APPLYING SPATIAL INFORMATION TO SUPPORT POVERTY ALLEVIATION BASED ON ECOLOGICAL LANDSCAPE CHARACTERISTICS: A CASE STUDY OF YASOTHON PROVINCE

Abstract

          The objectives of this study were to apply spatial information to classify and delineate ecological characteristics, and to group poor households based on spatial contexts and compare them with ecological characteristics to support the design of poverty reduction strategies in Yasothon Province. The study utilized a database of 5,041 poor households. A mixed-methods research approach was employed to integrate quantitative and qualitative data, including the participatory definition of geo-ecological characteristics, the delineation of boundaries using Geographic Information Systems (GIS), the categorization of poor households, and the analysis
of their distribution across the province’s geo-ecological landscapes using the Identity Analysis overlay technique. The results revealed that Yasothon Province could be classified into five ecological types of Phu (uplands), Khok (mounds), Thung (lowland fields), Tham (floodplains), and Mueang (urban areas) which were visualized into maps. The impoverished households were categorized into four groups based on their spatial context of struggling, difficult, manageable, and secure. The spatial analysis comparing the distribution of poor households with the eco-landscape types indicated that the majority of poor households (38.05% of all poor households) resided in the Thung (lowland fields) eco-landscape. However, when analyzing the density of poor households per eco-landscape area, it was found that the Mueang (urban areas) eco-landscape had the highest density, with 4.11 poor households per square kilometer. These variations among eco-landscapes indicated that physical characteristics significantly influenced poverty conditions, particularly in relation to livelihoods and occupations that differed across each eco-landscape type. This study demonstrated the vital role of spatial information systems as tools for defining ecological boundaries, managing physical data, and analyzing the spatial distribution of poor households. The findings could be beneficial in supporting future policy design and decision-making for poverty alleviation and development, enabling relevant agencies to formulate targeted and effective interventions that align with the specific contexts of each ecosystem.