Modern urban planning has been shaped by a car-centric vision, which is reliant on fossil fuels and fosters health-related and socio-economic issues, making transportation within cities unsustainable. Low Traffic Zones or – Neighborhoods, where through-traffic and high-speed driving are prevented, are one way of reducing car use and its impact. One example of a Low Traffic Neighborhood is the Superblock in Barcelona. A ‘Superblock’ is a set of adjacent urban blocks where vehicular through traffic is prevented or pacified, giving priority to people walking and cycling. Yet, implementing ‘superblocks’ is challenging, since they require a complex and extensive planning process involving different stakeholders and careful consideration of trade-offs.

To assist the planning process, new computational tools and available data sets could be used. This is the aim of Superblockify. Superblockify is a Python package for partitioning an urban street network into Superblock-like neighborhoods and for visualizing and analyzing the partition results.  It helps urban planners develop a city-wide or district-wide vision of where traffic could be reduced or removed. Superblocks, or any Low Traffic Zone schemes, are making streets more inclusive and safer for different sets of users, thus promoting sustainable and active modes of transport. The package is open and modular, allowing anyone to tailor the partition algorithm to specific needs and data, to make it more relevant in a local context. It is functional with just the name of the targeted city or arbitrarily selected area.

You can access the package on its website, where you can also find the associated public repository and scientific journal publication. This package has been developed in the context of the European project Just Streets, aiming to design safer, healthier, and happier streets.