Book excerpt: This thesis presents an exploratory visual analytics approach to identify temporal distributions, spatial clusters and popular routes of tourists in Amsterdam by making use of geotagged photos from social media platform Flickr. Our methods combine the analytical strength of humans with the data processing power of computers, using geovisualisations and charts to explore data, find patterns, and draw conclusions from its outcomes. For this research, the metadata of 2,849,261 geotagged photos was harvested from Flickr and stored in a spatial database. From this dataset, 393,828 photos were located in the municipality of Amsterdam. Our semi-automatic classification method classified 39,1% of the users as tourist with a very high precision and recall. The temporal distribution of tourists and locals is compared for different temporal granularities. A method is presented to assess photo timestamps by making use of photos that contain a real clock. We implemented and improved an existing grid-based clustering method to explore Amsterdam’s spatial distribution of tourists in Google Earth. The major tourist hotspots are detected using the density-based clustering algorithm DBSCAN. Finally, we estimated the most probable routes of tourists between subsequent photo locations and aggregated all route calculations into a route density map. A qualitative approach was used to validate the study outcomes by interviewing eight tourism experts of the municipality of Amsterdam. We found that their knowledge about the city bears a good resemblance with the detected spatial clusters and route density map of tourists. Despite several imperfections of geosocial data, we conclude that our methods provide meaningful insight into the spatial and temporal patterns of tourists in urban spaces and are a valuable addition to traditional tourism surveys.