Data is available in vast quantities nowadays, including spatial data, which has practical, necessary, and implicit knowledge. We need mining spatial data to obtain valuable and important information using a particular method. Data mining provides methods for mining extensive data, and we have Spatial Data Mining for spatial data. Co-location pattern mining is one of the essential methods in Spatial Data Mining, where there are some approaches. One of the approaches is the Event-Centric Model. We use Jakarta Province, Indonesia data. We also use PostgreSQL with PostGIS extension to save, process and analyse the data. Besides finding the Co-location pattern rule size 3, this paper compares ST_DWithin and ST_Distance, two methods in PostgreSQL with PostGIS, to determine which is faster in implementing the Co-location rule size 3. We find ST_DWithin is faster than ST_Distance, with a margin of 79.80%. We also find that in Co-location rule size 3 in Jakarta province, Indonesia with 10 major spatial features and 120 Co-location, Mosque-School-Hospital is the highest participation index, with 0.89, which means that 89% of Mosques are in the neighbourhood with School and Hospital. © 2025 IEEE.