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
This study fully addressed the modifiable areal unit problem （MAUP） that was well-known in geography but generally ignored by safety analysis. The basic issue of MAUP was introduced firstly with a case study to expl...This study fully addressed the modifiable areal unit problem （MAUP） that was well-known in geography but generally ignored by safety analysis. The basic issue of MAUP was introduced firstly with a case study to explicitly demonstrate the existence of the problem in macro level crash modeling, and then four potential strategies, i.e., using disaggregate data as possible, capturing spatial non-stationarity, designing optimal zoning systems, conducting sensitivity analysis to report the scope and magnitude of MAUP, were proposed and illustrated in an integrated way, followed by the future research directions. Results revealed that more efforts are desired to calibrate the state-of-art modeling technique at various levels of aggregation based on spatial homogeneity in traffic safety, transport characteristics, and demographical factors. The awareness of this problem in traffic safety domain is expected to the delineation of basic spatial units （e.g. the traffic safety analysis zones）, as well as to provide new insights into the nature of MAUP in statistics and geography.展开更多
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.
基金Natural Science Foundation of China (No. 71371192)the Research Fund for Fok Ying Tong Education Foundation of Hong Kong (No. 142005)Science Fund for Outstanding Young Scholars of Hunan Province (No. 2015JJ1017)
文摘This study fully addressed the modifiable areal unit problem （MAUP） that was well-known in geography but generally ignored by safety analysis. The basic issue of MAUP was introduced firstly with a case study to explicitly demonstrate the existence of the problem in macro level crash modeling, and then four potential strategies, i.e., using disaggregate data as possible, capturing spatial non-stationarity, designing optimal zoning systems, conducting sensitivity analysis to report the scope and magnitude of MAUP, were proposed and illustrated in an integrated way, followed by the future research directions. Results revealed that more efforts are desired to calibrate the state-of-art modeling technique at various levels of aggregation based on spatial homogeneity in traffic safety, transport characteristics, and demographical factors. The awareness of this problem in traffic safety domain is expected to the delineation of basic spatial units （e.g. the traffic safety analysis zones）, as well as to provide new insights into the nature of MAUP in statistics and geography.
