Data preprocessing
The Terrestrial Ecosystem Service Value Distribution Database used in
this study was constructed by Xie using attitudinal methods
(http://www.resdc.cn) (Table S1).
According to China’s green development policy (Ji et al. 2017), the
factors related to ecological conditions and sustainability were divided
based on six aspects: resource utilization, environmental governance,
environmental quality, ecological protection, growth quality, and green
life. Due to the spatial scale of this study, natural conditions such as
temperature, perception, and humidity are relatively similar across the
city, so they were not used as model input for differentiation. Taking
the correlation with ESV and anthropogenic controllability into account,
23 indexes from 2015 were chosen and altered from three perspectives
(Table S1) as input for the following ESV deep learning model. The data
resources and spatial revolution of raster data are shown in Table S1.
Multidata fusion on the same scale (Openshaw 1984; Perkins 2017;
Hodgkinson and Andresen 2019) is necessary to make labeled samples meet
the common format and quantity requirements for deep learning model
training. Considering the area of Nanjing and the ecological
significance of ESV, we used a 2*2 km grid for data processing by grid
transformation. A total of 2191 grid units were obtained as samples. The
socioeconomic data were
allocated by key weighting factors (Table S2) using Eq. 1. Spatial
socioeconomic data with a resolution of 2*2 km were obtained.
\(\text{Af}_{i}=\text{As}_{i}\times\frac{\text{Wf}_{i}}{\text{Ws}_{i}}\)(Eq. 1)