Urban cognizable synergistic features
As the activation function in the output layer is linear, it is possible
to extract cognitive and comprehensible features from the penultimate
layer (Yu & Seltzer 2011). Nine features with bottom-up information
were interpreted and understood. Considering the range of Y and the
weight of the features, significant features that contained more
information about the corresponding factors were selected. The features
were named according to X, which had a great influence on them, except
feature 7, which contained information on almost all the factors and
features 9 and 15, which contained little information on any factors.
Therefore, 6 cognizable synergistic features integrated from multiple
factors were extracted, including the urban expansion factor (0.0133),
land use-industrial structure-energy consumption structure (0.0146),
land use-energy consumption structure, agricultural development
(0.0146), city-scale factor (0.0151) and tourism exploitation potential
(0.0138) (Table 3). Among these factors, the city-scale factor was the
most significant urban cognizable feature with the highest weight
(0.0151), and it contained information about GDP, population and tourism
output. This finding demonstrated that the deep learning model may
predict ESV by quantifying regional characteristics, including land-use
structure, energy consumption structure, industrial structure, and city
scale. Therefore, it is possible to adjust the urban macro
characteristics to maintain or even improve the regional ESV.
Table 3. The extraction and analysis of urban cognizable
features