Table 1.
Evaluation indicator system for UGHQD.
Fig 1.
Location of China and four major regions.
Note: Quoted from the Tianditu map. Source of base map: the open source map data service provided by the National Platform for Common GeoSpatial Information Services (https://www.tianditu.gov.cn/).
Fig 2.
BP neural network prediction framework.
Fig 3.
Trends in the level of UGHQD in China and four major regions.
Fig 4.
Spatial Evolution Trends of UGHQD level in China.
Note: Quoted from the Tianditu map. Source of base map: the open source map data service provided by the National Platform for Common GeoSpatial Information Services (https://www.tianditu.gov.cn/).
Fig 5.
Spatial distribution of UGHQD level in China.
Note: Quoted from the Tianditu map. Source of base map: the open source map data service provided by the National Platform for Common GeoSpatial Information Services (https://www.tianditu.gov.cn/).
Table 2.
Results of the calculation of Moran’s I.
Fig 6.
The LISA cluster diagram of UGHQD.
Note: Quoted from the Tianditu map. Source of base map: the open source map data service provided by the National Platform for Common GeoSpatial Information Services (https://www.tianditu.gov.cn/).
Fig 7.
Cold and hot spot evolution of UGHQD.
Note: Quoted from the Tianditu map. Source of base map: the open source map data service provided by the National Platform for Common GeoSpatial Information Services (https://www.tianditu.gov.cn/).
Table 3.
Standard deviation ellipse parameters of UGHQD.
Fig 8.
The standard deviation ellipse of UGHQD.
Note: Quoted from the Tianditu map. Source of base map: the open source map data service provided by the National Platform for Common GeoSpatial Information Services (https://www.tianditu.gov.cn/).
Fig 9.
Distribution dynamics of UGHQD in China.
Fig 10.
Distribution dynamics of UGHQD in the four major regions.
Table 4.
Predicted results of UGHQD.
Fig 11.
Predicted and true value results.