[](https://superblockify.city/)
[](https://pypi.org/project/superblockify/)
[](https://pypi.org/project/superblockify/)
[](https://github.com/PyCQA/pylint)
[](https://github.com/psf/black)
[](https://pypi.org/project/superblockify/)
[](https://joss.theoj.org/papers/87426e988ebd0a6d7f592eb16d1fd209)
[](https://doi.org/10.5281/zenodo.13300610)
[](https://github.com/NERDSITU/superblockify/actions/workflows/docs.yml)
[](https://github.com/NERDSITU/superblockify/actions/workflows/lint.yml)
[](https://github.com/NERDSITU/superblockify/actions/workflows/test.yml)
[](https://codecov.io/gh/NERDSITU/superblockify)
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`](https://docs.conda.io/projects/conda/en/latest/index.html)
or [`mamba`](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html)
or [`micromamba`](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html)
to create the virtual environment `sb_env`:
```bash
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, use `conda` as instructed above to avoid problems.
*Alternatively*, or if you run into
issues, [clone this repository](https://github.com/NERDSITU/superblockify/archive/refs/heads/main.zip)
and create the environment via
the [`environment.yml`](https://github.com/NERDSITU/superblockify/blob/main/environment.yml)
file:
```bash
conda env create --file environment.yml
```
### Install package
Next, activate the environment and install the package:
```bash
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:
```bash
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:
* [Jupyter notebook (`00-mwe.ipynb`)](https://github.com/NERDSITU/superblockify/blob/main/examples/00-mwe.ipynb)
* [Python script (`00-mwe.py`)](https://github.com/NERDSITU/superblockify/blob/main/examples/00-mwe.py)
For a guided start after installation, see
the [usage section](https://superblockify.city/usage/) in the documentation. See
the [`examples/`](https://github.com/NERDSITU/superblockify/blob/main/examples/) folder
for more example scripts.
## Documentation
Read the [documentation](https://superblockify.city) to learn more
about `superblockify`.
## Testing
The tests are specified using the `pytest` signature,
see [`tests/`](https://github.com/NERDSITU/superblockify/blob/main/tests/) folder, and
can be run using a test runner of choice.
A pipeline is set up,
see [`.github/workflows/test.yml`](https://github.com/NERDSITU/superblockify/blob/main/.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](https://cordis.europa.eu/project/id/101104240)
Raw data
{
"_id": null,
"home_page": null,
"name": "superblockify",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": "Carlson B\u00fcth <carlson@cbueth.de>",
"keywords": "Low Traffic Neighborhoods, GIS, Networks, OpenStreetMap, Urban Planning, Urban Mobility, Urban Data",
"author": "Carlson B\u00fcth, Anastassia Vybornova, Michael Szell",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/b1/94/761c5bf48dc102f9df97d9ec644979fd5433e6ddb7e9e2c1761746f32d67/superblockify-1.0.0.tar.gz",
"platform": null,
"description": "\n\n[](https://superblockify.city/)\n[](https://pypi.org/project/superblockify/)\n[](https://pypi.org/project/superblockify/)\n[](https://github.com/PyCQA/pylint)\n[](https://github.com/psf/black)\n[](https://pypi.org/project/superblockify/)\n\n[](https://joss.theoj.org/papers/87426e988ebd0a6d7f592eb16d1fd209)\n[](https://doi.org/10.5281/zenodo.13300610)\n[](https://github.com/NERDSITU/superblockify/actions/workflows/docs.yml)\n[](https://github.com/NERDSITU/superblockify/actions/workflows/lint.yml)\n[](https://github.com/NERDSITU/superblockify/actions/workflows/test.yml)\n[](https://codecov.io/gh/NERDSITU/superblockify)\n\nSource code to `superblockify` an urban street network\n\n---\n\n`superblockify` is a Python package for partitioning an urban street network into\nSuperblock-like neighborhoods and for visualizing and analyzing the partition results. A\nSuperblock is a set of adjacent urban blocks where vehicular through traffic is\nprevented or pacified, giving priority to people walking and cycling.\n\n![superblockify concept](assets/superblockify_concept.png \"superblockify partitions an urban street network into Superblock-like neighborhoods\")\n\n## Installation\n\n### Set up environment\n\nUse [`conda`](https://docs.conda.io/projects/conda/en/latest/index.html)\nor [`mamba`](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html)\nor [`micromamba`](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html)\nto create the virtual environment `sb_env`:\n\n```bash\nconda create -n sb_env -c conda-forge python=3.12 osmnx=1.9.2\n```\n\n> **Note:** While `pip` can install OSMnx, it's not officially supported due to potential issues with C dependencies. If unsure, use `conda` as instructed above to avoid problems. \n\n*Alternatively*, or if you run into\nissues, [clone this repository](https://github.com/NERDSITU/superblockify/archive/refs/heads/main.zip)\nand create the environment via\nthe [`environment.yml`](https://github.com/NERDSITU/superblockify/blob/main/environment.yml)\nfile:\n\n```bash\nconda env create --file environment.yml\n```\n\n### Install package\n\nNext, activate the environment and install the package:\n\n```bash\nconda activate sb_env\npip install superblockify\n```\n\n### Set up Jupyter kernel\n\nIf you want to use `superblockify` with its environment `sb_env` in Jupyter, run:\n\n```bash\npip install --user ipykernel\npython -m ipykernel install --user --name=sb_env\n```\n\nThis allows you to run Jupyter with the kernel `sb_env` (Kernel > Change Kernel >\nsb_env)\n\n## Usage\n\nWe provide a minimum working example in two formats:\n\n* [Jupyter notebook (`00-mwe.ipynb`)](https://github.com/NERDSITU/superblockify/blob/main/examples/00-mwe.ipynb)\n* [Python script (`00-mwe.py`)](https://github.com/NERDSITU/superblockify/blob/main/examples/00-mwe.py)\n\nFor a guided start after installation, see\nthe [usage section](https://superblockify.city/usage/) in the documentation. See\nthe [`examples/`](https://github.com/NERDSITU/superblockify/blob/main/examples/) folder\nfor more example scripts.\n\n## Documentation\n\nRead the [documentation](https://superblockify.city) to learn more\nabout `superblockify`.\n\n## Testing\n\nThe tests are specified using the `pytest` signature,\nsee [`tests/`](https://github.com/NERDSITU/superblockify/blob/main/tests/) folder, and\ncan be run using a test runner of choice.\nA pipeline is set up,\nsee [`.github/workflows/test.yml`](https://github.com/NERDSITU/superblockify/blob/main/.github/workflows/test.yml).\n\n## Credits & Funding\n\n* Carlson M. B\u00fcth (Implementation)\n* Anastassia Vybornova (Supervision)\n* Michael Szell (Concept)\n\nFunded by the European\nUnion, [EU Horizon grant JUST STREETS](https://cordis.europa.eu/project/id/101104240)\n",
"bugtrack_url": null,
"license": "APGL-3.0-or-later",
"summary": "Automated Generation, Visualization, and Analysis of potential Superblocks in Cities",
"version": "1.0.0",
"project_urls": {
"Changelog": "https://superblockify.city/changelog/",
"Documentation": "https://superblockify.city/",
"Repository": "https://github.com/NERDSITU/superblockify"
},
"split_keywords": [
"low traffic neighborhoods",
" gis",
" networks",
" openstreetmap",
" urban planning",
" urban mobility",
" urban data"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "54632e0df4f81523526045ca861dda1ff3c1bda22003f753d7c91f1fa642cf6c",
"md5": "e65561c98e43c2115b846fee60f97007",
"sha256": "bfea689292f137e2b67cc8d069475ddd875e77dd3a7223081aa3a67098268539"
},
"downloads": -1,
"filename": "superblockify-1.0.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "e65561c98e43c2115b846fee60f97007",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 123304,
"upload_time": "2024-08-16T15:39:38",
"upload_time_iso_8601": "2024-08-16T15:39:38.042398Z",
"url": "https://files.pythonhosted.org/packages/54/63/2e0df4f81523526045ca861dda1ff3c1bda22003f753d7c91f1fa642cf6c/superblockify-1.0.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "b194761c5bf48dc102f9df97d9ec644979fd5433e6ddb7e9e2c1761746f32d67",
"md5": "83b5e12ad006f0885687c1dcd114e43e",
"sha256": "d1dd157f0b1e144c49137016fc704cb8bb5140eae9e558728fbd22e95538e8fc"
},
"downloads": -1,
"filename": "superblockify-1.0.0.tar.gz",
"has_sig": false,
"md5_digest": "83b5e12ad006f0885687c1dcd114e43e",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 120151,
"upload_time": "2024-08-16T15:39:40",
"upload_time_iso_8601": "2024-08-16T15:39:40.247917Z",
"url": "https://files.pythonhosted.org/packages/b1/94/761c5bf48dc102f9df97d9ec644979fd5433e6ddb7e9e2c1761746f32d67/superblockify-1.0.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-08-16 15:39:40",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "NERDSITU",
"github_project": "superblockify",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "superblockify"
}
JSON
