion rate of a given period is slower than the whole study period; X = 1 denotes that the expansion rate of a given period is equal to the whole study period; X > 1 denotes that the expansion rate of a given period is faster than the whole study period.

Comparing the variation rate of construction land in each town group with that of all town groups can signify regional differences between town groups. The spatial variation amongst town groups is calculated by Equation (2) [27]:

$$\mathrm{R}=\left(\left|{\mathrm{UA}}_{\mathrm{b}}-{\mathrm{UA}}_{\mathrm{a}}\right|\times {\mathrm{TA}}_{\mathrm{a}}\right)/\left({\mathrm{UA}}_{\mathrm{a}}\times \left|{\mathrm{TA}}_{\mathrm{b}}-{\mathrm{TA}}_{\mathrm{a}}\right|\right)$$

(2)

where $\mathrm{R}$ is the relative variation rate of construction land in a given town group, with ${\mathrm{UA}}_{\mathrm{b}}$ and ${\mathrm{UA}}_{\mathrm{a}}$ being the construction land area of a town group in the starting and ending years. ${\mathrm{TA}}_{\mathrm{a}}$ and ${\mathrm{TA}}_{\mathrm{b}}$ are the whole Xinjiang construction land area in the starting and ending years, respectively. $\mathrm{R}$ > 1 means that the construction land variation rate in a town group is faster than in the whole study area; $\mathrm{R}$ = 1 means that the construction land variation rate in a town group has the same rate as the whole study area. Finally, $\mathrm{R}$ < 1 means that the construction land variation land in a town group is slower than in the whole study area.

3.2. Analyzing Changes in the Centers of Gravity of Construction Land

The center of a gravity model was used to calculate the locational foci of construction land in the town groups from 2000–2018. It can explore the evolutionary characteristics of urban expansion and its temporal differences by comparing the changes in the centers of gravity in different years. The coordinates of the center of gravity are calculated by Equation (3) [28]:

$$\overline{\mathrm{x}}=\frac{{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{T}}_{\mathrm{i}}{\mathrm{X}}_{\mathrm{i}}}{{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{T}}_{\mathrm{i}}}; \overline{\mathrm{y}}=\frac{{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{T}}_{\mathrm{i}}{\mathrm{Y}}_{\mathrm{i}}}{{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{T}}_{\mathrm{i}}}$$

(3)

where $\overline{\mathrm{x}}$ and $\overline{\mathrm{y}}$ are the longitude and latitude values of the center of gravity of an attribute in the study area, respectively; n represents the pixel numbers of construction land; ${\mathrm{X}}_{\mathrm{i}}$ and ${\mathrm{Y}}_{\mathrm{i}}$ are the coordinates of the geographic center of gravity of the ith pixel; and ${\mathrm{T}}_{\mathrm{i}}$ denotes the value of an attribute of the whole study area. In this paper, the attribute refers to the construction land area. From the coordinates, the movement distance of an attribute’s center of gravity can be calculated by Equation (4) [28]:

$$\mathrm{d}=\mathsf{\rho }\sqrt{{\left({\mathrm{x}}_{\mathrm{i}+\mathrm{t}}-{\mathrm{x}}_{\mathrm{i}}\right)}^2+{\left({\mathrm{y}}_{\mathrm{i}+\mathrm{t}}-{\mathrm{y}}_{\mathrm{i}}\right)}^2}$$

(4)

where d denotes the distance traveled by the center of gravity; (${\mathrm{x}}_{\mathrm{i}},{ \mathrm{y}}_{\mathrm{i}}$), (${\mathrm{x}}_{\mathrm{i}+\mathrm{t}},{ \mathrm{y}}_{\mathrm{i}+\mathrm{t}}$) are the coordinates of the center of gravity of an attribute in the ith and (i + t)th year, respectively, and $\mathsf{\rho }$ is the conversion factor between the planar coordinates and geographical coordinates, which is generally taken as a constant.

3.3. Analyzing the Human–Land Coordination

The population elasticity coefficient, i.e., the ratio of the annual growth rate of construction land area and the annual growth rate of the urban population, is proven to be useful in reflecting the coordination between urban expansion and population evolution [29,30,31]. In this study, the per capita construction land use was applied to build and restrain the coefficient of coordination, which is calculated by Equation (5):

$$\{\begin{array}{c}\mathrm{CPI}=\frac{{\mathrm{CR}}_{\mathrm{i}}}{{\mathrm{PR}}_{\mathrm{i}}}\times \mathrm{R}\hfill \\ \mathrm{R}=\frac{{\mathrm{AC}}_{\mathrm{b}}/{\mathrm{AL}}_{\mathrm{b}}}{{\mathrm{AC}}_{\mathrm{a}}/{\mathrm{AL}}_{\mathrm{a}}}\hfill \end{array}$$

(5)

where CPI is the coefficient of coordination, ${\mathrm{CR}}_{\mathrm{i}}$ is the average annual growth rate of construction land, ${\mathrm{PR}}_{\mathrm{i}}$ is the average annual growth rate of the urban population, $\mathrm{R}$ is the correction coefficient and ${\mathrm{AL}}_{\mathrm{a}}$ and ${\mathrm{AL}}_{\mathrm{b}}$ are the ideal per capita construction land area in the starting and ending years. The ideal per capita construction land area is obtained from the “Urban Land Classification and Planning and Construction Standards” (GB/T 50137−2011). ${\mathrm{AC}}_{\mathrm{a}}$ and ${\mathrm{AC}}_{\mathrm{b}}$ are the actual construction land areas in the starting and ending years, respectively. In conjunction with existing studies, Xinjiang’s cities are classified according to their CPI and presented in

Table 2. Grading standards of the coordination between construction land expansion and population growth are based on the calculated coefficient of coordination (CPI) values.

Change Type a	Change Subtype a	CPI Grading Standard b	Comparison of Land Versus Population Changes a
Land expansion	Significant land expansion	CPI > 1.7	Land is expanding much faster than population growth
	Average land expansion	1.3 < CPI < 1.7	Land is expanding slightly faster than population growth
Land–population coordination	Land–population coordination	0.9 < CPI ≤ 1.3	Land expansion and population growth have the same rate generally
Population growth	Significant population growth	0 ≤ CP I≤ 0.5	Land expansion is much slower than population growth
	Average population growth	0.5 ≤ CPI ≤ 0.9	Land expansion is slower than population growth
Land–population contraction	Land–population contraction	CPI < 0 or CPI > 0 ${\mathrm{CR}}_{\mathrm{I}}$ < 0 or ${\mathrm{PR}}_{\mathrm{I}}$ < 0	Land expansion and/or population growth have/has declined

^a The term “land” in this table refers to “construction land”; ^b CPI stands for coefficient of coordination. Refer to Equation (5) for the meaning of CR_i and PR_i.

.

4. Results and Analysis

4.1. Time–Series Analysis of Construction Land

4.1.1. The Expansion Rate in Construction Land

From 2000 to 2018, the construction land area of 13 town groups in Xinjiang experienced rapid expansions, growing from 5828.00 to 8624.71 km² (Figure 2 and

Table 3. Changes in construction land area in the town groups from 2000 to 2018 unit: km².

Town Group	2000	2005	2010	2015	2018
UCS	1513.65	1587.96	1595.97	2154.62	2236.80
Aksu	334.59	351.89	353.66	385.51	400.98
Altay	188.35	193.42	196.23	288.23	304.27
BAJ	184.81	202.08	202.38	223.88	230.96
Hami	302.49	314.97	321.84	523.18	594.74
HML	163.55	176.57	176.84	199.56	269.36
Kashgar	648.62	672.45	684.05	830.33	854.25
KKU	388.54	432.00	435.28	520.25	535.98
Kuqa	231.27	246.90	250.19	326.01	333.76
Korla	436.99	515.46	633.83	920.97	967.95
TE	351.53	368.29	370.47	407.70	391.50
Turpan	217.93	230.76	232.18	336.56	379.36
Ili Valley	731.14	751.94	755.83	873.75	890.78
Total	5828.00	6200.68	6370.13	8214.55	8624.71

), indicating an increase to 147.98% of the original area, with an average annual expansion rate of 155.37 km²/a during the 18–year study period.

The results indicated that the construction land of the town groups grew continually in the study period but demonstrated a fluctuating trend. Specifically, from 2000 to 2005, the construction land expanded from 5828.00 to 6200.68 km² at an expansion rate of 74.54 km²/a (

Table 4. Changes in the expansion rate of construction land in the 13 town groups by five study periods.

Town Group	2000–2005		2005–2010		2010–2015		2015–2018		2000–2018
	Expansion Rate (km2/a)	Relative Expansion Rate (%)	Expansion Rate (km2/a)	Relative Expansion Rate (%)	Expansion Rate (km2/a)	Relative Expansion Rate (%)	Expansion Rate (km2/a)	Relative Expansion Rate (%)	Expansion Rate (km2/a)	Relative Expansion Rate (%)
All groups	74.54		33.89		368.88		136.72		155.37
UCS	14.86	0.20	1.60	0.05	111.73	0.30	27.40	0.20	40.17	0.26
Aksu	3.46	0.05	0.35	0.01	6.37	0.02	5.16	0.04	3.69	0.02
Altay	1.01	0.01	0.56	0.02	18.40	0.05	5.35	0.04	6.44	0.04
BAJ	3.45	0.05	0.06	0.00	4.30	0.01	2.36	0.02	2.56	0.02
Hami	2.50	0.03	1.37	0.04	40.27	0.11	23.85	0.17	16.24	0.10
HML	2.60	0.03	0.05	0.00	4.54	0.01	23.27	0.17	5.88	0.04
Kashgar	4.77	0.06	2.32	0.07	29.25	0.08	7.98	0.06	11.42	0.07
KKU	8.69	0.12	0.66	0.02	16.99	0.05	5.24	0.04	8.19	0.05
Kuqa	3.13	0.04	0.66	0.02	15.16	0.04	2.58	0.02	5.69	0.04
Korla	15.69	0.21	23.67	0.70	57.43	0.16	15.66	0.11	29.50	0.19
TE	3.35	0.04	0.44	0.01	7.44	0.02	−5.40	−0.04	2.22	0.01
Turpan	2.57	0.03	0.28	0.01	20.88	0.06	14.27	0.10	8.97	0.06
Ili Valley	4.16	0.06	0.78	0.02	23.58	0.06	5.67	0.04	8.87	0.06

Note: (1) Expansion rate: It refers to the absolute change of construction land in unit time. (2) Relative expansion rate: It refers to the ratio of the absolute amount of construction land in a certain area in the unit time sequence to the absolute amount of the overall construction land in the unit time sequence, which is used to reflect the difference between local and overall changes.

). The Korla and UCS town groups experienced the fastest expansion rates of 15.69 and 14.86 km²/a, respectively, equivalent to relative expansion rates of 0.21% and 0.20%, respectively.

From 2005 to 2010, the construction land in the whole study area expanded from 6200.68 to 6370.13 km², with an expansion rate of 33.89 km²/a. The Korla and Kashgar town groups had the fastest expansion rates of 23.67 and 2.32 km²/a, respectively, with relative expansion rates of 0.70% and 0.07%, respectively. Compared with the previous period, the expansion rate had slowed down.

From 2010 to 2015, the construction land of the whole study area expanded from 6370.13 to 8214.55 km², with an expansion rate of 366.88 km²/a. The UCS and Korla town groups demonstrated the fastest expansion rates of 111.73 and 57.43 km²/a, respectively, with relative expansion rates of 0.30% and 0.16%, respectively. This period experienced the fastest construction land expansion rate in the entire study period in most town groups.

From 2015 to 2018, the construction land of the whole study area expanded from 6370.13 to 8624.71 km², with an expansion rate of 136.72 km²/a. The Hami and HML town groups experienced the fastest expansion rates of 23.85 km²/a and 23.27 km²/a, respectively, with relative expansion rates of 0.17% and 0.17%, respectively. Compared with the previous period, the expansion had slowed down. For the first time, a negative growth (area contraction) occurred in the TE town group, registering a decrease in 5.40 km²/a, equivalent to a 0.04% drop in the relative expansion rate.

In recent years, with the financial support of the China government in infrastructure construction in remote western areas, the construction land for oasis town groups in Xinjiang has expanded rapidly. The urban system has shifted from small towns to large and medium–sized cities. The level of urbanization has gradually increased, thus affecting the expansion of its physical geographical scope [32]. In particular, the industrial and agricultural development conditions of the north Tianshan Mountain town group (UCS, KKU, Ili Valley) are much better than those of other areas. The overall urbanization process is fast, resulting in the corresponding construction land growth more rapidly [33]. TE town group amount conglomeration is located in frontier Xinjiang, the entire region takes the agriculture industry as the development pillar industry, and the urbanization process lags relatively backward. Therefore, there is more land distribution of industrial and mining conglomeration inside tower amount conglomeration [26]. The construction of ecological civilization became the main direction of development under the China development policy transformation this year, i.e., many mine sites were shut down. Therefore, the total amount of construction land in the region is reduced, which affects the overall expansion speed.

4.1.2. The Variation Rate in Construction Land

Significant spatial and temporal variabilities in urban land expansion could be discerned between them (

Table 5. The relative variation rate of town groups in five periods unit: (%).

Town Group	2000–2005	2005–2010	2010–2015	2015–2018	2000–2018
UCS	0.77	0.18	1.21	0.76	1.00
Aksu	0.81	0.18	0.31	0.80	0.41
Altay	0.42	0.53	1.62	1.11	1.28
BAJ	1.46	0.05	0.37	0.63	0.52
Hami	0.65	0.80	2.16	2.74	2.01
HML	1.25	0.06	0.44	7.01	1.35
Kashgar	0.57	0.63	0.74	0.58	0.66
KKU	1.75	0.28	0.67	0.61	0.79
Kuqa	1.06	0.49	1.05	0.48	0.92
Korla	2.81	8.40	1.56	1.02	2.53
TE	0.75	0.22	0.35	0.80	0.24
Turpan	0.92	0.22	1.55	2.55	1.54
Ili Valley	0.44	0.19	0.54	0.39	0.46

).

From the results, the construction land area of the UCS town group experienced the largest growth, from 1513.65 km² to 2236.80 km² from 2000 to 2018 (Table 3). The relative variation rate of 1.00 indicated a growth trend similar to the whole study area. Although the overall changes experienced fluctuations, the magnitude of change was small. The region where UCS is located is the political, economic, and cultural center of Xinjiang. It is also the most developed region in the whole region, and its construction land expansion is the fastest in the region. Variation rate refers to the ratio of change in the construction land area of a certain town group to that of the whole region [25]. We believe that the relative change rate of the UCS town group is basically the same as that of the whole region because the construction land area of the UCS town group occupies an absolute dominant proportion in this region from the beginning to the end period. Therefore, during the whole study period, the main change area of regional construction land is also concentrated in the UCS town group, while the changing area of construction land in other areas is very small. Therefore, the relative change rate of construction land in the UCS region is the same as that in the whole region. This research result is similar to the results from Gao [34].

The Korla town group experienced the most notable increase in construction land from 436.99 to 967.95 km² from 2000 to 2018, denoting a 2.53% relative variation rate. Especially in 2005–2010, the relative variation rate of Korla’s construction land increased significantly, far more than other town groups, and Korla also maintained a high relative variation rate in other periods. One explanation for this outstanding performance was its high urban development efficiency. The other reason was the successful implementation of many large–scale infrastructures from 2005 to 2010, such as the refinery at the Tarim oil field and the potassium salt field in Lop Nor, etc. Additionally, these large–scale infrastructure projects significantly increased the area of construction land to keep the relative variation rate of Korla at the forefront of the whole study area.

The relative variation rate of the TE town group was the smallest, with the construction land area increasing merely from 351.53 to 391.50 km² from 2000 to 2018. One explanation for the sluggish change was its dominant agricultural economic base, with little motivation for urban development. The other reason was its frontier location, far from the core town groups.

The Hami and Turpan town groups in eastern Xinjiang have experienced little variation in the early period, denoting the cold spots for construction land expansion in the study area. Overall, the base area of construction land in the northern Xinjiang town groups was much higher than in southern Xinjiang, especially on the north slope of the Tianshan Mountains. There, the UCS and KKU town groups constituted the hot spots of construction land growth. However, construction land expansion in the southern Xinjiang town groups shifted into a fast–increasing phase in 2010. Even though it still trailed behind northern Xinjiang, the southern Xinjiang growth rate, in tandem with strong development potential, had begun to surpass its northern counterpart.

In the second Central Xinjiang Work Conference held in May 2014, Xinjiang was identified as the “core area of the Silk Road Economic Belt” (http://xjdrc.xinjiang.gov.cn/xjfgw/c108303/202111/75aa6ebbfebb45c897640c57d3f851d8.shtml, accessed on 25 July 2022). With the support of relevant policies, China has continuously increased the infrastructure construction and investment in this region, ushering in a golden window period of economic development. The spatial features reflected a significant increase in the expansion rate of construction land during this period. The special geomorphic outline of three mountain systems surrounding two major basins in Xinjiang oasis city cluster is the rigid environmental constraint base for urban spatial expansion, with obvious regional differentiation characteristics [34]. It is affected by multiple natural background stress conditions and the process of economic and social development. The expansion of urban construction land for oasis town groups in Xinjiang has its particularity, which is mainly in cities with large construction land bases, especially in southern Xinjiang and some small and medium–sized border cities, where the expansion is still relatively slow.

4.2. Analyzing the Shifts of the Centers of Gravity of Construction Land Expansion

The center of gravity analysis was employed to evaluate the town groups’ uneven spatial changes of construction land. The shifts in the centers of gravity in individual town groups from 2000 to 2018 are plotted in longitude–latitude graphs (Figure 3). The distance (km) and compass direction of shifts in the centers of gravity of construction land are summarized in

Table 6. The distance (km) and compass direction of shifts in the centers of gravity of construction land in the 13 town groups in five study periods.

Town Group	2000–2005	2005–2010	2010–2015	2015–2018	2000–2018	Azimuth
All groups	0.04	0.11	0.46	0.08	0.67	Southeast
UCS	0.02	0.01	0.10	3.98	3.89	Southwest
Aksu	0.01	0.00	0.02	0.01	0.01	Northwest
Altay	0.02	0.02	0.00	0.24	0.20	Southeast
BAJ	0.02	0.00	0.02	0.01	0.03	Northeast
Hami	0.01	0.00	0.16	0.06	0.21	Southeast
HML	0.02	0.01	0.08	0.05	0.11	Southeast
Kashgar	0.20	0.20	0.08	0.01	0.10	Northwest
KKU	0.02	0.00	0.05	0.00	0.04	Southeast
Kuqa	0.02	0.00	0.03	0.01	0.04	Northeast
Korla	0.27	0.78	0.29	0.21	1.54	Southeast
TE	0.05	0.00	0.08	0.09	0.03	Southeast
Turpan	0.01	0.00	0.07	0.05	0.03	Northwest
Ili Valley	0.05	6.02	5.98	0.01	0.05	Northwest

.

From the results, the construction land in the town groups went through limited migration distances, mainly shifting toward the southeast direction. There was a tendency for the centers of gravity to aggregate toward the middle of Xinjiang. Combined with Xinjiang’s spatial distribution characteristics of construction land (Figure 2), an obvious siphon effect was expressed. In other words, the population, resources, and production factors of the towns in the region are concentrated in the central towns.

From 2000 to 2005, the center of gravity of construction land in the whole study area shifted 0.04 km to the southeast (Table 6). From 2005 to 2010, it continued to move 0.11 km to the southeast. From 2010 to 2015, it shifted the longest distance of 0.46 km in the same orientation, indicating a period of rapid expansion of construction land and reflecting rapid urban expansions. From 2015 to 2018, the centers of gravity shifted by a limited stretch of 0.08 km while the overall direction remained unchanged.

Throughout the study period, the centers of gravity experienced a continual migration toward the southeast, logging a cumulative distance of 0.67 km (Table 6). The southeastward direction witnessed the principal expansions. The town groups in the southeastern part of the study area developed at a relatively faster speed than others. A dichotomous development structure had formed in northern Xinjiang, with the UCS town group serving as the core, while in southern Xinjiang, with Kashgar as the core.

On the town–group scale, the shift distance of the centers of gravity did not change significantly from 2000 to 2005. The Kashgar and Korla town groups manifested the longest shifts compared with others, with migration distances reaching 0.20 km and 0.27 km, respectively (Table 6). The former expanded toward the southeast, and the latter expanded toward the northeast. They also experienced a much faster construction land expansion than others in this period. From 2005 to 2010, the Kashgar and Korla town groups maintained rapid growth. The Ili Valley town group also experienced rapid expansion, shifting its center of gravity by 6.02 km. Interestingly, from 2015 to 2018, the expansion slowed to a relatively stable phase. In the whole study period, the magnitude of construction land expansion in the southern town groups slightly exceeded the northern ones, which were relatively more convergent in their spatial changes.

4.3. Analyzing the Coordination between Construction Land Expansion and Population Growth

In this study, the coordination of construction land expansion and population growth in Xinjiang’s oasis town groups was quantitatively assessed using the coefficient of coordination (CPI) (Figure 4).

The results showed that the overall land–population coordination in the study area remained rather low throughout the study period. The town groups that concentrated mainly in the western part of the study area displayed the land expansion type followed by the population growth type. The KKU, Hami, and Altay town groups were the only ones achieving land–population coordination. In the spatial distribution, the western part was dominated by the population growth type, and the eastern part was the land expansion type. The town groups attaining land–population coordination was mainly located in the northern region, showing internal variations.

From 2000 to 2005, the population growth type was dominant in the town groups, accounting for 69.23%. At this stage, the study area had a low level of urbanization. The amount of construction land was small. Except for the Korla town group, the overall development was dominated by rapid population growth. From the spatial perspective, the northern slope of the Tianshan Mountains was the only area achieving land–population coordination in Xinjiang, serving as the core urban development area.

From 2005 to 2010, the land–population coordination structure began to show notable changes in some places. The Hami and Altay town groups in eastern Xinjiang started to convert to construction land. Meanwhile, the population–growth type of town groups dropped to 46.15%. The UCS town group changed from the land–population coordination to the population growth type. The Kuqa and Turpan town groups switched from population growth to the people–land coordination type. The Aksu town group changed from population growth to population contraction type. In comparison, the core urban areas on both sides of the Tianshan Mountains, showing an urban agglomeration trend, had relatively high land–population coordination.

From 2010 to 2015, land expansion became the dominant type, accounting for 61.54% of the town groups. The rate of construction land expansion far exceeded the urban population growth. Land expansion entered a stage of rapid development. The construction land expansion allowed regional infrastructure improvement to provide outstanding development potential.

From 2015 to 2018, the land–population contraction type evolved to become the dominant pattern, accounting for 61.52% of the town groups. This change was accompanied by slow economic growth and attenuation of regional population growth, which was associated with ecological civilization construction and environmental protection.

The essence of eco–city construction is to change the one–way linear development mode into the closed cycle development mode. The expansion of urban construction land is the result of the combined action of social and economic development and environmental background conditions. Generally, the urban expansion in economically developed regions is mainly driven by economic growth, while the less developed regions are mainly driven by natural expansion forces such as population influx. The natural background in arid Xinjiang determines that urban development is mainly supported by scattered oases. The oasis towns lie along the foothills of the three mountain ranges and are beaded along the edges of the two basins in northern and southern Xinjiang. The difference in construction land expansion is caused by the natural basis of regional differentiation and the historical process as well as the present situation of social and economic development [35]. Due to the inherent economic advantages of the north slope area of Tianshan Mountains (UCS town group, Hami town group, KKU town group), the population is denser, and the corresponding contradiction between man and land becomes more prominent. How to coordinate the contradiction between man and nature will become the main content of future development in this area. In other marginal city groups, the population distribution is more dispersed, and the economy is less developed. Therefore, the contradiction between man and land is relatively eased. In the future, infrastructure construction should be strengthened to absorb the population [19].

5. Discussion

Using the historical urban land use data, urban expansion rate index, and gravity center analysis of population elasticity coefficient, this study analyzed the construction land expansion and human–land coordination of all urban groups in Xinjiang. The results of this study provide theoretical guidance for improving urban human–land relationships, evaluating land use structure scientifically, optimizing urban land allocation and utilization, and promoting sustainable and high–quality urban development.

5.1. Analyzing the Human–Land Coordination

Human–land relationship is dynamic and influenced by economic development and institutional changes [35]. At present, China has put forward many policies and regulations aiming at homestead waste [36]. The difference in natural resources and climate in oasis towns is the root cause of regional urban expansion and human–land coordination differences. In the context of China’s urbanization, policies such as One Belt, One Road, ecological civilization construction, and western development directly affect the expansion of construction land and human–land coordination in Xinjiang. The urban expansion accelerated significantly after the Western development in 2000 and the implementation of the policy of matching aid to Xinjiang in 2010 [26]. In recent years, the promotion of ecological civilization construction and the concept of high–quality urbanization have reduced the urbanization rate in some regions and increased the coordination between humans and land [37]. The social structure of the population mainly includes quality structure, employment structure, and immigration structure. With the development of the social economy and the progress of ideology, people’s education level has been improved, the population has been increasing, and the living standard has been improving, which affects the change of the coordination between man and earth. The geographical structure of the population mainly includes the distribution of administrative regions, natural regions, economic regions, and urban and rural areas, which are the basic factors for the spatial evolution of construction land [38]. Therefore, it is necessary to take natural and human factors into full consideration in future studies to further explore the driving factors of oasis urban expansion and human–land coordination change in Xinjiang.

5.2. Applicability of Research Methods

This paper uses some classical methods in geography to explore the expansion of construction land and changes in human–land coordination in urban clusters in Xinjiang, and these estimation methods can effectively reveal and highlight the expansion of urban land and human–land coordination. Spatial analysis using ArcGIS has been widely used to study the spatial and temporal dynamics of urban land use. Alimujiang [39] used multi–temporal satellite remote sensing data and GIS technology to extract urban spatial information. The process and characteristics of spatio–temporal dynamic changes in Xinjiang cities from 1990–2010 were analyzed using sub–dimensional indices of urban sprawl intensity, urban growth rate, urban compactness, and urban spatial morphology; Chettry [40] used multi–source remote sensing data to study the urban sprawl of four rapidly expanding medium–sized Indian cities located in different geographical regions using relative entropy indices and urban sprawl indices Geospatial measures of sprawl, which can adequately reflect the rate of urban expansion and expansion trends. Moreover, population elasticity coefficients were used in related studies to reflect the coordination of urban expansion and population increase with good reflective results [41,42]. The overall urban expansion trend in Xinjiang, with the rate of urban expansion in northern Xinjiang being significantly higher than that in southern Xinjiang, shows significant regional differences in the coordination of people and land, which can prove and complement the results of previous studies [26,39].

5.3. Limitations and Prospects

The shortcoming of this study is that some model parameters select similar regional and pan–regional parameters due to the limited regional research, so the results fail to show regional characteristics. There is still room for improvement in the accuracy of the quantitative evaluation of the model, and we will further improve and supplement it in subsequent research. Xinjiang is a vast region with a diverse ecological environment and varied topography. Therefore, the natural local characteristics of small local regions from the micro perspective will inevitably be ignored, although the spatial and temporal characteristics of Xinjiang can be judged from the macro scale. Hence, local verification based on smaller scales and smaller regions can be carried out in subsequent studies to make overall research more scientific and applicable. Due to practical difficulties such as the availability of data, the next step in the research is to effectively obtain more comprehensive, refined, and targeted data in terms of the size of the study and the choice of indicators such as transport links and public services. The evolution of population distribution and drivers over time will be discussed in a more in–depth and comprehensive manner. Road network has a significant impact on the transfer of the center of gravity of buildings, so the paper does not consider the importance of road network for the transfer of the center of gravity of buildings. The core of the paper is to study the coordination relationship between urban expansion and human capital in Xinjiang, so future studies will focus on urban expansion and various driving factors of human–land coordination.

6. Conclusions and Recommendations

The construction land area of Xinjiang oasis town groups has been growing in recent decades. The expansion rate entailed significant changes through different phases. The year 2010 marked the turning point, showing a shift from limited to notable changes thereafter, where the construction land kept expanding, lapsing into the stage of rapid development.

The construction land expansion demonstrated obvious spatial divergence characteristics. Northern Xinjiang performed better as a whole than southern Xinjiang, especially notable for town groups on the northern slope of the Tianshan Mountains. There, the UCS town group furnished the core and crucial area of the evolution of Xinjiang’s town groups. It experienced the most significant increase in construction land. Further north, however, the Altay and TE town groups are located far from the core development area, well away from effective growth points, resulting in relatively slow development.

In contrast, the southern Xinjiang towns have remained relatively small in scale and beset by inherent shortcomings in the early period. Nevertheless, development efforts such as the continuous promotion of the western development initiative, various economic assistance efforts, and the Silk Road Economic Belt development have helped the southern Xinjiang town groups gain new vitality. The region has benefited from rapid growth in recent years. In comparison, the relatively stagnant eastern Xinjiang was endowed with convenient transportation, but its small towns and cities were in dire need of population supplementation to support urban growth.

Inspired by the quest of “The plan of the Xinjiang Uygur Autonomous Region Town System (2014–2030)” for the town groups, the findings of this study proposed the following recommendations to promote the efficiency and efficacy of regional town–scale development:

(1)

With the relatively active economic belt on the northern slope of the Tianshan Mountains serving as the development core in northern Xinjiang while the Kashgar town group serves as the core in southern Xinjiang, a dual–hub development strategy can be established to sustain the core–periphery linkages and mutualistic benefits. From the two primary nuclei, a series of feeder town groups should be strengthened to foster the radiation effect to disseminate from core town groups to fringe ones. The industrial chains can be improved through industrial structure adjustment to promote orderly extension to town groups at different nodes. Meanwhile, the development of fringe town groups should be reinforced to enhance land–use efficiency and attraction to talent. The binary north–south leading town groups, serving as the core, could intensify exchanges between the series of lesser peripheral town groups to sustain the development momentum.

(2)

The fundamental geographical constraints of Xinjiang have generated scattered and sparse oasis town groups located far apart from each other. As such, the road system as the essential infrastructure should be considerably improved to pump–prime the core–peripheral connections. In response to the significant variability in regional characteristics and development levels of different town groups, a “mutualistic” urban development strategy aiming at maintaining the small urban clusters while cultivating more synergies between them should be implemented to dovetail with local conditions. The sustained collaboration of different town groups offers the key to achieving the symbiotic growth objective.