Applying Space Syntax to Online Mapping Tools.

To walk around the city, individuals use mobile mapping services, and such services mostly suggest shortest routes. To go beyond recommending such walkable routes, we propose a new framework for automatic wayfinding for pedestrians. This framework tackles two main drawbacks from which past work suffers, namely coarse-grained representation of space and absence of contextual dynamics. We model the human tendency to regularize space by borrowing a spatial representation, Space Syntax, from the discipline of Architecture. Moreover, the proposed framework accounts for contextual dynamics of individual streets by predicting the popularity of each street under different contexts (e.g., at a given time, with a certain weather condition). Using Foursquare check-ins (i.e., whereabouts of the users of the popular location-based service) and publicly available weather data, we validate our framework in the entire city of Barcelona. We find that, with paths slightly longer than the shortest ones, our framework is able to accommodate our mental topography and effectively capture contextual changes.