Great efforts have been made toward malaria elimination in South-East Asia. Thailand has set a goal of halting malaria transmission by 2025. Despite overall lower malaria transmission, Yala is situated in Southern Thailand in one of the provinces with the highest burden of malaria. Estimating a geospatial malaria exposure risk surface can provide local health officials with information useful for targeting the remaining foci of transmission, as well as reducing the risk of exposure for local populations, promoting malaria elimination. To estimate a geospatial malaria exposure risk surface for the province of Yala, we use data on age, gender, occupation, mode of travel to work, type of malaria, and home and work village locations collected through a survey administered by a team of researchers from The Armed Forces Research Institute of Medical Sciences (AFRIMS) in Yala from January 2019 to April 2020. From this survey, there were a total of 54 cases, 36 Plasmodium vivax (P. vivax) malaria, 15 Plasmodium falciparum (P. falciparum) malaria, and 2 negative cases. In addition to the 54 infections identified through the AFRIMS study, we collected data on over 1401 P. vivax positive cases and 180 P. falciparum malaria cases and their village locations from the Ministry of Public Health of Thailand during the same time period. Remotely sensed environmental data was collected including minimum, maximum, and average temperature, precipitation, land cover, and elevation among other variables. A detailed road network was also captured from OpenStreetMap. We used a maximum entropy modeling tool (Maxent) to simulate and generate an estimate of the potential malaria exposure risk for Yala. The resulting probability surface captures the spatial variability of malaria risk within this province. We then use this risk surface to analyze the risk of exposure for different occupation groups, and how factors relating to local occupation-based travel are related to the risk of exposure to both P. vivax and P. falciparum malaria in this province.