KalmanFilterDEMO_IJGIS

The data and codes used in the paper published in IJGIS.<br>Title: Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach<br>Authors: Bisong Hu^a,b, Pan Ning^a, Yi Li^c, Chengdong Xu^b, George Christakos^d and Jinfeng Wang^b* ^aSchool of Geography and Environment, Jiangxi Normal University, Nanchang, China; ^bState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; ^cNational Engineering Research Center for Geoinformatics, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;^dGeography Department, San Diego State University, San Diego, USAContact: Jinfeng Wang, [e-mail]<br>Abstract: In this work, a synthesis of the Bayesian maximum entropy (BME) and the Kalman filter (KF) methods, which enhances their individual strengths and overcomes certain of their weaknesses for spatiotemporal mapping purposes, is proposed in a spatiotemporal disease mapping context. The proposed BME-Kalman synthesis allows BME to use information from both parametric regression modeling and KF estimation leading to enhanced knowledge bases. The BME-Kalman synthetic approach is used to study the space-time incidence mapping of the hand, foot and mouth disease (HFMD) in Shandong province (China) during the period May 1st, 2008 to March 19th, 2009. The results showed that the BME-Kalman approach exhibited very good regressive and predictive accuracies, maintained a very good performance even during low-incidence and extremely low-incidence periods, offered an improved description of hierarchical disease characteristics compared to traditional mapping techniques, and provided a clear explanation of the spatial stratified incidence heterogeneity at unsampled locations. The BME-Kalman approach is versatile and flexible so that it can be modified and adjusted according to the needs of the application.<br>Keywords: Bayesian maximum entropy; Kalman filter; Geostatistics; space-time analysis; hand, foot and mouth disease<br>

本数据集为发表于IJGIS的论文所用的数据与代码。
论文标题：结合贝叶斯最大熵（Bayesian maximum entropy, BME）与卡尔曼滤波（Kalman filter, KF）的时空疾病制图：BME-Kalman方法
作者：胡必松^a,b、潘宁^a、李毅^c、徐承栋^b、George Christakos^d、王劲峰^b*
作者单位：
^a 江西师范大学地理与环境学院，中国南昌；
^b 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，中国北京；
^c 中国科学院遥感与数字地球研究所国家地理信息工程技术研究中心，中国北京100101；
^d 圣地亚哥州立大学地理系，美国圣地亚哥
联系方式：通讯作者为王劲峰，邮箱：[e-mail]
摘要：本研究针对时空疾病制图需求，提出融合贝叶斯最大熵与卡尔曼滤波的合成方法，以充分发挥二者优势并弥补各自短板。所提出的BME-Kalman合成方法可使贝叶斯最大熵同时利用参数化回归建模与卡尔曼滤波估计的信息，进而扩充知识基础。本研究采用BME-Kalman合成方法，对2008年5月1日至2009年3月19日期间中国山东省手足口病（hand, foot and mouth disease, HFMD）的时空发病率制图开展研究。结果表明，BME-Kalman方法具备优异的回归与预测精度，即使在低发病率乃至极低发病率时段仍可保持良好性能；相较于传统制图技术，该方法可更好地刻画疾病的层级特征，并对未采样点位的空间分层发病率异质性给出清晰解释。BME-Kalman方法具有通用性与灵活性，可根据应用需求进行修改与调整。
关键词：贝叶斯最大熵；卡尔曼滤波；地统计学；时空分析；手足口病