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This is a proof of concept and a pet project for learning python while preparing data for importing it to OpenStreetMap. Although I will start small (importing trees from Barcelona city council), I aim to set the foundations for adding other types of imports that may (or may not) be added in the future.
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README.md

OpenStreetMap Imports preparations

Netlify Status All Contributors

This is a proof of concept and a pet project for learning python while preparing data for importing it to OpenStreetMap. Although I will start small (importing trees from Barcelona city council), I aim to set the foundations for adding other types of imports that may (or may not) be added in the future, hopefully with other people's contributions.

Documentation

Project's documentation can be found in docs folder and a live version, generated using mkdocs and deployed with Netlify, can be found here.

Contributors

Thanks goes to these wonderful people (emoji key):


Carlos Cámara

💻 📖

Miguel Sevilla-Callejo

📖

Gabriel de Marmiesse

💬

Alejandro Suarez

🚧

This project follows the all-contributors specification. Contributions of any kind welcome!

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── environment.yml    <- The environment file for reproducing the analysis environment, e.g.
│                         `conda activate osm_imports_preparations`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
    │
    ├── data           <- Scripts to download or generate data
    │   └── make_dataset.py
    │
    ├── features       <- Scripts to turn raw data into features for modeling
    │   └── build_features.py
    │
    ├── models         <- Scripts to train models and then use trained models to make
    │   │                 predictions
    │   ├── predict_model.py
    │   └── train_model.py
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations
        └── visualize.py

Project based on the cookiecutter data science project template. #cookiecutterdatascience

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