Meanwhile, our method can address the clustering issue in a dense area. Results show that the discovered POI/ROIs nearly match the official data in Tainan, whereas more commercial POI/ROIs are discovered in Taipei by the algorithm than official data. The developed method is demonstrated in two study areas in Taiwan: Tainan and Taipei, which are the oldest and densest cities, respectively. The discovered ROIs have a particular spatial overlap available which means the satisfied region of ROIs can be shared for appreciating attractions. POI and ROI, which are derived from the peak value and range of clusters, indicate the most popular location and range for appreciating attractions. Pattern discovery is combined with a novel algorithm, the spatial overlap (SO) algorithm, and the naming and merging method is conducted for attractive footprint clustering. Attractive footprints in photos with a local maximum that is beneficial for distinguishing clusters are first exploited. This study aims to develop an efficient method for POI/ROI discovery from Flickr. Point of interest (POI) and region of interest (ROI) are significant reference data that are widely used in geospatial applications. In the era of big data, ubiquitous Flickr geotagged photos have opened a considerable opportunity for discovering valuable geographic information.
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