Calculating the Hu Huanyong Line
Bibliographic Data
| ID | 4928705 |
|---|---|
| Authors | Jia Xiao (0000-0001-8351-1524, Central China Normal University, corresponding author) |
| Year | 2025 |
| Volume | 116 |
| Issue | 1 |
| Pages | 1-22 |
| Publication date | 2025-08-06 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Annals of the American Association of Geographers (JOURNAL) |
| Journal identifiers | ISSN: 2469-4452 • E-ISSN: 2469-4460 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/24694452.2025.2534549 |
| OpenAlex | W4413006871 |
| Language | EN |
| References cited | 16 |
The Hu Huanyong line (Hu line), also known as the Heihe-Tengchong line, has been acknowledged as the foremost geographical demarcation of China’s population since its proposal in 1935. It is drawn by subjective judgment based on geographers’ remarkable insight, and despite various explanations from different perspectives, no computational method can currently calculate it. This research aims to address this challenging issue. Calculating the Hu line essentially involves solving a regionalization problem that has yet to be fully resolved. Using spatial aggregation entropy (SAE), an effective measure of spatial heterogeneity, we propose a new SAE regionalization model that tackles the optimal regionalization problem. With the SAE regionalization model, by conducting the two-partition regionalization of China’s county population density vector data for seven census years, we successfully generate seven calculated Hu lines. The calculated Hu lines validate the efficacy of the SAE regionalization model and confirm the long-term stability of the Hu line on a macro scale. The results also show that the Hexi Corridor region is included within the southeastern side of the calculated Hu lines, which is opposite to the division by the Hu line. By analyzing the multiscale regionalization results, we conclude that the population distribution is dominated by natural environmental factors on the large scale whereas social-economic factors dominate on the small scale. This study’s significant contribution is constructing the SAE regionalization model, achieving optimal regionalization, based on which the data-driven computational method of the Hu line is proposed. The SAE regionalization model is not only a powerful computational tool for exploring the evolution of population distribution but also able to be applied to regionalizing calculations in any other field
Geography · Political science · Spatial and Panel Data Analysis
Regionalization with dynamically constrained agglomerative clustering and partitioning (Redcap)
A measure of spatial stratified heterogeneity
The Analysis of Spatial Association by Use of Distance Statistics
A Geographical Solution to Scale and Aggregation Problems in Region-Building, Partitioning and Spatial Modelling
Local Indicators of Spatial Association—Lisa
Spatial Aggregation Entropy
Calibrating Spatial Stratified Heterogeneity for Heavy-Tailed Distributed Data
Statistical Modeling of Spatially Stratified Heterogeneous Data
| Highly cited | No |
|---|