Spatio-Temporal Evolution Features and Impact Factors of Urban Expansion in Underdeveloped Cities: A Case Study of Nanchang, China

Abstract

Studying the expansion of urban construction land is necessary to promote rational land use and scientific territorial spatial planning. To reveal urban built-up areas, this study uses 1990–2020 Landsat remote sensing images, superimposed with NPP/VIIRS nighttime light. To extract urban construction land, support vector machines are then used to conduct classification experiments. The spatial-temporal features are analyzed using the expansion index, the shift of the center of gravity, and expansion direction, while influencing factors are analyzed using a Geodetector. The results show the following: (1) Urban construction land in Nanchang continued expanding from 1990 to 2020, by 385.22 km², with an average annual expansion intensity of 0.18% and an average annual growth rate of 6.2%. (2) During this time period, the expansion of urban construction land in Nanchang underwent three development stages from: low-strength with low-speed, low-strength with medium-speed, and medium-strength with low-speed expansion. The types of urban construction land expansion were primarily found to be edge expansion and outlying expansion. (3) The overall center of gravity of urban construction land shifts northwest, with significant expansion SW, NW, S, N, and W. (4) Urban planning policy is the dominant driving factor for urban expansion, whereas natural geographic factors have the weakest influence. The results suggest that planning policies should focus on strengthening the rational use and protection of land resources, and promoting the integration and coordinated development of urban functional spaces.

1. Introduction

With rapid urbanization, more than 50% of the population worldwide resides in urban areas, and this is predicted to reach 68% by 2050. [1]. The rapid growth of populations and the need for urban construction are causing global urban land to expand rapidly, especially in developing countries such as China and India. In China, this occurred following their 1978 “reform and opening up” [2], reducing the area of arable land. The People’s Republic of China Bulletin on the Third National Land Survey shows that China has: 0.13 billion ha of arable land, (an increase of 6.57 million ha from the previous survey, a yearly increase of 22.85%); cultivated land area decreased by 7.53 million ha and per capita cultivated land was 0.09 ha [3] (less than 40% of the world average). China, like other developing countries, faces serious arable land protection challenges. To alleviate the contradiction between urban land expansion and arable land protection, it is necessary to fully understand the dynamic expansion characteristics of urban land associated with different economic development levels. This is of great practical significance for rationally planning land resources, promoting healthy urban development, and maintaining ecological security.

Research on the expansion of construction land caused by urbanization and its driving forces has remained a focal point in global academic research [4,5,6,7,8]. Urban expansion is a complex process comprising numerous aspects, including the economy, population, and regional space. It is an evolutionary process involving the total amount of urban construction land and the outward direction of land use. This process implies the adjustment of the urban spatial structure and the evolution of form and density [9]. As the main form of land-use change, the urban expansion provides important support for urban economic growth, industrial upgrading, population agglomeration, and social development [10]. The sustainable development of socio-economic and ecological systems has had negative impacts [11], such as arable land degradation [12], environmental pollution [13,14], urban heat island effects [15,16], biodiversity reduction [17,18], deterioration of the ecological environment [19], increased urban traffic costs and congestion [20], and social inequity [21].

Many studies have investigated the spatial and temporal evolution of urban land at different scales including national [22], regional [23,24], provincial [25], and urban [26,27] scales. Current research on urban expansion mainly focuses on urban agglomerations and economically developed cities, such as Beijing [28], Shanghai [29], Guangzhou [30], Wuhan [31], and Nanjing [32] in China, and Atlanta [33] and Auckland [34] in Western countries, as this has significant economic, environmental, and social impacts. Developed and underdeveloped cities differ significantly in terms of urban expansion and development. Urban expansion is relatively slow and unique. In addition, underdeveloped cities currently face greater challenges of urban shrinkage and developmental instability [35]. However, due to the significant regional differences, research regarding the changes in construction land at different regional scales is insufficient. Extant research on urban expansion mainly focuses on urban agglomerations and economically developed cities, while the spatial and temporal characteristics and factors influencing urban expansion in underdeveloped cities have received little attention.

As shown in the following review of existing expansion factors, the factors influencing urban expansion are diverse, and they vary at different levels of development. The influencing factors can be divided into two major aspects: the driving force and resistance. On the one hand, scholars analyze the driving mechanism of urban expansion mainly from a single dimension or a combination of multiple dimensions of decentralization, globalization, and marketization, without accounting for the resistance factor of urban land expansion. Consequently, a systematic causal analysis that combines driving and resisting factors is lacking. Research gaps exist as to how globalization, marketization, decentralization, urbanization, and natural geographical conditions affect the urban expansion of underdeveloped cities, and to what extent. Will their role change with different development stages? On the other hand, urban planning, as a key indicator of decentralization and an important policy of urban development, reflects the local government’s development intentions and desires. Although urban planning is considered a factor driving the expansion of urban land, scholars mostly analyze the role of urban planning from a qualitative perspective and neglect quantitative analysis, especially for underdeveloped cities. This furthers the research gap regarding how the role of urban planning policy factors into the process of urban expansion.

Existing research on urban expansion mostly uses ready-made interpretations of remote sensing data for analysis. However, for accuracy of analysis (due to the existence of urban vacancies and the mixed phenomenon of urban and rural construction land), in addition to the use of single image spectral information, detailed evidence from urban nighttime light data (NTL) is needed. It is necessary to combine nighttime light data and remote sensing image data, interpreted using the method of multi-feature fusion.

To bridge the above-mentioned research gaps, this study researches Nanchang City —an underdeveloped city in central China. Nanchang, along with cities such as Tallinn, Aberdeen, Astana, Florence, and Hamilton, was rated as a city with the sufficiency of service. They are ranked fourth in the tier of the world cities according to GaWC 2020 [36]. Land expansion in Nanchang is representative of underdeveloped cities globally. Since the 1990s, the proportion of secondary and tertiary industries in Nanchang has increased. As an important city in the middle reaches of the Yangtze River, Nanchang is an important economic center and transportation hub linking the “Yangtze River Delta, Pearl River Delta, Fujian Delta” region, and the middle reaches of the Yangtze River. Despite its prime location as a transportation hub on a river, its economic development is still relatively backward.

Using Nanchang as an example, this study aims to analyze the spatio-temporal evolution characteristics and impact factors of urban land-use expansion in underdeveloped cities. Our research hypothesis is that decentralization, marketization, globalization, urbanization, and natural geographical conditions will affect the urban land expansion of undeveloped cities to varying degrees in different development stages. After the introduction, the second section reviews and summarizes the existing literature on urban expansion. The third section introduces our methods and data. The fourth section shows the research results. The spatio-temporal evolution characteristics of urban construction land are explored by using NTL data and remote sensing image data. By conceptualizing the socioeconomic driving factors of this urban land expansion as a multiple-factor process, the impact factors are analyzed. The fifth section summarizes and discusses the results.

2. Literature Review

2.1. Monitoring Urban Expansion

Remote sensing images [37,38] and NTL data [35,39] have been widely used to monitor urban expansion. Scholars have also explored the extraction method of urban land, including supervised and unsupervised classification, index methods, cellular automata, and artificial neural networks [40,41,42]. However, because of the existence of the phenomenon of “same subject with different spectra” and “different objects with the same spectrum,” remote sensing classification relying only on the spectral characteristics of ground objects will cause misclassification. More specifically, due to urban areas being mixed with man-made and vegetal land cover components, there are few thematically pure urban pixels; furthermore, there are significant spectral feature similarities between urban land and surrounding features, such as bare soil. These factors create difficulties in the extraction and change analysis of urban built-up areas using Landsat series data [43]. There are two ways to improve the accuracy of data sources for urban sprawl analysis. One is to superimpose NTL data to improve the accuracy of the spatial range extraction of urban land expansion [44,45], and the other is to use multiple features (e.g., spectral, texture, and other features) to improve the accuracy of remote sensing image interpretation and classification [43,46,47]. Existing studies have shown that combining the spectral features of DMSP/OLS nighttime light data and Landsat data, which are interpreted by extracting multi-band texture features, can effectively reduce the confusion between bare soil, rural construction land, and urban built-up areas, so that a higher accuracy can be obtained compared to using only single spectral information [43,48,49].

2.2. Uneven Urban Land Expansion

Studies have shown that the scale, speed, and pattern of urban expansion varies from city to city, and there is significant spatio-temporal heterogeneity and regional imbalance in urban expansion [50,51]. The most economically developed cities and regions show greater expansion intensity and faster expansion speed, such as the Pearl River Delta, Yangtze River Delta, and Beijing–Tianjin–Hebei urban agglomeration in China [52,53,54], Kolkata Urban Agglomeration in India [55], and Teheran in Iran [41]. In contrast, underdeveloped cities show lower expansion intensities and slower expansion speeds [56,57]. Relevant studies have shown that a developed city goes through a slow growth phase, enters a fast growth phase, and finally transforms into a steady slow growth rate [24,58]. In China, all first-tier cities except Beijing have experienced S-shaped expansion; that is, these cities have slowed down after rapid development. Most second-tier cities have maintained stable development and have not experienced high-speed expansion, while undeveloped cities (third-tier and fourth-tier cities) have a lower level of economic development, but the land is still growing rapidly [50]. An increasing number of scholars are studying urban sprawl; however, the research objects of urban expansion are mainly economically developed cities, and little attention is paid to undeveloped cities.

2.3. Driving Forces of Urban Expansion

Urban expansion in China is often the result of a combination of physical geographic, socioeconomic, and institutional factors [7,59,60,61]. The basic driving force is economic and population growth; leading to considerable urban expansion and land-use changes. Decentralization, marketization, globalization [62,63,64], and urbanization [65,66] are considered as significant factors.

Notably, the decision-making decentralization process [67] affects regional land-use changes and expansion through local government competition and demand for land finance. Urban construction land provides a space carrier for the local government’s domestic fixed asset investment, which is the main source of funds for urban construction and other development [68,69]. Studies have shown a positive correlation between fixed asset investment and urban land expansion [70]. The fiscal budget expenditures of Chinese local governments largely depend on land finance [71], and local governments rely on income from the transfer of land-use rights to maintain local fiscal expenditures.

Urban planning, as an important spatial policy and a means for local governments to plan urban development, plays an important role in the expansion of urban construction land [65]. Historically, owing to the varying functional scopes of departmental management, different planning systems existed in China, including land use planning, urban planning, and main functional area planning. Previous studies have shown that land-use planning can inhibit the expansion of construction land [72,73]. Urban master planning is considered as playing a strong role in promoting the expansion of urban construction land [27,74], but there is a lack of quantitative empirical evidence.

Globalization has produced a global flow of capital, promoted global industrial transfer and the development of the local economy, and increased the demand for urban land resources. Foreign direct investment is an important indicator of globalization, and has become a major factor in the expansion of urban construction land [75,76,77]. Market-oriented reforms have made land one of the most important tools for the country to attract foreign capital in China [78]. After Chinese market-oriented reforms, the number of non-state-owned urban enterprises grew rapidly, increasing the demand for land for industry and services. These enterprises have become an important indicator to measure the impact of marketization on urban expansion.

Rapid urbanization in developing countries results in the migration of rural populations to cities, increasing the demand for housing and commercial service industries [79] and transportation facilities, which drove the outward expansion of urban land [80,81,82,83]. The closer the location is to social and economic centers (e.g., city centers, central business districts (CBDs), suburban (county) centers), the higher the probability of urbanization [61].

Precipitation and topography affect urban sprawl by limiting water and land availability. Altitude is negatively correlated with urban sprawl, with cities with lower elevations or flat terrain experiencing faster urban sprawl and development [84]. The distance from the sea and river distribution may determine the potential, intensity, direction, and scale of urban-land expansion [85]. However, with the urbanization process and the improvement of social and economic development levels, the influence of physical factors is declining [61].

3. Materials and Methods

3.1. Study Area

Nanchang is a city in central China and the capital of Jiangxi Province. The terrain of the entire area is high in the northwest and low in the southeast. The northwest is hilly, the east is dominated by the Poyang Lake Plain, and the southeast is relatively flat. The Ganjiang River runs through Nanchang from southwest to northeast and divides the urban area of Nanchang into Changnan and Changbei. From 1990 to 2020, the total population of Nanchang increased from 3.7259 million to 6.255 million, the GDP increased from 9.467 billion to 574.551 billion yuan, and the output value of the tertiary industry continued to grow. In the past 30 years, although it has achieved certain development targets, as of 2020, its total GDP is only 15.12% of the first-ranking Shanghai. It ranks 40th in the country’s urban GDP. In the process of economic development, Nanchang City has made several adjustments to its administrative divisions (Table A1), expressing the local government’s strong desire to increase development. The total area of the current administrative area is 7194.98 km², including six districts (Donghu, Xihu, Qingyunpu, Qingshanhu, Honggutan, and Xinjian Districts), and three counties (Nanchang, Jinxian, and Anyi County) (Figure 1).

3.2. Data Sources and Processing

The main research data derived from the following sources.

• (1). NPP-VIIRS nighttime light data derive from the US National Oceanic and Atmospheric Administration (https://www.noaa.gov/, accessed on 17 February 2022). In the data processing, the built-up area is extracted using the threshold segmentation method. The average digital number (DN) value for 2020 ranges from 0 to 190. After preprocessing the NPP-VIIRS data such as cropping, projection conversion, and correction, we compared data with Google Images, conducted experiments, and analyzed the selection of the threshold. We found that DN = 2 is the optimal threshold for binarization. Therefore, DN ≥ 2 was set as urban land and DN < 2 was set as non-urban land.

• (2). The land-use data for Nanchang City were obtained from the interpretation of Landsat TM/ETM+/OLI data from 1990 to 2020. 30 m resolution image for 1990 to 2020 covering the study area was downloaded (e.g., LT05_L1TP_122040_19901123_20220210_02_T1, LC08_L1TP_122040_20181127_20220216_01_T1) from the U.S. Geological Survey (http://www.usgs.gov/, accessed on 10 February 2022). The period for image selection is October to December, and the selected images are as high-quality as possible. The remote sensing images were first subjected to radiometric calibration, atmospheric correction, image mosaicking, and image cropping in ENVI5.3 software, and then the images were cut using the built-up area extracted from NTL data. Support vector machines (SVM) have been used several times in geographic analysis [43,86,87]. Many researchers have added spatial information (such as image texture) and comprehensively used data from different sensors to improve accuracy [43,88]. We use the SVM classification method by combining multi-feature information to classify land into construction land, water area, cultivated land, forest land, grassland, and unused land, then extracting Construction land. Quantitative accuracy evaluation was then performed on the confusion matrix. We found that the overall accuracy is 85.31–93.94% and the Kappa coefficient is 0.84–0.90 in all years, which meets data accuracy and consistency requirements.

• (3). For influencing factors, the study selected 16 exploratory variables from the two dimensions of driving force and resistance (Table 1), including decentralization (GDP [24,89], fixed asset investment [70], urban planning [65], local fiscal budget expenditures [64,90]), marketization (non-state-owned enterprise income [52,64]), globalization (foreign direct investment [75,76,77]), urbanization (urbanization rate [91,92], population density [93,94], distance to city center [60,61], distance to county center [60,61], infrastructure such as distance to national highway, distance to highway, and distance to railway [95]). Natural geographic resistance factors include elevation, slope, and distance from rivers [6,60,61].

3.3. Methods

3.3.1. Research Flowchart

Analyzing the spatio-temporal characteristics of urban expansion aids in understanding the process of urban expansion, exploring its driving mechanism, and simulating and predicting future expansion trends. With the continuous deepening of urban expansion theory, indicators such as the spatial expansion index and landscape expansion index (LEI) have been widely used in quantitative research on spatio-temporal dynamic characteristics and patterns of urban expansion [96]. Annual increase (AI), annual growth rate (AGR), and urban expansion intensity (UEI) are typical indicators for quantification [23]. This study first uses the threshold segmentation method to extract urban built-up areas based on NTL data. Then, we use Landsat remote sensing images from 1990 to 2020, year by year, based on multi-feature information fusion including texture, spectrum, and index, and employ the remote sensing classification method of SVM to extract the annual urban construction land information. Next, based on GIS technology, we analyze the characteristics of land expansion over the past 30 years through analysis of the UEI, AGR, LEI, and gravity center shift. Finally, Geodetector is used to explore the factors influencing dynamic expansion. The specific research process is shown in Figure A1.

3.3.2. Urban Expansion Index Analysis

UEI and AGR are the most commonly used indicators for analyzing urban expansion. AI was used to characterize the overall scale and trends [24]. UEI refers to the ratio of the average annual increase in urban land in the study area to the total area, making it comparable across different periods [38]. According to research on urban expansion in Beijing, the expansion intensity of construction land can be divided into four levels: high strength (0.5 < UEI), medium strength (0.2 < UEI ≤ 0.5), low strength (0 < UEI ≤ 0.2), and non-strength (UEI = 0) [38]. UEI and AI can be used to compare the speed of urban expansion during different periods for the same city, whereas the AGR is effective in comparing urban expansion rates for different cities during the same period. The calculation formulae are as follows:

AI = ((S₂ − S₁))/T(1)

UEI = ((S₂ − S₁))/(T × S) × 100%(2)

AGR = [(S₂/S₁)^(1⁄T) − 1] × 100%(3)

where S is the total land area of the study area. S₁ and S₂ are the urban land areas in the first and last years of the study period, respectively, and T is the number of years.

3.3.3. Classification of Urban Expansion

The LEI intuitively depicts the dynamic process of urban land expansion by judging the spatial relationship between existing and new urban patches. The expression is as follows [54]:

LEI = L/P(4)

where L is the length of the common boundary between the new urban patch and existing urban patches and P is the perimeter of the new urban patch. Urban expansion can be classified into three types: infilling, edge expansion, and outlying [96,97,98]. (1) Outlying type (LEI = 0): the new urban patch is independent of the original urban patches. (2) In the edge-expansion type (0 < LEI ≤ 0.5), new urban patches appear along the edges of the original urban patch and extend outward. (3) Infilling type (0.5 < LEI ≤ 1), the new urban patch is inside the original urban patch gap.

3.3.4. Gravity-Center Shift Model

The geometric center of gravity coordinates of urban construction land and the average value of the linear direction of the geometric center of gravity shift are calculated using ArcGIS. Studying the average direction and average value of the center-of-gravity shift in each period can reflect the overall situation of Nanchang’s urban expansion. The average direction and transfer value of the center of gravity transfer in each period are studied to reflect the dynamic situation of urban expansion in Nanchang. The gravity center shift model is specified as follows [38]:

(5)$X_t=\frac{{{\displaystyle \sum }}_{i=1}^n\left(C_{ti}\times X_i\right)}{{{\displaystyle \sum }}_{i=1}^n{C}_{ti}}$

(6)$Y_t=\frac{{{\displaystyle \sum }}_{i=1}^n\left(C_{ti}\times Y_i\right)}{{{\displaystyle \sum }}_{i=1}^n{C}_{ti}}$

where X_t and Y_t are the longitude and latitude of the gravity center in year t; C_ti is the area of urban land in unit i; X_i and Y_i are the longitude and latitude coordinates of the geometric center in unit i.

3.3.5. Analyzing the Strength of Influencing Factors by Geodetector

Many scholars explore the driving mechanisms of urban expansion through multiple linear regression, logistic regression, spatial regression, and Geodetector methods [60,68,74,99]. Geodetector is a statistical method for detecting the spatial heterogeneity of geographic features and revealing the driving force behind each of them [100]. In this study, a grid of 2 km × 2 km is created in the study area as a sample, and the construction land expansion area and changes of influencing factors in each grid in each period are counted separately. Using the Geodetector model as follows:

(7)$q=1-\frac{1}{n{\sigma }^2}{\displaystyle \sum }_{i=1}^m{n}_i\times {\sigma }^2$

where q is the explanatory power of the driving factor of construction land expansion, n and σ² are the sample size and variance of the study area, respectively. The value interval of q is [0, 1], and the larger the value, the stronger the explanatory power of the factor on the expansion of construction land.

4. Results

4.1. Urban Expansion Characteristics in Nanchang

4.1.1. The Spatio-Temporal Characteristics of Urban Expansion in Nanchang

As shown in Figure 2, the area of urban construction land in Nanchang has increased over the past three decades (1990–2020) from 75.78 km² to 461 km². During 1990–2020, urban areas expanded by 385.22 km², with AI, AGR, and UEI values of 12.84 km²/a, 6.2%, and 0.18%. Although the scale of urban construction land continues to expand due to terrain and rivers, the expansion is limited. The expansion intensity has been slow in the past three decades, representing low-strength with medium-speed expansion (Table 2). The spatial distribution of the expansion shows a “core-edge” feature (Figure 3).

Previous studies show that from 1978 to 2015, the AGR of urban land in Shenzhen and Guangzhou was 8.07% and 11.02%, respectively, while the AGR of urban land in Beijing was 3.46% during the same period [24]. Taking the method of horizontal urban comparison, the AGR of cities can be divided into three categories: low-speed (AGR < 3.46%), medium-speed (3.46% ≤ AGR < 8.07%), and high-speed expansion (AGR ≥ 8.07%). According to the standard division of UEI, combined with the calculated values of UEI and AGR, the urban expansion of Nanchang can be divided into three stages: low-strength with low-speed, low-strength with medium-speed, and medium-strength with low-speed expansion. According to previous research, the AI of Nanchang was 0.86 km², the UEI was 0.012% from 1976 to 1989. The growth rate of construction land in this stage was significantly lower than that after 1990, the compactness of land use is low, and the spatial form is discrete, in addition to the urban land use being extensive [101]. From 1990 to 2000, the scale of urban construction land was relatively small, with AGR and UEI of 5.26% and 0.07%, respectively, which is the period of “low-strength with low-speed expansion.” During 2000–2010, Nanchang City proposed a spatial development strategy “to develop in the western area, expand in the eastern area, restrict in the northern area, and extend in the southern area” [102]. Urban construction land expanded rapidly, the area increased by 134.42 km², and the total area increased to 260.93 km². The UEI and AGR values were 0.19% and 7.51%, respectively. Both the UEI and AGR were higher than in the previous period, but still lower, which was the period of “low-strength with medium-speed expansion.” From 2010 to 2020, the AI was 20.01 km² and the AGR was 5.86%. The UEI was 0.28%, which is higher than the previous period, but the AGR slowed down, which was the period of “medium-strength with low-speed expansion.”

4.1.2. Urban Growth Type

The landscape expansion index can reflect changes in the spatial structure in the process of urbanization. Studying the spatial patterns of urban expansion can not only aid in understanding the development of a city but also provide a theoretical basis for improving the level of urban planning and management. The spatial expansion pattern of Nanchang City from 1990 to 2020 is calculated based on the landscape expansion index. From 1990 to 2020 it is dominated by edge expansion and outlying expansion, accounting for 56.3% and 32.32%, respectively, while the infilling expansion is the weakest (Figure 4).

Specifically, from 1990 to 2000, the edge-expansion and outlying growth patterns worked together. The expansion pattern shows typical spatial heterogeneity. Qingshanhu District, Nanchang County, Jinxian County, and Anyi County display edge expansion and outlying as the primary types. The expansion types of the old city (Donghu District, Xihu District, Qingyunpu District) are mainly infilling and edge expansion. The expansion of the Xinjian and Honggutan districts is mainly of the outlying type (Figure 5a). From 2000 to 2010, Nanchang relocated the city administrative center to the west bank of the Ganjiang River and started the construction of the Honggutan New District, which accelerated the cross-river outlying expansion of urban construction land. The scale of the outlying expansion reached its peak, increasing from 40.23% to 54.22%. Urban construction land grew in a decentralized manner. Nanchang County and Qingshanhu District have larger outlying expansion scales, followed by the Xinjian and Honggutan districts (Figure 5b). From 2010 to 2020, the proportion of edge expansion and infilling expansion increased to 78.5%, while the scale of outlying expansion decreased significantly. Urban construction land has transformed into agglomerative growth. During this period, urban expansion is characterized as an “internal infilling and external edge-expansion” model. The spatial expansion of urban construction land is dominated by the infilling expansion of the inner city and the edge expansion of the outer urban districts and counties (Figure 5c).

4.1.3. Spatial Shift of the Gravity Center of Urban Construction Land in Nanchang

According to the position of the gravity center and average value in the linear direction, since 1990, urban construction land has expanded to the northwest, and the center of gravity of the urban construction land has shifted 3552 m to the northwest as a whole, and the movement trajectory is “towards the northwest (1990–2000), towards the due west (2000–2010), towards the west by north (2010–2015), towards the east by north (2015–2020)” (Figure 6). The center of gravity remained in the Qingyunpu District and then gradually moved to the west-north, although it was always located on the south side of the geometric center, indicating that the spatial distribution of urban construction is uneven. The spatial differentiation characteristics of Nanchang’s urban expansion during the different periods are obvious. The distances shift in three time spans (1990–2000, 2000–2010, 2010–2020), 833 m, 1151 m, and 1567 m, respectively, (an increasing trend), which is also the result of the acceleration of the city’s recent overall urbanization process. Specifically, from 1990 to 2000, the center of gravity moved in a northwest direction, the movement range was relatively small and the urbanization process was slow. It was still in the initial stage of cross-river development and urban expansion was mainly blocked by the Ganjiang River. From 2000 to 2010, the center of gravity shifted towards the west with a large movement range. During this period, urbanization was rapid, and spatial expansion accelerated. From 2010 to 2020, the center of gravity moved northwest and the moving range continued to increase. The expansion of urban construction land in Honggutan District and Xinjian District in the west is evident, which has promoted the overall urbanization process of Nanchang. Later in this stage, the center of gravity shifted to the east by a large margin, indicating that urban construction gradually returned to the east.

4.1.4. Nanchang Expansion Direction Analysis

In the expansion direction, taking Nanchang Twin Towers (CBD) as the origin, each direction is equally spaced at 45°, and the study area is divided into eight directions including E, SE, S, SW, W, NW, N, and NE. Through the expansion speed radar maps of different aeolotropic areas in different periods, the differences in the expansion of urban construction land in different directions can be seen (Figure 7). From 1990 to 2020, the urban expansion directions of Nanchang are significantly different, with significant expansion in SW, NW, S, N, and W, with AGR of 12.39%, 10.64%, 9.58%, 9.39%, and 8.61%, respectively, while E and SE are the weakest directions of urban expansion, with AGR of only 4.01% and 4.62%, respectively, which is mainly limited by the resistance of natural lake systems (Junshan Lake and Qinglan Lake).

• (1). Before 1990, the urban construction of Nanchang focused on the old inner-city area on the east side of the Ganjiang River, while urban expansion in the west and north of Nanchang was blocked by the Ganjiang River. From 1990 to 2000, Nanchang gradually focused on the development of crossing the river to the northwest. The NW direction expands the fastest, with an AGR of 12.51%, and the N, W, and SW directions expanded rapidly, with AGRs of 11.08%, 9.65%, and 9.17%, while the AGR in the E, NE, and SE directions are lower, only 3.46%, 4.53%, and 4.64%, respectively. During this period, Nanchang City successively opened bridges across the river, such as the Nanchang Bridge and Bayi Bridge, and planned and constructed key urban construction projects such as the Nanchang High-tech Development Zone, Economic and Technological Development Zone, and Changbei University Town, which promoted the cross-river outlying expansion of urban construction land.

• (2). From 2000 to 2010, urban construction land in Nanchang entered a period of outlying expansion. The government was committed to creating an urban pattern of “one river and two banks.” The scale of the construction of new districts on the west bank of the Ganjiang River continues to expand. The SW, NW, and S orientations expanded rapidly, with AGR of 15.64%, 13.06%, and 12.45%, respectively. As mentioned above, during this period, the overall urban planning of Nanchang City proposed the development strategy of “develop in the western area, expand in the eastern area, restrict in the northern area, and extend in the southern area”. “To develop in the western area” mainly refers to focusing on the development of the Honggutan New Area and Hongjiaozhou Area on the west bank of the Ganjiang River. “To expand in the eastern area” mainly refers to the eastward expansion of urban construction, focusing on the development of high-tech development zones, Yao Lake, and the surrounding areas of Aixi Lake. “To restrict in the northern area” means restriction of the northward expansion of urban construction. “To extend in the southern area” mainly refers to the extension of urban construction to the south along the Ganjiang River, focusing on the development of the Changnan Area, Xiaolan Economic Development Zone, Liantang Town, and other areas in the south.

• (3). From 2010 to 2020, the fastest expansion rate of urban land occurred in the SW direction, with an AGR of 12.45%. The AGR in the S and NE directions was 8.90% and 8.75%, respectively. Except for the increase in the AGR in the NE direction, the AGR in all other directions decreased. During this period, urban construction shifted from multi-directional expansion to the local key expansion of Honggutan and Xinjian Districts on the west bank of the Ganjiang River. The key expansion areas include Chaoyang New Town, Honggutan New Area, Jiulong Lake New Town, the New Economic Development Zone, and the Airport Economic Zone.

4.2. Factors Influencing Urban Land Expansion in Nanchang

4.2.1. The Value and Spatial Distribution of Impact Factors

The natural breakpoint method was used to analyze the spatial distribution of each influencing factor. As shown in Figure 8, over the past three decades, the spatial distribution of globalization, decentralization, marketization, and urbanization factors at different stages varies greatly. Foreign direct investment (X1) increased significantly in all districts from 2000 to 2010, but negative growth occurred in some districts from 2010 to 2020, such as Qingshanhu, Anyi, and Jinxian. Non-state-owned enterprise income (X2), fixed asset investment (X3), and public finance expenditure (X4) increased the most in Nanchang County at each stage. The increase in GDP (X5) was highest in Xihu District from 2000 to 2010, while Nanchang County maintained rapid growth from 2010 to 2020, with the largest change in increase. The planned construction land in urban planning (X6) from 1990 to 2000 is primarily concentrated in the main urban area. The planning scope and planned construction land extended from 2000 to 2010. From 2010 to 2020, suburban counties, including Anyi County and Jinxian County, compiled urban master plans. The urbanization rate (X7) of Qingshanhu District showed the highest change during 2000–2010, while Nanchang County shows the largest change from 2010–2020. In terms of spatial variation characteristics, the population density (X8) changes are high in the center and low in suburban areas. The distance to the county or district center (X10) shows concentric circles that continuously increase outward. The distance from the national road (X11) is farther in the northeast direction, whereas the distance to the railway (X13) is lower in the inner city but increases outward. Geographic condition factors (X14, X15, and X16) show little change, and the altitude is lower in the southeast but higher in the northwest.

4.2.2. The Strength of Influencing Factors

Geodetector is employed to analyze the strength of each influencing factor in the three periods of 1990–2000, 2000–2010, and 2010–2020. The results show that urban planning is the dominant factor for land expansion at each stage, while the explanatory power of the geographic condition factor is the weakest, indicating that the urban expansion of the study area has little correlation with natural geographical conditions (Table 3).

From 1990 to 2000, the order of strength of explanation for the expansion of construction land is as follows: X6 (0.395) > X7 (0.262) > X10 (0.234) > X9 (0.192) > X2 (0.163) > X3 (0.156) > X8 (0.150). Decentralization and urbanization factors play a significant role at this stage, but the influence of natural factors cannot be ignored. Owing to the low level of economic development and insufficient transportation facilities, urban expansion is restricted by the Gangjiang River. Nanchang City mainly develops along the east bank of the Ganjiang River, and it is difficult to expand westward because of the high construction costs across the river.

From 2000 to 2010, the order of strength of the explanation for the expansion of construction land is as follows: X6 (0.504) > X5 (0.311) > X10 (0.305) > X8 (0.230) > X1 (0.217) > X2 (0.212) > X9 (0.202). Urban expansion is significantly dominated by decentralization, urbanization, globalization, and marketization. With ongoing development, the factors influencing the accelerated expansion of urban construction land in Nanchang have diversified, among which the leading factors are government planning guidance, economic development, population aggregation, and improvement of supporting facilities for urbanization construction. As a “top-down” urbanization force, the upper-level planning policy has played an important guiding role in its urban expansion. Government policies, such as the launch of the 2000 “one river and two banks’ strategy and the relocation of Nanchang’s administrative center to Honggutan in 2001, have effectively opened the framework for urban expansion. Nanchang’s GDP increased from 46.514 billion yuan in 2000 to 220.711 billion yuan in 2010, with an average annual growth rate of 37.45%, the development began to accelerate. Among the factors of urbanization, the total population increased from 4.32 million to 5.02 million between 2000 and 2010. The increased population created a large demand for land, which directly led to the expansion of urban construction spaces. Furthermore, the distance to the county center is also an important force driving the city’s outward expansion. Urbanization of the suburbs is accelerating, gradually forming a multi-center development pattern. From 2000 to 2010, foreign direct investment increased from 220 million to 14 billion yuan, a 64-fold increase. The rapid growth of foreign direct investment and globalization have contributed to economic and population growth, thus, affecting the accelerated urban land expansion. Market forces (X2) can drive the allocation of market factors, guide the efficient transformation and development of industries, inject new impetus into urban development, and become an important driving factor for the growth of the construction land scale.

From 2010 to 2020, the order of strength of explanation for the expansion of construction land is as follows: X6 (0.455) > X9 (0.255) > X10 (0.207) > X13 (0.147) > X1 (0.100). The driving force of urban expansion at this stage is mainly due to the combined effect of urban planning policies, improvement of facilities supporting urbanization, and globalization factors. The roles of globalization, marketization, and decentralization have all weakened. Among the urbanization factors, the explanatory power of distance to the district or county decreased compared to the previous stage, but the explanatory power of distance to the city center increased. This is mainly because after the Nanchang Municipal Government moved to Honggutan New Area, the Jiangxi Provincial Government also moved to Honggutan Area, which has brought Honggutan area more preferential policies for development and has driven the city to expand westward across the river. The effect of the distance to the railway is also stronger than that in the previous stage. This is because the completion and opening of the Changxi high-speed railway station and subway station promoted the spatial expansion of urban construction land.

4.3. Nanchang’s Urban Planning as the Main Driving Force

The results show that urban planning had the most significant effect on the expansion of urban construction land, with intensities of 0.395, 0.504, and 0.455 in 1990–2000, 2000–2010, and 2010–2020, respectively. The government’s policy intervention affected the urban spatial pattern and played an important guiding role. According to previous master plans, Nanchang only developed around the city center in the early 1980s (Table A2). After entering the 21st century, it turned into a network-like urban spatial pattern, and the city expanded rapidly. The guidance provided by government policies has promoted the development of “one river and two banks”. The Nanchang Municipal Government and Jiangxi Provincial Government have relocated to the west bank of the Ganjiang River one after another, attracting many enterprises, expanding the space for “westward expansion”, and promoting the coordinated development of the regions on both sides of the Ganjiang River. In 2010, the government proposed the development strategy of “develop in the western area, expand in the eastern area, and extend in the southern area”, and made corresponding administrative division adjustments, which promoted the edge expansion and outlying expansion of urban construction land.

5. Discussion and Conclusions

Based on Landsat remote sensing images and NTL data from 1990 to 2020, we extracted the urban construction land in Nanchang, an underdeveloped City, and performed a quantitative analysis of expansion speed, expansion intensity, expansion type, expansion orientation, and gravity center. In terms of methodology, we innovatively use multi-source data and multi-feature fusion methods to interpret remote sensing image data, thereby improving the accuracy of urban land use data interpretation. We studied the factors influencing urban expansion from the two dimensions of the driving force (globalization, marketization, decentralization, urbanization) and resistance (natural geographic conditions) using Geodetector. The following conclusions are drawn:

• (1). Over the past 30 years, urban construction land in Nanchang has continued to expand. The urban construction land area increased by 385.22 km², with an expansion intensity of 0.18% and an AGR of 6.2%, which is a low-strength with medium-speed expansion. As the capital city of Jiangxi Province, Nanchang City has advantages in policy, economy, and transportation; its construction land expansion process is not only the result of rapid economic growth, but also the spatial expression of urbanization policy. However, in general, compared with developed cities such as Beijing, Shanghai, Guangzhou, and Shenzhen, Nanchang’s expansion rate is slower, and the expansion intensity is lower. Expansion occurred in three stages: slow-strength with low-speed expansion during 1990–2000, low-strength with medium-speed expansion during 2000–2010, and medium-strength with low-speed expansion during 2010–2020.

• (2). The types of spatial expansion of construction land are mainly edge expansion and outlying expansion, whereas infilling expansion is weak, which is a representative feature of the expansion of underdeveloped cities. Urban development relies mainly on the outward sprawl of the city to achieve rapid economic growth rather than the infill expansion of the inner city. Previous studies have shown that rapidly expanding cities are likely to grow with edge expansion, whereas small cities tend to grow in an outlying pattern [2,103]. The expansion types of Nanchang City have a typical pattern of edge expansion and outlying expansion, which is consistent with the previous results on the type of expansion for small cities and rapidly expanding cities. At the same time, it also shows that the future development of underdeveloped cities needs to adopt more infilling expansion to improve the quality of urban development.

• (3). From the perspective of the expansion direction and shift of the gravity center from 1990 to 2020, the center of gravity of urban land in Nanchang shifted 3552 m to the northwest, and the directions of SW, NW, S, N, and W expanded significantly.

• (4). The influence of each factor on the expansion of Nanchang’s land use varies significantly during the study period. Urban planning policy has the strongest influence and is the dominant driving factor for urban expansion, whereas natural geographic factors have the weakest influence. However, natural geographic conditions, such as resistance factors, have different effects at different stages. From 1990 to 2000, the resistance factor of natural geography (e.g., rivers, lakes, and mountains) had a significant impact on urban land expansion due to underdeveloped economic conditions. After 2000, since the rapid economic development and improvement of infrastructure, the expansion of urban land in Nanchang gradually overcame the obstacles of natural geographic factors. From the perspective of driving factors, urban planning and the urbanization rate played a significant role from 1990 to 2000. From 2000 to 2010, urban expansion is dominated by decentralization, urbanization, globalization, and marketization, while urban planning policies still maintained a significant positive impact. From 2010 to 2020, globalization, marketization, and decentralization continued to play an important role; however, the strength of their roles decreased, and the active role of urban planning policies was still significant.

Our research shows that in order to make urban construction more compact and sustainable, urban planning policies play an important role in the process of land expansion in undeveloped cities. For underdeveloped cities in China and abroad, the rational preparation of urban planning is conducive to guiding the orderly and sustainable expansion of the city and avoiding extensive urban sprawl. In planning policies, it is necessary to reasonably consider the population size, rationally cultivate urban sub-centers, strengthen land-use management, avoid excessive occupation of cultivated land for urban construction, reasonably protect cultivated land resources, and promote the transformation of urban expansion from edge expansion and outlying expansion to infill expansion.

This study of Nanchang helped strengthen the understanding of the characteristics and challenges of land use in underdeveloped cities, which provides a basis for land regulation, improving urban land’s intensive and economical use, optimizing the urban spatial structure, and achieving smart growth. However, this study has some limitations, which provide potential directions for future research. First, timely and accurate extraction of urban land information is extremely important for exploring urban expansion. Using medium-resolution Landsat series data to extract urban land is still a complex task. The extraction method needs to be further optimized, and the extraction accuracy further improved. Second, the policy factor is only measured by the variable of urban planning. Future research should consider more variables from the perspective of policy.

Author Contributions

Conceptualization, K.L., W.H., and R.W.; methodology, K.L., W.H., and C.W.; software, W.H. and Y.H.; validation, K.L., C.W., and R.W.; formal analysis, K.L., W.H., and R.W.; investigation, W.H. and Y.H.; resources, W.H.; data curation, W.H.; writing—original draft preparation, K.L. and W.H.; writing—review and editing, K.L., C.W., and R.W.; visualization, W.H. and Y.H.; supervision, K.L., C.W., and R.W.; project administration, K.L.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Note

1. The base map is the administrative division map of 2020. Honggutan District is a new district set aside from the Xinjian District in 2019, so no relevant available data is available. The values of Honggutan District shown in the Figure are historical data of the Xinjian District.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Study area.

Figure 2. Quantity Change of urban land in Nanchang city from 1990 to 2020.

Figure 3. Quantity Change of urban land in Nanchang city from 1990 to 2020.

Figure 4. Percentages of the three spatial growth types during 1990–2020.

Figure 5. Spatial distribution characteristics of three types of spatial expansion from 1990 to 2020.

Figure 6. The shift trajectory of the center of gravity of construction land in Nanchang from 1990 to 2020.

Figure 7. Eight-direction radar chart of construction land expansion in Nanchang from 1990 to 2020. +++, ++, + represent the expansion speeds from high to low, respectively.

Figure 8. Spatial distribution of the value of impact factors1.

Figure 8. Spatial distribution of the value of impact factors1.

Table 1

Selected Variables that study the influencing factors of urban expansion.

Variable Category	Description	Variable	Variable Types/Assignments	Sources
Independent variable	Urban construction land expansion area (km2)	Land expansion(Y)	Use the natural breakpoint method to divide into six grades	Extracted from remote sensing images
Globalization	Foreign direct investment (yuan)	FDI (X1)	Use the natural breakpoint method to divide into six grades	Nanchang Statistical Yearbook
Marketization	Non-state-owned enterprise income (100 million yuan)	NSOEI (X2)	Use the natural breakpoint method to divide into six grades
Decentralization	Fixed asset investment (100 million yuan)	FAI (X3)	Use the natural breakpoint method to divide into six grades
	Public finance expenditure (100 million yuan)	PFE (X4)	Use the natural breakpoint method to divide into six grades
	GDP (10,000 yuan/km2)	GDP (X5)	Use the natural breakpoint method to divide into six grades	GDP Grid dataset from the China Resources and Environmental Science Data Center (https://www.resdc.cn/, accessed on 20 June 2022)
	Urban planning	UP (X6)	Binary variables (0: Non-planned urban area; 1: Planned urban area)	The Urban Master Plan of Nanchang City (1981–2000), The Urban Master Plan of Nanchang City (2001–2020), The Urban Master Plan of Nanchang County (2008–2030), The Urban Master Plan of Wanli District (2013–2030), The Urban Master Plan of Nanchang County (2011–2030), The Urban Master Plan of Anyi County (2010–2030), The Urban Master Plan of Jinxian County (2010–2030)
Urbanization	Urbanization rate	UR (X7)	Use the natural breakpoint method to divide into six grades	Nanchang Statistical Yearbook
	Population density (10,000 people/km2)	PD (X8)	Use the natural breakpoint method to divide into six grades	Population Grid dataset from the China Resources and Environmental Science Data Center (https://www.resdc.cn/, accessed on 20 June 2022)
	Distance to the city center (m)	DisTocity (X9)	Use the natural breakpoint method to divide into six grades	City center map and county center map computed using the Euclidean Distance and Zonal Statistics tool in ArcGIS 10.2. (original map sourced National Catalogue Ser-vice For Geographic Information, https://www.webmap.cn/, accessed on 20 June 2022)
	Distance to county (District) center (m)	DisTocounty (X10)	Use the natural breakpoint method to divide into six grades
	Distance to the national way (m)	DisTonationalway (X11)	Use the natural breakpoint method to divide into six grades	National way, highway, and railway maps were calculated using the Euclidean Distance Analysis tool in ArcGIS 10.2 (original map sourced from National Catalogue Service For Geographic Information, https://www.webmap.cn/, accessed on 20 June 2022)
	Distance to the highway (m)	DisTohighway (X12)	Use the natural breakpoint method to divide into six grades
	Distance to railway (m)	DisTorailway (X13)	Use the natural breakpoint method to divide into six grades
Natural geographic conditions	Elevation (m)	Elevation (X14)	Use the natural breakpoint method to divide into six grades	Geospatial Data Cloud (http://www.gscloud.cn, accessed on 20 June 2022)
	Slope (Degree)	Slope (X15)	Use the natural breakpoint method to divide into six grades	Slope map calculated using the Slope tool in ArcGIS 10.2
	Distance to the river (m)	DisToriver (X16)	Use the natural breakpoint method to divide into six grades	River map computed using the Euclidean Distance Analysis and Zonal Statistics tool in ArcGIS 10.2 (original map sourced from National Catalogue Service For Geo-graphic Information, https://www.webmap.cn/, accessed on 20 June 2022)

Table 2

Statistical table of urban construction land area change in Nanchang city from 1990 to 2020.

Period	Increased Area (km2)	AI (km2)	UEI/%	AGR (%)	Stage
1990–1995	33.41	6.68	0.09	7.58	low-strength with medium-speed
1995–2000	17.32	3.46	0.05	2.99	low-strength with low-speed
1990–2000	50.73	5.07	0.07	5.26	low-strength with low-speed
2000–2005	58.83	11.77	0.16	7.94	low-strength with medium-speed
2005–2010	75.59	15.12	0.21	7.08	medium strength with medium-speed
2000–2010	134.42	13.44	0.19	7.51	low-strength with medium-speed
2010–2015	134.25	26.85	0.37	8.66	medium strength with high speed
2015–2020	65.82	13.16	0.18	3.13	low-strength with low-speed
2010–2020	200.07	20.01	0.28	5.86	medium-strength with low-speed
1990–2020	385.22	12.84	0.18	6.20	low-strength with medium-speed

Table 3

Detection results of impact factors of urban construction land in Nanchang.

Variable Category	Variable	q Statistic
		1990–2000	2000–2010	2010–2020
Globalization	FDI (X1)	0.138 ***	0.217 ***	0.100 ***
Marketization	NSOEI (X2)	0.163 ***	0.212 ***	0.033 ***
Decentralization	FAI (X3)	0.156 ***	0.116 ***	0.055 ***
	PFE (X4)	0.023 ***	0.180 ***	0.043 ***
	GDP (X5)	0.132 ***	0.311 ***	0.086 ***
	UP (X6)	0.395 ***	0.504 ***	0.455 ***
Urbanization	UR (X7)	0.262 ***	0.187 ***	0.097 ***
	PD (X8)	0.150 ***	0.230 ***	0.066 ***
	DisTocity (X9)	0.192 ***	0.202 ***	0.255 ***
	DisTocounty (X10)	0.234 ***	0.305 ***	0.207 ***
	DisTonationalway (X11)	0.068 ***	0.075 ***	0.077 ***
	DisTohighway (X12)	0.022 ***	0.036 ***	0.038 ***
	DisTorailway (X13)	0.086 ***	0.115 ***	0.147 ***
Natural geographic conditions	Elevation (X14)	0.021 ***	0.011 ***	0.018 ***
	Slope (X15)	0.009 ***	0.012 ***	0.011 ***
	DisToriver (X16)	0.021 ***	0.024 ***	0.023 ***

*** represents significant at the 1% statistical level.

Appendix A

Table A1

Changes in the administrative divisions of Nanchang City.

Time	Number of Counties	Number of District	Name of County or District	Administrative Division Changes	GDP(Billion Yuan)
1983	4	5	Donghu District, Xihu District, Qingyunpu District, Wanli District, Suburban District, Nanchang County, Xinjian County, Jinxian County, Anyi County	Anyi County and Jinxian County were incorporated into Nanchang City	21.2
2002	4	5	Donghu District, Xihu District, Qingyunpu District, Wanli District, Qingshanhu District, Nanchang County, Xinjian County, Jinxian County, Anyi County	Suburban District renamed Qingshan Lake District	552.37
2015	3	6	Donghu District, Xihu District, Qingyunpu District, Wanli District, Qingshanhu District, Xinjian District, Nanchang County, Jinxian County, Anyi County	Transforming Xinjian county into an urban district	3778.82
2019	3	6	Donghu District, Xihu District, Qingyunpu District, Qingshanhu District, Honggutan District, Xinjian District, Nanchang County, Jinxian County, Anyi County	Withdraw Wanli District and merge it into Xinjian District; the newly established Honggutan District	5536.66

Table A2

Nanchang’s urban planning policies.

Planning	Related Information
The Urban Master Plan of Nanchang City (1981–2000)	The development of the land “is centered on the inner-city area. The Changbei area and Luojia Industrial Zone should closely connect with the inner-city area, and set up some satellite towns such as Wanli, Shigang, and Liantang on the periphery.”
Land Use Plan of Nanchang City (1997–2010)	“Changnan City will develop moderately, Changbei City will be built in a centralized manner and will be scaled, and the new construction land quotas will be mainly arranged in the towns such as Dongxin and Liantang; basically, a city development pattern of ‘one river and two banks’ will be formed.”
The Urban Master Plan of Nanchang City (2001–2020)	Put forward the development strategy “develop in the western area, expand in the eastern area, restrict in the northern area, extension in the southern area.” “With the development of the central city as the core and the traffic line as the main axis (for outward expansion), the central city should first develop Changbei New City, and arranges a large number of small and medium-sized towns on the periphery.”
Land Use Plan of Nanchang City (2006–2020)	Efforts will be made to form an overall spatial pattern of “taking the Ganjiang River as the main axis, forming one river with two banks, two city cores in the north and the south, developing east and west urban areas around the Ganjiang River, and forming group-like and network-like development.”
Nanchang’s Fourteenth Five-Year Plan for National Economic and Social Development and Outline of Long-term Goals for 2035	Establish the urban spatial development orientation of “Nanchang develops to the south,” form a metropolis with mountains, rivers, and lakes, and build a spatial layout of “leading by one (Gangjiang) river, integrating the north and south parts of the city, connecting three rings (connecting the urban areas with three rapid traffic ring lines in the city), and surrounded by five stars (the periphery of the city consists of five sub-center groups and functional areas that are closely connected with the main urban area)”

Appendix B

Figure A1. Flowchart for revealing Nanchang’s urban expansion and its impact factors.

References

1. 68% of the World Population Projected to Live in Urban Areas by 2050, Says UN | UN DESA | United Nations Department of Economic and Social Affairs. Available online: https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html (accessed on 30 September 2022).

2. Yu, X.J.; Ng, C.N. Spatial and Temporal Dynamics of Urban Sprawl along Two Urban–Rural Transects: A Case Study of Guangzhou, China. Landsc. Urban Plan.; 2007; 79, pp. 96-109. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2006.03.008]

3. Ministry of Natural Resources of the People’s Republic of China Bulletin on Main Data of the Third National Land Survey. Available online: https://www.mnr.gov.cn/dt/ywbb/202108/t20210826_2678340.html (accessed on 13 September 2022).

4. Xie, H.; Zhang, Y.; Duan, K. Evolutionary Overview of Urban Expansion Based on Bibliometric Analysis in Web of Science from 1990 to 2019. Habitat Int.; 2020; 95, 102100. [DOI: https://dx.doi.org/10.1016/j.habitatint.2019.102100]

5. Hamidi, S.; Ewing, R. A Longitudinal Study of Changes in Urban Sprawl between 2000 and 2010 in the United States. Landsc. Urban Plan.; 2014; 128, pp. 72-82. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2014.04.021]

6. Braimoh, A.K.; Onishi, T. Spatial Determinants of Urban Land Use Change in Lagos, Nigeria. Land Use Policy; 2007; 24, pp. 502-515. [DOI: https://dx.doi.org/10.1016/j.landusepol.2006.09.001]

7. Dewan, A.M.; Yamaguchi, Y. Land Use and Land Cover Change in Greater Dhaka, Bangladesh: Using Remote Sensing to Promote Sustainable Urbanization. Appl. Geogr.; 2009; 29, pp. 390-401. [DOI: https://dx.doi.org/10.1016/j.apgeog.2008.12.005]

8. Bhatta, B. Analysis of Urban Growth Pattern Using Remote Sensing and GIS: A Case Study of Kolkata, India. Int. J. Remote Sens.; 2009; 30, pp. 4733-4746. [DOI: https://dx.doi.org/10.1080/01431160802651967]

9. Herold, M.; Goldstein, N.C.; Clarke, K.C. The Spatiotemporal Form of Urban Growth: Measurement, Analysis and Modeling. Remote Sens. Environ.; 2003; 86, pp. 286-302. [DOI: https://dx.doi.org/10.1016/S0034-4257(03)00075-0]

10. Seto, K.C.; Sánchez-Rodríguez, R.; Fragkias, M. The New Geography of Contemporary Urbanization and the Environment. Annu. Rev. Environ. Resour.; 2010; 35, pp. 167-194. [DOI: https://dx.doi.org/10.1146/annurev-environ-100809-125336]

11. Srinivasan, V.; Seto, K.C.; Emerson, R.; Gorelick, S.M. The Impact of Urbanization on Water Vulnerability: A Coupled Human–Environment System Approach for Chennai, India. Glob. Environ. Chang.; 2013; 23, pp. 229-239. [DOI: https://dx.doi.org/10.1016/j.gloenvcha.2012.10.002]

12. Imbrenda, V.; Quaranta, G.; Salvia, R.; Egidi, G.; Salvati, L.; Prokopova, M.; Coluzzi, R.; Lanfredi, M. Land Degradation and Metropolitan Expansion in a Peri-Urban Environment. Geomat. Nat. Hazards Risk; 2021; 12, pp. 1797-1818. [DOI: https://dx.doi.org/10.1080/19475705.2021.1951363]

13. Stone, B., Jr. Urban Sprawl and Air Quality in Large US Cities. J. Environ. Manag.; 2008; 86, pp. 688-698. [DOI: https://dx.doi.org/10.1016/j.jenvman.2006.12.034]

14. Foley, J.A.; DeFries, R.; Asner, G.P.; Barford, C.; Bonan, G.; Carpenter, S.R.; Chapin, F.S.; Coe, M.T.; Daily, G.C.; Gibbs, H.K. et al. Global Consequences of Land Use. Science; 2005; 309, pp. 570-574. [DOI: https://dx.doi.org/10.1126/science.1111772] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16040698]

15. Zhao, M.; Cai, H.; Qiao, Z.; Xu, X. Influence of Urban Expansion on the Urban Heat Island Effect in Shanghai. Int. J. Geogr. Inf. Sci.; 2016; 30, pp. 2421-2441. [DOI: https://dx.doi.org/10.1080/13658816.2016.1178389]

16. Halder, B.; Bandyopadhyay, J.; Banik, P. Monitoring the Effect of Urban Development on Urban Heat Island Based on Remote Sensing and Geo-Spatial Approach in Kolkata and Adjacent Areas, India. Sustain. Cities Soc.; 2021; 74, 103186. [DOI: https://dx.doi.org/10.1016/j.scs.2021.103186]

17. Güneralp, B.; Seto, K. Futures of Global Urban Expansion: Uncertainties and Implications for Biodiversity Conservation. Environ. Res. Lett.; 2013; 8, 014025. [DOI: https://dx.doi.org/10.1088/1748-9326/8/1/014025]

18. Pauchard, A.; Aguayo, M.; Peña, E.; Urrutia, R. Multiple Effects of Urbanization on the Biodiversity of Developing Countries: The Case of a Fast-Growing Metropolitan Area (Concepción, Chile). Biol. Conserv.; 2006; 127, pp. 272-281. [DOI: https://dx.doi.org/10.1016/j.biocon.2005.05.015]

19. Han, J. Can Urban Sprawl Be the Cause of Environmental Deterioration? Based on the Provincial Panel Data in China. Environ. Res.; 2020; 189, 109954. [DOI: https://dx.doi.org/10.1016/j.envres.2020.109954]

20. Zhao, P.; Lu, B.; de Roo, G. Urban Expansion and Transportation: The Impact of Urban Form on Commuting Patterns on the City Fringe of Beijing. Environ. Plan. A; 2010; 42, pp. 2467-2486. [DOI: https://dx.doi.org/10.1068/a4350]

21. Monkkonen, P.; Comandon, A.; Escamilla, J.A.M.; Guerra, E. Urban Sprawl and the Growing Geographic Scale of Segregation in Mexico, 1990–2010. Habitat Int.; 2018; 73, pp. 89-95. [DOI: https://dx.doi.org/10.1016/j.habitatint.2017.12.003]

22. Ji, C.; Liu, Q.; Sun, D.; Wang, S.; Lin, P.; Li, X. Monitoring Urban Expansion with Remote Sensing in China. Int. J. Remote Sens.; 2001; 22, pp. 1441-1455. [DOI: https://dx.doi.org/10.1080/01431160117207]

23. Yang, C.; Li, Q.; Hu, Z.; Chen, J.; Shi, T.; Ding, K.; Wu, G. Spatiotemporal Evolution of Urban Agglomerations in Four Major Bay Areas of US, China and Japan from 1987 to 2017: Evidence from Remote Sensing Images. Sci. Total Environ.; 2019; 671, pp. 232-247. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2019.03.154] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30928752]

24. Fei, W.; Zhao, S. Urban Land Expansion in China’s Six Megacities from 1978 to 2015. Sci. Total Environ.; 2019; 664, pp. 60-71. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2019.02.008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30739854]

25. Shi, G.; Jiang, N.; Li, Y.; He, B. Analysis of the Dynamic Urban Expansion Based on Multi-Sourced Data from 1998 to 2013: A Case Study of Jiangsu Province. Sustainability; 2018; 10, 3467. [DOI: https://dx.doi.org/10.3390/su10103467]

26. Xiao, J.; Shen, Y.; Ge, J.; Tateishi, R.; Tang, C.; Liang, Y.; Huang, Z. Evaluating Urban Expansion and Land Use Change in Shijiazhuang, China, by Using GIS and Remote Sensing. Landsc. Urban Plan.; 2006; 75, pp. 69-80. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2004.12.005]

27. Liu, G.; Li, J.; Nie, P. Tracking the History of Urban Expansion in Guangzhou (China) during 1665–2017: Evidence from Historical Maps and Remote Sensing Images. Land Use Policy; 2022; 112, 105773. [DOI: https://dx.doi.org/10.1016/j.landusepol.2021.105773]

28. Deng, F.; Huang, Y. Uneven Land Reform and Urban Sprawl in China: The Case of Beijing. Prog. Plan.; 2004; 61, pp. 211-236. [DOI: https://dx.doi.org/10.1016/j.progress.2003.10.004]

29. Li, H.; Wei, Y.; Huang, Z. Urban Land Expansion and Spatial Dynamics in Globalizing Shanghai. Sustainability; 2014; 6, pp. 8856-8875. [DOI: https://dx.doi.org/10.3390/su6128856]

30. Gong, J.; Hu, Z.; Chen, W.; Liu, Y.; Wang, J. Urban Expansion Dynamics and Modes in Metropolitan Guangzhou, China. Land Use Policy; 2018; 72, pp. 100-109. [DOI: https://dx.doi.org/10.1016/j.landusepol.2017.12.025]

31. Du, N.; Ottens, H.; Sliuzas, R. Spatial Impact of Urban Expansion on Surface Water Bodies-A Case Study of Wuhan, China. Landsc. Urban Plan.; 2010; 94, pp. 175-185. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2009.10.002]

32. Luo, J.; Wei, Y.D. Modeling Spatial Variations of Urban Growth Patterns in Chinese Cities: The Case of Nanjing. Landsc. Urban Plan.; 2009; 91, pp. 51-64. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2008.11.010]

33. Hu, Z.; Lo, C. Modeling Urban Growth in Atlanta Using Logistic Regression. Comput. Environ. Urban Syst.; 2007; 31, pp. 667-688. [DOI: https://dx.doi.org/10.1016/j.compenvurbsys.2006.11.001]

34. Xu, T.; Gao, J.; Coco, G.; Wang, S. Urban Expansion in Auckland, New Zealand: A GIS Simulation via an Intelligent Self-Adapting Multiscale Agent-Based Model. Int. J. Geogr. Inf. Sci.; 2020; 34, pp. 2136-2159. [DOI: https://dx.doi.org/10.1080/13658816.2020.1748192]

35. Zhou, Y.; Li, C.; Zheng, W.; Rong, Y.; Liu, W. Identification of Urban Shrinkage Using NPP-VIIRS Nighttime Light Data at the County Level in China. Cities; 2021; 118, 103373. [DOI: https://dx.doi.org/10.1016/j.cities.2021.103373]

36. GaWC-The World According to GaWC 2020. Available online: https://www.lboro.ac.uk/microsites/geography/gawc/world2020t.html (accessed on 3 October 2022).

37. Rimal, B.; Sloan, S.; Keshtkar, H.; Sharma, R.; Rijal, S.; Shrestha, U.B. Patterns of Historical and Future Urban Expansion in Nepal. Remote Sens.; 2020; 12, 628. [DOI: https://dx.doi.org/10.3390/rs12040628]

38. Yang, Y.; Liu, Y.; Li, Y.; Du, G. Quantifying Spatio-Temporal Patterns of Urban Expansion in Beijing during 1985–2013 with Rural-Urban Development Transformation. Land Use Policy; 2018; 74, pp. 220-230. [DOI: https://dx.doi.org/10.1016/j.landusepol.2017.07.004]

39. Alahmadi, M.; Atkinson, P.M. Three-Fold Urban Expansion in Saudi Arabia from 1992 to 2013 Observed Using Calibrated DMSP-OLS Night-Time Lights Imagery. Remote Sens.; 2019; 11, 2266. [DOI: https://dx.doi.org/10.3390/rs11192266]

40. Weng, Q. Remote Sensing of Impervious Surfaces in the Urban Areas: Requirements, Methods, and Trends. Remote Sens. Environ.; 2012; 117, pp. 34-49. [DOI: https://dx.doi.org/10.1016/j.rse.2011.02.030]

41. Arsanjani, J.J.; Helbich, M.; Kainz, W.; Boloorani, A.D. Integration of Logistic Regression, Markov Chain and Cellular Automata Models to Simulate Urban Expansion. Int. J. Appl. Earth Obs. Geoinf.; 2013; 21, pp. 265-275. [DOI: https://dx.doi.org/10.1016/j.jag.2011.12.014]

42. Tayyebi, A.; Pijanowski, B.C.; Tayyebi, A.H. An Urban Growth Boundary Model Using Neural Networks, GIS and Radial Parameterization: An Application to Tehran, Iran. Landsc. Urban Plan.; 2011; 100, pp. 35-44. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2010.10.007]

43. Zhang, J.; Li, P.; Wang, J. Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture. Remote Sens.; 2014; 6, pp. 7339-7359. [DOI: https://dx.doi.org/10.3390/rs6087339]

44. Ma, X.; Tong, X.; Liu, S.; Luo, X.; Xie, H.; Li, C. Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-up Areas. Remote Sens.; 2017; 9, 236. [DOI: https://dx.doi.org/10.3390/rs9030236]

45. Castrence, M.; Nong, D.H.; Tran, C.C.; Young, L.; Fox, J. Mapping Urban Transitions Using Multi-Temporal Landsat and DMSP-OLS Night-Time Lights Imagery of the Red River Delta in Vietnam. Land; 2014; 3, pp. 148-166. [DOI: https://dx.doi.org/10.3390/land3010148]

46. Guindon, B.; Zhang, Y.; Dillabaugh, C. Landsat Urban Mapping Based on a Combined Spectral–Spatial Methodology. Remote Sens. Environ.; 2004; 92, pp. 218-232. [DOI: https://dx.doi.org/10.1016/j.rse.2004.06.015]

47. Bramhe, V.; Ghosh, S.K.; Garg, P.K. Extraction of Built-up Area by Combining Textural Features and Spectral Indices from Landsat-8 Multispectral Image. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.; 2018; 42, pp. 727-733. [DOI: https://dx.doi.org/10.5194/isprs-archives-XLII-5-727-2018]

48. Chen, Z.; Hu, D.; Zeng, W.; Deng, L. TM Image and Nighttime Light Data to Monitoring Regional Urban Expansion: A Case Study of Zhejiang Province. Remote Sens. Nat. Resour.; 2014; 26, pp. 83-89.

49. Yi, K.; Zeng, Y.; Wu, B. Mapping and Evaluation the Process, Pattern and Potential of Urban Growth in China. Appl. Geogr.; 2016; 71, pp. 44-55. [DOI: https://dx.doi.org/10.1016/j.apgeog.2016.04.011]

50. Shen, Y.; Shen, H.; Cheng, Q.; Huang, L.; Zhang, L. Monitoring Three-Decade Expansion of China’s Major Cities Based on Satellite Remote Sensing Images. Remote Sens.; 2020; 12, 491. [DOI: https://dx.doi.org/10.3390/rs12030491]

51. Qiu, R.; Xu, W. Modes of Land Development in Shanghai. Land Use Policy; 2017; 61, pp. 475-486. [DOI: https://dx.doi.org/10.1016/j.landusepol.2016.12.006]

52. Wei, Y.; Li, H.; Yue, W. Urban Land Expansion and Regional Inequality in Transitional China. Landsc. Urban Plan.; 2017; 163, pp. 17-31. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2017.02.019]

53. Zhang, Q.; Su, S. Determinants of Urban Expansion and Their Relative Importance: A Comparative Analysis of 30 Major Metropolitans in China. Habitat Int.; 2016; 58, pp. 89-107. [DOI: https://dx.doi.org/10.1016/j.habitatint.2016.10.003]

54. Zhao, S.; Zhou, D.; Zhu, C.; Qu, W.; Zhao, J.; Sun, Y.; Huang, D.; Wu, W.; Liu, S. Rates and Patterns of Urban Expansion in China’s 32 Major Cities over the Past Three Decades. Landsc. Ecol.; 2015; 30, pp. 1541-1559. [DOI: https://dx.doi.org/10.1007/s10980-015-0211-7]

55. Sahana, M.; Hong, H.; Sajjad, H. Analyzing Urban Spatial Patterns and Trend of Urban Growth Using Urban Sprawl Matrix: A Study on Kolkata Urban Agglomeration, India. Sci. Total Environ.; 2018; 628, pp. 1557-1566. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2018.02.170] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30045573]

56. Angel, S.; Parent, J.; Civco, D.L.; Blei, A.; Potere, D. The Dimensions of Global Urban Expansion: Estimates and Projections for All Countries, 2000–2050. Prog. Plan.; 2011; 75, pp. 53-107. [DOI: https://dx.doi.org/10.1016/j.progress.2011.04.001]

57. Deng, Y.; Qi, W.; Fu, B.; Wang, K. Geographical Transformations of Urban Sprawl: Exploring the Spatial Heterogeneity across Cities in China 1992-2015. Cities; 2020; 105, 102415. [DOI: https://dx.doi.org/10.1016/j.cities.2019.102415]

58. Fang, C.; Zhao, S. A Comparative Study of Spatiotemporal Patterns of Urban Expansion in Six Major Cities of the Yangtze River Delta from 1980 to 2015. Ecosyst. Health Sustain.; 2018; 4, pp. 95-114. [DOI: https://dx.doi.org/10.1080/20964129.2018.1469960]

59. Bürgi, M.; Hersperger, A.M.; Schneeberger, N. Driving Forces of Landscape Change-Current and New Directions. Landsc. Ecol.; 2005; 19, pp. 857-868. [DOI: https://dx.doi.org/10.1007/s10980-005-0245-3]

60. Li, G.; Sun, S.; Fang, C. The Varying Driving Forces of Urban Expansion in China: Insights from a Spatial-Temporal Analysis. Landsc. Urban Plan.; 2018; 174, pp. 63-77. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2018.03.004]

61. Li, X.; Zhou, W.; Ouyang, Z. Forty Years of Urban Expansion in Beijing: What Is the Relative Importance of Physical, Socioeconomic, and Neighborhood Factors?. Appl. Geogr.; 2013; 38, pp. 1-10. [DOI: https://dx.doi.org/10.1016/j.apgeog.2012.11.004]

62. Wei, Y.D. Decentralization, Marketization, and Globalization: The Triple Processes Underlying Regional Development in China. Asian Geogr.; 2001; 20, pp. 7-23. [DOI: https://dx.doi.org/10.1080/10225706.2001.9684073]

63. Zhang, Y.; Chang, X.; Liu, Y.; Lu, Y.; Wang, Y.; Liu, Y. Urban Expansion Simulation under Constraint of Multiple Ecosystem Services (MESs) Based on Cellular Automata (CA)-Markov Model: Scenario Analysis and Policy Implications. Land Use Policy; 2021; 108, 105667. [DOI: https://dx.doi.org/10.1016/j.landusepol.2021.105667]

64. Huang, Z.; Wei, Y.D.; He, C.; Li, H. Urban Land Expansion under Economic Transition in China: A Multi-Level Modeling Analysis. Habitat Int.; 2015; 47, pp. 69-82. [DOI: https://dx.doi.org/10.1016/j.habitatint.2015.01.007]

65. Tian, L.; Ge, B.; Li, Y. Impacts of State-Led and Bottom-up Urbanization on Land Use Change in the Peri-Urban Areas of Shanghai: Planned Growth or Uncontrolled Sprawl?. Cities; 2017; 60, pp. 476-486. [DOI: https://dx.doi.org/10.1016/j.cities.2016.01.002]

66. Chen, J.; Gao, J.; Chen, W. Urban Land Expansion and the Transitional Mechanisms in Nanjing, China. Habitat Int.; 2016; 53, pp. 274-283. [DOI: https://dx.doi.org/10.1016/j.habitatint.2015.11.040]

67. Zhao, P. Managing Urban Growth in a Transforming China: Evidence from Beijing. Land Use Policy; 2011; 28, pp. 96-109. [DOI: https://dx.doi.org/10.1016/j.landusepol.2010.05.004]

68. Wu, R.; Li, Z.; Wang, S. The Varying Driving Forces of Urban Land Expansion in China: Insights from a Spatial-Temporal Analysis. Sci. Total Environ.; 2021; 766, 142591. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.142591] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33601670]

69. Zhong, Y.; Lin, A.; He, L.; Zhou, Z.; Yuan, M. Spatiotemporal Dynamics and Driving Forces of Urban Land-Use Expansion: A Case Study of the Yangtze River Economic Belt, China. Remote Sens.; 2020; 12, 287. [DOI: https://dx.doi.org/10.3390/rs12020287]

70. Zou, Y.; Peng, H.; Liu, G.; Yang, K.; Xie, Y.; Weng, Q. Monitoring Urban Clusters Expansion in the Middle Reaches of the Yangtze River, China, Using Time-Series Nighttime Light Images. Remote Sens.; 2017; 9, 1007. [DOI: https://dx.doi.org/10.3390/rs9101007]

71. He, C.; Huang, Z.; Wang, R. Land Use Change and Economic Growth in Urban China: A Structural Equation Analysis. Urban Stud.; 2014; 51, pp. 2880-2898. [DOI: https://dx.doi.org/10.1177/0042098013513649]

72. Zhou, Y.; Huang, X.; Chen, Y.; Zhong, T.; Xu, G.; He, J.; Xu, Y.; Meng, H. The Effect of Land Use Planning (2006–2020) on Construction Land Growth in China. Cities; 2017; 68, pp. 37-47. [DOI: https://dx.doi.org/10.1016/j.cities.2017.04.014]

73. Zhang, L.; Liu, Y.; Jing, Y.; Zhang, Y. How Does Quota-Oriented Land Use Planning Affect Urban Expansion? A Spatial Analysis of 280 Chinese Cities. Land; 2022; 11, 528. [DOI: https://dx.doi.org/10.3390/land11040528]

74. Tian, L.; Li, Y.; Yan, Y.; Wang, B. Measuring Urban Sprawl and Exploring the Role Planning Plays: A Shanghai Case Study. Land Use Policy; 2017; 67, pp. 426-435. [DOI: https://dx.doi.org/10.1016/j.landusepol.2017.06.002]

75. Shi, Y.; Yang, J.; Hu, X. How Economic Globalization Affects Urban Expansion: An Empirical Analysis of 30 Chinese Provinces for 2000-2010. Qual. Quant.; 2016; 50, pp. 1117-1133. [DOI: https://dx.doi.org/10.1007/s11135-015-0193-1]

76. Park, J.-I.; Leigh, N.G. Urban Industrial Land Loss and Foreign Direct Investment-Related Manufacturing Job Sprawl: An Atlanta, Georgia MSA Case Study. J. Urban Technol.; 2017; 24, pp. 95-113. [DOI: https://dx.doi.org/10.1080/10630732.2017.1348883]

77. Gao, J.; Wei, Y.; Chen, W.; Yenneti, K. Urban Land Expansion and Structural Change in the Yangtze River Delta, China. Sustainability; 2015; 7, pp. 10281-10307. [DOI: https://dx.doi.org/10.3390/su70810281]

78. Li, H.; Wei, Y.D.; Liao, F.H.; Huang, Z. Administrative Hierarchy and Urban Land Expansion in Transitional China. Appl. Geogr.; 2015; 56, pp. 177-186. [DOI: https://dx.doi.org/10.1016/j.apgeog.2014.11.029]

79. You, H.; Yang, X. Urban Expansion in 30 Megacities of China: Categorizing the Driving Force Profiles to Inform the Urbanization Policy. Land Use Policy; 2017; 68, pp. 531-551. [DOI: https://dx.doi.org/10.1016/j.landusepol.2017.06.020]

80. Deng, T.; Wang, D.; Hu, Y.; Liu, S. Did High-Speed Railway Cause Urban Space Expansion? -Empirical Evidence from China’s Prefecture-Level Cities. Res. Transp. Econ.; 2020; 80, 100840. [DOI: https://dx.doi.org/10.1016/j.retrec.2020.100840]

81. Wu, R.; Li, Y.; Wang, S. Will the Construction of High-Speed Rail Accelerate Urban Land Expansion? Evidences from Chinese Cities. Land Use Policy; 2022; 114, 105920. [DOI: https://dx.doi.org/10.1016/j.landusepol.2021.105920]

82. Li, X.; Ma, X. Impacts of High-Speed Rail Development on Urban Land Expansion and Utilization Intensity in China. J. Transp. Land Use; 2021; 14, pp. 583-601. [DOI: https://dx.doi.org/10.5198/jtlu.2021.1804]

83. Zhu, X.; Qian, T.; Wei, Y. Do High-Speed Railways Accelerate Urban Land Expansion in China? A Study Based on the Multi-Stage Difference-in-Differences Model. Socioecon. Plann. Sci.; 2020; 71, 100846. [DOI: https://dx.doi.org/10.1016/j.seps.2020.100846]

84. Ustaoglu, E.; Williams, B. Determinants of Urban Expansion and Agricultural Land Conversion in 25 EU Countries. Environ. Manage.; 2017; 60, pp. 717-746. [DOI: https://dx.doi.org/10.1007/s00267-017-0908-2] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28685244]

85. Yan, Y.; Ju, H.; Zhang, S.; Jiang, W. Spatiotemporal Patterns and Driving Forces of Urban Expansion in Coastal Areas: A Study on Urban Agglomeration in the Pearl River Delta, China. Sustainability; 2020; 12, 191. [DOI: https://dx.doi.org/10.3390/su12010191]

86. Elhamdouni, D.; Arioua, A.; Karaoui, I. Monitoring Urban Expansion Using SVM Classification Approach in Khenifra City (Morocco). Model. Earth Syst. Environ.; 2022; 8, pp. 293-298. [DOI: https://dx.doi.org/10.1007/s40808-021-01092-w]

87. Gong, P.; Wang, J.; Yu, L.; Zhao, Y.; Zhao, Y.; Liang, L.; Niu, Z.; Huang, X.; Fu, H.; Liu, S. et al. Finer Resolution Observation and Monitoring of Global Land Cover: First Mapping Results with Landsat TM and ETM+ Data. Int. J. Remote Sens.; 2013; 34, pp. 2607-2654. [DOI: https://dx.doi.org/10.1080/01431161.2012.748992]

88. Griffiths, P.; Hostert, P.; Gruebner, O.; van der Linden, S. Mapping Megacity Growth with Multi-Sensor Data. Remote Sens. Environ.; 2010; 114, pp. 426-439. [DOI: https://dx.doi.org/10.1016/j.rse.2009.09.012]

89. Colsaet, A.; Laurans, Y.; Levrel, H. What Drives Land Take and Urban Land Expansion? A Systematic Review. Land Use Policy; 2018; 79, pp. 339-349. [DOI: https://dx.doi.org/10.1016/j.landusepol.2018.08.017]

90. Liu, Y.; Fan, P.; Yue, W.; Song, Y. Impacts of Land Finance on Urban Sprawl in China: The Case of Chongqing. Land Use Policy; 2018; 72, pp. 420-432. [DOI: https://dx.doi.org/10.1016/j.landusepol.2018.01.004]

91. Li, Z.; Luan, W.; Zhang, Z.; Su, M. Relationship between Urban Construction Land Expansion and Population/Economic Growth in Liaoning Province, China. Land Use Policy; 2020; 99, 105022. [DOI: https://dx.doi.org/10.1016/j.landusepol.2020.105022]

92. Wang, X. Relationship Between Population Urbanization and Urban Sprawl Across Different City Sizes in China. Environ. Urban. ASIA; 2021; 12, pp. 202-219. [DOI: https://dx.doi.org/10.1177/09754253211040196]

93. Sudhira, H.; Ramachandra, T.; Jagadish, K. Urban Sprawl: Metrics, Dynamics and Modelling Using GIS. Int. J. Appl. Earth Obs. Geoinf.; 2004; 5, pp. 29-39. [DOI: https://dx.doi.org/10.1016/j.jag.2003.08.002]

94. Li, Y.; Kong, X.; Zhu, Z. Multiscale Analysis of the Correlation Patterns between the Urban Population and Construction Land in China. Sustain. Cities Soc.; 2020; 61, 102326. [DOI: https://dx.doi.org/10.1016/j.scs.2020.102326]

95. Xu, T.; Gao, J. Directional Multi-Scale Analysis and Simulation of Urban Expansion in Auckland, New Zealand Using Logistic Cellular Automata. Comput. Environ. Urban Syst.; 2019; 78, 101390. [DOI: https://dx.doi.org/10.1016/j.compenvurbsys.2019.101390]

96. Xu, C.; Liu, M.; Zhang, C.; An, S.; Yu, W.; Chen, J.M. The Spatiotemporal Dynamics of Rapid Urban Growth in the Nanjing Metropolitan Region of China. Landsc. Ecol.; 2007; 22, pp. 925-937. [DOI: https://dx.doi.org/10.1007/s10980-007-9079-5]

97. Liu, F.; Zhang, Z.; Wang, X. Forms of Urban Expansion of Chinese Municipalities and Provincial Capitals, 1970s–2013. Remote Sens.; 2016; 8, 930. [DOI: https://dx.doi.org/10.3390/rs8110930]

98. Liu, X.; Li, X.; Chen, Y.; Tan, Z.; Li, S.; Ai, B. A New Landscape Index for Quantifying Urban Expansion Using Multi-Temporal Remotely Sensed Data. Landsc. Ecol.; 2010; 25, pp. 671-682. [DOI: https://dx.doi.org/10.1007/s10980-010-9454-5]

99. Shu, B.; Zhang, H.; Li, Y.; Qu, Y.; Chen, L. Spatiotemporal Variation Analysis of Driving Forces of Urban Land Spatial Expansion Using Logistic Regression: A Case Study of Port Towns in Taicang City, China. Habitat Int.; 2014; 43, pp. 181-190. [DOI: https://dx.doi.org/10.1016/j.habitatint.2014.02.004]

100. Wang, Y.; Wang, S.; Li, G.; Zhang, H.; Jin, L.; Su, Y.; Wu, K. Identifying the Determinants of Housing Prices in China Using Spatial Regression and the Geographical Detector Technique. Appl. Geogr.; 2017; 79, pp. 26-36. [DOI: https://dx.doi.org/10.1016/j.apgeog.2016.12.003]

101. Zuo, L.; Mu, F.; Zhang, Z.; Dong, T. Analysis on Urban Expansion and Spatial Morphology Evolution of Nanchang Based on GIS and RS in the Recent Three Decades. Proceedings of the MIPPR 2007: Remote Sensing and GIS Data Processing and Applications and Innovative Multispectral Technology and Applications (SPIE); Wuhan, China, 15 November 2007; Volume 6790, pp. 85-91.

102. Nanchang Urban Planning Bureau The Urban Master Plan of Nanchang City (2001–2020). Available online: http://bnr.nc.gov.cn/ncszrzyj/lbzg/202103/123cbe1867004f418d22c61c5a1bbae1.shtml (accessed on 13 September 2022).

103. Xian, S.; Li, L.; Qi, Z. Toward a Sustainable Urban Expansion: A Case Study of Zhuhai, China. J. Clean. Prod.; 2019; 230, pp. 276-285. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.05.087]