Pub Date : 2019-09-04DOI: 10.1007/s40980-019-00053-3
Rachel J. Bacon
{"title":"Racial-Ethnic Diversity and the Decline of Predominantly-White Mainline and Evangelical Protestant Denominations: A Spatial Fixed-Effects Approach","authors":"Rachel J. Bacon","doi":"10.1007/s40980-019-00053-3","DOIUrl":"https://doi.org/10.1007/s40980-019-00053-3","url":null,"abstract":"","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"7 1","pages":"195 - 218"},"PeriodicalIF":1.9,"publicationDate":"2019-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40980-019-00053-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44392891","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}
Pub Date : 2019-07-18DOI: 10.1007/s40980-019-00052-4
Matthew M. Brooks
Although scholarship regarding spatial inequality has grown in recent years, past research has seen limited use of spatial statistics—let alone comparison between spatial statistical techniques. Comparing and contrasting the application and use of spatial statistics is valuable in research because it allows for more precise identification of spatial patterns, and highlights results that may be hidden when only using a single method. This study serves as a demonstration on how the use of multiple LISA statistics can benefit inequality related research. Analyzing changes in county level poverty in the rural United States from 1990 to 2015 serves as a tool to demonstrate these techniques and this study examined how the geographic distribution of poverty has changed, and well as if there is evidence of diffusion effects. The three featured techniques utilized Local Indicators of Spatial Association (LISA) statistics. The techniques are Bivariate LISA, LISA Cluster Transitions, and LISA Diffusion Transitions, with the last technique specifically designed for this study. Each technique varies in how it reports the changes in the spatial structure of poverty. Bivariate LISA and LISA Cluster Transitions are complementary to each other—with the former technique providing a single global statistic while the latter is more easily interpretable. Diffusion Transitions show how the highest and lowest values of a variable may be spreading over time. The study also produces new findings regarding rural poverty, with poverty in Mountain-West and rural Sun Belt counties on the rise. Analysis shows a diffusion effect for poverty in Southeastern metropolitan fringe counties.
尽管近年来有关空间不平等的学术研究有所增长，但过去的研究对空间统计的使用却很有限，更不用说对空间统计技术进行比较了。比较和对比空间统计的应用和使用在研究中很有价值，因为这样可以更精确地识别空间模式，并突出仅使用单一方法时可能被掩盖的结果。本研究展示了多种 LISA 统计方法的使用如何有益于与不平等相关的研究。分析 1990 年至 2015 年美国农村地区县级贫困人口的变化是展示这些技术的工具，本研究考察了贫困人口的地理分布发生了怎样的变化，以及是否存在扩散效应的证据。三种特色技术利用了地方空间关联指标（LISA）统计。这三种技术分别是双变量 LISA、LISA 集群过渡和 LISA 扩散过渡，其中最后一种技术是专门为本研究设计的。每种技术报告贫困空间结构变化的方式各不相同。双变量 LISA 和 LISA 聚类过渡互为补充--前者提供单一的总体统计数据，而后者更易于解释。扩散过渡显示了变量的最高值和最低值是如何随着时间的推移而扩散的。研究还得出了有关农村贫困的新发现，即西部山区县和阳光带农村县的贫困率在上升。分析表明，东南部大都市边缘县的贫困现象具有扩散效应。
{"title":"The Advantages of Comparative LISA Techniques in Spatial Inequality Research: Evidence from Poverty Change in the United States","authors":"Matthew M. Brooks","doi":"10.1007/s40980-019-00052-4","DOIUrl":"https://doi.org/10.1007/s40980-019-00052-4","url":null,"abstract":"Although scholarship regarding spatial inequality has grown in recent years, past research has seen limited use of spatial statistics—let alone comparison between spatial statistical techniques. Comparing and contrasting the application and use of spatial statistics is valuable in research because it allows for more precise identification of spatial patterns, and highlights results that may be hidden when only using a single method. This study serves as a demonstration on how the use of multiple LISA statistics can benefit inequality related research. Analyzing changes in county level poverty in the rural United States from 1990 to 2015 serves as a tool to demonstrate these techniques and this study examined how the geographic distribution of poverty has changed, and well as if there is evidence of diffusion effects. The three featured techniques utilized Local Indicators of Spatial Association (LISA) statistics. The techniques are Bivariate LISA, LISA Cluster Transitions, and LISA Diffusion Transitions, with the last technique specifically designed for this study. Each technique varies in how it reports the changes in the spatial structure of poverty. Bivariate LISA and LISA Cluster Transitions are complementary to each other—with the former technique providing a single global statistic while the latter is more easily interpretable. Diffusion Transitions show how the highest and lowest values of a variable may be spreading over time. The study also produces new findings regarding rural poverty, with poverty in Mountain-West and rural Sun Belt counties on the rise. Analysis shows a diffusion effect for poverty in Southeastern metropolitan fringe counties.","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"37 3 1","pages":"167-193"},"PeriodicalIF":1.9,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884780","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}
Pub Date : 2019-05-08DOI: 10.1007/s40980-019-00051-5
Pedzisai Ndagurwa, Clifford Odimegwu
This study assesses the capabilities of the 4-parameters own children method (4-pOCM) approach in the estimation of fertility rates of small areas using Schoumaker’s (2013) Poisson regression-based person-period approach (PPA). The paper was designed to appraise the Excel toolkit designed by Garenne and McCaa (2017) to implement the 4-pOCM in relation to Schoumaker’s (2013) Stata software command tfr2 which implements a Poisson regression-based PPA to calculate fertility rates. Using a descriptive approach, analyses were conducted on the 2015 Zimbabwe Demographic and Health Survey, applying the two tools and methods to the estimation of national and subnational fertility rates. The results showed that the 4-pOCM was able to maintain consistency in its estimates between national to subnational levels just like the proven tfr2. The study concluded that the 4-pOCM can be a reliable reference method for studying fertility trends of small areas especially in African contexts where reliable vital registration data are limited.
{"title":"Small Area Estimation of Fertility: Comparing the 4-Parameters Own-Children Method and the Poisson Regression-Based Person-Period Approach","authors":"Pedzisai Ndagurwa, Clifford Odimegwu","doi":"10.1007/s40980-019-00051-5","DOIUrl":"https://doi.org/10.1007/s40980-019-00051-5","url":null,"abstract":"This study assesses the capabilities of the 4-parameters own children method (4-pOCM) approach in the estimation of fertility rates of small areas using Schoumaker’s (2013) Poisson regression-based person-period approach (PPA). The paper was designed to appraise the Excel toolkit designed by Garenne and McCaa (2017) to implement the 4-pOCM in relation to Schoumaker’s (2013) Stata software command tfr2 which implements a Poisson regression-based PPA to calculate fertility rates. Using a descriptive approach, analyses were conducted on the 2015 Zimbabwe Demographic and Health Survey, applying the two tools and methods to the estimation of national and subnational fertility rates. The results showed that the 4-pOCM was able to maintain consistency in its estimates between national to subnational levels just like the proven tfr2. The study concluded that the 4-pOCM can be a reliable reference method for studying fertility trends of small areas especially in African contexts where reliable vital registration data are limited.","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"114 1","pages":"149-165"},"PeriodicalIF":1.9,"publicationDate":"2019-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884845","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}
Pub Date : 2019-04-01Epub Date: 2018-06-18DOI: 10.1007/s40980-018-0044-5
Barrett A Lee, Chad R Farrell, Sean F Reardon, Stephen A Matthews
Most quantitative studies of neighborhood racial change rely on census tracts as the unit of analysis. However, tracts are insensitive to variation in the geographic scale of the phenomenon under investigation and to proximity among a focal tract's residents and those in nearby territory. Tracts may also align poorly with residents' perceptions of their own neighborhood and with the spatial reach of their daily activities. To address these limitations, we propose that changes in racial structure (i.e., in overall diversity and group-specific proportions) be examined within multiple egocentric neighborhoods, a series of nested local environments surrounding each individual that approximate meaningful domains of experience. Our egocentric approach applies GIS procedures to census block data, using race-specific population densities to redistribute block counts of whites, blacks, Hispanics, and Asians across 50-meter by 50-meter cells. For each cell, we then compute the proximity-adjusted racial composition of four different-sized local environments based on the weighted average racial group counts in adjacent cells. The value of this approach is illustrated with 1990-2000 data from a previous study of 40 large metropolitan areas. We document exposure to increasing neighborhood racial diversity during the decade, although the magnitude of this increase in diversity-and of shifts in the particular races to which one is exposed-differs by local environment size and racial group membership. Changes in diversity exposure at the neighborhood level also depend on how diverse the metro area as a whole has become.
{"title":"From Census Tracts to Local Environments: An Egocentric Approach to Neighborhood Racial Change.","authors":"Barrett A Lee, Chad R Farrell, Sean F Reardon, Stephen A Matthews","doi":"10.1007/s40980-018-0044-5","DOIUrl":"https://doi.org/10.1007/s40980-018-0044-5","url":null,"abstract":"<p><p>Most quantitative studies of neighborhood racial change rely on census tracts as the unit of analysis. However, tracts are insensitive to variation in the geographic scale of the phenomenon under investigation and to proximity among a focal tract's residents and those in nearby territory. Tracts may also align poorly with residents' perceptions of their own neighborhood and with the spatial reach of their daily activities. To address these limitations, we propose that changes in racial structure (i.e., in overall diversity and group-specific proportions) be examined within multiple egocentric neighborhoods, a series of nested local environments surrounding each individual that approximate meaningful domains of experience. Our egocentric approach applies GIS procedures to census block data, using race-specific population densities to redistribute block counts of whites, blacks, Hispanics, and Asians across 50-meter by 50-meter cells. For each cell, we then compute the proximity-adjusted racial composition of four different-sized local environments based on the weighted average racial group counts in adjacent cells. The value of this approach is illustrated with 1990-2000 data from a previous study of 40 large metropolitan areas. We document exposure to increasing neighborhood racial diversity during the decade, although the magnitude of this increase in diversity-and of shifts in the particular races to which one is exposed-differs by local environment size and racial group membership. Changes in diversity exposure at the neighborhood level also depend on how diverse the metro area as a whole has become.</p>","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"7 1","pages":"1-26"},"PeriodicalIF":1.9,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40980-018-0044-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37352635","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}
Pub Date : 2019-04-01DOI: 10.1007/S40980-019-00047-1
David W. S. Wong
{"title":"Thomas, Richard K.: Concepts, Methods and Practical Applications in Applied Demography: An Introductory Text","authors":"David W. S. Wong","doi":"10.1007/S40980-019-00047-1","DOIUrl":"https://doi.org/10.1007/S40980-019-00047-1","url":null,"abstract":"","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"7 1","pages":"103-104"},"PeriodicalIF":1.9,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/S40980-019-00047-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46410174","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}
Pub Date : 2019-03-22DOI: 10.1007/s40980-019-00049-z
Ron Johnston
{"title":"David Darmofal and Ryan Strickler: Demography, Politics and Partisan Polarization in the United States, 1828–2016","authors":"Ron Johnston","doi":"10.1007/s40980-019-00049-z","DOIUrl":"https://doi.org/10.1007/s40980-019-00049-z","url":null,"abstract":"","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"18 1","pages":"105-108"},"PeriodicalIF":1.9,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884659","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}
Pub Date : 2019-03-04DOI: 10.1007/s40980-019-00048-0
Maria E. Kamenetsky, G. Chi, Donghui Wang, Jun Zhu
{"title":"Spatial Regression Analysis of Poverty in R","authors":"Maria E. Kamenetsky, G. Chi, Donghui Wang, Jun Zhu","doi":"10.1007/s40980-019-00048-0","DOIUrl":"https://doi.org/10.1007/s40980-019-00048-0","url":null,"abstract":"","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"7 1","pages":"113 - 147"},"PeriodicalIF":1.9,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40980-019-00048-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47969465","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}
Pub Date : 2018-11-27DOI: 10.1007/s40980-018-00046-8
Tara A. Smith, J. Sandoval
{"title":"A Spatial Analysis of Homicides in Saint Louis: The Importance of Scale","authors":"Tara A. Smith, J. Sandoval","doi":"10.1007/s40980-018-00046-8","DOIUrl":"https://doi.org/10.1007/s40980-018-00046-8","url":null,"abstract":"","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"7 1","pages":"57 - 82"},"PeriodicalIF":1.9,"publicationDate":"2018-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40980-018-00046-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53017372","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}
Pub Date : 2018-03-21DOI: 10.1007/s40980-018-0043-6
Cecilia Reynaud, Sara Miccoli, Francesco Lagona
Population ageing is one of the most important demographic phenomena of this century. Driven by fertility decline and the continuing extension of the life expectancy, the process of population ageing has not been uniform across time and space. Italy has one of the oldest populations in the world. The combination of a very old population and large territorial differences has made Italy an interesting laboratory for studying population ageing. The purpose of this paper is to study how population ageing developed between 2002 and 2014 across different geographical areas within Italy. We analyse patterns of population ageing across the five major socio-economic regions using the 110 provinces of Italy as our spatial units of analysis. We use a statistical model that integrates patterns of variation of population ageing data by accounting for autocorrelation in space and time. The results indicate that the provincial age structures tend to converge and demonstrate the importance of considering the role of space in studies of population ageing.
{"title":"Population Ageing in Italy: An Empirical Analysis of Change in the Ageing Index Across Space and Time","authors":"Cecilia Reynaud, Sara Miccoli, Francesco Lagona","doi":"10.1007/s40980-018-0043-6","DOIUrl":"https://doi.org/10.1007/s40980-018-0043-6","url":null,"abstract":"Population ageing is one of the most important demographic phenomena of this century. Driven by fertility decline and the continuing extension of the life expectancy, the process of population ageing has not been uniform across time and space. Italy has one of the oldest populations in the world. The combination of a very old population and large territorial differences has made Italy an interesting laboratory for studying population ageing. The purpose of this paper is to study how population ageing developed between 2002 and 2014 across different geographical areas within Italy. We analyse patterns of population ageing across the five major socio-economic regions using the 110 provinces of Italy as our spatial units of analysis. We use a statistical model that integrates patterns of variation of population ageing data by accounting for autocorrelation in space and time. The results indicate that the provincial age structures tend to converge and demonstrate the importance of considering the role of space in studies of population ageing.","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"5 1","pages":"235-251"},"PeriodicalIF":1.9,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884662","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}
Pub Date : 2018-03-07DOI: 10.1007/s40980-018-0042-7
Kyle E. Walker
Distance profiles have long been used in urban demography to explore how demographic characteristics of metropolitan areas vary by distance from their urban cores. Distance profile visualizations graphically illustrate these relationships and are useful in exploratory demographic data analysis of urban areas. The purpose of this article is to demonstrate how to build distance profile visualizations reproducibly within R, a free and open-source programming language and data analysis environment. The approach to distance profile visualization in this article involves the graphical display of a smoothed relationship between the location quotient of a demographic group for a metropolitan Census tract and the distance between the tract centroid and its respective urban core. Data acquisition, analysis, and visualization are all handled in R. The tidycensus, sf, and ggplot2 R packages are featured in this framework. Distance profile visualizations for educational attainment are used as illustrative examples, and reveal how the geography of metropolitan educational attainment varies both over time and across different types of metropolitan areas.
长期以来，城市人口学一直使用距离剖面图来探讨大都市地区的人口特征如何因其与城市核心的距离而变化。距离剖面可视化以图形方式说明了这些关系，在对城市地区进行探索性人口数据分析时非常有用。本文旨在演示如何在 R（一种免费开源的编程语言和数据分析环境）中可重复地构建距离剖面可视化。本文中的距离剖面可视化方法涉及以图形方式显示大都市人口普查区人口群体的位置商数与人口普查区中心点与其各自城市核心之间的距离之间的平滑关系。数据采集、分析和可视化均由 R 语言处理。在此框架中使用了 tidycensus、sf 和 ggplot2 R 软件包。教育程度的距离剖面可视化被用作示例，揭示了大都市教育程度的地理分布如何随时间和不同类型的大都市地区而变化。
{"title":"A Reproducible Framework for Visualizing Demographic Distance Profiles in US Metropolitan Areas","authors":"Kyle E. Walker","doi":"10.1007/s40980-018-0042-7","DOIUrl":"https://doi.org/10.1007/s40980-018-0042-7","url":null,"abstract":"Distance profiles have long been used in urban demography to explore how demographic characteristics of metropolitan areas vary by distance from their urban cores. Distance profile visualizations graphically illustrate these relationships and are useful in exploratory demographic data analysis of urban areas. The purpose of this article is to demonstrate how to build distance profile visualizations reproducibly within R, a free and open-source programming language and data analysis environment. The approach to distance profile visualization in this article involves the graphical display of a smoothed relationship between the location quotient of a demographic group for a metropolitan Census tract and the distance between the tract centroid and its respective urban core. Data acquisition, analysis, and visualization are all handled in R. The tidycensus, sf, and ggplot2 R packages are featured in this framework. Distance profile visualizations for educational attainment are used as illustrative examples, and reveal how the geography of metropolitan educational attainment varies both over time and across different types of metropolitan areas.","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"10 1","pages":"207-233"},"PeriodicalIF":1.9,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884661","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}
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