In [ ]:
#PREVIO A LA PREGUNTA 1
In [ ]:
#CONSEGUIR LOS MAPAS DE PUNTOS
#AEROPUERTO
import os
import geopandas as gpd
import pandas as pd
from google.colab import drive
from IPython.display import display
drive.mount('/content/drive')
shapefile_path = "/content/drive/My Drive/SHAPEFILE/contdv250kgw/contdv250kgw.shp"
mexico = gpd.read_file(shapefile_path)
airports=pd.read_csv("/content/drive/My Drive/SHAPEFILE/Aeropuertos/airports.csv")


keep=['type', 'name', 'latitude_deg', 'longitude_deg',
       'elevation_ft', 'continent', 'iso_country', 'iso_region',
       'municipality']
airports=airports.loc[:,keep]
numeros_airpt=['latitude_deg', 'longitude_deg', 'elevation_ft']
airports[numeros_airpt]=airports[numeros_airpt].apply(pd.to_numeric)
aeropuertos_mex=gpd.GeoDataFrame(airports, geometry=gpd.points_from_xy(airports.longitude_deg, airports.latitude_deg), crs=mexico.crs.to_epsg())
airports_projected_for_filter = aeropuertos_mex.to_crs(mexico.crs)
airports_in_mexico = gpd.sjoin(airports_projected_for_filter, mexico, predicate='within')
filtered_aeropuertos_mex = aeropuertos_mex.loc[airports_in_mexico.index]

mexico6362=mexico.to_crs(6362)
base=mexico6362.plot(color='white', edgecolor='black')
aerop_6362=filtered_aeropuertos_mex.to_crs(6362)
aerop_6362.plot(ax=base, color='red')

Mounted at /content/drive
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
#CONSEGUIR MAPA DE PUNTOS
#PUERTOS MARITIMOS
portsFileLink="https://github.com/CienciaDeDatosEspacial/GeoDataFrame_Analytics/raw/main/data/UpdatedPub150.csv"
infoseaports=pd.read_csv(portsFileLink)
In [ ]:
infoseaports.rename(columns={'Main Port Name':'portName'},inplace=True)
infoseaports=infoseaports.loc[:,['portName', 'Country Code','Latitude', 'Longitude']]

infoseaports.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3739 entries, 0 to 3738
Data columns (total 4 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   portName      3739 non-null   object 
 1   Country Code  3739 non-null   object 
 2   Latitude      3739 non-null   float64
 3   Longitude     3739 non-null   float64
dtypes: float64(2), object(2)
memory usage: 117.0+ KB
In [ ]:
#Selecionamos los puertos maritimos de Mexico
seaports_mex=infoseaports[infoseaports['Country Code']=='Mexico']
seaports_mex.head()
Out[ ]:
portName Country Code Latitude Longitude
432 San Juan De La Costa Mexico 24.400000 -110.700000
1027 Ensenada Mexico 31.866667 -116.633333
1029 Nanchital Mexico 18.066667 -94.416667
1104 Puerto Madero Mexico 14.700000 -92.450000
1119 Salina Cruz Mexico 16.166667 -95.200000
In [ ]:
seaports_mex_gdf = gpd.GeoDataFrame(seaports_mex, geometry=gpd.points_from_xy(seaports_mex.Longitude, seaports_mex.Latitude), crs=mexico.crs.to_epsg())
type(seaports_mex_gdf)
Out[ ]:
geopandas.geodataframe.GeoDataFrame
def __init__(data=None, *args, geometry=None, crs=None, **kwargs)
/usr/local/lib/python3.11/dist-packages/geopandas/geodataframe.pyA GeoDataFrame object is a pandas.DataFrame that has one or more columns
containing geometry. In addition to the standard DataFrame constructor arguments,
GeoDataFrame also accepts the following keyword arguments:

Parameters
----------
crs : value (optional)
    Coordinate Reference System of the geometry objects. Can be anything accepted by
    :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
    such as an authority string (eg "EPSG:4326") or a WKT string.
geometry : str or array-like (optional)
    Value to use as the active geometry column.
    If str, treated as column name to use. If array-like, it will be
    added as new column named 'geometry' on the GeoDataFrame and set as the
    active geometry column.

    Note that if ``geometry`` is a (Geo)Series with a
    name, the name will not be used, a column named "geometry" will still be
    added. To preserve the name, you can use :meth:`~GeoDataFrame.rename_geometry`
    to update the geometry column name.

Examples
--------
Constructing GeoDataFrame from a dictionary.

>>> from shapely.geometry import Point
>>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
>>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")
>>> gdf
    col1     geometry
0  name1  POINT (1 2)
1  name2  POINT (2 1)

Notice that the inferred dtype of 'geometry' columns is geometry.

>>> gdf.dtypes
col1          object
geometry    geometry
dtype: object

Constructing GeoDataFrame from a pandas DataFrame with a column of WKT geometries:

>>> import pandas as pd
>>> d = {'col1': ['name1', 'name2'], 'wkt': ['POINT (1 2)', 'POINT (2 1)']}
>>> df = pd.DataFrame(d)
>>> gs = geopandas.GeoSeries.from_wkt(df['wkt'])
>>> gdf = geopandas.GeoDataFrame(df, geometry=gs, crs="EPSG:4326")
>>> gdf
    col1          wkt     geometry
0  name1  POINT (1 2)  POINT (1 2)
1  name2  POINT (2 1)  POINT (2 1)

See also
--------
GeoSeries : Series object designed to store shapely geometry objects
In [ ]:
seaports_mex_6362=seaports_mex_gdf.to_crs(6362)
seaports_mex_6362.head()
#los reproyectamos
Out[ ]:
portName Country Code Latitude Longitude geometry
432 San Juan De La Costa Mexico 24.400000 -110.700000 POINT (1622766.872 1400102.095)
1027 Ensenada Mexico 31.866667 -116.633333 POINT (1110312.055 2270684.066)
1029 Nanchital Mexico 18.066667 -94.416667 POINT (3301657.436 696363.442)
1104 Puerto Madero Mexico 14.700000 -92.450000 POINT (3534131.97 336173.831)
1119 Salina Cruz Mexico 16.166667 -95.200000 POINT (3228888.129 482014.432)
In [ ]:
type(seaports_mex_6362)
Out[ ]:
geopandas.geodataframe.GeoDataFrame
def __init__(data=None, *args, geometry=None, crs=None, **kwargs)
/usr/local/lib/python3.11/dist-packages/geopandas/geodataframe.pyA GeoDataFrame object is a pandas.DataFrame that has one or more columns
containing geometry. In addition to the standard DataFrame constructor arguments,
GeoDataFrame also accepts the following keyword arguments:

Parameters
----------
crs : value (optional)
    Coordinate Reference System of the geometry objects. Can be anything accepted by
    :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
    such as an authority string (eg "EPSG:4326") or a WKT string.
geometry : str or array-like (optional)
    Value to use as the active geometry column.
    If str, treated as column name to use. If array-like, it will be
    added as new column named 'geometry' on the GeoDataFrame and set as the
    active geometry column.

    Note that if ``geometry`` is a (Geo)Series with a
    name, the name will not be used, a column named "geometry" will still be
    added. To preserve the name, you can use :meth:`~GeoDataFrame.rename_geometry`
    to update the geometry column name.

Examples
--------
Constructing GeoDataFrame from a dictionary.

>>> from shapely.geometry import Point
>>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
>>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")
>>> gdf
    col1     geometry
0  name1  POINT (1 2)
1  name2  POINT (2 1)

Notice that the inferred dtype of 'geometry' columns is geometry.

>>> gdf.dtypes
col1          object
geometry    geometry
dtype: object

Constructing GeoDataFrame from a pandas DataFrame with a column of WKT geometries:

>>> import pandas as pd
>>> d = {'col1': ['name1', 'name2'], 'wkt': ['POINT (1 2)', 'POINT (2 1)']}
>>> df = pd.DataFrame(d)
>>> gs = geopandas.GeoSeries.from_wkt(df['wkt'])
>>> gdf = geopandas.GeoDataFrame(df, geometry=gs, crs="EPSG:4326")
>>> gdf
    col1          wkt     geometry
0  name1  POINT (1 2)  POINT (1 2)
1  name2  POINT (2 1)  POINT (2 1)

See also
--------
GeoSeries : Series object designed to store shapely geometry objects
In [ ]:
mexico6362=mexico.to_crs(6362)
base=mexico6362.plot(color='white', edgecolor='black')
seaports_mex_6362.plot(ax=base, color='blue')
#Mapa de Mexico con sus puertos maritimos
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
largeAirports=aerop_6362[aerop_6362['type']=='large_airport']
#seleccionamos un tipo de aeropuerto
largeAirports.reset_index(drop=True, inplace=True)

#proyectamos los puertos maritimos usando como base a los aeropuertos seleccionados
base=largeAirports.plot(color='red',marker="^")
seaports_mex_6362.plot(ax=base,alpha=0.5,markersize=3)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
!pip install folium matplotlib mapclassify

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PREGUNTA 1

A)Use two maps of points from your country. B)Compute the distance matrix for both maps. C)Select one row of the distance matrix, and plot the two points with the minimal distance on top of the country of your choosing

In [ ]:
seaports_mex_6362.head()
Out[ ]:
portName Country Code Latitude Longitude geometry
432 San Juan De La Costa Mexico 24.400000 -110.700000 POINT (1622766.872 1400102.095)
1027 Ensenada Mexico 31.866667 -116.633333 POINT (1110312.055 2270684.066)
1029 Nanchital Mexico 18.066667 -94.416667 POINT (3301657.436 696363.442)
1104 Puerto Madero Mexico 14.700000 -92.450000 POINT (3534131.97 336173.831)
1119 Salina Cruz Mexico 16.166667 -95.200000 POINT (3228888.129 482014.432)
In [ ]:
largeAirports.head()
Out[ ]:
type name latitude_deg longitude_deg elevation_ft continent iso_country iso_region municipality geometry
0 large_airport Cozumel International Airport 20.514922 -86.928538 15.0 NaN MX MX-ROO Cozumel POINT (4062709.679 1027637.886)
1 large_airport General Guadalupe Victoria International Airport 24.125468 -104.527938 6104.0 NaN MX MX-DUR Durango POINT (2244428.234 1345494.944)
2 large_airport Guadalajara International Airport 20.523342 -103.310108 5016.0 NaN MX MX-JAL Guadalajara POINT (2363919.298 947002.294)
3 large_airport Manuel Crescencio Rejón International Airport 20.930485 -89.645462 38.0 NaN MX MX-YUC Mérida POINT (3777833.47 1046337.156)
4 large_airport Benito Juárez International Airport 19.435137 -99.071328 7316.0 NaN MX MX-DIF Ciudad de México POINT (2806634.918 829468.369)
In [ ]:
#Computamos la matriz de distancia entre los puertos maritimos de Mexico(seaports)
#y algunos aeropuertos de Mexico (largeAirporsts)
matrix=seaports_mex_6362.set_index('portName').geometry.apply\
(lambda g: largeAirports.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)
matrix.head()
Out[ ]:
name Benito Juárez International Airport Cancún International Airport Cozumel International Airport General Abelardo L. Rodriguez International Airport General Felipe Ángeles International Airport General Guadalupe Victoria International Airport General Rafael Buelna International Airport Guadalajara International Airport Los Cabos International Airport Manuel Crescencio Rejón International Airport Monterrey International Airport Puerto Vallarta International Airport
portName
Acapulco 300.009668 1447.671526 1427.327486 2436.172560 333.091078 933.871862 959.542103 539.548143 1234.657733 1171.411274 985.082999 701.753499
Altamira 359.027190 1140.476352 1146.638959 2184.736951 326.350438 702.973342 862.284620 602.995418 1213.989654 861.780327 428.797200 788.434353
Campeche 891.937200 403.896260 384.168611 2973.572326 885.104175 1510.771263 1660.111532 1330.344491 2008.512254 151.792990 1174.016600 1532.344468
Cayo Arcas Terminal 744.487297 538.996937 527.130695 2829.699088 736.060499 1358.754282 1506.779974 1178.429198 1855.051505 256.980248 1033.611211 1380.169135
Ciudad Del Carmen 761.692373 584.878290 556.679267 2937.065625 760.707537 1439.635686 1571.962002 1214.663089 1914.004365 343.597467 1155.056189 1417.094496
In [ ]:
#Seleccionamos una fila de la matrix
fila=matrix.iloc[0]
fila.head()
Out[ ]:
Acapulco
name
Benito Juárez International Airport 300.009668
Cancún International Airport 1447.671526
Cozumel International Airport 1427.327486
General Abelardo L. Rodriguez International Airport 2436.172560
General Felipe Ángeles International Airport 333.091078

In [ ]:
#La distancia mínima entre el seaport Acapulco y un aeropuerto
dist_min_puntos=fila.idxmin()
dist_min_puntos
Out[ ]:
'Benito Juárez International Airport'
In [ ]:
#Proyectamos la distancia mínima
base=mexico6362.plot(color='white', edgecolor='black')
seaports_mex_6362[seaports_mex_6362['portName'] == 'Acapulco'].plot(ax=base, color='blue', marker='o')
largeAirports[largeAirports['name'] == dist_min_puntos].plot(ax=base, color='red', marker='*')
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
base=seaports_mex_6362[seaports_mex_6362['portName'] == 'Acapulco'].explore(color='purple', marker_kwds=dict(radius=10)) #tomamos como base a Acapulco

largeAirports[largeAirports['name'] == dist_min_puntos].explore(m=base, color='red', marker_kwds=dict(radius=10)) #aeropuerto mas cercano a Acapulto
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook

PREVIO A LA PREGUNTA 2

In [ ]:
!pip install fiona

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In [ ]:
shapefile_path = "/content/drive/MyDrive/SHAPEFILE/MAPA6362.gpkg"
from  fiona import listlayers
listlayers(shapefile_path)
Out[ ]:
['México',
 'Estados de México',
 'Municipios de México',
 'Ríos de México',
 'Estados_de_México',
 'Municipalidades_de_México',
 'aeropuerto',
 'Aereopuertos de Mexico',
 'aaeropuertointento']
In [ ]:
#Mapa de lineas (Rios de Mexico)
Rios=gpd.read_file(shapefile_path,layer='Ríos de México')

EJERCICIO 2

Use a map of points and a map of lines from your country.

Compute the distance matrix for both.

Select one line of the distance matrix, and plot the closests and the farthest point to that line.

In [ ]:
Rios.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 2234 entries, 0 to 2233
Data columns (total 9 columns):
 #   Column    Non-Null Count  Dtype   
---  ------    --------------  -----   
 0   FNODE_    2234 non-null   int64   
 1   TNODE_    2234 non-null   int64   
 2   LPOLY_    2234 non-null   int64   
 3   RPOLY_    2234 non-null   int64   
 4   LENGTH    2234 non-null   float64 
 5   COV_      2234 non-null   int64   
 6   COV_ID    2234 non-null   int64   
 7   NOMBRES   582 non-null    object  
 8   geometry  2234 non-null   geometry
dtypes: float64(1), geometry(1), int64(6), object(1)
memory usage: 157.2+ KB
In [ ]:
Rios.dropna(inplace=True)
In [ ]:
distanceMatrixKM_riv_air=Rios.set_index('NOMBRES').geometry.apply\
(lambda g: largeAirports.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)
distanceMatrixKM_riv_air #Matriz con las distancias de cada aeropuerto con cada río
Out[ ]:
name Benito Juárez International Airport Cancún International Airport Cozumel International Airport General Abelardo L. Rodriguez International Airport General Felipe Ángeles International Airport General Guadalupe Victoria International Airport General Rafael Buelna International Airport Guadalajara International Airport Los Cabos International Airport Manuel Crescencio Rejón International Airport Monterrey International Airport Puerto Vallarta International Airport
NOMBRES
A. Alamos 1244.767837 2287.881034 2302.410690 1000.630406 1227.792932 487.518091 427.021477 843.485115 358.872781 2022.847588 857.522507 713.816263
A. Ballenas 1292.290887 2450.402408 2457.989365 1129.665872 1282.849189 617.660977 453.276932 843.057805 126.964982 2173.264372 1075.591885 660.179863
A. Baroten 1218.344428 2266.951093 2280.923453 1066.985599 1201.262794 460.703212 403.545164 818.756607 345.245778 2000.780636 840.047138 691.748270
A. Blanco 518.347853 1320.811277 1334.149105 1909.732722 486.422183 465.609534 652.013756 525.901967 999.764276 1054.448037 183.032359 664.248183
A. Cabrera 1186.224588 2240.885335 2254.666664 1107.012632 1169.530735 430.685215 368.052189 783.955417 333.001912 1974.344282 815.560199 656.040741
... ... ... ... ... ... ... ... ... ... ... ... ...
R. la Culebra 320.962245 1600.315614 1591.674640 2068.322539 329.102760 574.910948 592.076210 171.648471 882.124227 1313.460401 735.136938 350.505705
R. lazos 590.406199 1685.603046 1692.443417 1663.536896 570.742566 131.522464 284.653260 291.964090 635.533576 1407.596842 417.602305 344.786145
R. verde 1289.357071 2230.689712 2250.133053 960.451810 1267.591675 534.876639 558.514515 940.231038 578.179094 1977.298409 788.595836 849.617326
R.Canas 741.270676 1915.089614 1919.077229 1557.281292 730.266149 173.839472 86.183663 310.367930 419.617803 1633.604466 634.224019 201.633902
R.Chico 1417.680322 2395.318245 2413.837686 853.075270 1398.041831 654.753848 630.560935 1038.751107 550.650731 2139.367972 953.087277 921.113684

582 rows × 12 columns

In [ ]:
#quieremos una fila de la matrix

fila_Canas=distanceMatrixKM_riv_air.loc['R.Canas'].sort_values()
fila_Canas
Out[ ]:
R.Canas
name
General Rafael Buelna International Airport 86.183663
General Guadalupe Victoria International Airport 173.839472
Puerto Vallarta International Airport 201.633902
Guadalajara International Airport 310.367930
Los Cabos International Airport 419.617803
Monterrey International Airport 634.224019
General Felipe Ángeles International Airport 730.266149
Benito Juárez International Airport 741.270676
General Abelardo L. Rodriguez International Airport 1557.281292
Manuel Crescencio Rejón International Airport 1633.604466
Cancún International Airport 1915.089614
Cozumel International Airport 1919.077229

In [ ]:
#aeropuerto mas cercano al río
aerop_close=fila_Canas.idxmin()
aerop_close
Out[ ]:
'General Rafael Buelna International Airport'
In [ ]:
#aeropuerto más lejano al río
aerop_far=fila_Canas.idxmax()
aerop_far
Out[ ]:
'Cozumel International Airport'
In [ ]:
#Usamos plot para mostrar el punto mas lejano y cercano
base=mexico6362.plot(color='white', edgecolor='black')
Rios[Rios['NOMBRES'] == 'R.Canas'].plot(ax=base, color='blue', linewidth=4)
largeAirports[largeAirports['name'] == aerop_close].plot(ax=base, color='green', marker='*', markersize=100)

largeAirports[largeAirports['name'] == aerop_far].plot(ax=base, color='red', marker='X', markersize=100)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
#Tambien podemos mostrar los puntos usando explore
base= largeAirports[largeAirports['name'] == aerop_close].explore(color='purple', marker_kwds=dict(radius=10)) #tomamos como base el aeropuerto mas cercano al río

largeAirports[largeAirports['name'] == aerop_far].explore(m=base, color='red', marker_kwds=dict(radius=10)) #aeropuerto mas lejano al rio

Rios[Rios.NOMBRES.str.contains('Canas')].explore(m=base, color='blue')#río que seleccionamos
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook

PREGUNTA 3

In [ ]:
#a)Create a HULL for some set of line map.
In [ ]:
Rios.head()
Out[ ]:
FNODE_ TNODE_ LPOLY_ RPOLY_ LENGTH COV_ COV_ID NOMBRES geometry
4 0 0 0 0 26475.678766 5 5 R. Bravo o Grande del Norte LINESTRING (2082127.527 2190850.698, 2093579.3...
13 0 0 0 0 7572.959387 14 14 R. Bravo o Grande del Norte LINESTRING (2131589.714 2139272.086, 2135237.0...
25 0 0 0 0 9258.807764 26 26 R. Bravo o Grande del Norte LINESTRING (2198018.156 2081989.6, 2204844.998...
46 0 0 0 0 10825.842400 47 47 R. Bravo o Grande del Norte LINESTRING (2250306.564 1969443.84, 2253439.91...
66 0 0 0 0 7225.017265 67 67 R. Bravo o Grande del Norte LINESTRING (2345812.884 1900118.722, 2351842.8...
In [ ]:
#disolver los que pertenecen a un mismo sistema
systems=Rios.dissolve(by='NOMBRES')
systems
Out[ ]:
geometry FNODE_ TNODE_ LPOLY_ RPOLY_ LENGTH COV_ COV_ID
NOMBRES
A. Alamos LINESTRING (1827223.452 1597713.219, 1828053.2... 2626 2627 0 0 112126.518570 1815 1733
A. Ballenas LINESTRING (1615448.003 1337202.901, 1615650.9... 3051 3052 0 0 23444.916115 2229 2082
A. Baroten LINESTRING (1829698.577 1582451.727, 1830801.3... 856 857 0 0 24556.034866 1182 946
A. Blanco LINESTRING (2768259.317 1350673.637, 2766344.9... 2612 2613 0 0 67200.850123 1797 578
A. Cabrera MULTILINESTRING ((1857380.806 1530778.593, 185... 1310 1312 0 0 24406.014760 1294 944
... ... ... ... ... ... ... ... ...
R. la Culebra LINESTRING (2471140.017 765900.302, 2471146.66... 0 0 0 0 65730.623333 2196 1947
R. lazos LINESTRING (2383841.249 1241566.649, 2381243.5... 967 968 0 0 50216.582547 1878 1079
R. verde LINESTRING (1919940.263 1871845.167, 1920952.3... 0 0 0 0 95524.412166 1709 1728
R.Canas LINESTRING (2126384.075 1179539.954, 2129075.6... 2658 2659 0 0 62309.980888 1835 2507
R.Chico LINESTRING (1760099.821 1825632.185, 1762761.5... 0 470 0 0 30342.572775 1113 2147

390 rows × 8 columns

In [ ]:
# formatear
systems.reset_index(drop=False,inplace=True)
systems
Out[ ]:
NOMBRES geometry FNODE_ TNODE_ LPOLY_ RPOLY_ LENGTH COV_ COV_ID
0 A. Alamos LINESTRING (1827223.452 1597713.219, 1828053.2... 2626 2627 0 0 112126.518570 1815 1733
1 A. Ballenas LINESTRING (1615448.003 1337202.901, 1615650.9... 3051 3052 0 0 23444.916115 2229 2082
2 A. Baroten LINESTRING (1829698.577 1582451.727, 1830801.3... 856 857 0 0 24556.034866 1182 946
3 A. Blanco LINESTRING (2768259.317 1350673.637, 2766344.9... 2612 2613 0 0 67200.850123 1797 578
4 A. Cabrera MULTILINESTRING ((1857380.806 1530778.593, 185... 1310 1312 0 0 24406.014760 1294 944
... ... ... ... ... ... ... ... ... ...
385 R. la Culebra LINESTRING (2471140.017 765900.302, 2471146.66... 0 0 0 0 65730.623333 2196 1947
386 R. lazos LINESTRING (2383841.249 1241566.649, 2381243.5... 967 968 0 0 50216.582547 1878 1079
387 R. verde LINESTRING (1919940.263 1871845.167, 1920952.3... 0 0 0 0 95524.412166 1709 1728
388 R.Canas LINESTRING (2126384.075 1179539.954, 2129075.6... 2658 2659 0 0 62309.980888 1835 2507
389 R.Chico LINESTRING (1760099.821 1825632.185, 1762761.5... 0 470 0 0 30342.572775 1113 2147

390 rows × 9 columns

In [ ]:
#creando convex hulls para los sistemas
systems.convex_hull.plot()
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
systems_hulls = systems.copy()
systems_hulls = systems_hulls.set_geometry('geometry')
systems_hulls.crs = 'EPSG:6362'
systems_hulls
Out[ ]:
NOMBRES geometry FNODE_ TNODE_ LPOLY_ RPOLY_ LENGTH COV_ COV_ID
0 A. Alamos LINESTRING (1827223.452 1597713.219, 1828053.2... 2626 2627 0 0 112126.518570 1815 1733
1 A. Ballenas LINESTRING (1615448.003 1337202.901, 1615650.9... 3051 3052 0 0 23444.916115 2229 2082
2 A. Baroten LINESTRING (1829698.577 1582451.727, 1830801.3... 856 857 0 0 24556.034866 1182 946
3 A. Blanco LINESTRING (2768259.317 1350673.637, 2766344.9... 2612 2613 0 0 67200.850123 1797 578
4 A. Cabrera MULTILINESTRING ((1857380.806 1530778.593, 185... 1310 1312 0 0 24406.014760 1294 944
... ... ... ... ... ... ... ... ... ...
385 R. la Culebra LINESTRING (2471140.017 765900.302, 2471146.66... 0 0 0 0 65730.623333 2196 1947
386 R. lazos LINESTRING (2383841.249 1241566.649, 2381243.5... 967 968 0 0 50216.582547 1878 1079
387 R. verde LINESTRING (1919940.263 1871845.167, 1920952.3... 0 0 0 0 95524.412166 1709 1728
388 R.Canas LINESTRING (2126384.075 1179539.954, 2129075.6... 2658 2659 0 0 62309.980888 1835 2507
389 R.Chico LINESTRING (1760099.821 1825632.185, 1762761.5... 0 470 0 0 30342.572775 1113 2147

390 rows × 9 columns

In [ ]:
#b)Compute the distance matrix between the HULLS and a map of points.
In [ ]:
#se observan valores duplicados sólo en aeropuertos, por lo que no podremos hacer la matriz de distancia.
aerop_6362['name'].duplicated().any()
Out[ ]:
np.True_
In [ ]:
#para no mover nada del original, se trabajara con la copia de aerop6362
copia=aerop_6362.copy()
copia = copia.drop_duplicates(subset='name')
In [ ]:
#la matriz de distancia
# Restablecer el índice para asegurar que sea único antes de establecer 'name' como índice
distanceMatrixKM_sysHull_air = systems_hulls.set_index('NOMBRES').geometry.apply\
(lambda g: copia.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)

distanceMatrixKM_sysHull_air
Out[ ]:
name (Old) Rancho Oaxaca Airport 33rd Infantry Battalion Heliport 8 de Mayo Airstrip AVE Arandas Helipad Aboreachi Airport Abraham González International Airport Acaponeta Airport Acapulco ISSSTE Regional Hospital Helliport Acatita Airport Acebuches Airport ... Zapotiltic - Tuxpan Airport Zarupa Airstrip Zimas Heliport Zongozotla Heliport Zoquipan Airstrip Álamos Airport Álamos de Márquez Airport Ángeles Hospital Helipad Ébano Airport Úrsulo Galván Airstrip
NOMBRES
A. Alamos 407.717139 541.056452 327.959006 894.320054 130.809888 550.036667 539.105025 1388.147822 129.622790 577.668665 ... 920.096386 88.757301 829.573427 1321.598382 635.828444 13.741184 543.212027 905.621995 1137.719876 213.911846
A. Ballenas 758.753755 751.463521 546.211624 921.182804 478.674981 939.077906 557.432160 1368.227640 298.706392 898.553374 ... 889.623386 392.429976 1043.683932 1397.767617 658.636985 373.990347 886.399647 1000.500968 1262.131157 447.356770
A. Baroten 500.899396 521.017945 398.670461 868.661531 159.520328 636.213402 514.939829 1363.210594 107.307414 598.281194 ... 896.249570 78.494392 811.536618 1294.814991 610.972913 96.151224 576.014776 878.812250 1112.759834 280.738496
A. Blanco 1158.604717 378.569868 1252.243429 449.986828 810.251057 1048.861289 586.609293 797.234242 827.161649 542.133806 ... 610.275275 830.368612 175.022413 488.713717 524.745680 958.206363 621.378214 242.252769 234.501915 1151.107595
A. Cabrera 520.511633 495.509811 436.489844 826.167236 151.642332 641.907443 467.741965 1317.836585 57.649670 581.231261 ... 848.112438 64.525506 786.793320 1261.068243 565.647638 125.499910 561.937436 846.040440 1084.526287 319.446180
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
R. la Culebra 1428.412404 687.250616 1400.907340 147.139662 1000.782894 1417.538634 477.580203 341.324857 886.806475 976.715873 ... 126.668435 967.304242 712.084103 467.344078 379.047471 1088.508914 1041.992448 327.194946 501.153668 1283.273158
R. lazos 976.909641 233.036664 990.934094 283.895518 561.582271 953.670902 222.373425 779.386327 504.988891 532.945191 ... 394.025551 548.230853 385.193938 660.828182 185.312029 677.912492 590.101915 245.385113 496.955551 878.083922
R. verde 238.166849 510.010606 332.188344 970.311593 108.020653 346.785772 655.434529 1472.364579 305.429882 465.765920 ... 1029.465771 177.418170 766.033155 1342.498096 736.859482 163.235798 419.354421 931.869120 1130.830051 273.880195
R.Canas 940.823572 368.749683 890.224479 373.301046 508.839561 978.111984 4.433554 855.250609 375.677237 649.213670 ... 386.010402 462.743435 600.546677 843.848526 107.011184 579.294725 686.817613 453.669593 721.572315 772.386269
R.Chico 271.592948 662.182619 187.190530 1081.065936 231.128741 475.480796 739.056799 1580.514661 338.030731 633.835828 ... 1120.460763 241.595208 929.375598 1481.361136 831.115610 128.229111 584.286863 1066.469735 1279.828721 107.668704

390 rows × 2552 columns

In [ ]:
#diferencias minimas
mins=distanceMatrixKM_sysHull_air.idxmin(axis="columns")
mins
Out[ ]:
0
NOMBRES
A. Alamos El Fuerte Airport
A. Ballenas El Quelele Airport
A. Baroten El Fuerte Airport
A. Blanco Aramberri Airport
A. Cabrera Portugués de Gálvez Airport
... ...
R. la Culebra Los Barajas Airport
R. lazos Aeródromo de "Ganadería Valparaíso"
R. verde Arisiachi Airport
R.Canas Acaponeta Airport
R.Chico Movas Airport

390 rows × 1 columns


In [ ]:
#c)Plot the HULLS and the points. Show the closest and farthest points to the HULL.
In [ ]:
base=systems_hulls.explore()
copia[copia.name.isin(mins)].explore(m=base,color='red',marker_kwds=dict(radius=10)) #más cercanos a cada sistema
copia[~copia.name.isin(mins)].explore(m=base,color='blue',marker_kwds=dict(radius=5)) #más lejanos a cada sistema
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [ ]:
#tomando un sistema fluvial en específico, "A.Alamos"
# crear un mapa base con el hull del sistema
hull_aa = systems_hulls[systems_hulls['NOMBRES'] == 'A. Alamos']
base = hull_aa.explore(color='green', style_kwds=dict(fillOpacity=0.2))
#Obtener el nombre del aeropuerto más cercano
aeropuerto_cercano = mins['A. Alamos']
#Mostrar el aeropuerto más cercano en rojo
copia[copia['name'] == aeropuerto_cercano].explore(
    m=base,
    color='red',
    marker_kwds=dict(radius=10)
)

#Mostrar los demás aeropuertos en azul
copia[copia['name'] != aeropuerto_cercano].explore(
    m=base,
    color='blue',
    marker_kwds=dict(radius=5)
)
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook

PREGUNTA 4

In [ ]:
#Select a line map and a point one.

#Get the buffer for the lines, select a distance.

#Keep the points that are within the buffer (you might need to play with differn distances until you show something interesting.
In [ ]:
distanceMatrixKM_riv_air=Rios.set_index('NOMBRES').geometry.apply\
(lambda g: largeAirports.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)
In [ ]:
print(Rios['NOMBRES'].unique())

['R. Bravo o Grande del Norte' 'A. Ocoroni' 'A. Cerro Gordo' 'R. Colorado'
 'A. San Fernando' 'A. Santa Catarina' 'A. Jaraguay' 'A. Delfin'
 'R. San Andres' 'A. Santa Isabel' 'A. Calamajue' 'A. Zamora'
 'A. El Camelo' 'R. Hardy' 'R. Santa Catarina' 'R. Tijuana' 'R. Guadalupe'
 'R. Sto. Tomas' 'A. Grande' 'A. Taraisa' 'A. Sta. Ana' 'R. San Ignacio'
 'A. San Benito' 'A. San Jose Gracia' 'R. San Gregorio' 'A. La Purisima'
 'A. San Venancio' 'A. Santo Domingo' 'A. San Luis' 'A. Salado' 'A. Malva'
 'A. Carrizal' 'A. Rivera' 'R. Sonoita' 'R. Concepcion' 'A. El Coyote'
 'R. El Coyote' 'A. El Tren' 'R. Seco' 'R. Altar' 'A. Cocospera'
 'A. Tezota' 'R. Bacabachic' 'A. del Carrizo' 'A. El Cajonzito'
 'R. Sonora' 'R. Zanjon' 'R. San Miguel' 'A. La Junta' 'R. Guaymas'
 'R. Matape' 'R. Yaqui' 'A. Machomabampo' 'A. Sahuraral' 'R. Tecoripa'
 'R.Chico' 'R. Chico' 'A. La Trinidad' 'R. Bacanora' 'R. Moctezuma'
 'R. Bavispe' 'R. Mulalos' 'A. de Nacalli' 'R. Bapepito' 'A. Cajon Bonito'
 'A. Cocoraque' 'R. Mayo' 'A. Quiriego' 'R. Babanore' 'R. Morris'
 'A. Baroten' 'R. Nazas' 'A. de Matalotes' 'R. de Sextin' 'A. Cabrera'
 'R. Petatlan' 'R. Badiraguato' 'R. Humaya' 'R. Abelardo Rodriguez'
 'R, Coatzacoalcos' 'R. Casas Grandes' 'R. San Pedro' 'R. Piedras Verdes'
 'R. Sta. Maria' 'R. del Carmen' 'R. Sta. Clara' 'A. de los Sauces'
 'R. San Carlos' 'R. San Antonio' 'A. del Marquez' 'R. Conchos'
 'R. del Parral' 'R. Florida' 'A. de la India' 'R. Balleza' 'R. Agujas'
 'R. Nonoava' 'R. Chuviscar' 'R. Sta. Isabel' 'R. Satevo' 'R. Papigochi'
 'R. Sirupa' 'R. Papigochic' 'R. verde' 'R. Oteros' 'R. Chinipas'
 'R. Fuerte' 'R. Verde' 'R. Chinatu' 'R. de los Loera' 'R. Calabaci'
 'A. Tenoriba' 'R. Basonapa' 'R. Urique' 'R. Tepochique' 'R. Batopilas'
 'R. Mohinora' 'R. de la Costura' 'A. del Caballo' 'A. del Buey'
 'A. de las Vacas' 'A. San Diego' 'A. Navajas' 'A. San Nicolas' 'A. Texas'
 'A. de la Resaca' 'A. de la Castana' 'R. Sabinas' 'A. de la Zorra'
 'R. Alamos' 'A. del Pino' 'R. Salado' 'R. Monclova' 'A. Pajaros Azules'
 'A. de Patos' 'R. Alamo' 'R. San juan' 'R. Huizache' 'R. Sosa'
 'R. Pesqueria' 'A. Salinas' 'R. San Juan' 'R. Sta. Catarina'
 'A. San Lorenzo' 'A. Las Jaras' 'A. de la Joya' 'R. Potosi' 'R. Pablillo'
 'A. de los Anegados' 'R. San Fernando' 'A. Chorreras' 'R. Pilon'
 'A. Blanco' 'R. Purificacion' 'R. San Marcos' 'A. Gallinas'
 'R. Soto la Marina' 'A. San Carlos' 'R. Carrizal' 'R. Tigre'
 'R. Guayalejo' 'R. Tamesis' 'R. del Cojo' 'R. Puerco' 'R. Valles'
 'A. Alamos' 'R. Sinaloa' 'R. Mocorito' 'R. Tule' 'R. Culiacan'
 'R. San Lorenzo' 'R. Venaditos' 'R. Elota' 'R. Piaxtla' 'R. Otelite'
 'R. Presidio' 'R. Baluarte' 'R.Canas' 'R. Badiroguato'
 'A. Sn. Jose del Oro' 'R. Tamazula' 'R. de los Remedios'
 'R. del Espiritu Santo' 'R. de Ramos' 'A. Potrillos' 'R. del Penon'
 'A. Cuenaame' 'R. Aguanaval' 'R. Tepehuanes' 'R. de Santiago'
 'R. San Diego' 'R. Mezquital' 'R. Sainalto' 'R. Trujillo' 'R. lazos'
 'R. Atengo' 'R. Huajimic' 'A. Rasal' 'R. Jerez' 'R. del Salto'
 'R. Cataflan' 'R. Juchipila' 'R. Cuixtla' 'R. Aguascalientes'
 'R. Tampaon' 'R. Sta. Martha' 'A. el Molino' 'R. Tamuin' 'R. Panuco'
 'R. Ameca' 'R. Huicicila' 'R. Bejuco' 'R. Acaponeta' 'R. Ruiz'
 'R. San Blas' 'R. Ingenio' 'R. Tepic' 'A. de Vance' 'R. Huichol'
 'R. Grande o Verde' 'R. San Juan de los Lagos' 'R. Mascota'
 'R. del Tuito' 'R. Tomatlan' 'R. San Nicolas' 'R. Cuitzmala'
 'R. Cihuatlan' 'R. Armeria' 'R. Ayutla' 'R. Santiago'
 'R. Grande de Santiago' 'R. Bolanos' 'R. Valparaiso' 'R. Coahuayana'
 'R. Barreras' 'R. Ahuijilla' 'R. Quitupan' 'R. Itzicaro' 'R. Coberon'
 'R. Duero' 'R. Lerma' 'R. Tilostoc' 'R. Ixtapan' 'R. La Laja' 'R. Jalpan'
 'R. Extoraz' 'R. Tobila' 'R. Chicayon' 'R. Tempoal' 'R. Calabozo'
 'R. Amajac' 'R. Amajaque' 'R. Alcalayuca' 'R. Tula' 'R. Tuxpan'
 'R. Vimazco' 'R. Cazones' 'R. Tecolutla' 'R. Nautla' 'R. Misantla'
 'R. Colina' 'R. Juchique' 'R. Actopan' 'R. Idolos' 'R. La Antigua'
 'R. Jamapa' 'R. Atoyac' 'R. Blanco' 'R. Santo Domingo' 'R. Usila'
 'R. Valle Nacional' 'R. Laxamilpa' 'R. Quilotepec' 'R. Papaloapan'
 'R. Tonto' 'R. Mezcala' 'R. Nexapa' 'R. Cuautla' 'R. Yautepec'
 'R. Tembembe' 'R. Grande de Amacuizoc' 'R. Tlapaneca' 'R. Mixteco'
 'R. Huajuapan' 'R. Tamazulapa' 'R. Chiquito' 'R. Alcozauco'
 'R. Atlamajac' 'R. Pachumo' 'R. Tesechoacan' 'R. de la Lana'
 'R. Jaltepec' 'R. Cachapa' 'R. Uspanapa' 'R. San Crispin' 'R. Tonala'
 'R. Tancochapa' 'R. Zanapa' 'R. Pedregal' 'R. Grijalva' 'R. Mezcalapa'
 'R. Chilapilla' 'R. Tepetitlan' 'R. Tulija' 'R. Bascan' 'R. Mascupana'
 'R. Tocotalpa' 'R. Pichucalco' 'R. Usumacinta' 'R. San Pedro y San Pablo'
 'R. Palizada' 'R. Chumpan' 'R. Candelaria' 'R. Champoton' 'R. Lacantun'
 'R. Jatate' 'R. Tzanconeja' 'R. Grande de Comitan' 'R. Comitan'
 'R. Grande' 'R. Chixov o Salinas' 'R. Suchate' 'R. Suchiapa'
 'R. de la Vemta' 'R. Cintalapa' 'R. Encajonada' 'R. Osuta' 'R. Niltepec'
 'R. San Jose o Cazadero' 'R. Chicapa' 'R. de los Perros' 'R. Copalita'
 'R. Colaltepec' 'R. Zapote' 'R. Sordo' 'R. Nochixtlan' 'R. Penoles'
 'R. Tehuantepec' 'R. Pazmolacon' 'R. Coatzacoalcos' 'R. Malatonap'
 'R. Almolonga' 'R. Chichihua' 'R. Ometepec' 'R. Puente' 'R. Cuetzala'
 'R. Marquelia' 'R. Ayutla o Nexpa' 'R. Tecomate' 'R. Papagayo'
 'R. Omitlan' 'R. Azul' 'R. Union' 'R. Sabina' 'R. Lecpun' 'R. San luis'
 'R. Ixtapa' 'R. de la Union' 'R. Balsas' 'R. Tepecuacunco' 'R. Huautla'
 'R. Poliutla' 'R. Apahuitla' 'R. Cuirio' 'R. Amuco' 'R. Cutzamala'
 'R. Carrizar' 'R. Oscura' 'R. Coalcoman' 'A. Pizandaran'
 'R. Tepalcatepec' 'R. el Marquez' 'R. Cupatitzio' 'R. la Culebra'
 'R. Apatzingo' 'R. Placeres de oro' 'A. Churumuco' 'R. Purungueo'
 'R. Zitacuaro' 'R. Grande de Morelia' 'R. San Vicente' 'R. San Rafael'
 'A. Huatamote' 'A. San Simon' 'R. del Rosario' 'A. San Miguel'
 'A. Ballenas' 'A. Conejo' 'A. Cedro' 'R. San Jose']
In [ ]:
distanceMatrixKM_riv_air.loc['R. San Lorenzo'].min()
Out[ ]:
159.09472240460212
In [ ]:
minMTS=distanceMatrixKM_riv_air.loc['R. San Lorenzo'].min()*1000
Rios[Rios.NOMBRES=='R. San Lorenzo'].buffer(distance=minMTS)
Out[ ]:
0
1827 POLYGON ((1820052.164 1454900.302, 1829487.312...

In [ ]:
buff_San_Lorenzo=Rios[Rios.NOMBRES=='R. San Lorenzo'].buffer(distance=minMTS)
buffASbase=buff_San_Lorenzo.explore(color='red')
Rios[Rios.NOMBRES=='R. San Lorenzo'].explore(m=buffASbase,color='blue',style_kwds={'weight':0.5})
Out[ ]:
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In [ ]:
smallAirports=aerop_6362[aerop_6362['type']=='small_airport']
Rios[Rios.NOMBRES=='R. San Lorenzo'].explore(m=buffASbase,color='blue',style_kwds={'weight':0.5})
smallAirports.explore(m=buffASbase,color='yellow')
Out[ ]:
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In [ ]:
Rios_dentro_buff=smallAirports.clip(mask=buff_San_Lorenzo)
Rios_dentro_buff
Out[ ]:
type name latitude_deg longitude_deg elevation_ft continent iso_country iso_region municipality geometry
46668 small_airport Mazfly S.C. Airport 23.255225 -106.214995 59.0 NaN MX MX-SIN Mazatlán POINT (2071091.083 1253686.861)
46432 small_airport Campo El Patole Airport 23.738610 -106.830833 66.0 NaN MX MX-SIN San Ignacio POINT (2010239.995 1308876.736)
46091 small_airport Ejidal Airstrip 23.909576 -106.897357 NaN NaN MX MX-SIN La Cruz POINT (2004141.206 1327927.002)
46090 small_airport Elota Airstrip 23.935466 -106.872801 NaN NaN MX MX-SIN La Cruz POINT (2006723.427 1330692.607)
47035 small_airport Juan Murillo Airport 23.950528 -106.927335 43.0 NaN MX MX-SIN Cruz de Elota POINT (2001262.069 1332539.419)
... ... ... ... ... ... ... ... ... ... ...
45788 small_airport El Limon de Badiraguato Airstrip 25.824200 -107.232700 4060.0 NaN MX MX-SIN Badiraguato POINT (1977898.714 1539980.611)
46213 small_airport Loma Linda Airstrip 25.840642 -107.322625 2763.0 NaN MX MX-SIN Badiraguato POINT (1968997.655 1542122.587)
46828 small_airport El Roble Gordo Airstrip 25.849289 -107.337456 2100.0 NaN MX MX-SIN Badiraguato POINT (1967554.225 1543130.425)
47550 small_airport Tecuixiapa Airstrip 25.853054 -107.384566 1893.0 NaN MX MX-SIN Badiraguato POINT (1962872.48 1543720.972)
46636 small_airport El Palmito Airstrip 25.942433 -107.143053 4603.0 NaN MX MX-CHH Guadalupe y Calvo POINT (1987306.971 1552686.737)

217 rows × 10 columns

In [ ]:
buffASbase=buff_San_Lorenzo.explore(color='red')
Rios_dentro_buff.explore(m=buffASbase,color='blue',style_kwds={'weight':0.5})
Rios_dentro_buff.explore(m=buffASbase,color='orange')
Out[ ]:
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In [ ]:
distanceMatrixKM_riv_air.min(axis=1).min()
MinminMTS_5=5*distanceMatrixKM_riv_air.min(axis=1).min()*1000
Allminbuff=Rios.buffer(distance=MinminMTS_5).explore(color='red')
Rios.explore(m=Allminbuff,color='blue',style_kwds={'weight':0.5})
Out[ ]:
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In [ ]:
RiosAll_buff=Rios.buffer(distance=MinminMTS_5)
RiosAll_buff.explore(color='blue')
Out[ ]:
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In [ ]:
RiosAll_buff=smallAirports.clip(RiosAll_buff)
RiosAll_buff.explore(color='blue')
Out[ ]:
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In [ ]:
base=RiosAll_buff.plot(color='yellow')
Rios_dentro_buff.plot(ax=base,color='red', markersize=1)
Out[ ]:
<Axes: >
No description has been provided for this image
In [ ]:
base=RiosAll_buff.explore(color='yellow')
Rios_dentro_buff.explore(m=base,color='green')
Out[ ]:
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