tesspy 0.1.2
Tessellation of Urban Areas
Requires Python: >=3.7

# tesspy
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<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 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
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
```

## Install from the repository
If you want to work with the latest development version, you can directly install it from GitHub.
To do that, it is recommended to first install all the dependencies using conda. (preferably in a newly created env).

```shell
conda install -c conda-forge geopandas scipy h3-py osmnx hdbscan mercantile scikit-learn
```

Then install `TessPy` using:

```shell
pip install git+git://github.com/siavash-saki/tesspy
```


## Dependencies

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


## 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

Follow the instructions [here](https://tesspy.readthedocs.io/en/latest/Contribution.html)
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](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]