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Tourism is continuously developing as a new economic source in Indonesia. Tourism activities extend to the various services, products, and experiences provided in the tourism site’s surrounding area. Tourism development requires information on possible related activities with tourism. However, there was a lack of studies that examined the relationship between tourism sites and the simultaneous presence of multiple public facilities, which would reveal the value of proximity. This paper aims to investigate the proximity patterns of tourism sites and the support facilities, to develop a strategy for tourism sites. The average nearest-neighbor results verify that there are clustering tendencies for almost all datasets. The Kernel Density Estimation (KDE)-based raster’s were created to visualize the patterns of tourism sites and nearby public facilities, which located near three world cultural heritage sites in Indonesia. Co-location pattern mining was applied to examine the co-location behavior between tourism sites and tourism support facilities using the Participation Index (PI) as the measurement parameter. This study provides knowledge, specifically the existence of co-location rules between tourism sites and tourism support facilities, which consist of food services, accommodations, transportation, shopping, and other tourism support facilities. The network graph shows that the location of tourism support facilities can be affected by the types of tourism sites, providing practical implications for individuals, business owners, and policymakers. Government policies related to planning for tourism destination development that consider the characteristics of spatial interactions are expected to be able to support government targets for increasing lengths of stay and tourist expenditures.

Tourism is continuously developing as a new economic source in Indonesia. Tourism activities extend to the various services, products, and experiences provided in the tourism site’s surrounding area. Tourism development requires information on possible related activities with tourism. However, there was a lack of studies that examined the relationship between tourism sites and the simultaneous presence of multiple public facilities, which would reveal the value of proximity. This paper aims to investigate the proximity patterns of tourism sites and the support facilities, to develop a strategy for tourism sites. The average nearest-neighbor results verify that there are clustering tendencies for almost all datasets. The Kernel Density Estimation (KDE)-based raster’s were created to visualize the patterns of tourism sites and nearby public facilities, which located near three world cultural heritage sites in Indonesia. Co-location pattern mining was applied to examine the co-location behavior between tourism sites and tourism support facilities using the Participation Index (PI) as the measurement parameter. This study provides knowledge, specifically the existence of co-location rules between tourism sites and tourism support facilities, which consist of food services, accommodations, transportation, shopping, and other tourism support facilities. The network graph shows that the location of tourism support facilities can be affected by the types of tourism sites, providing practical implications for individuals, business owners, and policymakers. Government policies related to planning for tourism destination development that consider the characteristics of spatial interactions are expected to be able to support government targets for increasing lengths of stay and tourist expenditures.

Tourism is continuously developing as a new economic source in Indonesia. Tourism activities extend to the various services, products, and experiences provided in the tourism site’s surrounding area. Tourism development requires information on possible related activities with tourism. However, there was a lack of studies that examined the relationship between tourism sites and the simultaneous presence of multiple public facilities, which would reveal the value of proximity. This paper aims to investigate the proximity patterns of tourism sites and the support facilities, to develop a strategy for tourism sites. The average nearest-neighbor results verify that there are clustering tendencies for almost all datasets. The Kernel Density Estimation (KDE)-based raster’s were created to visualize the patterns of tourism sites and nearby public facilities, which located near three world cultural heritage sites in Indonesia. Co-location pattern mining was applied to examine the co-location behavior between tourism sites and tourism support facilities using the Participation Index (PI) as the measurement parameter. This study provides knowledge, specifically the existence of co-location rules between tourism sites and tourism support facilities, which consist of food services, accommodations, transportation, shopping, and other tourism support facilities. The network graph shows that the location of tourism support facilities can be affected by the types of tourism sites, providing practical implications for individuals, business owners, and policymakers. Government policies related to planning for tourism destination development that consider the characteristics of spatial interactions are expected to be able to support government targets for increasing lengths of stay and tourist expenditures.