Exploring the industrial growth and poverty alleviation through space-time data mining from night-time light images: a case study in Eastern Economic Corridor (EEC), Thailand

Abstract

ABSTRACT In low-middle income countries, industrial development is essential in driving economic growth, improving social progress, and creating employment to alleviate poverty. This study used the time series of night-time light (NTL) from 2000 to 2020 to assess the industrial park growth and its impact on poverty reduction at the village level. The emerging hotspot analysis has been used to identify the space-time pattern of night-time light data over the study period. Our study reveals that industrial parks keep growing and generate new neighbourhood growth. NTL growth patterns and Gross Provincial Product (GPP) are significantly correlated. Also, the multidimensional poverty index (MPI) increases with the distance from industrial park areas. Conclusively, using space-time data mining with NTL time series data can help the local government understand and monitor industrial growth at a low cost. The NTL data is a suitable proxy indicator for studying human activities and solving the incomplete and discontinued dataset. A further study utilizing the time series NTL data is still beneficial for developing countries.