## 3 Methods and Data Sources
### Study Area Selection and Processing  
In this paper, two cases of Fangshan District, Beijing, and the border of Tongzhou District, Chaoyang District, are selected for the study. The pre-selected locations and service stores of waste disposal centers are selected using remote sensing technology in combination with relevant policies.  
In this section, we present the selected study areas and the processing of the data. The pre-selected locations and service points for waste disposal centers in Fangshan District and the border area of Tongzhou District and Chaoyang District are illustrated in Figure 2. This figure provides a visual representation of the spatial distribution of the service points and pre-sited centers, which helps in understanding the geographical context and the layout of the waste management system.  
Chaoyang District is located in the northeastern part of Beijing and is the city's center of commerce and business and a gathering place for high-end talents. Tongzhou District is located in the southeastern part of Beijing and is one of the core areas of the new round of urban expansion. The border area between the two districts is one of the city's central urban areas, with high population density, rapid urbanization, and the presence of a number of commercial and catering enterprises, which contribute significantly to waste generation and disposal. There is increasing pressure on the waste disposal and recycling system in the area.  
Located in the southern part of Beijing, Fangshan District is a typical combination of urban and rural areas and is a less developed area. The district has great potential for future development as one of the demonstration areas for the collaborative development of Tianjin and Hebei, but the existing waste treatment and recycling system is relatively imperfect.  
In this paper, Google Earth satellite images and road network data extracted from Open Street Map are used for the study. These data are subjected to a series of pre-processing procedures including geographic alignment and geometric correction before being used. We classify them according to the following rules:  
* Urban and rural areas are considered different areas.
* Villages are considered the same object, unless they are separated by large parcels of farmland, industrial parks, railroads, or highways.
* Urban areas separated by major roads are considered different areas.
* Different neighborhoods that do not distinguish between visible arterials are considered the same area.  
A total of 98 urban objects and 10 pre-sited centers were selected in the border area of Tongzhou District, Chaoyang District, and 64 urban objects, 36 village objects, and 10 presited centers were selected in the case of Fangshan District. We use the night light remote sensing data to process the brightness value of each district to get the real brightness value of each district, and at the same time, this paper generates the other four data (number of household population, per capita disposable income, per capita consumption expenditure, and resident population density) of the service point randomly, which will fluctuate within an interval of 20% above the mean value of the district for urban areas and 20% below for rural areas. Then, the data of each point in the region is substituted into the machine learning model to predict the mean value of its garbage production, and its variance is set to 0.1 to collate the data set with realism for each region.