Spatio-temporal models are valuable tools for disease mapping and understanding the geographical distribution of diseases and temporal dynamics. Spatio-temporal models have been proven empirically to be very complex a...Spatio-temporal models are valuable tools for disease mapping and understanding the geographical distribution of diseases and temporal dynamics. Spatio-temporal models have been proven empirically to be very complex and this complexity has led many to oversimply and model the spatial and temporal dependencies independently. Unlike common practice, this study formulated a new spatio-temporal model in a Bayesian hierarchical framework that accounts for spatial and temporal dependencies jointly. The spatial and temporal dependencies were dynamically modelled via the matern exponential covariance function. The temporal aspect was captured by the parameters of the exponential with a first-order autoregressive structure. Inferences about the parameters were obtained via Markov Chain Monte Carlo (MCMC) techniques and the spatio-temporal maps were obtained by mapping stable posterior means from the specific location and time from the best model that includes the significant risk factors. The model formulated was fitted to both simulation data and Kenya meningitis incidence data from 2013 to 2019 along with two covariates;Gross County Product (GCP) and average rainfall. The study found that both average rainfall and GCP had a significant positive association with meningitis occurrence. Also, regarding geographical distribution, the spatio-temporal maps showed that meningitis is not evenly distributed across the country as some counties reported a high number of cases compared with other counties.展开更多
近30年来,随着对地观测技术、资源调查与信息传输技术的增强,地里国情各领域积累了海量的空间数据。在时间序列内对海量空间数据处理分析,为环境变化预测、生态恢复重建、资源合理开发提供科学的数据参考。国家对土地资源普查监测频次...近30年来,随着对地观测技术、资源调查与信息传输技术的增强,地里国情各领域积累了海量的空间数据。在时间序列内对海量空间数据处理分析,为环境变化预测、生态恢复重建、资源合理开发提供科学的数据参考。国家对土地资源普查监测频次逐年提高,以此来了解资源现状、以及变化情况。传统模型对数据处理分析存在一定限制。在此背景下,选取黑河上游山区作为实验区,构建Logistic-CA-Markov(LCM)模拟与预测模型,探讨其对实验区LUCC(land use and cover change)的模拟效果,以及预测未来30年实验区LUCC情况。结果表明,对时空数据的时间序列变化与空间维度演化,LCM模型具有较强的模拟能力。展开更多
文摘Spatio-temporal models are valuable tools for disease mapping and understanding the geographical distribution of diseases and temporal dynamics. Spatio-temporal models have been proven empirically to be very complex and this complexity has led many to oversimply and model the spatial and temporal dependencies independently. Unlike common practice, this study formulated a new spatio-temporal model in a Bayesian hierarchical framework that accounts for spatial and temporal dependencies jointly. The spatial and temporal dependencies were dynamically modelled via the matern exponential covariance function. The temporal aspect was captured by the parameters of the exponential with a first-order autoregressive structure. Inferences about the parameters were obtained via Markov Chain Monte Carlo (MCMC) techniques and the spatio-temporal maps were obtained by mapping stable posterior means from the specific location and time from the best model that includes the significant risk factors. The model formulated was fitted to both simulation data and Kenya meningitis incidence data from 2013 to 2019 along with two covariates;Gross County Product (GCP) and average rainfall. The study found that both average rainfall and GCP had a significant positive association with meningitis occurrence. Also, regarding geographical distribution, the spatio-temporal maps showed that meningitis is not evenly distributed across the country as some counties reported a high number of cases compared with other counties.
文摘近30年来,随着对地观测技术、资源调查与信息传输技术的增强,地里国情各领域积累了海量的空间数据。在时间序列内对海量空间数据处理分析,为环境变化预测、生态恢复重建、资源合理开发提供科学的数据参考。国家对土地资源普查监测频次逐年提高,以此来了解资源现状、以及变化情况。传统模型对数据处理分析存在一定限制。在此背景下,选取黑河上游山区作为实验区,构建Logistic-CA-Markov(LCM)模拟与预测模型,探讨其对实验区LUCC(land use and cover change)的模拟效果,以及预测未来30年实验区LUCC情况。结果表明,对时空数据的时间序列变化与空间维度演化,LCM模型具有较强的模拟能力。