International Science and Technology Journal

With the proliferation of technology and the advancement of mobile devices and the internet, the use of Location-Based Social Networks (LBSNs) has become popular in urban communities. That providing a rich source of data for analyzing social and urban behaviour in cities. Which allows for the tracking of urban interaction patterns and human activity to understand visitor behaviour in these places. This paper aims to identify spatial patterns and the distribution the attractions using LBSNs. Density estimation, spatial direction monitoring, and identification of spatial correlation of attractions within Riyadh city were conducted. Heat maps were utilized to reveal the relative density of attraction spots in Riyadh. On the other hand, the distribution trend was analysed using the SDE tool. Moreover, Moran's I index was employed to measure the spatial correlation of attractions within the city. The results of directional and density analysis of attractions indicate a tendency for attraction spots to concentrate in the city centre. Additionally, the results reflect variations between types of places across the studied categories, including parks, shopping centres, entertainment venues, and tourist attractions. The Moran index indicates spatial correlation for the attractions. Overall, LBSNs can be considered an additional and reliable source of big data for social media to monitor urban places and interactions within the city. Future integration of temporal analysis with spatial analysis can be pursued to identify spatiotemporal changes over time. Key words: GIS, Spatial Analysis, LBSNs, Google maps.