Building City-Scale Walking Itineraries Using Large Geospatial Datasets

Abstract

Nowadays, social networks play an important role in many aspects of people's life and in traveling in particular. People share their experience and opinions not only on specialized sites, like TripAdvisor, but also in social networks, e.g. Instagram. Combining information from different sources we can get a manifold dataset, which covers main sights, famous buildings as well as places popular with city residents. In this paper, we propose method for generation of walking tours based on large multi-source dataset. In order to create this dataset, we developed data crawling framework, which is able to collect data from Instagram at high speed. We provide several use cases for the developed itinerary generation method and demonstrate that it can significantly enrich standard touristic paths provided by official site.

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