普查数据是地理学空间分析的重要数据源。由于受到数据与计算机处理能力的限制,以往的研究对普查数据空间分析的不确定性未给予足够重视,也未形成成熟的研究方法。在建筑物单元的人口普查数据支持下,本文基于多边形统计数据的可塑面积...普查数据是地理学空间分析的重要数据源。由于受到数据与计算机处理能力的限制,以往的研究对普查数据空间分析的不确定性未给予足够重视,也未形成成熟的研究方法。在建筑物单元的人口普查数据支持下,本文基于多边形统计数据的可塑面积单元问题(Modifiable areal unit problem,MAUP)特征,设计了一种该类数据空间分析不确定性的研究方法,采用不同的尺度(Scale)及分区(Zoning)系统对多边形的统计数据空间分析的准确性进行了分析。实验引入尺度与形态指数,利用可视化分析和数据拟合的研究方法,对尺度及分区对空间分析结果的影响模式进行了模拟。研究结果表明:(1)以统计小区的空间分析,其结果受统计小区空间形态的影响较大,不确定性强,不能充分反映统计数据本身的空间特征;(2)规则格网能较好地保持原始统计数据的空间分布特征,但仍然受尺度及分区影响;(3)规则格网的空间分析结果及其准确性与尺度有较好的拟合关系,不同尺度下的分析结果不确定性是原始数据不同尺度特征的体现;(4)分区效应受空间分析方法的计算尺度影响,两者共同对空间分析结果产生影响。对于固定尺度的规则格网,其邻接多边形数目是分析结果不确定的主要原因。本文研究结果表明,在多边形统计数据空间分析时,应该对其使用规则格网重新聚合,并根据实际应用的需求选择多尺度分析方法,以达到实际应用目的。展开更多
This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include...This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include SARS cases,climate data,hospitalization records,and COVID-19 vaccination information,our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset.The analysis reveals significant variations in the incidence of SARS cases over time,particularly during and between the distinct eras of pre-COVID-19,during,and post-COVID-19.Our modeling approach accommodates explanatory variables such as humidity,temperature,and COVID-19 vaccine doses,providing a comprehensive understanding of the factors influencing SARS dynamics.Our modeling revealed unique temporal trends in SARS cases for each region,resembling neighborhood patterns.Low temperature and high humidity were linked to decreased cases,while in the COVID-19 era,temperature and vaccination coverage played significant roles.The findings contribute valuable insights into the spatial and temporal patterns of SARS in Brazil,offering a foundation for targeted public health interventions and preparedness strategies.展开更多
The conditional autoregressive model is a routinely used statistical model for areal data thatarise from, for instances, epidemiological, socio-economic or ecological studies. Various multivariate conditional autoregr...The conditional autoregressive model is a routinely used statistical model for areal data thatarise from, for instances, epidemiological, socio-economic or ecological studies. Various multivariate conditional autoregressive models have also been extensively studied in the literatureand it has been shown that extending from the univariate case to the multivariate case is nottrivial. The difficulties lie in many aspects, including validity, interpretability, flexibility and computational feasibility of the model. In this paper, we approach the multivariate modelling froman element-based perspective instead of the traditional vector-based perspective. We focus onthe joint adjacency structure of elements and discuss graphical structures for both the spatialand non-spatial domains. We assume that the graph for the spatial domain is generally knownand fixed while the graph for the non-spatial domain can be unknown and random. We proposea very general specification for the multivariate conditional modelling and then focus on threespecial cases, which are linked to well-known models in the literature. Bayesian inference forparameter learning and graph learning is provided for the focused cases, and finally, an examplewith public health data is illustrated.展开更多
In this article,a new unit level model based on a pairwise penalised regression approach is proposed for problems in small area estimation(SAE).Instead of assuming common regression coefficients for all small domains ...In this article,a new unit level model based on a pairwise penalised regression approach is proposed for problems in small area estimation(SAE).Instead of assuming common regression coefficients for all small domains in the traditional model,the new estimator is based on a subgroup regression model which allows different regression coefficients in different groups.The alternating direction method of multipliers(ADMM)algorithm is used to find subgroups with different regression coefficients.We also consider pairwise spatial weights for spatial areal data.In the simulation study,we compare the performances of the new estimator with the traditional small area estimator.We also apply the new estimator to urban area estimation using data from the National Resources Inventory survey in Iowa.展开更多
文摘普查数据是地理学空间分析的重要数据源。由于受到数据与计算机处理能力的限制,以往的研究对普查数据空间分析的不确定性未给予足够重视,也未形成成熟的研究方法。在建筑物单元的人口普查数据支持下,本文基于多边形统计数据的可塑面积单元问题(Modifiable areal unit problem,MAUP)特征,设计了一种该类数据空间分析不确定性的研究方法,采用不同的尺度(Scale)及分区(Zoning)系统对多边形的统计数据空间分析的准确性进行了分析。实验引入尺度与形态指数,利用可视化分析和数据拟合的研究方法,对尺度及分区对空间分析结果的影响模式进行了模拟。研究结果表明:(1)以统计小区的空间分析,其结果受统计小区空间形态的影响较大,不确定性强,不能充分反映统计数据本身的空间特征;(2)规则格网能较好地保持原始统计数据的空间分布特征,但仍然受尺度及分区影响;(3)规则格网的空间分析结果及其准确性与尺度有较好的拟合关系,不同尺度下的分析结果不确定性是原始数据不同尺度特征的体现;(4)分区效应受空间分析方法的计算尺度影响,两者共同对空间分析结果产生影响。对于固定尺度的规则格网,其邻接多边形数目是分析结果不确定的主要原因。本文研究结果表明,在多边形统计数据空间分析时,应该对其使用规则格网重新聚合,并根据实际应用的需求选择多尺度分析方法,以达到实际应用目的。
文摘This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include SARS cases,climate data,hospitalization records,and COVID-19 vaccination information,our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset.The analysis reveals significant variations in the incidence of SARS cases over time,particularly during and between the distinct eras of pre-COVID-19,during,and post-COVID-19.Our modeling approach accommodates explanatory variables such as humidity,temperature,and COVID-19 vaccine doses,providing a comprehensive understanding of the factors influencing SARS dynamics.Our modeling revealed unique temporal trends in SARS cases for each region,resembling neighborhood patterns.Low temperature and high humidity were linked to decreased cases,while in the COVID-19 era,temperature and vaccination coverage played significant roles.The findings contribute valuable insights into the spatial and temporal patterns of SARS in Brazil,offering a foundation for targeted public health interventions and preparedness strategies.
文摘The conditional autoregressive model is a routinely used statistical model for areal data thatarise from, for instances, epidemiological, socio-economic or ecological studies. Various multivariate conditional autoregressive models have also been extensively studied in the literatureand it has been shown that extending from the univariate case to the multivariate case is nottrivial. The difficulties lie in many aspects, including validity, interpretability, flexibility and computational feasibility of the model. In this paper, we approach the multivariate modelling froman element-based perspective instead of the traditional vector-based perspective. We focus onthe joint adjacency structure of elements and discuss graphical structures for both the spatialand non-spatial domains. We assume that the graph for the spatial domain is generally knownand fixed while the graph for the non-spatial domain can be unknown and random. We proposea very general specification for the multivariate conditional modelling and then focus on threespecial cases, which are linked to well-known models in the literature. Bayesian inference forparameter learning and graph learning is provided for the focused cases, and finally, an examplewith public health data is illustrated.
基金This research was supported in part by the Natural ResourcesConservation Service of the U.S. Department of Agriculture.
文摘In this article,a new unit level model based on a pairwise penalised regression approach is proposed for problems in small area estimation(SAE).Instead of assuming common regression coefficients for all small domains in the traditional model,the new estimator is based on a subgroup regression model which allows different regression coefficients in different groups.The alternating direction method of multipliers(ADMM)algorithm is used to find subgroups with different regression coefficients.We also consider pairwise spatial weights for spatial areal data.In the simulation study,we compare the performances of the new estimator with the traditional small area estimator.We also apply the new estimator to urban area estimation using data from the National Resources Inventory survey in Iowa.
