Influenza-like illness (ILI) is an acute respiratory disease that remains a public health concern for its ability to circulate globally affecting any age group and gender causing serious illness with mortality risk. Comprehensive assessment of the spatio-temporal dynamics of ILI is a prerequisite for effective risk assessment and application of control measures. Though meteorological parameters, such as rainfall, average relative humidity and temperature, influence ILI and represent crucial information for control of this disease, the relation between the disease and these variables is not clearly understood in tropical climates. The aim of this study was to analyse the epidemiology of ILI cases using integrated methods (space-time analysis, spatial autocorrelation and other correlation statistics). After 2009s H1N1 influenza pandemic, Phitsanulok Province in northern Thailand was strongly affected by ILI for many years. This study is based on ILI cases in villages in this province from 2005 to 2012. We used highly precise weekly incidence records covering eight years, which allowed accurate estimation of the ILI outbreak. Comprehensive methodology was developed to analyse the global and local patterns of the spread of the disease. Significant space-time clusters were detected over the study region during eight different periods. ILI cases showed seasonal clustered patterns with a peak in 2010 (P>0.05-9.999 iterations). Local indicators of spatial association identified hotspots for each year. Statistically, the weather pattern showed a clear influence on ILI cases and it strongly correlated with humidity at a lag of 1 month, while temperature had a weaker correlation.

Imaging spectroscopy in the remote sensing is an ever emerging platform that has offered the hyperspectral imaging (HSI) which delivers the Earth's object information in hundreds of bands. HSI integrates conventional imaging with spectroscopy to get rich spectral and spatial features of the object. However, the challenges associated with HSI are its huge dimensionality and data redundancy that requests huge space, complex computations, and lengthier processing time. Therefore, this study aims to find the optimal bands to characterize the roof surfaces using supervised classifiers. To deal with high dimensionality of hyperspectral data, this study assesses the band selection method over data transformation methods. This study provides the comparison between data reduction methods and used classifiers. The height information from LiDAR was used to characterize urban roofs above the height of 2.5 meters. The optimal bands were investigated using supervised classifiers such as artificial neural network (ANN), support vector machine (SVM), and spectral angle mapper (SAM) by comparing accuracies. The classification result shows that ANN and SVM classifiers outperform whereas SAM performed poorly in roof characterization. The band selection method worked efficiently than the transformation methods. The classification algorithm successfully identifies the optimum bands with significant accuracy.