Spatial Demography最新文献_第3页

Introduction to the Special Issue on Population Dynamics in Africa 非洲人口动态特刊简介

IF 1.9

Spatial Demography Pub Date : 2022-08-01 DOI: 10.1007/s40980-022-00111-3

E. Gayawan

引用次数: 0

Modelling the Spatial Heterogeneity of Female-Male Ratio in West Bengal, India 印度西孟加拉邦男女比例的空间异质性建模

IF 1.9

Spatial Demography Pub Date : 2022-06-09 DOI: 10.1007/s40980-022-00107-z

Indrita Saha

引用次数: 0

Examining Spatial Heterogeneity and Potential Risk Factors of Childhood Undernutrition in High-Focus Empowered Action Group (EAG) States of India 研究印度高度关注授权行动小组（EAG）各州儿童营养不良的空间异质性和潜在风险因素

IF 1.9

Spatial Demography Pub Date : 2022-06-08 DOI: 10.1007/s40980-022-00108-y

Pravat Bhandari, E. Gayawan

引用次数: 2

Trends in the “Ecological Distance” of Ethnoracial Group Suburbanization in U.S. Metropolitan Areas, 1970–2019 1970-2019年美国大都市族群郊区化的“生态距离”趋势

IF 1.9

Spatial Demography Pub Date : 2022-05-30 DOI: 10.1007/s40980-022-00106-0

Jeffrey M. Timberlake, A. J. Howell

引用次数: 2

A Spatial Approach to the Study of the Electoral Resurgence of the Extreme Right in Southern Spain 西班牙南部极右翼选举复苏的空间研究方法

IF 1.9

Spatial Demography Pub Date : 2022-04-04 DOI: 10.1007/s40980-022-00105-1

Ricardo Iglesias-Pascual, Federico Benassi, Virginia Paloma

引用次数: 2

Decomposing County-Level Working-Age Mortality Trends in the United States Between 1999-2001 and 2015-2017. 1999-2001 年至 2015-2017 年美国县级工龄死亡率趋势分解》（Decomposing County-Level Working-Age Mortality Trends in the United States Between 1999-2001 and 2015-2017）。

IF 1.9

Spatial Demography Pub Date : 2022-04-01 Epub Date: 2021-08-24 DOI: 10.1007/s40980-021-00095-6

Nick Graetz, Irma T Elo

{"title":"Decomposing County-Level Working-Age Mortality Trends in the United States Between 1999-2001 and 2015-2017.","authors":"Nick Graetz, Irma T Elo","doi":"10.1007/s40980-021-00095-6","DOIUrl":"10.1007/s40980-021-00095-6","url":null,"abstract":"Studies have documented significant geographic divergence in U.S. mortality in recent decades. However, few studies have examined the extent to which county-level trends in mortality can be explained by national, state, and metropolitan-level trends, and which county-specific factors contribute to remaining variation. Combining vital statistics data on deaths and Census data with time-varying county-level contextual characteristics, we use a spatially explicit Bayesian hierarchical model to analyze the associations between working-age mortality, state, metropolitan status and county-level socioeconomic conditions, family characteristics, labor market conditions, health behaviors, and population characteristics between 2000 and 2017. Additionally, we employ a Shapley decomposition to illustrate the additive contributions of each changing county-level characteristic to the observed mortality change in U.S. counties between 1999-2001 and 2015-2017 over and above national, state, and metropolitan-nonmetropolitan mortality trends. Mortality trends varied by state and metropolitan status as did the contribution of county-level characteristics. Metropolitan status predicted more of the county-level variance in mortality than state of residence. Of the county-level characteristics, changes in percent college-graduates, smoking prevalence and the percent of foreign-born population contributed to a decline in all-cause mortality over this period, whereas increasing levels of poverty, unemployment, and single-parent families and declines manufacturing employment slowed down these improvements, and in many nonmetropolitan areas were large enough to overpower the positive contributions of the protective factors.","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9435968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40351237","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: 0

Identifying Geographical Heterogeneity in Associations between Under-Five Child Nutritional Status and Its Correlates Across Indian Districts 确定印度各区五岁以下儿童营养状况及其相关因素之间的地理异质性

IF 1.9

Spatial Demography Pub Date : 2022-03-08 DOI: 10.1007/s40980-022-00104-2

Monirujjaman Biswas

{"title":"Identifying Geographical Heterogeneity in Associations between Under-Five Child Nutritional Status and Its Correlates Across Indian Districts","authors":"Monirujjaman Biswas","doi":"10.1007/s40980-022-00104-2","DOIUrl":"https://doi.org/10.1007/s40980-022-00104-2","url":null,"abstract":"India has substantially reduced the burden of under-five child malnutrition over the last two decades. Despite this, it is still gigantic and differs remarkably across districts, while the demographic and socio-economic groups are most affected by it. This paper aimed to decrypt the place-specific spatial dependence and heterogeneity in associations between district-level nutritional status (stunting, wasting and underweight) and its considered correlates using a geocoded database for all 640 Indian districts from the latest fourth wave of the National Family Health Survey, 2015–16. Univariate Moran’s I and LISA statistics were used to confirm the spatial clustering and dependence in under-five nutritional status. The Ordinary Least Square (OLS), Geographically Weighted Regression (GWR), Spatial (lag/error) models were employed to examine the effects of correlates on the district-level nutritional status. The mean (Moran’s I) district-level stunting, wasting and underweight were 38% (0.634), 21% (0.488) and 36% (0.721), respectively. The GWR results disclosed that the spatial heterogeneity in relationships between district-level nutritional status and its driving forces were strongly location-based, altering their direction, magnitude and strength across districts. Overall, the localized model performed better, and best fit the data than the OLS and spatial (lag/error) models. This nationwide study confirmed that the spatial dependencies and heterogeneities in the district-level nutritional status indicators were strongly explained by a multitude of factors and thus can help policymakers in formulating effective nutrition-specific programmatic interventions to speed up the coverage of under-five malnutrition status in most priority districts and geographical hot spots across India.","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138528426","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: 3

Social Vulnerability and Childhood Health: Bayesian Spatial Models to Assess Risks from Multiple Stressors on Childhood Diarrhoea in Malawi 社会脆弱性和儿童健康:贝叶斯空间模型评估马拉维儿童腹泻多重压力源的风险

IF 1.9

Spatial Demography Pub Date : 2022-01-14 DOI: 10.1007/s40980-021-00101-x

Lawrence N. Kazembe

{"title":"Social Vulnerability and Childhood Health: Bayesian Spatial Models to Assess Risks from Multiple Stressors on Childhood Diarrhoea in Malawi","authors":"Lawrence N. Kazembe","doi":"10.1007/s40980-021-00101-x","DOIUrl":"https://doi.org/10.1007/s40980-021-00101-x","url":null,"abstract":"Childhood diarrhoea accounts for over 15% of all under-five deaths in Africa. The disease is exacerbated by social vulnerability. This study operationalizes social vulnerability by using three indicators: water poverty, sanitation and assets, to capture social disadvantage, which measures individual or community resources to prevent or mitigate health effects. We particularly investigated the relationship between childhood diarrhoea and risks emanating from multiple stressors: water poverty, poor sanitation and low wealth status, which define social vulnerability. Using data from the 2013/14 Malawi MDG Endline Survey (MMES), we fitted spatial models assuming that the combined effect of social vulnerability indicators, together with individual covariates, exhibit spatial correlation and heterogeneity on the outcome-diarrhoea status. Findings showed evidence of spatially varying risk imposed by social vulnerability indicators on childhood diarrhoea. We established a positive relationship between diarrhoea and water poverty, and negative association with poor sanitation and low wealth status. Spatial characterization of health effects of social vulnerability presents an important step towards targeted interventions in diarrhoea management. Our use of district level mapping provides for optimal planning and implementation, particularly, for the lowly placed individuals who are geographically located in high risk areas, since most decentralized decision making processes are made at this level.\u0000","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138528436","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

Evaluating Alternative Implementations of the Hamilton-Perry Model for Small Area Population Forecasts: the Case of Australia 评估汉密尔顿-佩里模型在小区域人口预测中的替代实施:以澳大利亚为例

IF 1.9

Spatial Demography Pub Date : 2022-01-05 DOI: 10.1007/s40980-021-00103-9

Tom Wilson, Irina Grossman

{"title":"Evaluating Alternative Implementations of the Hamilton-Perry Model for Small Area Population Forecasts: the Case of Australia","authors":"Tom Wilson, Irina Grossman","doi":"10.1007/s40980-021-00103-9","DOIUrl":"https://doi.org/10.1007/s40980-021-00103-9","url":null,"abstract":"Small area population forecasts are widely used across the public and private sectors, with many users requiring forecasts broken down by sex and age group. The preparation of small area age-sex population forecasts across a whole country or State with a multiregional cohort-component model is usually a time-consuming and expensive task. It involves the purchase of large datasets, considerable amounts of complex data preparation and assumption-setting, and substantial amounts of staff time. A quicker and lower-cost alternative is to use a reduced form cohort projection model, such as the Hamilton-Perry model. This paper presents an evaluation of various implementations of the Hamilton-Perry model, including an alternative version employing a combination of Cohort Change Ratios and Cohort Change Differences. It also evaluates the effects on forecast accuracy of smoothing the age profiles of Cohort Change Ratios and Differences, and constraining to independent population forecasts. Population ‘forecasts’ were created for small areas of Australia over the horizon 2006–16 and compared against population estimates. The most accurate implementation is found to be the Hamilton-Perry model using a combination of Cohort Change Ratios and Cohort Change Differences, smoothed age profiles, and with constraining to independent forecasts.","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138528438","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: 3

Community Boosts Immunity? Exploring the Relationship Between Social Capital and COVID-19 Social Distancing. 社区提高免疫力?探讨社会资本与COVID-19社会距离的关系

IF 1.9

Spatial Demography Pub Date : 2022-01-01 Epub Date: 2021-10-04 DOI: 10.1007/s40980-021-00096-5

Joseph Gibbons, Tse-Chuan Yang, Eyal Oren

引用次数: 3