Metadata-Version: 2.1
Name: tesspy
Version: 0.1.0
Summary: Tessellation of Urban Areas
Home-page: https://github.com/siavash-saki/tesspy
Author: tesspy Developers
Author-email: jonas.hamann@fb3.fra-uas.de
License: UNKNOWN
Description: # tesspy
        [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
        [![Documentation Status](https://readthedocs.org/projects/tesspy/badge/?version=latest)](https://tesspy.readthedocs.io/en/latest/?badge=latest)
        ![version](https://img.shields.io/badge/version-0.1.0-blue)
        [![Conda Version](https://img.shields.io/conda/vn/conda-forge/tesspy.svg)](https://anaconda.org/conda-forge/tesspy)
        
        <img align="left" src="docs/readme_pics/logo.jpg">
        
        `tesspy` is a python library for geographical tessellation.
        
        The process of discretization of space into subspaces without overlaps and gaps is called tessellation. 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
        You can install ``tesspy`` from PyPI using pip (**Not Recommended**):
        ```
        pip install tesspy
        ```
        
        and from conda (**Recommended**):
        ```
        conda install -c conda-forge tesspy
        ```
        
        ## Creating a new environment for tesspy
        
        `tesspy` depends on `geopandas`, which could make the installation sometimes tricky because of the conflicts with the current packages. Therefore, we recommend creating a new clean environment and installing the dependencies from the conda-forge channel.
        
        
        Create a new environment:
        ```shell
        conda create -n tesspy_env -c conda-forge
        ```
        
        Activate this environment:
        ```shell
        conda activate tesspy_env
        ```
        
        Install tesspy from conda-forge channel:
        ```shell
        conda install -c conda-forge tesspy
        ```
        
        
        ## Dependencies
        
        `tesspy`'s dependencies are: `geopandas`,`h3-py`,`osmnx`,`hdbscan`, and`mercantile`.
        
        
        ## Documentation
        The official documentation is hosted on **[ReadTheDocs](https://tesspy.readthedocs.io)**.
        
        
        ## 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.
        ```python
        from tesspy import Tessellation
        ffm= Tessellation('Frankfurt am Main')
        ```
        
        
        ### Squares 
        ```python
        ffm_sqruares = ffm.squares(resolution=15)
        ```
        ![Squares_tessellation](docs/readme_pics/Squares.png)
        
        ### Hexagons
        ```python
        ffm_hex_8 = ffm.hexagons(resolution=8)
        ```
        ![hexagon_tessellation](docs/readme_pics/Hexagons.png)
        
        
        ### Adaptive Squares
        ```python
        ffm_asq = ffm.adaptive_squares(start_resolution=14, threshold=100, poi_categories=['amenity'])
        ```
        
        ![adaptive_squares_tessellation](docs/readme_pics/Adaptive_Squares.png)
        
        ### Voronoi Polygons
        ```python
        ffm_voronoi = ffm.voronoi(poi_categories=['amenity'], n_polygons=500)
        ```
        ![Voronoi_tessellation](docs/readme_pics/Voronoi.png)
        
        ### City Blocks
        ```python
        ffm_city_blocks = ffm.city_blocks(n_polygons=500)
        ```
        ![city_blocks_tessellation](docs/readme_pics/CB.png)
        
        ## 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
        
        If you want to contribute to code:
        1. Fork the latest main branch
        2. Create a dev environment: install dependencies and install tesspy in [develop mode](https://python-packaging-tutorial.readthedocs.io/en/latest/setup_py.html#develop-mode)
        3. Write failing tests
        4. Write new code
        5. Run tests and make sure they pass
        6. Update documentation
        7. Pull Request
        
        If you have any ideas or questions, feel free to open an issue.
        
        
        ## Acknowledgements
        `tesspy` is the result of the research project [ClusterMobil](https://www.frankfurt-university.de/de/hochschule/fachbereich-1-architektur-bauingenieurwesen-geomatik/forschungsinstitut-ffin/fachgruppen-des-ffin/fg-neue-mobilitat/relut/forschungsprojekte-relut/clustermobil/) conducted by the [Research Lab for Urban Transport](https://www.frankfurt-university.de/en/about-us/faculty-1-architecture-civil-engineering-geomatics/research-institute-ffin/specialist-groups-of-the-ffin/specialist-group-new-mobility/relut/). This research project is funded by the state of Hesse and [HOLM](https://frankfurt-holm.de/) 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]
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: GIS
Requires-Python: >=3.7
Description-Content-Type: text/markdown
