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<p>Bangladesh faces significant earthquake risks due to its complex tectonic setting, rapid urbanization, and socio-economic vulnerability. This study introduces a two-stage spatial meta-regression to model these multidimensional risks, combining seismic records (1910–2024), urbanization data (2024), socio-economic information (2016 and 2022), and geophysical data from national sources. Statistical tests (Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling) confirmed non-normality (<i>p</i> &lt; 0.05), leading to the use of Principal Axis Factoring (PAF). The factor analysis identified four main dimensions influencing earthquake risk: seismic hazard, geophysical features, urbanization and land use, and socio-economic vulnerability. The proposed framework demonstrates strong structural coherence, with the model’s explanatory power (R²) increasing from 0.228 (Model 1) to 0.898 (Model 4). Key contributors to earthquake risk include vulnerable populations (0.67), earthquake magnitude (0.28), slope (0.18), and proximity to fault lines (-0.15). Spatial analysis identified Gazipur (3.23), Rangamati (1.52), Bandarban (1.05), Dhaka (1.00), Narayanganj (0.97), and Khagrachhari (0.82) as critical hotspots. A comparison with the Bangladesh National Building Code (BNBC) indicates that current seismic zoning underestimates risks, especially in densely populated and rapidly urbanizing areas. These findings emphasize the urgent need to update earthquake preparedness and risk reduction strategies by including multidimensional indicators that reflect tectonic activity, land-use patterns, and social vulnerability, to promote resilient and sustainable urban and infrastructure development in Bangladesh.</p> Graphical Abstract <p></p> <p>The graphical abstract outlines the framework and key findings of earthquake risk assessment in Bangladesh. Multisource seismic, geophysical, urban, and socioeconomic data are initially screened through exploratory data analysis, including outlier detection, normality tests, and assessments of sampling adequacy using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test. Exploratory Factor Analysis (EFA), employing Principal Axis Factoring with oblimin rotation, is then used to identify latent risk dimensions. Factor loadings are applied to derive standardized weight scores for all indicators. These EFA-derived weights serve as inputs to four nested spatial meta-regression models, which add predictor groups step-by-step to explain spatial variation in earthquake risk and to assess model stability. Finally, earthquake risk zones are established by classifying division-level META scores into five risk categories using hierarchical clustering, Jenks natural breaks, and K-means clustering. The findings highlight Gazipur, Rangamati, Bandarban, Dhaka, Narayanganj, and Khagrachhari as the most at-risk districts, emphasizing the importance of combining statistically derived weights with spatial modeling to enhance earthquake preparedness in Bangladesh.</p>

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What Shapes Earthquake Risk in Bangladesh? Geospatial Insights from Physical and Social Factors Using a Spatial Meta-Regression Approach

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Md. Mominul Islam,
  • Md. Najmul Kabir

摘要

Bangladesh faces significant earthquake risks due to its complex tectonic setting, rapid urbanization, and socio-economic vulnerability. This study introduces a two-stage spatial meta-regression to model these multidimensional risks, combining seismic records (1910–2024), urbanization data (2024), socio-economic information (2016 and 2022), and geophysical data from national sources. Statistical tests (Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling) confirmed non-normality (p < 0.05), leading to the use of Principal Axis Factoring (PAF). The factor analysis identified four main dimensions influencing earthquake risk: seismic hazard, geophysical features, urbanization and land use, and socio-economic vulnerability. The proposed framework demonstrates strong structural coherence, with the model’s explanatory power (R²) increasing from 0.228 (Model 1) to 0.898 (Model 4). Key contributors to earthquake risk include vulnerable populations (0.67), earthquake magnitude (0.28), slope (0.18), and proximity to fault lines (-0.15). Spatial analysis identified Gazipur (3.23), Rangamati (1.52), Bandarban (1.05), Dhaka (1.00), Narayanganj (0.97), and Khagrachhari (0.82) as critical hotspots. A comparison with the Bangladesh National Building Code (BNBC) indicates that current seismic zoning underestimates risks, especially in densely populated and rapidly urbanizing areas. These findings emphasize the urgent need to update earthquake preparedness and risk reduction strategies by including multidimensional indicators that reflect tectonic activity, land-use patterns, and social vulnerability, to promote resilient and sustainable urban and infrastructure development in Bangladesh.

Graphical Abstract

The graphical abstract outlines the framework and key findings of earthquake risk assessment in Bangladesh. Multisource seismic, geophysical, urban, and socioeconomic data are initially screened through exploratory data analysis, including outlier detection, normality tests, and assessments of sampling adequacy using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test. Exploratory Factor Analysis (EFA), employing Principal Axis Factoring with oblimin rotation, is then used to identify latent risk dimensions. Factor loadings are applied to derive standardized weight scores for all indicators. These EFA-derived weights serve as inputs to four nested spatial meta-regression models, which add predictor groups step-by-step to explain spatial variation in earthquake risk and to assess model stability. Finally, earthquake risk zones are established by classifying division-level META scores into five risk categories using hierarchical clustering, Jenks natural breaks, and K-means clustering. The findings highlight Gazipur, Rangamati, Bandarban, Dhaka, Narayanganj, and Khagrachhari as the most at-risk districts, emphasizing the importance of combining statistically derived weights with spatial modeling to enhance earthquake preparedness in Bangladesh.