Mining Social Images to Analyze Routing Preferences in Tourist Areas

Social media platforms provide a useful source of data for environmental planning. In the last years these datahave been exploited to perform social behaviour analysis. This work uses the huge amount of georeferenced imagespublicly available on social media as a source of information to infer the behaviour of tourists. Visual analyticmapping tools combined with the Parzen-Rosenblatt non-parametric kernel density estimation give us visual cluesto assess the attractiveness of tourist geographical areas. To investigate the preferred combinations of locationsvisited by the tourists within a time window of few days we propose to mine association rules using the Apriorialgorithm. A prototype of an integrated system to visually perform the suggested analysis has been realized andthe paper reports about some of case studies performed with it.