This paper first applies the sequential cluster method to set up the classification standard of infectious disease incidence state based on the fact that there are many uncertainty characteristics in the incidence cou...This paper first applies the sequential cluster method to set up the classification standard of infectious disease incidence state based on the fact that there are many uncertainty characteristics in the incidence course.Then the paper presents a weighted Markov chain,a method which is used to predict the future incidence state.This method assumes the standardized self-coefficients as weights based on the special characteristics of infectious disease incidence being a dependent stochastic variable.It also analyzes the characteristics of infectious diseases incidence via the Markov chain Monte Carlo method to make the long-term benefit of decision optimal.Our method is successfully validated using existing incidents data of infectious diseases in Jiangsu Province.In summation,this paper proposes ways to improve the accuracy of the weighted Markov chain,specifically in the field of infection epidemiology.展开更多
With the emergence of the Internet of Things(IoT), there has been a proliferation of urban studies using big data. Yet, another type of urban research innovations that involve interdisciplinary thinking and methods re...With the emergence of the Internet of Things(IoT), there has been a proliferation of urban studies using big data. Yet, another type of urban research innovations that involve interdisciplinary thinking and methods remains underdeveloped. This paper represents an attempt to adopt a Hidden Markov Model(HMM) toolbox developed in Computer Science for the analysis of eye movement patterns in Psychology to answer urban mobility questions in Geography. The main idea is that both people’s eye movements and travel behavior follow the stop-travel-stop pattern, which can be summarized using HMM. Methodological challenges were addressed by adjusting the HMM to analyze territory-wide travel survey data in Hong Kong, China. By using the adjusted toolbox to identify the activitytravel patterns of working adults in Hong Kong, two distinctive groups of balanced(38.4%) and work-oriented(61.6%) lifestyles were identified. With some notable exceptions, working adults living in the urban core were having a more work-oriented lifestyle. Those with a balanced lifestyle were having a relatively compact zone of non-work activities around their homes but a relatively long commuting distance. Furthermore, working females tend to spend more time at home than their counterparts, regardless of their marital status and lifestyle. Overall, this interdisciplinary research demonstrates an attempt to integrate spatial, temporal, and sequential information for understanding people’s behavior in urban mobility research.展开更多
现有住宅建筑在室行为预测模型缺乏对住户差异性的合理考虑,导致模型往往存在整体预测精度不高和适用性受限等问题.针对这一问题,提出一种考虑住户差异性的马尔可夫链在室状态预测模型.该模型首先通过Spearman相关性分析确定了不同影响...现有住宅建筑在室行为预测模型缺乏对住户差异性的合理考虑,导致模型往往存在整体预测精度不高和适用性受限等问题.针对这一问题,提出一种考虑住户差异性的马尔可夫链在室状态预测模型.该模型首先通过Spearman相关性分析确定了不同影响因素(即特征参数)与住户总在室时长的相关性,将相关系数作为特征参数权值并结合聚类分析对住户群体进行分类.在此基础上采用马尔可夫链模型对住户在室状态进行预测.为评估所建立预测模型的性能,以英国TUS(Time Use Survey)数据库为例,将改进模型与传统马尔可夫链模型进行对比分析.结果表明,该方法能够综合考虑不同住户特征参数及其对在室行为的影响,对住户进行合理的分类,与传统马尔可夫模型相比,所建预测模型显著提升了整体性能,平均绝对误差和均方根误差分别减小了20.57%和15.35%.展开更多
基金supported in part by"National S&T Major Project Foundation of China"(2009ZX10004-904)Universities Natural Science Foundation of Jiangsu Province(09KJB330004),National Science Foundation Grant DMS-9971405National Institutes of Health Contract N01-HV-28183
文摘This paper first applies the sequential cluster method to set up the classification standard of infectious disease incidence state based on the fact that there are many uncertainty characteristics in the incidence course.Then the paper presents a weighted Markov chain,a method which is used to predict the future incidence state.This method assumes the standardized self-coefficients as weights based on the special characteristics of infectious disease incidence being a dependent stochastic variable.It also analyzes the characteristics of infectious diseases incidence via the Markov chain Monte Carlo method to make the long-term benefit of decision optimal.Our method is successfully validated using existing incidents data of infectious diseases in Jiangsu Province.In summation,this paper proposes ways to improve the accuracy of the weighted Markov chain,specifically in the field of infection epidemiology.
文摘With the emergence of the Internet of Things(IoT), there has been a proliferation of urban studies using big data. Yet, another type of urban research innovations that involve interdisciplinary thinking and methods remains underdeveloped. This paper represents an attempt to adopt a Hidden Markov Model(HMM) toolbox developed in Computer Science for the analysis of eye movement patterns in Psychology to answer urban mobility questions in Geography. The main idea is that both people’s eye movements and travel behavior follow the stop-travel-stop pattern, which can be summarized using HMM. Methodological challenges were addressed by adjusting the HMM to analyze territory-wide travel survey data in Hong Kong, China. By using the adjusted toolbox to identify the activitytravel patterns of working adults in Hong Kong, two distinctive groups of balanced(38.4%) and work-oriented(61.6%) lifestyles were identified. With some notable exceptions, working adults living in the urban core were having a more work-oriented lifestyle. Those with a balanced lifestyle were having a relatively compact zone of non-work activities around their homes but a relatively long commuting distance. Furthermore, working females tend to spend more time at home than their counterparts, regardless of their marital status and lifestyle. Overall, this interdisciplinary research demonstrates an attempt to integrate spatial, temporal, and sequential information for understanding people’s behavior in urban mobility research.
文摘现有住宅建筑在室行为预测模型缺乏对住户差异性的合理考虑,导致模型往往存在整体预测精度不高和适用性受限等问题.针对这一问题,提出一种考虑住户差异性的马尔可夫链在室状态预测模型.该模型首先通过Spearman相关性分析确定了不同影响因素(即特征参数)与住户总在室时长的相关性,将相关系数作为特征参数权值并结合聚类分析对住户群体进行分类.在此基础上采用马尔可夫链模型对住户在室状态进行预测.为评估所建立预测模型的性能,以英国TUS(Time Use Survey)数据库为例,将改进模型与传统马尔可夫链模型进行对比分析.结果表明,该方法能够综合考虑不同住户特征参数及其对在室行为的影响,对住户进行合理的分类,与传统马尔可夫模型相比,所建预测模型显著提升了整体性能,平均绝对误差和均方根误差分别减小了20.57%和15.35%.
