Spatiotemporal heterogeneity in associations of national population ageing with socioeconomic and environmental factors at the global scale

Wan, Qing; Tang, Zhangying; Ju, Ke; Xie, Mingyu; Wang, Shaobin; Yin, Hao; Li, Junming; Liu, Xin; Yang, Yang; Song, Chao

doi:10.1016/j.jclepro.2022.133781

Cited by 10 publications

(7 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A variety of factors can influence population ageing, including demographic, economic, social, health and healthcare, and environmental factors (Wan et al, 2022;Beard & Bloom, 2015;Almedia de Melo et al, 2010). Many different factors influenced the ageing process of Bosnia and Herzegovina's population, but demographic factors and changes were the largest ones.…”

Section: Resultsmentioning

confidence: 99%

“…Population ageing is influenced by a variety of factors, including demographic, economic, social, environmental, health, and health care (Wan et al, 2022;Beard & Bloom, 2015;Almedia de Melo et al, 2010). Various studies have shown that numerous variables contribute to population ageing disparities in different areas of Bosnia and Herzegovina, with social, economic, and political factors being the most significant (Gekic et al, 2020;Gekic et al, 2021;Kadusic, Suljic, 2018).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Geospatial analysis of population ageing in Bosnia and Herzegovina

Kadusic¹,

Smajic²,

Kunosić³

et al. 2023

Geographica Pannonica

View full text Add to dashboard Cite

The aim of this research is to analyze the spatial distribution of the population ageing in Bosnia and Herzegovina and identify areas of the country that are particularly vulnerable to negative demographic trends. To achieve the goal of the study, data on the ageing coefficient and ageing index for the period 2013-2020 were used. The geospatial analysis of these indicators was performed using global (Global Moran's I and Getis-Ord General G) and local (Anselin Local Moran's I and Getis-Ord G*) indexes of spatial autocorrelation. The research results confirmed the clustering of both indicators. Ageing coefficient values are clustered in municipalities in western, northwestern, Eastern, and central Bosnia and Herzegovina. Ageing index values are clustered in municipalities in central, western, northwestern, and northeastern Bosnia and Herzegovina. This study provides insight into the research methods of spatial demographic trends and phenomena, and its findings can serve as a basis for future demographic research and development in Bosnia and Herzegovina.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Geospatial analysis of population ageing in Bosnia and Herzegovina

Kadusic¹,

Smajic²,

Kunosić³

et al. 2023

Geographica Pannonica

View full text Add to dashboard Cite

show abstract

“…Therefore, future research should consider more reasonable allocation methods to further improve the accuracy of landslide susceptibility assessments. Moreover, considering the second law of geography, a more reasonable screening of regional risk factors should take into account their spatial local heterogeneous (SLH) associations with landslides, and such SLH-based factor screening methods [90,91] are also worthy of continued research in the future.…”

Section: Limitations Of This Study and Prospects Of Future Researchmentioning

confidence: 99%

An Optimization of Statistical Index Method Based on Gaussian Process Regression and GeoDetector, for Higher Accurate Landslide Susceptibility Modeling

et al. 2022

Self Cite

View full text Add to dashboard Cite

Landslide susceptibility assessment is an effective non-engineering landslide prevention at the regional scale. This study aims to improve the accuracy of landslide susceptibility assessment by using an optimized statistical index (SI) method. A landslide inventory containing 493 historical landslides was established, and 20 initial influencing factors were selected for modeling. First, a combination of GeoDetector and recursive feature elimination was used to eliminate the redundant factors. Then, an optimization method for weights of SI was adopted based on Gaussian process regression (GPR). Finally, the predictive abilities of the original SI model, the SI model with optimized factors (GD-SI), and the SI model with optimized factors and weights (GD-GPR-SI) were compared and evaluated by the area under the receiver operating characteristic curve (AUC) on the testing datasets. The GD-GPR-SI model has the highest AUC value (0.943), and the GD-SI model (0.936) also has a higher value than the SI model (0.931). The results highlight the necessity of factor screening and weight optimization. The factor screening method used in this study can effectively eliminate factors that negatively affect the SI model. Furthermore, by optimizing the SI weights through GPR, more reasonable weights can be obtained for model performance improvement.

show abstract

“…Beyond the identification of spatiotemporally heterogeneous influence effects of potential factors, the more important concern lies in pinpointing the key determinants for healthcare resources, as they provide the foundation for potential intervention strategies. The spatiotemporal variance partitioning index (STVPI) offers an advanced means of evaluating the relative percentage contribution of each explanatory factor after accounting for spatiotemporal non-stationarity, aiming to elucidate pivotal spatiotemporal determinants [ 51 ]. Unfortunately, no studies to date have discovered the key spatiotemporal determinants driving small-area healthcare resources by assessing the contributions of influencing factors over space and time.…”

Section: Introductionmentioning

confidence: 99%

Revealing spatiotemporal inequalities, hotspots, and determinants in healthcare resource distribution: insights from hospital beds panel data in 2308 Chinese counties

Song,

Fang,

Xie

et al. 2024

BMC Public Health

Self Cite

View full text Add to dashboard Cite

Background Ensuring universal health coverage and equitable access to health services requires a comprehensive understanding of spatiotemporal heterogeneity in healthcare resources, especially in small areas. The absence of a structured spatiotemporal evaluation framework in existing studies inspired us to propose a conceptual framework encompassing three perspectives: spatiotemporal inequalities, hotspots, and determinants. Methods To demonstrate our three-perspective conceptual framework, we employed three state-of-the-art methods and analyzed 10 years’ worth of Chinese county-level hospital bed data. First, we depicted spatial inequalities of hospital beds within provinces and their temporal inequalities through the spatial Gini coefficient. Next, we identified different types of spatiotemporal hotspots and coldspots at the county level using the emerging hot spot analysis (Getis-Ord Gi* statistics). Finally, we explored the spatiotemporally heterogeneous impacts of socioeconomic and environmental factors on hospital beds using the Bayesian spatiotemporally varying coefficients (STVC) model and quantified factors’ spatiotemporal explainable percentages with the spatiotemporal variance partitioning index (STVPI). Results Spatial inequalities map revealed significant disparities in hospital beds, with gradual improvements observed in 21 provinces over time. Seven types of hot and cold spots among 24.78% counties highlighted the persistent presence of the regional Matthew effect in both high- and low-level hospital bed counties. Socioeconomic factors contributed 36.85% (95% credible intervals [CIs]: 31.84–42.50%) of county-level hospital beds, while environmental factors accounted for 59.12% (53.80–63.83%). Factors’ space-scale variation explained 75.71% (68.94–81.55%), whereas time-scale variation contributed 20.25% (14.14–27.36%). Additionally, six factors (GDP, first industrial output, local general budget revenue, road, river, and slope) were identified as the spatiotemporal determinants, collectively explaining over 84% of the variations. Conclusions Three-perspective framework enables global policymakers and stakeholders to identify health services disparities at the micro-level, pinpoint regions needing targeted interventions, and create differentiated strategies aligned with their unique spatiotemporal determinants, significantly aiding in achieving sustainable healthcare development.

show abstract

Spatiotemporal heterogeneity in associations of national population ageing with socioeconomic and environmental factors at the global scale

Cited by 10 publications

References 68 publications

Geospatial analysis of population ageing in Bosnia and Herzegovina

Geospatial analysis of population ageing in Bosnia and Herzegovina

An Optimization of Statistical Index Method Based on Gaussian Process Regression and GeoDetector, for Higher Accurate Landslide Susceptibility Modeling

Revealing spatiotemporal inequalities, hotspots, and determinants in healthcare resource distribution: insights from hospital beds panel data in 2308 Chinese counties

Contact Info

Product

Resources

About