Pub Date : 2019-10-02DOI: 10.1080/08898480.2019.1653058
S. Matthews
The two thematic issues 26(4) and 27(1) of Mathematical Population Studies deal with “methods and applications in spatial demography.” The five articles they contain, and which are listed below, show how population studies can be informed through an integration of appropriate spatial theory, data, and methods. In the editorial to appear in the next issue, 27(1), I will provide a summary of each article and discuss its contribution to this very lively field. I would like to thank the authors, the journal, and Taylor and Francis for these issues, which I had the pleasure of organizing. We wish you a pleasant reading.
{"title":"Methods and applications in spatial demography","authors":"S. Matthews","doi":"10.1080/08898480.2019.1653058","DOIUrl":"https://doi.org/10.1080/08898480.2019.1653058","url":null,"abstract":"The two thematic issues 26(4) and 27(1) of Mathematical Population Studies deal with “methods and applications in spatial demography.” The five articles they contain, and which are listed below, show how population studies can be informed through an integration of appropriate spatial theory, data, and methods. In the editorial to appear in the next issue, 27(1), I will provide a summary of each article and discuss its contribution to this very lively field. I would like to thank the authors, the journal, and Taylor and Francis for these issues, which I had the pleasure of organizing. We wish you a pleasant reading.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1653058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47249999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-02DOI: 10.1080/08898480.2019.1669363
S. Matthews
{"title":"In memoriam: Jennifer Buher Kane (1979–2019)","authors":"S. Matthews","doi":"10.1080/08898480.2019.1669363","DOIUrl":"https://doi.org/10.1080/08898480.2019.1669363","url":null,"abstract":"","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1669363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44556794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-09DOI: 10.1080/08898480.2019.1656491
Chouaib Beldjoudi, T. Kernane, Hamid El Maroufy
ABSTRACT A Bayesian data-augmentation method allows estimating the parameters in a susceptible-exposed-infected-recovered (SEIR) epidemic model, which is formulated as a continuous-time Markov process and approximated by a diffusion process using the convergence of the master equation. The estimation was carried out with latent data points between every pair of observations simulated through the Euler-Maruyama scheme, which involves imputing the missing data in addition to the model parameters. The missing data and parameters are treated as random variables, and a Markov-chain Monte-Carlo algorithm updates the missing data and the parameter values. Numerical simulations show the effectiveness of the proposed Markov-chain Monte-Carlo algorithm.
{"title":"Bayesian inference for a susceptible-exposed-infected-recovered epidemic model with data augmentation","authors":"Chouaib Beldjoudi, T. Kernane, Hamid El Maroufy","doi":"10.1080/08898480.2019.1656491","DOIUrl":"https://doi.org/10.1080/08898480.2019.1656491","url":null,"abstract":"ABSTRACT A Bayesian data-augmentation method allows estimating the parameters in a susceptible-exposed-infected-recovered (SEIR) epidemic model, which is formulated as a continuous-time Markov process and approximated by a diffusion process using the convergence of the master equation. The estimation was carried out with latent data points between every pair of observations simulated through the Euler-Maruyama scheme, which involves imputing the missing data in addition to the model parameters. The missing data and parameters are treated as random variables, and a Markov-chain Monte-Carlo algorithm updates the missing data and the parameter values. Numerical simulations show the effectiveness of the proposed Markov-chain Monte-Carlo algorithm.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1656491","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43385656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-25DOI: 10.1080/08898480.2019.1636574
Zack W. Almquist, Nathaniel E. Helwig, Yun You
ABSTRACT In 2007, the Department of Housing and Urban Development initiated a point-in-time count of the homeless across the United States. The counts are administered by the Continuum of Care Program, which provides spatial and temporal data for the homeless population over the last decade. Unfortunately, this administrative spatial unit does not align with the more common areal units defined by the United States Census Bureau, which limits usability of these data. To unify these two areal units, spatial disaggregation, matching, and imputation allow for aligning Continuum of Care data with county data. The resulting county-level homeless counts for the years 2005 to 2017 are provided as an R package. The county-level data display more spatial precision and more temporal variation than the Continuum of Care-level data. Nonparametric regression analyses reveal that the spatiotemporal variation in the data can be well approximated by additive spatial and temporal effects at both the county and Continuum of Care level.
2007年，美国住房和城市发展部发起了一项针对全美无家可归者的时点统计。这些统计是由连续关怀计划管理的，该计划提供了过去十年无家可归人口的空间和时间数据。不幸的是，这个行政空间单位与美国人口普查局定义的更常见的面积单位不一致，这限制了这些数据的可用性。为了统一这两个区域单位，空间分解、匹配和imputation允许将Continuum of Care数据与县数据对齐。由此产生的2005年至2017年县级无家可归者统计数据作为R包提供。县级数据的空间精度和时间变异性均高于关爱级连续体数据。非参数回归分析表明，在县域和连续关怀水平上，数据的时空变化可以很好地近似于加性时空效应。
{"title":"Connecting Continuum of Care point-in-time homeless counts to United States Census areal units","authors":"Zack W. Almquist, Nathaniel E. Helwig, Yun You","doi":"10.1080/08898480.2019.1636574","DOIUrl":"https://doi.org/10.1080/08898480.2019.1636574","url":null,"abstract":"ABSTRACT In 2007, the Department of Housing and Urban Development initiated a point-in-time count of the homeless across the United States. The counts are administered by the Continuum of Care Program, which provides spatial and temporal data for the homeless population over the last decade. Unfortunately, this administrative spatial unit does not align with the more common areal units defined by the United States Census Bureau, which limits usability of these data. To unify these two areal units, spatial disaggregation, matching, and imputation allow for aligning Continuum of Care data with county data. The resulting county-level homeless counts for the years 2005 to 2017 are provided as an R package. The county-level data display more spatial precision and more temporal variation than the Continuum of Care-level data. Nonparametric regression analyses reveal that the spatiotemporal variation in the data can be well approximated by additive spatial and temporal effects at both the county and Continuum of Care level.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1636574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42686396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-03DOI: 10.1080/08898480.2019.1626189
E. Chernousova, O. Hryniv, S. Molchanov
ABSTRACT In a population model in continuous space, individuals evolve independently as branching random walks subject to immigration. If the underlying branching mechanism is subcritical, the model has a unique steady state for each value of the immigration intensity. Convergence to the equilibrium is exponentially fast. The resulting dynamics are Lyapunov stable in that their qualitative behavior does not change under suitable perturbations of the main parameters of the model.
{"title":"Population model with immigration in continuous space","authors":"E. Chernousova, O. Hryniv, S. Molchanov","doi":"10.1080/08898480.2019.1626189","DOIUrl":"https://doi.org/10.1080/08898480.2019.1626189","url":null,"abstract":"ABSTRACT In a population model in continuous space, individuals evolve independently as branching random walks subject to immigration. If the underlying branching mechanism is subcritical, the model has a unique steady state for each value of the immigration intensity. Convergence to the equilibrium is exponentially fast. The resulting dynamics are Lyapunov stable in that their qualitative behavior does not change under suitable perturbations of the main parameters of the model.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1626189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42375749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-19DOI: 10.1080/08898480.2019.1626635
Saurav Guha, Hukum Chandra
ABSTRACT Chain-ratio estimators are often used to improve the efficiency of the estimation of the population total or the mean using two auxiliary variables, available in two different phases. An improved chain-ratio estimator for the population total based on double sampling is proposed when auxiliary information is available for the first variable and not available for the second variable. The bias and the mean square error of this estimator are obtained for a large sample. Empirical evaluations using both model-based and design-based simulations show that the proposed estimator performs better than the ratio, the regression, and the difference estimators.
{"title":"Improved chain-ratio type estimator for population total in double sampling","authors":"Saurav Guha, Hukum Chandra","doi":"10.1080/08898480.2019.1626635","DOIUrl":"https://doi.org/10.1080/08898480.2019.1626635","url":null,"abstract":"ABSTRACT Chain-ratio estimators are often used to improve the efficiency of the estimation of the population total or the mean using two auxiliary variables, available in two different phases. An improved chain-ratio estimator for the population total based on double sampling is proposed when auxiliary information is available for the first variable and not available for the second variable. The bias and the mean square error of this estimator are obtained for a large sample. Empirical evaluations using both model-based and design-based simulations show that the proposed estimator performs better than the ratio, the regression, and the difference estimators.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1626635","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48595379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-17DOI: 10.1080/08898480.2019.1626633
Xiaoni Li, Xining Li, Qimin Zhang
ABSTRACT A stochastic susceptible-infected-recovered-susceptible model with vaccination includes stochastic variation in its parameters. Sufficient conditions for the extinction and the existence of the stationary distribution of the population are proved.
{"title":"Time to extinction and stationary distribution of a stochastic susceptible-infected-recovered-susceptible model with vaccination under Markov switching","authors":"Xiaoni Li, Xining Li, Qimin Zhang","doi":"10.1080/08898480.2019.1626633","DOIUrl":"https://doi.org/10.1080/08898480.2019.1626633","url":null,"abstract":"ABSTRACT A stochastic susceptible-infected-recovered-susceptible model with vaccination includes stochastic variation in its parameters. Sufficient conditions for the extinction and the existence of the stationary distribution of the population are proved.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1626633","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45208367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-21DOI: 10.1080/08898480.2019.1592638
Gillian Dunn, Glen D. Johnson, D. Balk, Grace Sembajwe
ABSTRACT Diarrhea is a major contributor to child morbidity and mortality in West Africa. Non-spatial regression and geographically weighted Poisson regression applied to data from 10 Demographic and Health Surveys conducted in West Africa from 2008 to 2013 show that water source, toilet type, mother’s education, latitude, temperature, rainfall, altitude, and population density influence the risk of diarrhea. The risk associated with these factors is dependent on location and may be higher or lower than the rest of the study area. Areas with increased relative risk for diarrhea include several urban centers, low-elevation areas (coastal and along rivers), remote areas such as western Mali, and conflict zones (northeast Nigeria).
{"title":"Spatially varying relationships between risk factors and child diarrhea in West Africa, 2008-2013","authors":"Gillian Dunn, Glen D. Johnson, D. Balk, Grace Sembajwe","doi":"10.1080/08898480.2019.1592638","DOIUrl":"https://doi.org/10.1080/08898480.2019.1592638","url":null,"abstract":"ABSTRACT Diarrhea is a major contributor to child morbidity and mortality in West Africa. Non-spatial regression and geographically weighted Poisson regression applied to data from 10 Demographic and Health Surveys conducted in West Africa from 2008 to 2013 show that water source, toilet type, mother’s education, latitude, temperature, rainfall, altitude, and population density influence the risk of diarrhea. The risk associated with these factors is dependent on location and may be higher or lower than the rest of the study area. Areas with increased relative risk for diarrhea include several urban centers, low-elevation areas (coastal and along rivers), remote areas such as western Mali, and conflict zones (northeast Nigeria).","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1592638","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47205859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-03DOI: 10.1080/08898480.2017.1418113
Biagio Aragona, R. De Rosa
ABSTRACT A review of studies based on big data shows that big data advantageously complete surveys and censuses, nurture policy making, and highlight effects of a given policy in real time.
基于大数据的研究综述表明，大数据有利于实时完成调查和普查，培育政策制定，并突出特定政策的效果。
{"title":"Big data in policy making","authors":"Biagio Aragona, R. De Rosa","doi":"10.1080/08898480.2017.1418113","DOIUrl":"https://doi.org/10.1080/08898480.2017.1418113","url":null,"abstract":"ABSTRACT A review of studies based on big data shows that big data advantageously complete surveys and censuses, nurture policy making, and highlight effects of a given policy in real time.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2017.1418113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45676558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-03DOI: 10.1080/08898480.2019.1597577
Enrica Amaturo, Biagio Aragona
The diffusion of digital technologies and social networks has multiplied the forms of digital data that can be employed for social research. The main two forms are native digital data, which are produced in social networks, search engines, or blogging, and digitized data, which are analog data transformed into digital (Rogers, 2013). Big data are originally produced in the Internet. They allow for analyzing behaviors without interfering with individuals (Webb et al., 1966). An example is the data used in web platforms analytics, such as Google Correlate, whose purpose is to reveal the co-occurrences associated with a keyword searched through the Google search engine. This tool helped to predict the flu epidemic in the US, well before the US Centre for Disease Control and Prevention (Ginsberg et al., 2009). This example demonstrates that digital web platforms enable innovations in data analysis. Another example of native digital data is the data voluntarily uploaded on social networks, blogs, and websites. These are mainly textual or visual (images and videos), often unstructured. A third example is transactional data and the Internet of things. Transactions made through digital devices, such as smart-phones, scanners, tablets, and cards with chips (credit cards, shopping cards) produce data with some structure. These data comprise metadata (date, time, duration, or expenditures) associated with transactions. The objects connected to the Internet (the Internet of things), such as sensors for health monitoring, house automation, and driving aid, usually produce structured data, which can be organized and analyzed. Digitized data previously existed in analog form, for example images, videos, and scanned or digitally photographed documents uploaded on the web, such as museum collections or libraries available on-line. Digital humanities have converted this material into digital form. Another example is the surveys assisted by computers, where the data are inserted into digital databases. Web surveys now are conducted through the Internet (by e-mail) (Amaturo and Aragona, 2016), and allow for reaching a large sample with a small budget.
数字技术和社交网络的传播使可用于社会研究的数字数据形式成倍增加。主要的两种形式是在社交网络、搜索引擎或博客中产生的原生数字数据，以及数字化数据，即转换为数字的模拟数据（Rogers，2013）。大数据最初是在互联网上产生的。它们允许在不干扰个人的情况下分析行为（Webb等人，1966）。一个例子是网络平台分析中使用的数据，如Google Correlate，其目的是揭示与通过Google搜索引擎搜索的关键词相关联的共同出现。早在美国疾病控制和预防中心（Ginsberg et al.，2009）之前，这一工具就有助于预测美国的流感疫情。这个例子表明，数字网络平台能够实现数据分析的创新。本地数字数据的另一个例子是自愿上传到社交网络、博客和网站上的数据。这些主要是文本或视觉（图像和视频），通常是非结构化的。第三个例子是事务数据和物联网。通过数字设备进行的交易，如智能手机、扫描仪、平板电脑和带芯片的卡（信用卡、购物卡），产生具有某种结构的数据。这些数据包括与交易相关联的元数据（日期、时间、持续时间或支出）。连接到互联网（物联网）的对象，如用于健康监测、房屋自动化和驾驶辅助的传感器，通常会产生结构化数据，这些数据可以进行组织和分析。数字化数据以前以模拟形式存在，例如图像、视频，以及上传到网络上的扫描或数字拍摄文件，例如博物馆藏品或在线图书馆。数字人文学科已经将这些材料转化为数字形式。另一个例子是由计算机辅助的调查，将数据插入数字数据库。现在，网络调查是通过互联网（通过电子邮件）进行的（Amaturo和Aragona，2016），可以用小预算接触到大样本。
{"title":"Methods for big data in social sciences","authors":"Enrica Amaturo, Biagio Aragona","doi":"10.1080/08898480.2019.1597577","DOIUrl":"https://doi.org/10.1080/08898480.2019.1597577","url":null,"abstract":"The diffusion of digital technologies and social networks has multiplied the forms of digital data that can be employed for social research. The main two forms are native digital data, which are produced in social networks, search engines, or blogging, and digitized data, which are analog data transformed into digital (Rogers, 2013). Big data are originally produced in the Internet. They allow for analyzing behaviors without interfering with individuals (Webb et al., 1966). An example is the data used in web platforms analytics, such as Google Correlate, whose purpose is to reveal the co-occurrences associated with a keyword searched through the Google search engine. This tool helped to predict the flu epidemic in the US, well before the US Centre for Disease Control and Prevention (Ginsberg et al., 2009). This example demonstrates that digital web platforms enable innovations in data analysis. Another example of native digital data is the data voluntarily uploaded on social networks, blogs, and websites. These are mainly textual or visual (images and videos), often unstructured. A third example is transactional data and the Internet of things. Transactions made through digital devices, such as smart-phones, scanners, tablets, and cards with chips (credit cards, shopping cards) produce data with some structure. These data comprise metadata (date, time, duration, or expenditures) associated with transactions. The objects connected to the Internet (the Internet of things), such as sensors for health monitoring, house automation, and driving aid, usually produce structured data, which can be organized and analyzed. Digitized data previously existed in analog form, for example images, videos, and scanned or digitally photographed documents uploaded on the web, such as museum collections or libraries available on-line. Digital humanities have converted this material into digital form. Another example is the surveys assisted by computers, where the data are inserted into digital databases. Web surveys now are conducted through the Internet (by e-mail) (Amaturo and Aragona, 2016), and allow for reaching a large sample with a small budget.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2019-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1597577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41950429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：