Sometimes we take a longer route because it’s more scenic or more interesting. Also, while on the go, people may not always want to supply lengthy preferences about what they want. Here, we tackled this problem: how can we build city route planners that ‘automatically’ compute route plans based not on efficiency, but on people’s trailing city experiences?
To define an interesting walkable route, we assumed that the locations of photographs are potentially interesting as a photographer found it worthwhile to take a picture in that sequence. To make our walkable routes, we used sequence alignment methods, which we borrowed from bioinformatics.
We tested our approach on two routes in Amsterdam (The Netherlands): Central Station to Museumplein and another from Waterlooplein to Westerkerk. We compared our photographer paths with two baselines (Fig. 1): a) a Google Maps shortest path route variation and b) a Photo Density route variation, based on the volume of photos at a location.
Drawing on questionnaires, web surveys, and user interviews with Amsterdam residents, our results showed that our photographer paths were perceived as most stimulating and suitable for city exploration. With our proof-of-concept approach, we have shown it is possible to leverage social geotagged data to cater for the hard problem of automatically generating exploration-based route plans.
For more, see our full paper, Photographer Paths: Sequence Alignment of Geotagged Photos for Exploration-based Route Planning.