  Directions and paths, as commonly provided by navigation systems, are usually
derived considering absolute metrics, e.g., finding the shortest path within an
underlying road network. With the aid of crowdsourced geospatial data we aim at
obtaining paths that do not only minimize distance but also lead through more
popular areas using knowledge generated by users. We extract spatial relations
such as "nearby" or "next to" from travel blogs, that define closeness between
pairs of points of interest (PoIs) and quantify each of these relations using a
probabilistic model. Subsequently, we create a relationship graph where each
node corresponds to a PoI and each edge describes the spatial connection
between the respective PoIs. Using Bayesian inference we obtain a probabilistic
measure of spatial closeness according to the crowd. Applying this measure to
the corresponding road network, we obtain an altered cost function which does
not exclusively rely on distance, and enriches an actual road networks taking
crowdsourced spatial relations into account. Finally, we propose two routing
algorithms on the enriched road networks. To evaluate our approach, we use
Flickr photo data as a ground truth for popularity. Our experimental results --
based on real world datasets -- show that the paths computed w.r.t.\ our
alternative cost function yield competitive solutions in terms of path length
while also providing more "popular" paths, making routing easier and more
informative for the user.
