The nighttime light satellite data has been proven effective for examining global urbanization. The data have informative value for areas with low-quality statistical systems or no recent socioeconomic data. However, most of the studies examine urban growth at national or regional levels in a macroscopic scale. In Thailand, most of socioeconomic data are not often updated and rarely available at the sub-provincial level. The implication of nighttime light satellite images, which are publicly available and frequently updated, is assumed to be useful for analyzing urbanization or related issues. This study will be among the first studies to examine the changes of urban areas at the provincial level in Thailand using nighttime light satellite images. The objective of this study is to investigate if the nighttime light satellite data can serve as a proxy for socioeconomic data in the framework of an economic base analysis at the provincial level in Thailand by examining the time series data of relationship of 1995-2010 urban growth with nighttime light data and socioeconomic data. The study finds that both dark and bright nighttime light and the intensity of the light can be used as a proxy for gross provincial products (GPP) and population data. However, the intensity of the light is a better dataset than the dark and bright nighttime light data. In the national level, the intensity of light data can be used as a proxy of GPP data to analyze urban growth at 61% and as a proxy for population data at 98 %. The results also show that the use of the nighttime light data as a proxy of GPP is suitable for analyzing the provinces that are national trade and service center, small and medium business centers, and agricultural based. As a proxy of population data, the use of the nighttime light data is more appropriate for the provinces with medium scale with high density. On the other hand, the nighttime light data is not suitable to analyze the provinces that are regional center because the different of urban growth between districts in the provinces are very high.