Floods are one of the most damaging natural hazards globally, but measuring local impacts is complicated by challenges in the measurement of flood incidence, especially in Global South contexts. This paper examines household-level exposure and vulnerability to the major 2012 floods in Nigeria and studies how sensitive estimates are to different approaches to flood measurement. We construct seven different measures of flood incidence from survey reports, flood databases, and satellite imagery, which capture both physical inundation and reported damages. We find limited cross-measure correlations in flood incidence, while exposure estimates range from 0.03 to 13
Development conditions in ethnic villages in China’s western mountainous regions exhibit pronounced spatial heterogeneity shaped by the interplay of geographic constraints, socio-cultural structures, demographic conditions, and infrastructure accessibility. Drawing on a complex-systems-informed perspective, this study examines 300 multi-ethnic villages in Sichuan Province through an integrated cross-sectional analytical framework. Village development is conceptualized as a multidimensional configuration rather than as a single economic outcome. Spatial statistics, including Global Moran’s I and Getis-Ord Gi*, are first used to identify spatial patterns and localized clustering. PCA and K-means clustering are then employed to derive multidimensional development typologies. XGBoost-SHAP analysis used as a post-hoc interpretive tool to identify variables that most strongly distinguish the derived typologies, while mutual information analysis further examines interdependence among variables. Four development typologies are identified: economically advantaged; education-supported transitional; culturally resource-dependent under geographic constraints; and infrastructure-facilitated but constrained-return profiles. The results show that transportation accessibility, ethnic attributes, education quality, and elevation are the most salient variables differentiating these typologies, while conventional economic indicators play a comparatively limited role in the model structure. Mutual information analysis further reveals strong dependence between ethnicity and elevation, suggesting that ethnic attributes should be interpreted as a proxy for historically embedded, geographic, cultural, and institutional c. This study provides an interpretable framework for diagnosing multidimensional rural differentiation and offers implications for place-sensitive governance in ethnic regions.
South Korea is experiencing rapid population ageing, with pronounced differences between rural and urban areas. Internal migration, which plays an important role in South Korean society, may contribute to these regional disparities. This study examines the contribution of internal migration to regional age-structure changes in South Korea between 2012 and 2023. Using administrative data from 17 provinces and 229 municipal units, distinguished by their degree of urbanization, we perform a decomposition analysis to assess the impact of internal migration on regional old-age dependency ratios. The results reveal substantial differences in internal migration patterns and their effects on regional age structures. Notably, internal migration has decelerated population ageing in the provinces of Jeju, Sejong, and Seoul, while accelerating it in most other provinces. At the municipal level, towns and semi-dense areas have benefited the most, reflecting evolving suburbanization processes as well as the influence of large-scale spatial development policies. While the overall impact of internal migration on population ageing is moderate, it has reduced disparities in old-age dependency ratios between highly and moderately densely populated areas. In contrast, the faster population ageing in rural areas can be partially attributed to internal migration. Beyond migration among young and middle-aged adults, migration at older ages also plays a meaningful role in shaping these dynamics. Overall, the findings highlight the importance of considering internal migration when analyzing regional disparities in ageing societies.
Infectious disease spread occurs both temporally and spatially at a variety of scales. These scales could be temporal and/or spatial. In the case of the COVID-19 pandemic, much effort has been made to consider temporal modeling and prediction of mortality and case incidence in the time domain. Meanwhile the spatial structure of the epidemic spread has had less attention, and in particular the explicit linkage between multiple spatial scales, has received comparatively less attention. We have addressed this evaluation based on CDC data archived from the COVID-19 dashboard for the weeks of January 2020 to June 2023, available for the counties and state level of South Carolina. Our results indicate that joint models which have linked scale components are to be preferred in terms of goodness of fit compared to separate models. Multi-scale components also benefit for separate models, where time series models applied solely to the state level do not fit as well as models, which exploit linkages with case counts and cumulative counts of disease. Spatial correlation effects are supported in multi-scale models but are not favored when separate models are considered. These findings suggest that basing policy decisions on models which disregard linkage across different scale levels could be biased and incorrectly estimated.
Hidden urbanization is often framed as the under-recognition of settlements that exhibit urban characteristics but remain classified as rural. Existing approaches, ranging from density-based methods to satellite-derived and hybrid classifications, frequently produce higher estimates of urbanization level, yet rely on differing definitions of what constitutes urban, thereby limiting comparability. This paper adopts a complementary perspective by shifting attention from measurement outcomes to the processes that shape them. Focusing on two eastern Indian states (West Bengal and Bihar), we examine how demographic dynamics interact with statistical classification systems to influence whether rural settlements are formally recognized as urban. We show that the reclassification of rural settlements, a key pathway of urbanization, depends on demographic momentum. When rural population growth slows, settlements may fail to reach population thresholds and therefore remain unrecognized as urban, despite ongoing economic transformation. This process gives rise to distinct geographies of hidden urbanization, including dispersed and independent forms beyond peri-urban contexts. A comparison with DEGURBA shows that under-recognition persists across frameworks, revealing how classification criteria shape the visibility of settlement transformation. Our study demonstrates that hidden urbanization reveals how demographic dynamics, in conjunction with other processes, influence the statistical classification systems that determine what is officially recognized as urban.
