Urban expansion is considered as one of the most important problems in several developing countries. Bangkok Metropolitan Region (BMR) is the urbanized and agglomerated area of Bangkok Metropolis (BM) and its vicinity, which confronts the expansion problem from the center of the city. Landsat images of 1988, 1993, 1998, 2003, 2008, and 2011 were used to detect the land use and land cover (LULC) changes. The demographic and economic data together with corresponding maps were used to determine the driving factors for land conversions. This study applied Cellular Automata-Markov Chain (CA-MC) and Multi-Layer Perceptron-Markov Chain (MLP-MC) to model LULC and urban expansions. The performance of the CA-MC and MLP-MC yielded more than 90% overall accuracy to predict the LULC, especially the MLP-MC method. Further, the annual population and economic growth rates were considered to produce the land demand for the LULC in 2014 and 2035 using the statistical extrapolation and system dynamics (SD). It was evident that the simulated map in 2014 resulting from the SD yielded the highest accuracy. Therefore, this study applied the SD method to generate the land demand for simulating LULC in 2035. The outcome showed that urban occupied the land around a half of the BMR.

Hand, Foot and Mouth Disease (HFMD) is an emerging viral disease, and at present, there are no antiviral drugs or vaccines available to control it. Outbreaks have persisted for the past 10 years, particularly in northern Thailand. This study aimed to elucidate the phenomenon of HFMD outbreaks from 2003 to 2012 using general statistics and spatial-temporal analysis employing a GIS-based method. The spatial analysis examined data at the village level to create a map representing the distribution pattern, mean center, standard deviation ellipse and hotspots for each outbreak. A temporal analysis was used to analyze the correlation between monthly case data and meteorological factors. The results indicate that the disease can occur at any time of the year, but appears to peak in the rainy and cold seasons. The distribution of outbreaks exhibited a clustered pattern. Most mean centers and standard deviation ellipses occurred in similar areas. The linear directional mean values of the outbreaks were oriented toward the south. When separated by season, it was found that there was a significant correlation with the direction of the southwest monsoon at the same time. An autocorrelation analysis revealed that hotspots tended to increase even when patient cases subsided. In particular, a new hotspot was found in the recent year in Mae Hong Son province.

In recent years, research and development on liveable cities has gained much attention due to the complexity and diversity of liveability standards. Due to the already-existing grand-scale developments commonly found in most capitals, research on liveability is often conducted in smaller semi-urban cities. Using Khon Kaen District in Thailand as a case study, we have developed a Liveable City Index (LCI) based on residents’ opinions and experts’ recommendations with the integration of Geographic Information System (GIS) techniques. The first stage of the survey (out of three), identifies marked variations in attitudes towards the liveability of a city. The survey evaluates nine significant factors (Safety, Economy, Environment, Education, Health, Transportation, Recreation, Population Density, and Public Utility) through the Analytical Hierarchy Process (AHP) for LCI development. The LCI map reveals that only 3.49% of the Khon Kaen area corresponds to the highest and high liveable city levels. This contradicts the earlier ranking of the city as the most liveable city in 2010, which was only based on economic factors. Moreover, the proposed method was applied to another area—the Muang district of Suphanburi in western Thailand—in order to test its reliability, and the results were found to be similar. This clearly supports the integration of residents’ participation in assessing the liveability of a city, and it is evident that this proposed approach can be adopted in other areas for LCI development.