KalmanFilterDEMO_IJGIS

The data and codes used in the paper published 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>