Spatial Demography最新文献_第6页

Housing Values and the Residential Settlement of Migrants: Zooming in on Neighbourhoods in Italian Provincial Capitals 住房价值与移民的居住安置——聚焦意大利省会城市的邻里关系

IF 1.9

Spatial Demography Pub Date : 2020-11-07 DOI: 10.1007/s40980-020-00068-1

Sona Kalantaryan, A. Alessandrini

引用次数: 3

Correction to: Residential Mobility, Gentrification and Neighborhood Change in Spanish Cities: A Post-Crisis Perspective 修正:西班牙城市的住宅流动性、中产阶级化和邻里变化:危机后的视角

IF 1.9

Spatial Demography Pub Date : 2020-11-02 DOI: 10.1007/s40980-020-00070-7

Antonio López-Gay, Andrea Andújar-Llosa, Luca Salvati

引用次数: 0

Residential Mobility, Gentrification and Neighborhood Change in Spanish Cities: A Post-Crisis Perspective 西班牙城市的住宅流动性、中产阶级化和邻里变化:后危机视角

IF 1.9

Spatial Demography Pub Date : 2020-09-24 DOI: 10.1007/s40980-020-00069-0

A. López‐Gay, Andrea Andújar-Llosa, L. Salvati

引用次数: 21

Residential Segregation by Race and Ethnicity and the Changing Geography of Neighborhood Poverty 按种族和民族划分的居住隔离与邻里贫困的地理变化

IF 1.9

Spatial Demography Pub Date : 2020-08-25 DOI: 10.1007/s40980-020-00066-3

Chris Hess

引用次数: 17

Correction to: Comparing Residential Segregation of Migrant Populations in Selected European Urban and Metropolitan Areas 更正:比较欧洲部分城市和大都市地区移民人口的居住隔离

IF 1.9

Spatial Demography Pub Date : 2020-08-20 DOI: 10.1007/s40980-020-00067-2

Federico Benassi, Corrado Bonifazi, Frank Heins, Fabio Lipizzi, Salvatore Strozza

引用次数: 0

Comparing Residential Segregation of Migrant Populations in Selected European Urban and Metropolitan Areas 比较欧洲部分城市和大都市移民人口的居住隔离

IF 1.9

Spatial Demography Pub Date : 2020-07-14 DOI: 10.1007/s40980-020-00064-5

F. Benassi, C. Bonifazi, F. Heins, Fabio Lipizzi, S. Strozza

引用次数: 17

Revealing Multiscale Segregation Effects from Fine-Scale Data: A Case Study of Two Communities in Paris 从精细尺度数据揭示多尺度隔离效应:以巴黎两个社区为例

IF 1.9

Spatial Demography Pub Date : 2020-07-13 DOI: 10.1007/s40980-020-00065-4

Madalina Olteanu, Cecile de Bezenac, William Clark, Julien Randon-Furling

引用次数: 1

Super Aging in South Korea Unstoppable but Mitigatable: A Sub-National Scale Population Projection for Best Policy Planning. 韩国的超级老龄化势不可挡，但可以缓解:为最佳政策规划进行的次国家规模人口预测。

IF 1.9

Spatial Demography Pub Date : 2020-07-01 Epub Date: 2020-06-12 DOI: 10.1007/s40980-020-00061-8

Kee Whan Kim, Oh Seok Kim

{"title":"Super Aging in South Korea Unstoppable but Mitigatable: A Sub-National Scale Population Projection for Best Policy Planning.","authors":"Kee Whan Kim, Oh Seok Kim","doi":"10.1007/s40980-020-00061-8","DOIUrl":"https://doi.org/10.1007/s40980-020-00061-8","url":null,"abstract":"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.","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40980-020-00061-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39151734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 52

Modelling Age Patterns of Internal Migration at the Highest Ages 对最高年龄的国内移民年龄模式进行建模

IF 1.9

Spatial Demography Pub Date : 2020-06-25 DOI: 10.1007/s40980-020-00062-7

Tom Wilson

引用次数: 4

U.S. Fertility Decline and Recuperation Following the Great Recession by County-Level Industrial Composition of the Labor Force 大衰退后美国生育率的下降和恢复——以县级劳动力工业构成为例

IF 1.9

Spatial Demography Pub Date : 2020-06-16 DOI: 10.1007/s40980-020-00063-6

Jeongsoo Kim, Lloyd B. Potter

{"title":"U.S. Fertility Decline and Recuperation Following the Great Recession by County-Level Industrial Composition of the Labor Force","authors":"Jeongsoo Kim, Lloyd B. Potter","doi":"10.1007/s40980-020-00063-6","DOIUrl":"https://doi.org/10.1007/s40980-020-00063-6","url":null,"abstract":"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.","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138510193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0