Spatial Demography最新文献

In above mentioned article first and family names in the author group have been published in reversed order.

Fine-scale data is particularly important for the analysis of multiscalar segregation phenomena. Using dis-aggregated data from an EU data challenge, we show here how to apply a recently developed method that measures segregation at multiple scales and provides a visualization of the levels of segregation across scale and space. We illustrate the technique with results for two groups of citizen migrants in the city of Paris.

This research portrays the spatial and temporal progression of super-aging in regions throughout South Korea. Using a single-year population projection considering gross domestic migration, this research identifies which regions will shortly become a super-aged society. A cohort-component method with a migrant pool model is applied. The county-level national population registration data (2000-2018) are aggregated into 37 regions for the model run. In 2020, 16 rural regions will become super-aged societies. By 2029, all 37 regions, including the metropolitan areas, will join the group, with Sejong, the administrative capital, being the last to enter. In brief, the rural areas become super-aged earlier than the metropolitan areas, and within a decade, those 65 years old or older will make up the majority of the national population. Among all the metropolitan areas, Busan, the largest harbor city, will be the first to be super-aged in 2023. Sejong will experience the most radical change between 2020 and 2050. The research outcomes demonstrate that demographic changes in the rural and metropolitan areas are different; hence, the recent population policies, such as promoting fertility, may not work in the rural areas as they have already lost their population momentum due to the extreme and ongoing urbanization throughout the nation. The unstoppable aging will pose adverse effects on future citizens (who are mostly senior) both financially and medically. An increase in health care expenditure and a nationwide blood shortage for transfusion are anticipated, for example.

Model migration age schedules have proved valuable to demographers for a range of applications for over 40 years. The original Rogers-Castro curve has been extended over time to include a retirement curve, a post-retirement curve, and a student peak. With demographic analyses extending to higher age groups than in the past due to population ageing, it is important for the model schedule to faithfully reflect migration patterns at advanced ages. Recent data on internal migration in the nonagenarian and centenarian ages reveals several examples of rising then falling mobility with increasing age. This paper suggests an alternative specification of the post-retirement curve of the model schedule to reflect this pattern. The modified model migration schedule is successfully fitted to example internal migration age patterns from Australia, Canada and the Netherlands. The modified schedule should prove useful in preparing input data for population projections and analyses of migration age patterns extending to the highest ages.

Amid a persistent U.S. fertility decline since the Great Recession, fertility recuperation patterns by geographic regions were not homogeneous. This study hypothesizes that the geographic discrepancies in fertility patterns are attributable to different labor force compositions by the regions. We use data from the U.S. Census Bureau’s Annual Estimates of the Resident Population County Components of Population Change to estimate the discrepancy in fertility variations at the county-level. By comparing the slopes of births before and following the recession, we visualize the characteristics of fertility variations at the U.S. county-level. Also, a multiple linear regression model estimates that the counties with a greater share of labor force in wholesale trade, information & technology, finance & insurance, and professional & scientific industry show greater volatility in fertility trends throughout the Great Recession. On the contrary, the counties with higher proportions of the labor force in agriculture, retail trade, and education industry tend to less change over the years of the economic recession. However, fertility recuperation is limitedly identified amid the structural fertility decline after the Great Recession.