Spatial DemographyPub Date : 2020-01-01Epub Date: 2020-12-21DOI: 10.1007/s40980-020-00071-6
Gemma Catney, Christopher D Lloyd
{"title":"Population Grids for Analysing Long-Term Change in Ethnic Diversity and Segregation.","authors":"Gemma Catney, Christopher D Lloyd","doi":"10.1007/s40980-020-00071-6","DOIUrl":"https://doi.org/10.1007/s40980-020-00071-6","url":null,"abstract":"<p><p>Changes in the spatial patterns of ethnic diversity and residential segregation are often highly localized, but inconsistencies in geographical data units across different time points limit their exploration. In this paper, we argue that, while they are often over-looked, population grids provide an effective means for the study of long-term fine-scale changes. Gridded data represent population structures: there are gaps where there are no people, and they are not (unlike standard zones) based on population distributions at any one time point. This paper uses an innovative resource, <i>PopChange</i>, which provides spatially fine-grained (1 km by 1 km) gridded data on country of birth (1971-2011) and ethnic group (1991-2011). These data enable insight into micro-level change across a long time period. Exploring forty years of change over five time points, measures of residential ethnic diversity and segregation are employed here to create a comprehensive 'atlas' of ethnic neighbourhood change across the whole of Britain. Four key messages are offered: (1) as Britain's ethnic diversity has grown, the spatial complexity of this diversity has also increased, with greater diversity in previously less diverse spaces; (2) ethnic residential segregation has steadily declined at this micro-scale; (3) as neighbourhoods have become more diverse, they have become more spatially integrated; (4) across the whole study period, the most dynamic period of change was between 2001 and 2011. While concentrating on Britain as a case study, the paper explores the potential offered by gridded data, and the methods proposed to analyse them, for future allied studies within and outside this study area.</p>","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"8 3","pages":"215-249"},"PeriodicalIF":1.9,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40980-020-00071-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39087332","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}
{"title":"The Unique Case of Minneapolis–St. Paul, MN: Locational Attainments and Segregation in the Twin Cities","authors":"Amber R. Crowell, Mark Fossett","doi":"10.1007/s40980-019-00056-0","DOIUrl":"https://doi.org/10.1007/s40980-019-00056-0","url":null,"abstract":"The Minneapolis–St. Paul Metropolitan Area has a rapidly growing foreign-born population in part due to its high levels of refugee reception and migrants drawn to the burgeoning high-tech and manufacturing industries. As a result, the Twin Cities are unique in that every major racial group has a sizable foreign-born segment with a wide range of U.S. entry experiences and thus the area offers an opportunity to investigate the dynamics of locational attainments and segregation of a highly diverse non-White population. Accordingly, we examine the residential outcomes of Blacks, Latinos and Asians, investigate how nativity, socioeconomic gains, and acculturation translate into residential contact with Whites, and draw the link between these micro-level locational attainments and overall segregation patterns for the area. We find Latinos and Asians experience traditional spatial assimilation dynamics but a different pattern is seen for Blacks wherein foreign-born Blacks are less segregated than U.S.-born Blacks, reversing the expected role of nativity and acculturation and suggesting a more complicated story of ethnic stratification and assimilation supported by the segmented assimilation framework.","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"114 1","pages":"1-31"},"PeriodicalIF":1.9,"publicationDate":"2019-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884558","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}
Maria Kamenetsky, Guangqing Chi, Donghui Wang, Jun Zhu
{"title":"Spatial Regression Analysis of Poverty in R.","authors":"Maria Kamenetsky, Guangqing Chi, Donghui Wang, Jun Zhu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Poverty has been studied across many social science disciplines, resulting in a large body of literature. Scholars of poverty research have long recognized that the poor are not uniformly distributed across space. Understanding the spatial aspect of poverty is important because it helps us understand place-based structural inequalities. There are many spatial regression models, but there is a learning curve to learn and apply them to poverty research. This manuscript aims to introduce the concepts of spatial regression modeling and walk the reader through the steps of conducting poverty research using R: standard exploratory data analysis, standard linear regression, neighborhood structure and spatial weight matrix, exploratory spatial data analysis, and spatial linear regression. We also discuss the spatial heterogeneity and spatial panel aspects of poverty. We provide code for data analysis in the R environment and readers can modify it for their own data analyses. We also present results in their raw format to help readers become familiar with the R environment.</p>","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"7 2-3","pages":"113-147"},"PeriodicalIF":1.9,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6857788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141284921","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}
{"title":"Demographic Ageing in the Mediterranean: The End of the Spatial Dichotomy Between the Shores?","authors":"Yoann Doignon","doi":"10.1007/s40980-019-00054-2","DOIUrl":"https://doi.org/10.1007/s40980-019-00054-2","url":null,"abstract":"","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"8 1","pages":"85 - 117"},"PeriodicalIF":1.9,"publicationDate":"2019-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40980-019-00054-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53017712","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}
{"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}
{"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}
{"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}
Spatial DemographyPub 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
{"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}
{"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}