.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_plot_rio_de_janeiro_favelas.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_rio_de_janeiro_favelas.py:


Favelas in Rio de Janeiro
=========================

This is another example in which we use data from the
ArcGIS server of Rio de Janeiro city.

This time, we'll try to visualize the distribution of slums or favelas
in the city.

Let's first try to get the map of the city and afterwards plot the favelas
geometries.


.. code-block:: default


    import pandas as pd

    pd.set_option("max_rows", 10)

    from mapsbr import arcgis

    districts = arcgis.get_map(
        service="Basicos/mapa_basico_UTM",
        layer=15,
        baseurl="https://pgeo3.rio.rj.gov.br/arcgis/rest/services/",
    )

    favelas = arcgis.get_map(
        service="SMH/Limites_de_Favelas",
        layer=0,
        baseurl="https://pgeo3.rio.rj.gov.br/arcgis/rest/services/",
    )








Now, just to see what other interesting things we can do with GeoPandas
and the Shapely library in general: let's calculate the geometries' areas.

This will give us a rough idea about which favela is the biggest:


.. code-block:: default


    favelas["area"] = favelas.geometry.area
    favelas = favelas.sort_values("area", ascending=False)








Now, here is what I wanna do: highlight the five biggest ones.

This will be straight-forward to do with annotate method of matplotlib.


.. code-block:: default


    favelas["point"] = favelas.geometry.centroid  # calculate the geometry centroid

    import matplotlib.pyplot as plt

    fig, ax = plt.subplots(figsize=(10, 7), constrained_layout=True)
    districts.plot(color="white", edgecolor="slategray", ax=ax)
    favelas.plot(color="orange", ax=ax)
    favelas.query("Nome in @favelas.Nome.head()").plot(color="gray", ax=ax)

    ax.axis("off")
    ax.set_title("Favelas in Rio\nBiggest 5 highlighted")

    for _, row in favelas.head().iterrows():
        ax.annotate(
            row.Nome,
            xy=(row.point.x, row.point.y),
            xytext=(5, 10),
            textcoords="offset points",
            arrowprops=dict(arrowstyle="->", color="black", connectionstyle="angle3"),
            bbox=dict(boxstyle="round", alpha=0.7, facecolor="silver"),
            color="k",
        )



.. image:: /auto_examples/images/sphx_glr_plot_rio_de_janeiro_favelas_001.png
    :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  2.359 seconds)

**Estimated memory usage:**  21 MB


.. _sphx_glr_download_auto_examples_plot_rio_de_janeiro_favelas.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_rio_de_janeiro_favelas.py <plot_rio_de_janeiro_favelas.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_rio_de_janeiro_favelas.ipynb <plot_rio_de_janeiro_favelas.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_