Application of Deep Learning in Ecological Resource Research: Theories, Methods, and Challenges
Abstract
The spatial inequilibrium phenomenon is apparent during China’s rapid urbanization in the past four decades. As the fertility rate decreases and the population ages, this phenomenon will become more critical. To accurately forecast the future economic development of China, it is necessary to quantify the attractiveness of individual cities. This study introduces web search data to quantify the attractiveness of cities with a fine spatial scale (prefecture-level city) and relatively long-term span (nine years). Results confirm that the estimated city attractiveness can unravel a city’s capability to attract labor force, and suggest that tourism and health care functions of a city have a positive effect to the city’s attractiveness. Additionally, China’s north-south gap in economic development has been widened in the past decade, and 11 cities with nationwide influence have prosperous development potential. This study provides a new lens for predicting China’s economic development, as well as its spatial patterns
Hao Guo
Grad Student
2020 - present
My research interests lie in the general area of spatial analysis theory and methods, including spatial statistics, GeoAI, and spatial optimization.
Weiyu Zhang
PhD Student
2022 - present
I’m a P.H.D. student in the S3-Lab and supervised by Prof. Yu Liu. My research interests include Geographical Information Systems, Deep Learning(GeoAI), Urban Science, and Spatial-temporal Data Mining.
Haode Du
Grad Student
2022 - present
My research interests include Change Detection, GeoAI and Remote sensing
