tesspy 0.2.0

Tessellation of Urban Areas

tesspy

tesspy is a python library for geographical tessellation.

The process of discretization of space into subspaces without overlaps and gaps is called tessellation and is of interest to researchers in the field of spatial analysis. Tessellation is essential in understanding geographical space and provides a framework for analyzing geospatial data. Different tessellation methods are implemented in tesspy. They can be divided into two groups. The first group is regular tessellation methods: square grid and hexagon grid. The second group is irregular tessellation methods based on geospatial data. These methods are adaptive squares, Voronoi diagrams, and city blocks. The geospatial data used for tessellation is retrieved from the OpenStreetMap database.

Installation

Install tesspy using uv (Recommended):

uv pip install tesspy

or using pip:

pip install tesspy

tesspy is also available on conda-forge:

conda install -c conda-forge tesspy

Creating a new environment for tesspy

We recommend using uv to create and manage a virtual environment:

uv venv tesspy_env
source tesspy_env/bin/activate  # Linux/macOS
# tesspy_env\Scripts\activate   # Windows
uv pip install tesspy

To also install dependencies for running the example notebooks:

uv pip install "tesspy[examples]"

Dependencies

tesspy's dependencies are: geopandas, scipy, h3-py, osmnx, hdbscan, mercantile, matplotlib and scikit-learn.

Documentation

The official documentation is hosted on ReadTheDocs.

Logging

tesspy uses Python's standard logging module. By default, library logging is silent unless you configure it.

Basic progress logs:

from tesspy import Tessellation, configure_logging

configure_logging("INFO")
t = Tessellation("Frankfurt am Main")
t.city_blocks(verbose=True)

Use verbose=True on POI-driven methods to emit progress events.

Examples

The city of "Frankfurt am Main" in Germany is used to showcase different tessellation methods. This is how a tessellation object is built, and different methods are called. For the tessellation methods based on Points of Interests (adaptive squares, Voronoi polygons, and City Blocks), we use amenity data from the OpenStreetMap.

from tesspy import Tessellation
ffm= Tessellation('Frankfurt am Main')

Squares

ffm_sqruares = ffm.squares(resolution=15)

Hexagons

ffm_hex_8 = ffm.hexagons(resolution=8)

Adaptive Squares

ffm_asq = ffm.adaptive_squares(start_resolution=14, threshold=100, poi_categories=['amenity'])

Voronoi Polygons

ffm_voronoi = ffm.voronoi(poi_categories=['amenity'], n_polygons=500)

City Blocks

ffm_city_blocks = ffm.city_blocks(n_polygons=500)

Citing Tesspy

We would be very grateful if you would cite Tesspy in your scientific publications. Please feel free to use the following citation for this purpose: Saki et al., (2022). TessPy: a python package for geographical tessellation. Journal of Open Source Software, 7(76), 4620, https://doi.org/10.21105/joss.04620

or the bibtex citation directly:

@article{Saki2022, 
        doi = {10.21105/joss.04620}, 
        url = {https://doi.org/10.21105/joss.04620}, 
        year = {2022}, 
        publisher = {The Open Journal}, 
        volume = {7}, 
        number = {76}, 
        pages = {4620}, 
        author = {Siavash Saki and Jonas Hamann and Tobias Hagen}, 
        title = {TessPy: a python package for geographical tessellation}, 
        journal = {Journal of Open Source Software}}

Contributing to tesspy

All kind of contributions are welcome:

• Improvement of code with new features, bug fixes, and bug reports
• Improvement of documentation
• Additional tests

To set up a development environment, clone the repo and install in editable mode:

git clone https://github.com/siavash-saki/tesspy.git
cd tesspy
uv pip install -e ".[dev,examples]"

Follow the instructions here for submitting a PR.

If you have any ideas or questions, feel free to open an issue.

Acknowledgements

tesspy is the result of the research project ClusterMobil conducted by the Research Lab for Urban Transport. This research project is funded by the state of Hesse and HOLM funding under the “Innovations in Logistics and Mobility” measure of the Hessian Ministry of Economics, Energy, Transport and Housing. [HA Project No.: 1017/21-19]