Cities are complex systems, and understanding their structure is critical for multiple applications. However, traditional urban planning is challenged by the dynamics of the urban system. Fortunately, in recent years, multiple datasets reflecting human activity in nearly real-time have become available. This paper leverages geo-tagged data from VKontakte, Google Places social media and Nash Petersburg urban issue-reporting portal for building a multi-layered social activity network and revealing the structure of the city through the community structure in this network. The ability of this structure to capture meaningful socioeconomic patterns across the city is evaluated. Results will aid urban, transportation, infrastructural planning, policy-making, real estate and socioeconomic development initiatives.

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Mistakes in pedestrian infrastructure design in modern cities decrease transfer comfort for people, impact greenery due to appearance of desire paths, and thus increase the amount of dust in the air because of open ground. These mistakes can be avoided if optimal path networks are created considering behavioral aspects of pedestrian traffic, which is a challenge. In this article, we introduce Ant Road Planner, a new method of computer simulation for estimation and creation of optimal path networks which not only considers pedestrians' behavior but also helps minimize the total length of the paths so that the area is used more efficiently. The method, which includes a modeling algorithm and its software implementation with a user-friendly web interface, makes it possible to predict pedestrian networks for new territories with high precision and detect problematic areas in existing networks. The algorithm was successfully tested on real territories and proved its potential as a decision making support system for urban planners.

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