## Materials and Methods
### Data Collection  
During this research, LUCC data from 1990, 2000 (based on Landsat MSS/TM/ETM+/OLI), and 2017 from Gaofen-1 (GF-1) ([[http://www.cresda.com/CN/Satellite/3076.shtml](http://www.cresda.com/CN/Satellite/3076.shtml)]([http://www.cresda.com/CN/Satellite/3076.shtml](http://www.cresda.com/CN/Satellite/3076.shtml))) were used as the  
Figure 1: Location of the study area and its eleven municipalities. key information sources (Figure 2). The spatial resolution of the data was 30 m for 1990 and 2000 and 8 m for 2017, which was sufficient for land-use analysis [1; 2; 6]. The land-use and land-cover data were produced by the Chinese Academy of Science (CAS). During the 1990s, the CAS created the National Resource and Environments Database and has since accumulated massive amounts of LUCC data [43]. The original data source for these LUCC data was remote images. More than 500 remote sensing images from Landsat TM/ETM+/OLI ([[https://www.usgs.gov/](https://www.usgs.gov/)]([https://www.usgs.gov/](https://www.usgs.gov/))) have been used to map LUCC data for China [6; 43]. Most of the images were obtained from similar seasons (July-October) and under cloud-free conditions. Human-computer interactive interpretation, renowned for its high accuracy, was used to digitize the LUCC data at a spatial scale of 1:100,000 [16; 41]. The interpretation was approached based on 25 subclasses of land-use types that were defined by [PERSON] et al. [16]. The data were then classified into six classes--including cropland, woodland, grassland, water body, unused land, and built-up land--and 25 subclasses (Table 1, [PERSON] et al., and [PERSON] et al. [16; 41]). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classes** & **Code** & **Description** \\ \hline Cropland & 1 & Land for growing crops. \\ \hline Woodland & 2 & Land for growing trees, arbor, shrubs, bamboo, and forestry use. \\ \hline Grassland & 3 & Land for herbaceous plants. \\ \hline Water bodies & 4 & Natural water areas, including constructed reservoirs as well as water \\ \hline Unused land & 6 & Land that is not put into practical use or that is difficult to use. \\ \hline Built-up land & 5 & Human settlements in urban and rural areas as well as factories and transportation facilities. \\ \hline Urban land & 51 & Built-up areas used for urban settlements \\ \hline Rural settlements & 52 & Land used for small towns or village settlements. \\ \hline Infrastructure areas & 53 & Land used for factories, quarries, mining, and oil fields outside cities, as well as land for roads and other transportation infrastructure \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The land-use classification system. Figure 2: Land use maps in 1990, 2000, and 2017. The accuracy of the LUCC data was validated by field surveys. The results indicated that the overall accuracy of the first-level land use types was 94.0%, and the accuracy for the 25 sub-classes was 91.2% [41]. These data have been used previously to evaluate land degradation [13], to identify the major driver of LUCC change [2; 41], to predict future land-use change [44], and to model optimal land-use strategies [45]. The classification system is important for the production of LUCC data. The classification used in this study was based on six land-use types defined by [PERSON] et al. [16]. Twenty-five subclasses were grouped into the following six classes: cropland, woodland, grassland, water bodies, built-up land, and unused land. To measure urban expansion in the ZXEB, the authors also analyzed the following sub-classes: urban land, rural settlements, and infrastructure areas, all of which belong to the general class of built-up land (Table 1). To analyze the socio-economic drivers of built-up land expansion, the authors also collected gross domestic product (GDP) (for ZXEB, except Chongzuo), urban and rural net income (for ZXEB, except Chongzuo), and total population (for Guangdong and Guangxi province); data were collected from 1994 to 2015.