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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
Pub Date : 2018-01-29DOI: 10.1007/s40980-018-0041-8
M. Kritz, D. Gurak
{"title":"Ethno-Racial and Nativity Group Differences in U.S. Intercounty Migration and Move Distances","authors":"M. Kritz, D. Gurak","doi":"10.1007/s40980-018-0041-8","DOIUrl":"https://doi.org/10.1007/s40980-018-0041-8","url":null,"abstract":"","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2018-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40980-018-0041-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53018009","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 : 2018-01-19DOI: 10.1007/s40980-017-0038-8
Michael J. R. Martin, Christopher S. Fowler
{"title":"Connecting the Dots: The Spatial Processes Underlying Place-Level Diversity Change in U.S. Metros Between 1990 and 2010","authors":"Michael J. R. Martin, Christopher S. Fowler","doi":"10.1007/s40980-017-0038-8","DOIUrl":"https://doi.org/10.1007/s40980-017-0038-8","url":null,"abstract":"","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2018-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40980-017-0038-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53017558","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 : 2017-11-29DOI: 10.1007/s40980-017-0040-1
Bhaswati Das, Rabiul Ansary
{"title":"Bangladeshi and Inter-state Migrants: Differential Adaptation and Acceptance by the Locals in West Bengal, India","authors":"Bhaswati Das, Rabiul Ansary","doi":"10.1007/s40980-017-0040-1","DOIUrl":"https://doi.org/10.1007/s40980-017-0040-1","url":null,"abstract":"","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40980-017-0040-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53017230","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 : 2017-10-22DOI: 10.1007/s40980-017-0039-7
Finn Hedefalk, Karolina Pantazatou, Luciana Quaranta, Lars Harrie
Geocoding longitudinal and individual-level historical demographic databases enables novel analyses of how micro-level geographic factors affected demographic outcomes over long periods. However, such detailed geocoding involves high costs. Additionally, the high spatial resolution cannot be properly utilized if inappropriate methods are used to quantify the geographic factors. We assess how different geocoding levels and methods used to define geographic variables affects the outcome of detailed spatial and historical demographic analyses. Using a longitudinal and individual-level demographic database geocoded at the property unit level, we analyse the effects of population density and proximity to wetlands on all-cause mortality for individuals who lived in five Swedish parishes, 1850–1914. We compare the results from analyses on three detailed geocoding levels using two common quantification methods for each geographic variable. Together with the method selected for quantifying the geographic factors, even small differences in positional accuracy (20–50 m) between the property units and slightly coarser geographic levels heavily affected the results of the demographic analyses. The results also show the importance of accounting for geographic changes over time. Finally, proximity to wetlands and population density affected the mortality of women and children, respectively. However, all possible determinants of mortality were not evaluated in the analyses. In conclusion, for rural historical areas, geocoding to property units is likely necessary for fine-scale analyses at distances within a few hundred metres. We must also carefully consider the quantification methods that are the most logical for the geographic context and the type of analyses.
{"title":"Importance of the Geocoding Level for Historical Demographic Analyses: A Case Study of Rural Parishes in Sweden, 1850–1914","authors":"Finn Hedefalk, Karolina Pantazatou, Luciana Quaranta, Lars Harrie","doi":"10.1007/s40980-017-0039-7","DOIUrl":"https://doi.org/10.1007/s40980-017-0039-7","url":null,"abstract":"Geocoding longitudinal and individual-level historical demographic databases enables novel analyses of how micro-level geographic factors affected demographic outcomes over long periods. However, such detailed geocoding involves high costs. Additionally, the high spatial resolution cannot be properly utilized if inappropriate methods are used to quantify the geographic factors. We assess how different geocoding levels and methods used to define geographic variables affects the outcome of detailed spatial and historical demographic analyses. Using a longitudinal and individual-level demographic database geocoded at the property unit level, we analyse the effects of population density and proximity to wetlands on all-cause mortality for individuals who lived in five Swedish parishes, 1850–1914. We compare the results from analyses on three detailed geocoding levels using two common quantification methods for each geographic variable. Together with the method selected for quantifying the geographic factors, even small differences in positional accuracy (20–50 m) between the property units and slightly coarser geographic levels heavily affected the results of the demographic analyses. The results also show the importance of accounting for geographic changes over time. Finally, proximity to wetlands and population density affected the mortality of women and children, respectively. However, all possible determinants of mortality were not evaluated in the analyses. In conclusion, for rural historical areas, geocoding to property units is likely necessary for fine-scale analyses at distances within a few hundred metres. We must also carefully consider the quantification methods that are the most logical for the geographic context and the type of analyses.","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884804","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 : 2017-10-01Epub Date: 2016-12-19DOI: 10.1007/s40980-016-0030-8
Michael J R Martin, Stephen A Matthews, Barrett A Lee
Although increasing racial and ethnic diversity is a demographic trend with society-wide implications, it has advanced farther in some parts of the United States than others. Our research seeks to understand this unevenness at the local level. Drawing on 1980-2010 census data, we use an innovative spatial analytic approach to examine the spread or diffusion of diversity across counties in the 48 contiguous states. Three perspectives-locational persistence, spatial assimilation, and institutional hub-offer different expectations about the nature of the diffusion process. The perspectives are evaluated by mapping changes in the magnitude and structure of diversity and by tracing county transitions between types of diversity clusters. We document considerable stability in diversity patterns over a 30-year period, consistent with the logic of locational persistence. But support is also found for the spatial assimilation and institutional hub models in the form of cluster-type transitions that reflect contagious diffusion and hierarchical diffusion, respectively.
{"title":"The Spatial Diffusion of Racial and Ethnic Diversity Across U.S. Counties.","authors":"Michael J R Martin, Stephen A Matthews, Barrett A Lee","doi":"10.1007/s40980-016-0030-8","DOIUrl":"10.1007/s40980-016-0030-8","url":null,"abstract":"<p><p>Although increasing racial and ethnic diversity is a demographic trend with society-wide implications, it has advanced farther in some parts of the United States than others. Our research seeks to understand this unevenness at the local level. Drawing on 1980-2010 census data, we use an innovative spatial analytic approach to examine the spread or diffusion of diversity across counties in the 48 contiguous states. Three perspectives-locational persistence, spatial assimilation, and institutional hub-offer different expectations about the nature of the diffusion process. The perspectives are evaluated by mapping changes in the magnitude and structure of diversity and by tracing county transitions between types of diversity clusters. We document considerable stability in diversity patterns over a 30-year period, consistent with the logic of locational persistence. But support is also found for the spatial assimilation and institutional hub models in the form of cluster-type transitions that reflect contagious diffusion and hierarchical diffusion, respectively.</p>","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5847321/pdf/nihms947652.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35921076","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}
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