.. _cartecarreaurst:

=================
Données carroyées
=================


.. only:: html

    **Links:** :download:`notebook <carte_carreau.ipynb>`, :downloadlink:`html <carte_carreau2html.html>`, :download:`PDF <carte_carreau.pdf>`, :download:`python <carte_carreau.py>`, :downloadlink:`slides <carte_carreau.slides.html>`, :githublink:`GitHub|_doc/notebooks/visualisation/carte_carreau.ipynb|*`


Les `données
carroyées <https://www.insee.fr/fr/statistiques/2520034#titre-bloc-5>`__
sont des données économiques agrégées sur tout le territoire français
sur des carrés de 200m de côté.

.. code:: ipython3

    from jyquickhelper import add_notebook_menu
    add_notebook_menu()






.. contents::
    :local:





Département de la réunion
-------------------------

Le code suivant utilise les données de la réunion disponible sur le site
de l’INSEE : `Données carroyées à 200
mètres <https://www.insee.fr/fr/statistiques/2520034>`__. Voir aussi la
fonction
`load_carreau_from_zip <https://github.com/sdpython/papierstat/blob/master/src/papierstat/datasets/carreau.py#L184>`__
et aussi la fonction
`from_file <http://geopandas.org/reference.html?highlight=from_file#GeoDataFrame.from_file>`__
du module `geopandas <http://geopandas.org/index.html>`__.

.. code:: ipython3

    from papierstat.datasets import load_carreau_from_zip
    dfcar, shpcar, dfrect, shprect = load_carreau_from_zip()

.. code:: ipython3

    dfcar.head()






.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>id</th>
          <th>idINSPIRE</th>
          <th>idk</th>
          <th>ind_c</th>
          <th>nbcar</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>UTM40S200M_N38171E01797</td>
          <td>CRS2975RES200mN7634200E0359400</td>
          <td>N38171E01797-N38172E01797</td>
          <td>16.0</td>
          <td>2.0</td>
        </tr>
        <tr>
          <th>1</th>
          <td>UTM40S200M_N38172E01779</td>
          <td>CRS2975RES200mN7634400E0355800</td>
          <td>N38172E01779-N38172E01779</td>
          <td>40.0</td>
          <td>1.0</td>
        </tr>
        <tr>
          <th>2</th>
          <td>UTM40S200M_N38172E01780</td>
          <td>CRS2975RES200mN7634400E0356000</td>
          <td>N38172E01780-N38172E01780</td>
          <td>64.0</td>
          <td>1.0</td>
        </tr>
        <tr>
          <th>3</th>
          <td>UTM40S200M_N38172E01781</td>
          <td>CRS2975RES200mN7634400E0356200</td>
          <td>N38172E01781-N38172E01782</td>
          <td>106.0</td>
          <td>2.0</td>
        </tr>
        <tr>
          <th>4</th>
          <td>UTM40S200M_N38172E01782</td>
          <td>CRS2975RES200mN7634400E0356400</td>
          <td>N38172E01781-N38172E01782</td>
          <td>6.0</td>
          <td>2.0</td>
        </tr>
      </tbody>
    </table>
    </div>



.. code:: ipython3

    shpcar.head()






.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>idINSPIRE</th>
          <th>id</th>
          <th>geometry</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>CRS2975RES200mN7634200E0359400</td>
          <td>UTM40S200M_N38171E01797</td>
          <td>POLYGON ((359400.000 7634200.000, 359600.000 7...</td>
        </tr>
        <tr>
          <th>1</th>
          <td>CRS2975RES200mN7634400E0355800</td>
          <td>UTM40S200M_N38172E01779</td>
          <td>POLYGON ((355800.000 7634400.000, 356000.000 7...</td>
        </tr>
        <tr>
          <th>2</th>
          <td>CRS2975RES200mN7634400E0356000</td>
          <td>UTM40S200M_N38172E01780</td>
          <td>POLYGON ((356000.000 7634400.000, 356200.000 7...</td>
        </tr>
        <tr>
          <th>3</th>
          <td>CRS2975RES200mN7634400E0356200</td>
          <td>UTM40S200M_N38172E01781</td>
          <td>POLYGON ((356200.000 7634400.000, 356400.000 7...</td>
        </tr>
        <tr>
          <th>4</th>
          <td>CRS2975RES200mN7634400E0356400</td>
          <td>UTM40S200M_N38172E01782</td>
          <td>POLYGON ((356400.000 7634400.000, 356600.000 7...</td>
        </tr>
      </tbody>
    </table>
    </div>



.. code:: ipython3

    dfrect.head().T






.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>0</th>
          <th>1</th>
          <th>2</th>
          <th>3</th>
          <th>4</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>idk</th>
          <td>N38171E01797-N38172E01797</td>
          <td>N38172E01779-N38172E01779</td>
          <td>N38172E01780-N38172E01780</td>
          <td>N38172E01781-N38172E01782</td>
          <td>N38172E01798-N38173E01798</td>
        </tr>
        <tr>
          <th>men</th>
          <td>32</td>
          <td>15</td>
          <td>39</td>
          <td>44</td>
          <td>14</td>
        </tr>
        <tr>
          <th>men_surf</th>
          <td>2263</td>
          <td>1202</td>
          <td>2605</td>
          <td>3103</td>
          <td>1379</td>
        </tr>
        <tr>
          <th>men_occ5</th>
          <td>17</td>
          <td>8</td>
          <td>3</td>
          <td>21</td>
          <td>14</td>
        </tr>
        <tr>
          <th>men_coll</th>
          <td>12</td>
          <td>0</td>
          <td>0</td>
          <td>1</td>
          <td>0</td>
        </tr>
        <tr>
          <th>men_5ind</th>
          <td>7</td>
          <td>2</td>
          <td>2</td>
          <td>6</td>
          <td>1</td>
        </tr>
        <tr>
          <th>men_1ind</th>
          <td>7</td>
          <td>3</td>
          <td>22</td>
          <td>14</td>
          <td>1</td>
        </tr>
        <tr>
          <th>i_1ind</th>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
        </tr>
        <tr>
          <th>men_prop</th>
          <td>17</td>
          <td>10</td>
          <td>8</td>
          <td>22</td>
          <td>11</td>
        </tr>
        <tr>
          <th>i_prop</th>
          <td>0</td>
          <td>0</td>
          <td>2</td>
          <td>0</td>
          <td>1</td>
        </tr>
        <tr>
          <th>men_basr</th>
          <td>15</td>
          <td>4</td>
          <td>20</td>
          <td>16</td>
          <td>0</td>
        </tr>
        <tr>
          <th>i_basr</th>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
        </tr>
        <tr>
          <th>ind_r</th>
          <td>101</td>
          <td>40</td>
          <td>64</td>
          <td>112</td>
          <td>38</td>
        </tr>
        <tr>
          <th>ind_age1</th>
          <td>7</td>
          <td>3</td>
          <td>4</td>
          <td>7</td>
          <td>1</td>
        </tr>
        <tr>
          <th>ind_age2</th>
          <td>2</td>
          <td>1</td>
          <td>1</td>
          <td>3</td>
          <td>0</td>
        </tr>
        <tr>
          <th>ind_age3</th>
          <td>6</td>
          <td>5</td>
          <td>0</td>
          <td>12</td>
          <td>2</td>
        </tr>
        <tr>
          <th>ind_age4</th>
          <td>5</td>
          <td>2</td>
          <td>2</td>
          <td>5</td>
          <td>3</td>
        </tr>
        <tr>
          <th>ind_age5</th>
          <td>8</td>
          <td>0</td>
          <td>0</td>
          <td>4</td>
          <td>3</td>
        </tr>
        <tr>
          <th>ind_age6</th>
          <td>62</td>
          <td>26</td>
          <td>55</td>
          <td>69</td>
          <td>26</td>
        </tr>
        <tr>
          <th>ind_age7</th>
          <td>3</td>
          <td>7</td>
          <td>34</td>
          <td>14</td>
          <td>4</td>
        </tr>
        <tr>
          <th>i_age7</th>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
        </tr>
        <tr>
          <th>ind_age8</th>
          <td>1</td>
          <td>2</td>
          <td>12</td>
          <td>9</td>
          <td>1</td>
        </tr>
        <tr>
          <th>i_age8</th>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
        </tr>
        <tr>
          <th>ind_srf</th>
          <td>1.01807e+06</td>
          <td>459078</td>
          <td>536160</td>
          <td>1.09746e+06</td>
          <td>687108</td>
        </tr>
        <tr>
          <th>nbcar</th>
          <td>2</td>
          <td>1</td>
          <td>1</td>
          <td>2</td>
          <td>2</td>
        </tr>
      </tbody>
    </table>
    </div>



.. code:: ipython3

    shprect.head()






.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>idk</th>
          <th>geometry</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>N38171E01797-N38172E01797</td>
          <td>POLYGON ((359400.000 7634200.000, 359600.000 7...</td>
        </tr>
        <tr>
          <th>1</th>
          <td>N38172E01779-N38172E01779</td>
          <td>POLYGON ((355800.000 7634400.000, 356000.000 7...</td>
        </tr>
        <tr>
          <th>2</th>
          <td>N38172E01780-N38172E01780</td>
          <td>POLYGON ((356000.000 7634400.000, 356200.000 7...</td>
        </tr>
        <tr>
          <th>3</th>
          <td>N38172E01781-N38172E01782</td>
          <td>POLYGON ((356200.000 7634400.000, 356600.000 7...</td>
        </tr>
        <tr>
          <th>4</th>
          <td>N38172E01798-N38173E01798</td>
          <td>POLYGON ((359600.000 7634400.000, 359800.000 7...</td>
        </tr>
      </tbody>
    </table>
    </div>



Il faut lire la page `Données carroyées à 200
mètres <https://www.insee.fr/fr/statistiques/2520034#documentation>`__
pour découvrir le système de coordonnées utilisée sur la Réunion et plus
précisément Saint-Denis (-20.887090, 55.451716).

.. code:: ipython3

    import warnings
    warnings.simplefilter("ignore", (FutureWarning, DeprecationWarning))

.. code:: ipython3

    from pyproj import Proj, transform
    
    p1 = Proj(init='epsg:4326')  # longitude / latidude
    p2 = Proj(init='epsg:2975')  # 
    coor = transform(p1, p2, 55.451716, -20.887090)
    coor




.. parsed-literal::
    (338953.2627389685, 7689572.419008633)



.. code:: ipython3

    from shapely.geometry import Point
    sd = Point(coor)

.. code:: ipython3

    jardin = shprect[shprect.geometry.contains(sd)]
    jardin






.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>idk</th>
          <th>geometry</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>7217</th>
          <td>N38447E01694-N38447E01694</td>
          <td>POLYGON ((338800.000 7689400.000, 339000.000 7...</td>
        </tr>
      </tbody>
    </table>
    </div>



Conversion du geodataframe en un autre référenciel
--------------------------------------------------

.. code:: ipython3

    jardinll = jardin.to_crs({'init':'epsg:4326'})
    jardinll






.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>idk</th>
          <th>geometry</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>7217</th>
          <td>N38447E01694-N38447E01694</td>
          <td>POLYGON ((55.45023 -20.88863, 55.45215 -20.888...</td>
        </tr>
      </tbody>
    </table>
    </div>



Premier carreau avec folium
---------------------------

La carte suivante le carreau sur la carte
d’\ `OpenStreetMap <https://www.openstreetmap.org/>`__.

.. code:: ipython3

    import folium
    map_osm = folium.Map(location=[-20.887090, 55.451716], zoom_start=16)
    folium.GeoJson(jardinll).add_to(map_osm)
    
    map_osm






.. raw:: html

    <div style="width:100%;"><div style="position:relative;width:100%;height:0;padding-bottom:60%;"><iframe 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