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.
Child care and early education plays a vital role in the lives of young children and the social infrastructure of the United States. Extensive and emerging research to understand the accessibility of child care in the United States has focused more on the supply side of the industry than the demand side; there lacks reliable, accessible data regarding parents’ need or desire for child care across time and space. This study proposes using Google Trends data as an ancillary source for understanding child care demand across diverse temporal and spatial contexts because of the dataset’s free and accessible nature. The methods build on a growing body of literature examining best practices of using Google Trends for research, especially in harnessing geographic insights for the spatial components of the data. It contributes to this literature methodologically by demonstrating the usefulness of comprehensive replicate sampling in presenting Google Trends data and how what is considered “comprehensive” depends on the scale of the spatial unit being studied. This case study demonstrates that while Google Trends is a subtly complex data source that should be used judiciously, it is a particularly valuable surrogate for geographers and social scientists to use when no other viable datasets exist. The results reaffirm the need for multiple samples of the same Google Trends query over many days for accurate research insights, and it builds upon recent literature by exploring the use of bias-correcting techniques at different geographic scales. Furthermore, caution is warranted when using Google Trends to study search interest in precise geographies where there is little stability in the data.
Modern contraceptives are multifaceted, and delivery methods are obtained through various sources, such as private healthcare providers, public healthcare facilities, and others. The availability and distribution of these sources vary by geographical location, socio-economic factors, and government policies. Thus, the study analyzed the geospatial pattern of different sources of modern contraceptives in Nigeria. Data for the study were obtained from the Nigeria Demographic and Health Survey carried out between 2003 and 2018 involving women of reproductive age. The study adopts a robust and applicable model using a multicategorical response and a multinomial logistic model to investigate the importance of socio-demographic characteristics and geographical locations in determining the sources of modern contraceptives. The results show north-south geographical disparities in the use of private and public sectors for sourcing of modern contraceptives. The majority of women from most states in the northern part of Nigeria were more likely to source contraceptives from public sectors while most states in the south show preference for private sectors. The findings also indicate that as the number of children ever born increases, women were less likely to patronize private sectors for contraceptives. Other contributing socio-demographic characteristics include ethnicity and wealth index. The study suggests that creating unified educational campaigns that encourage the use of modern contraceptives regardless their source and structuring an intervention to correct the socioeconomic inequalities between the north and south is recommended could help improve access to the commodities.
Jammu, the second-largest urban centre in Jammu and Kashmir after Srinagar, has experienced rapid urbanisation accompanied by significant spatial restructuring. Between 1921 and 2011, the city’s population increased from 31,506 to 576,198, while the level of urbanisation rose from 11
Timely emergency department (ED) access is vital for young children under five (U-5) years and adults aged ≥ 85 years. This study assessed the spatial equity of populations at higher risk of delayed emergency care across Greater Melbourne to identify priority areas for intervention. We integrated 2021 mesh-block demographics with the geolocations of 39 public and private EDs. Using Voronoi tessellation, we defined service catchments and employed 15 km straight-line buffers as proxies for a 15-minute ambulance response range. Of 386,663 children U-5 and adults ≥ 85 across 46,608 populated mesh blocks, 92.9 ∼ 50
Influenza remains a significant and recurrent public health burden in temperate regions. Meteorological factors such as temperature, humidity, and rainfall are recognised as associated with influenza transmission patterns, exhibiting complex, nonlinear, temporally lagged, and spatially heterogeneous effects. This study employed a Spatial Bayesian Distributed Lag Non-Linear Model (SB-DLNM) to investigate the associations between meteorological factors and influenza incidence across 15 Local Health Districts, New South Wales, Australiathe short-term meteorological variables on influenza incidence across multiple Local Health Districts within New South Wales, Australia. The method incorporates (i) cross-basis functions to model delayed and non-linear meteorological impacts; (ii) a comparative analysis of case-crossover and time-series designs to distinguish monthly-lag associations from broader temporal trends; and (iii) spatial partial pooling to enhance the stability of estimates, particularly in data-sparse regions. Temperature demonstrated the strongest associations with influenza risk (Relative Risk (RR) range: 1.16–3.90), with elevated risks observed predominantly at cold temperature extremes. While exposure-response curves suggest minimum risk at moderate temperatures ( 18-22^∘C ), the available data primarily capture cold-related effects; warm-temperature associations remain uncertain due to limited extreme heat observations. Humidity showed marked spatial heterogeneity with variable effects across districts (RR range: 1.32–5.69), while rainfall demonstrated minimal associations (RR typically 1.03–1.42). Exceedance probabilities for RR>1 were moderate across all variables, ranging from 17.5 20-22^∘C ) A schematic overview of the workflow from merging meteorological and influenza data, evaluating four modelling approaches (with Model 3 highlighted as the best), to generating spatial risk maps and relative risk estimates for influenza in NSW.
This study examines intra-urban socio-economic inequalities in Palermo, analysing the degree of variation in household consumption expenditure across three geographical units: census tracts, First-Level Unit, and neighbourhoods. For each geographical unit, we present the distinct patterns of intra-urban inequalities identified and discuss them in the light of the socio-urban evolution of the city of Palermo. The analysis relies on the integration of two different data sources, the 2011 Census microdata and the 2019 Household Budget Survey, through a statistical matching technique. In this way, a synthetic dataset was obtained that includes information on household expenditure and their area of residence. A multilevel modelling approach is therefore used to exploit the hierarchical structure of our data, where households are grouped in nested territorial units. The results show that, even if most of the variation in consumption expenditure is due to household characteristics, significant territorial differences persist. The greatest between-area variation emerges at the census tract and neighbourhood levels, revealing patterns of macro- and micro-segregation.
In this paper, we propose a novel approach for measuring the presence of Organized Crime across Italian provinces. Recognizing that Organized Crime is a spatially heterogeneous phenomenon, we introduce a composite indicator that accounts for such structural heterogeneity while weighting individual indicators. Our method combines spatially-constrained hierarchical clustering with cluster-specific Principal Component Analysis. Italian provinces are grouped based on a spatially informed dissimilarity matrix, and a separate Principal Component Analysis is performed within each cluster to extract context-sensitive indicator weights. Using publicly available data, we adopt this procedure and new elementary indicators to provide an updated map of the Organized Crime phenomenon diffusion at the provincial scale in Italy.
Italy reports some of the lowest levels of mortality in the developed world. Recent evidence, however, suggests that even in low mortality countries improvements may be slowing and regional inequalities widening. This study contributes new empirical evidence to the debate by analysing mortality data by single year of age for males and females across 107 provinces in Italy from 2002 to 2019. We extend the widely used Lee Carter model to include spatially varying age specific effects, and further specify it to capture space age time interactions. The model is estimated in a Bayesian framework using the inlabru package, which builds on INLA (Integrated Nested Laplace Approximation) for non linear models and facilitates the use of smoothing priors. This approach borrows strength across provinces and years, mitigating random fluctuations in small area death counts. Results demonstrate the value of such a granular approach, highlighting the existence of an uneven geography of mortality despite overall national improvements. Mortality disadvantage is concentrated in parts of the Centre South and North West, while the Centre North and North East fare relatively better. These geographical differences have widened since 2010, with clear age and gender specific patterns, being more pronounced at younger adult ages for men and at older adult ages for women. Future work may involve refining the analysis to mortality by cause of death or socioeconomic status, informing more targeted public health policies to address mortality disparities across Italy's provinces.
The aim of this study is to examine the effects of hot days on birth rates in Serbia and its districts. This is the first study to address this issue in Serbia, with a regional focus stemming from the pronounced spatial heterogeneity in birth rates and climate conditions. The research utilizes data from demographic statistics on nearly 382,000 live births across 25 Serbian districts during the period 2015–2020, alongside data from the Digital Atlas of Serbia on daily average temperatures in the same timeframe. Inspired by methodologies from previous studies in other regions, this approach allows for an in-depth analysis of the impact of variations in daily air temperature distribution, particularly hot days with an average temperature exceeding 25 °C, on birth rates up to 12 months post-exposure. The results reveal that hot days (>25 °C) lead to a significant decline in birth rates approximately 9–10 months later. This is followed by a partial rebound, with a slight increase in birth rates observed in the 11th and 12th months. Regional variations within Serbia highlight differing levels of population adaptability, reflecting the diverse socio-climatic resilience across districts. These findings have important implications for developing locally tailored policies and responses to climate change. Enhancing population adaptability at the regional level may serve as a mechanism to mitigate the fertility impacts associated with climate change.
This paper explores the connection between interpersonal trust (both generalised and particularised) and health outcomes (chronic diseases, functional disabilities/ ADLs, and depression) in India, taking into account the potential influence of district-level developmental indicators as moderators. Multi-level regression analysis is conducted based on nationally representative data from WHO’s Study on global AGEing and adult health (SAGE) Wave-1 (2007/10) and district-level data from multiple sources. The study tests two hypotheses. The first, the buffer/compensatory hypothesis, suggests that in disadvantaged districts, trust compensates for resource scarcity, enhancing its positive impact on health. The second, the dependency hypothesis, posits that trust’s influence on health is stronger in advantaged districts, amplifying its effects in modern, resource-rich environments. The study confirms that both generalised and particularised trust positively affect health, acting as a buffer in resource-poor districts, reducing disability and depression, especially in districts with high scheduled caste populations. It also supports the dependency hypothesis, which suggests that trust is correlated with better health in socioeconomically advanced districts. For instance, generalised trust is associated with fewer disabilities and chronic diseases in areas with higher HDI, while particularised trust is particularly effective against disabilities in urban settings. The study highlights the importance of generalised and particularised trust in influencing health across different socio-economic contexts.
Holidays represent peak periods for population mobility. However, existing research has paid limited attention to the differences in mobility patterns across distinct holidays, and methodological approaches have often been confined to social network analysis. This study examines the spatial patterns and regional disparities of population mobility during China’s three major public holidays—the Spring Festival, May Day, and National Day—using Baidu migration big data from 2024. By integrating social network analysis with exploratory spatiotemporal data analysis, the study explores network structures, rank-size distributions, regional and provincial disparities, and local spatial dynamics across the three holiday periods. The key findings are as follows: (1) Population movement peaks during the Spring Festival, with May Day having the highest daily mobility and National Day falling in between. (2) The Spring Festival features both short- and long-distance inter-regional flows. May Day travel is mostly short-distance around major cities, while National Day involves more inter-regional movement to small and medium-sized cities. Intra-provincial travel dominates all three holidays. (3) Mobility patterns exhibit a southeast–northwest divide along the “Hu Line” and a diamond-shaped structure in the southeast. City mobility ranks follow the Rank-Size Rule but diverge to varying degrees from Zipf’s Law. (4) All holidays show strong spatial spillover and polarization effects. May Day has the most dynamic clustering, while Spring Festival patterns remain relatively stable. These findings provide valuable insights for informing targeted interventions and enhancing policy responsiveness to population mobility surges during major holidays.
A number of studies have shown that fertility levels differ substantially across settlement types in modern societies. As a rule, as the population density increases, fertility decreases. Such differences can occur due to a combination of three factors: (1) direct contextual influence, (2) differences in population composition between settlement types, and (3) selective migration. In this study, we aim to disentangle these factors with respect to completed fertility. We use linked micro-data from the Estonian population and housing censuses of 2000 and 2021 that we analyse with Poisson regression. The results show large fertility differences between settlement types, with women living in the capital city having the lowest, and women living in the more distant countryside having the highest number of children. Control of individual socio-demographic and housing-related variables (size, type, ownership), as well as migration experience, somewhat reduces the effects of settlement type. The analysis underlines the size of the dwellings to be especially relevant for the contextual effect. Looking at intercensal migration flows in greater detail reveals that the completed fertility of migrants lies mostly between the origin and destination groups.
Global gridded population (GGP) datasets provide estimates of a population within a grid and are used across various disciplines. However, their accuracy, particularly at the spatially fine grid-cell level, is poorly understood. Therefore, in this study, we empirically evaluated the accuracy of four common GGP datasets (GHS-POP, GPWv4, LandScan, and WorldPop) for Japan and several EU countries (France, Germany, Italy, and Sweden) at approximately 1-km2 resolution. In addition, we examined an ensemble of GGPs and smart selection of GGPs using a random forest model. The results indicated that (1) GHS-POP achieved at least second-best accuracy in most cases for both Japan and the EU; (2) simple averaging of GHS-POP, GPWv4, and WorldPop may improve the accuracy, although the sole use of GHS-POP was generally associated with better accuracy; and (3) dataset selection using a random forest model with the GGP datasets and infrastructure as explanatory variables did not improve accuracy outside the country for which the model was trained.
To address the imbalance between rapid land urbanization and the slower pace of population urbanization, China has recently implemented the “People-Land Linkage” policy, which aims to promote the urban integration of rural migrants through land use reforms. This paper investigates how urban land growth exerts both facilitative and crowding-out effects on the urbanization of agricultural migrants. Using data from CHIP 2008 and 2018, the Seventh National Population Census, and urban land expansion records, we employ ordered probit and conditional mixed process (CMP) models to identify the effects of newly added urban construction land on rural-to-urban migration. Our findings show that the growth of new urban land significantly increases migrants’ willingness to urbanize and improves both de facto and hukou-based urbanization rates. However, it simultaneously crowds out local farmers in terms of hukou conversion. Mechanism analyses indicate that the promotive effect is driven by improvements in social security coverage and public infrastructure, while the crowding-out effect is associated with lower satisfaction with work and living conditions. These findings suggest that greater coordination between land and labor markets is needed to optimize resource allocation and enhance overall social welfare.
Previous studies have attempted to define segregation succinctly but have not reached a consensus. Adding to the unsettled debates was the shift from using place-based to person-based data in segregation studies. Deduced from the literature, the meanings of segregation may be summarized by the two essences of measuring the distribution of and interaction among different population groups, and measures may be characterized by four elements of segregation: dimensions, spatial extent, social extent, and data type. Besides showing a trend from using spatially aggregated place-based data to individual-level person-based data, a review of selected papers confirms the salience of distribution and interaction in measuring segregation and highlights the limitations of existing measures in capturing social interaction between population groups and the impacts of segregation, even in recent studies that utilize person-based data. Finally, we propose a segregation measurement framework to accommodate the increasingly hybrid physical-virtual world using person-based data with three components: distributions of different groups, interactions among groups, and inequality experienced by different groups due to the disparities in accessing resources and opportunities. This review paper charts directions for future studies using segregation measures.
