利用元胞自动机模型进行城市扩张模拟时,其使用的栅格数据格式和基于统计的转换规则提取方法必然会导致可变面积单元问题(the Modifiable Areal Unit Problem,MAUP)的出现。采用系统的敏感性分析方法对该问题的粒度效应、划区效应和综...利用元胞自动机模型进行城市扩张模拟时,其使用的栅格数据格式和基于统计的转换规则提取方法必然会导致可变面积单元问题(the Modifiable Areal Unit Problem,MAUP)的出现。采用系统的敏感性分析方法对该问题的粒度效应、划区效应和综合效应进行了分析,研究表明:1)MAUP问题在CA模拟时是客观存在的,且会对模拟结果造成影响,研究时不能忽视该问题。应进行系统的敏感性分析,获取其对研究问题的影响,寻找适宜的研究粒度和分区方案。2)该研究中粒度效应会呈现明显的尺度阈值,尺度域内的拟合优度和模拟精度较为稳定。尺度阈值在最精细粒度后随即出现,且该阈值与景观指数的尺度阈值一致,反映了景观对象大小对城市扩张模拟的重要影响。3)良好的划区方案能够提高拟合优度和模拟精度,其划区方案应使区域内城市扩张规律差异最小,区域间城市扩张规律差异最大。在大尺度城市扩张模拟时更应采用合理的划区方案以提高模拟精度。4)粒度效应和划区效应的综合影响表现为各区域模型的拟合优度和模拟精度在粒度范围内所受到的影响存在差异,但大多数区域都较为明显地表现出相同的尺度阈值。展开更多
可塑性面积单元问题(modifiable areal unit problem,MAUP)效应是对空间数据分析结果产生不确定性影响的主要原因之一,在空间自相关分析中也不例外。本文分别利用网格模拟数据和中国人均GDP实例数据为数据源,以全局Moran's I系数来...可塑性面积单元问题(modifiable areal unit problem,MAUP)效应是对空间数据分析结果产生不确定性影响的主要原因之一,在空间自相关分析中也不例外。本文分别利用网格模拟数据和中国人均GDP实例数据为数据源,以全局Moran's I系数来探究空间自相关统计中的MAUP效应,分析结果表明,变量的空间自相关程度依赖于空间的粒度大小与单元的划分方法,但空间单元的变化与自相关性并不存在某种函数关系。因此,在进行空间自相关研究时必须选择合适的地理单元的粒度大小和分区。最后本文给出一种基于地统计内插方法来降低MAUP对空间自相关分析影响。展开更多
Accurate identification of spatial patterns and risk factors of disease occurrence is crucial for public health interventions.However,the Modifiable Areal Unit Problem(MAUP)poses challenges in disease modelling by imp...Accurate identification of spatial patterns and risk factors of disease occurrence is crucial for public health interventions.However,the Modifiable Areal Unit Problem(MAUP)poses challenges in disease modelling by impacting the reliability of statistical inferences drawn from spatially aggregated data.This study examines the effect of MAUP on ecological model inference using locally and overseas-acquired COVID-19 case data from 2020 to 2023 in Queensland,Australia.Bayesian spatial Besag-York-Mollié(BYM)models were applied across four Statistical Area(SA)levels,as defined by the Australian Statistical Geography Standard,with and without covariates:Socio-Economic Indexes for Areas(SEIFA)and overseas-acquired(OA)COVID-19 cases.OA COVID-19 cases were also considered a response variable in our study.Results indicated that finer spatial scales(SA1 and SA2)captured localized patterns and significant spatial autocorrelation,while coarser levels(SA3 and SA4)smoothed spatial variability,masking potential outbreak clusters.Incorporating SEIFA as a covariate in locally-acquired(LA)cases reduced spatial autocorrelation in residuals,effectively capturing socioeconomic disparities.Conversely,OA cases showed limited effectiveness in reducing autocorrelation at finer scales.For LA cases,higher socioeconomic disadvantage was associated with increased COVID-19 incidence at finer scales,but this association became non-significant at coarser scales.OA cases showed significant positive association with higher SEIFA scores at finer scales.Model parameters displayed narrower credible intervals at finer scales,indicating greater precision,while coarser levels had increased uncertainty.SA2 emerged as an arguably optimal scale,striking a balance between spatial resolution,model stability,and interpretability.To improve inference on COVID-19 incidence,it is recommended to use data from both SA1 and SA2 levels to leverage their respective strengths.The findings emphasize the importance of selecting appropriate spatial scales and covariates or evaluating the inferential impacts of multiple scales,to address MAUP to facilitate more reliable spatial analysis.The study advocates exploring intermediate aggregation levels and multi-scale approaches to better capture nuanced disease dynamics and extend these analyses across Australia and replicating in other countries with low population densities to enhance generalizability.展开更多
文摘利用元胞自动机模型进行城市扩张模拟时,其使用的栅格数据格式和基于统计的转换规则提取方法必然会导致可变面积单元问题(the Modifiable Areal Unit Problem,MAUP)的出现。采用系统的敏感性分析方法对该问题的粒度效应、划区效应和综合效应进行了分析,研究表明:1)MAUP问题在CA模拟时是客观存在的,且会对模拟结果造成影响,研究时不能忽视该问题。应进行系统的敏感性分析,获取其对研究问题的影响,寻找适宜的研究粒度和分区方案。2)该研究中粒度效应会呈现明显的尺度阈值,尺度域内的拟合优度和模拟精度较为稳定。尺度阈值在最精细粒度后随即出现,且该阈值与景观指数的尺度阈值一致,反映了景观对象大小对城市扩张模拟的重要影响。3)良好的划区方案能够提高拟合优度和模拟精度,其划区方案应使区域内城市扩张规律差异最小,区域间城市扩张规律差异最大。在大尺度城市扩张模拟时更应采用合理的划区方案以提高模拟精度。4)粒度效应和划区效应的综合影响表现为各区域模型的拟合优度和模拟精度在粒度范围内所受到的影响存在差异,但大多数区域都较为明显地表现出相同的尺度阈值。
文摘可塑性面积单元问题(modifiable areal unit problem,MAUP)效应是对空间数据分析结果产生不确定性影响的主要原因之一,在空间自相关分析中也不例外。本文分别利用网格模拟数据和中国人均GDP实例数据为数据源,以全局Moran's I系数来探究空间自相关统计中的MAUP效应,分析结果表明,变量的空间自相关程度依赖于空间的粒度大小与单元的划分方法,但空间单元的变化与自相关性并不存在某种函数关系。因此,在进行空间自相关研究时必须选择合适的地理单元的粒度大小和分区。最后本文给出一种基于地统计内插方法来降低MAUP对空间自相关分析影响。
基金The National Health and Medical Research Council(NHMRC)Special Initiative in Human Health and Environmental Change(Grant No.2008937).
文摘Accurate identification of spatial patterns and risk factors of disease occurrence is crucial for public health interventions.However,the Modifiable Areal Unit Problem(MAUP)poses challenges in disease modelling by impacting the reliability of statistical inferences drawn from spatially aggregated data.This study examines the effect of MAUP on ecological model inference using locally and overseas-acquired COVID-19 case data from 2020 to 2023 in Queensland,Australia.Bayesian spatial Besag-York-Mollié(BYM)models were applied across four Statistical Area(SA)levels,as defined by the Australian Statistical Geography Standard,with and without covariates:Socio-Economic Indexes for Areas(SEIFA)and overseas-acquired(OA)COVID-19 cases.OA COVID-19 cases were also considered a response variable in our study.Results indicated that finer spatial scales(SA1 and SA2)captured localized patterns and significant spatial autocorrelation,while coarser levels(SA3 and SA4)smoothed spatial variability,masking potential outbreak clusters.Incorporating SEIFA as a covariate in locally-acquired(LA)cases reduced spatial autocorrelation in residuals,effectively capturing socioeconomic disparities.Conversely,OA cases showed limited effectiveness in reducing autocorrelation at finer scales.For LA cases,higher socioeconomic disadvantage was associated with increased COVID-19 incidence at finer scales,but this association became non-significant at coarser scales.OA cases showed significant positive association with higher SEIFA scores at finer scales.Model parameters displayed narrower credible intervals at finer scales,indicating greater precision,while coarser levels had increased uncertainty.SA2 emerged as an arguably optimal scale,striking a balance between spatial resolution,model stability,and interpretability.To improve inference on COVID-19 incidence,it is recommended to use data from both SA1 and SA2 levels to leverage their respective strengths.The findings emphasize the importance of selecting appropriate spatial scales and covariates or evaluating the inferential impacts of multiple scales,to address MAUP to facilitate more reliable spatial analysis.The study advocates exploring intermediate aggregation levels and multi-scale approaches to better capture nuanced disease dynamics and extend these analyses across Australia and replicating in other countries with low population densities to enhance generalizability.
