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The study investigates how urbanization and demographic changes affect social vulnerability in Chinese floodplains.

April 12, 2026Open Access

Spatial heterogeneity and drivers of social vulnerability in chinese floodplains: a PCA-MGWR approach

Key Points

The study investigates how urbanization and demographic changes affect social vulnerability in Chinese floodplains.
Integrated census data with high-resolution floodplain maps.
Utilized a pixel-based spatial aggregation approach.
Employed Principal Component Analysis and Multiscale Geographically Weighted Regression (PCA-MGWR) framework.
Analyzed vulnerability dimensions including floating population and low education.
Identified four vulnerability dimensions: floating population, livelihood instability, low education, and lack of action capabilities.
The PCA-MGWR model significantly outperformed traditional models with an R2 of 0.713.
Revealed a higher flood exposure in affluent regions, challenging existing narratives on poverty and vulnerability.
Highlighted the importance of mobility and aging as critical vulnerability drivers.

Abstract

The spatial heterogeneity of social vulnerability in China’s floodplains remains underexplored, particularly regarding how rapid urbanization and unique demographic shifts-specifically the “floating population” and an aging society-reshape human-water relationships. This study quantifies flood exposure across 361 Chinese cities by integrating the 2020 Seventh National Population Census with high-resolution (250 m) floodplain maps via a pixel-based spatial aggregation approach. This methodology captures both absolute population scales and relative structural dependencies within hazard zones, effectively eliminating estimation bias from administrative-level averages. We propose a coupled Principal Component Analysis and Multiscale Geographically Weighted Regression (PCA-MGWR) framework to mitigate multicollinearity among socioeconomic variables while capturing spatially varying scale effects. The analysis identifies four distinct vulnerability dimensions: floating population (PC-FP), livelihood instability (PC-LI), low education (PC-EL), and lack of action capabilities (PC-AC). The PCA-MGWR model (R2 = 0.713) significantly outperforms traditional OLS and GWR models in explanatory power. Crucially, results reveal a “Paradox of Wealth Exposure” along the developed Yangtze River Economic Belt, where affluent regions exhibit higher flood exposure, challenging the conventional poverty-driven vulnerability narrative. Furthermore, the driving mechanisms exhibit significant spatial non-stationarity: (1) In eastern megacities, the floating population shows a statistical “risk aversion” effect driven by housing market sorting, yet remains vulnerable due to “institutional segregation” in emergency management; (2) In old industrial bases and eastern plains, an “in-situ aging” phenomenon has created a “spatial lock-in,” trapping elderly populations in high-risk zones. These findings suggest that “mobility” and “aging” have emerged as critical drivers of vulnerability in China alongside economic factors. Consequently, we advocate for spatially differentiated governance strategies, shifting from “one-size-fits-all” defenses to adaptive policies that address specific regional socio-hydrological traps.

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