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Sprawl v1.1
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Expand Up @@ -10,56 +10,63 @@ It uses OpenStreetMap data to calculate its sprawling indices, divided in Access

* Gervasoni Luciano, Bosch Martí, Fenet Serge, and Sturm Peter. 2017. "[LUM_OSM: une plateforme pour l'évaluation de la mixité urbaine à partir de données participatives](https://hal.inria.fr/hal-01548341)." GAST Workshop, Conférence Extraction et Gestion de Connaissances (EGC 2017).

* Gervasoni Luciano, Bosch Martí, Fenet Serge, and Sturm Peter. 2017. "[Calculating spatial urban sprawl indices using
open data](https://hal.inria.fr/hal-01535469)." 15th International Conference on Computers in Urban Planning and Urban Management.
* Gervasoni Luciano, Bosch Martí, Fenet Serge, and Sturm Peter. 2017. "[Calculating spatial urban sprawl indices using open data](https://hal.inria.fr/hal-01535469)." 15th International Conference on Computers in Urban Planning and Urban Management.

NOTE: The implementation for the previous publications can be found at version 1.0

## Dependencies

urbansprawl works with Python 2+3.

- Python dependencies:
```sh
osmnx utm scipy statsmodels h5py
osmnx scikit-learn
```

* Using anaconda:
```sh
conda update -c conda-forge --all
conda install -c conda-forge osmnx utm scipy statsmodels h5py
conda install -c conda-forge osmnx scikit-learn
```

## Example

PostGIS-enabled PostgreSQL databases are used in this framework (refer to [OSM2PGSQL tools](https://mapzen.com/data/metro-extracts/)):
OpenStreetMap is retrieved using the Overpass API.

Results are depicted for the city of **Lyon, France**:

- Locations of residential and activity land uses are retrieved

* Activity uses:

<img src="examples/images/Lyon_activities.png" width="550" height="500">

* Residential uses:

Results are depicted for the city of **Grenoble, France**:
<img src="examples/images/Lyon_residential.png" width="550" height="500">

- Locations and densities of residential and activity land usages
- Densities for each land use are estimated:

<img src="examples/images/Grenoble_Activity_points.png" width="550">
<img src="examples/images/Grenoble_Activity_densities.png" width="550">
<img src="examples/images/Grenoble_Residential_points.png" width="550">
<img src="examples/images/Grenoble_Residential_densities.png" width="550">
<img src="examples/images/Lyon_densities.png" width="1200" height="500">

- Street network
* Activity uses can be further classified:

<img src="examples/images/Grenoble.png" width="550">
<img src="examples/images/Lyon_activities_densities.png" width="1200" height="500">

- Buildings
- Street network:

<img src="examples/images/Grenoble_Buildings.png" width="550">
<img src="examples/images/Lyon_graph.png" width="550" height="500">

**Sprawling indices:**

- Land use mix indices
- Land use mix indices:

<img src="examples/images/Grenoble_Land_use_mix.png" width="550">
<img src="examples/images/Grenoble_Bubble_land_use_mix.png" width="550">
<img src="examples/images/Lyon_Landusemix.png" width="550" height="500">

- Accessibility indices
- Accessibility indices:

<img src="examples/images/Grenoble_Accessibility.png" width="550">
<img src="examples/images/Lyon_Accessibility.png" width="550" height="500">

- Dispersion indices
- Dispersion indices:

<img src="examples/images/Grenoble_Dispersion.png" width="550">
<img src="examples/images/Lyon_Dispersion.png" width="550" height="500">
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