Automated Generation, Visualization, and Analysis of potential Superblocks in Cities
Project description
Source code to superblockify
an urban street network
superblockify
is a Python package for partitioning an urban street network into
Superblock-like neighborhoods and for visualizing and analyzing the partition results. A
Superblock is a set of adjacent urban blocks where vehicular through traffic is
prevented or pacified, giving priority to people walking and cycling.
Installation
Set up environment
Use conda
or mamba
or micromamba
to create the virtual environment sb_env
:
conda create -n sb_env -c conda-forge python=3.12 osmnx=1.9.2
Note: While
pip
can install OSMnx, it's not officially supported due to potential issues with C dependencies. If unsure, useconda
as instructed above to avoid problems.
Alternatively, or if you run into
issues, clone this repository
and create the environment via
the environment.yml
file:
conda env create --file environment.yml
Install package
Next, activate the environment and install the package:
conda activate sb_env
pip install superblockify
Set up Jupyter kernel
If you want to use superblockify
with its environment sb_env
in Jupyter, run:
pip install --user ipykernel
python -m ipykernel install --user --name=sb_env
This allows you to run Jupyter with the kernel sb_env
(Kernel > Change Kernel >
sb_env)
Usage
We provide a minimum working example in two formats:
For a guided start after installation, see
the usage section in the documentation. See
the examples/
folder
for more example scripts.
Documentation
Read the documentation to learn more
about superblockify
.
Testing
The tests are specified using the pytest
signature,
see tests/
folder, and
can be run using a test runner of choice.
A pipeline is set up,
see .github/workflows/test.yml
.
Credits & Funding
- Carlson M. Büth (Implementation)
- Anastassia Vybornova (Supervision)
- Michael Szell (Concept)
Funded by the European Union, EU Horizon grant JUST STREETS
Project details
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