LECTURA DE DATOS¶

In [38]:
import geopandas as gpd
import pandas as pd
australia_link='https://github.com/Tracy-12/TAREA_GRUPAL_3/raw/refs/heads/main/mapas%20obtenidos/australiaMaps_3112.gpkg'
ports=pd.read_csv('https://github.com/CienciaDeDatosEspacial/GeoDataFrame_Analytics/raw/main/data/UpdatedPub150.csv')
border=gpd.read_file(australia_link,layer='border')
gpd.list_layers(australia_link)
Out[38]:
name geometry_type
0 country MultiPolygon
1 cities Point
2 rivers MultiLineString
3 airports Point
4 border MultiLineString

EJERCICIO 1¶

In [39]:
ports.columns
Out[39]:
Index(['World Port Index Number', 'Region Name', 'Main Port Name',
       'Alternate Port Name', 'UN/LOCODE', 'Country Code', 'World Water Body',
       'IHO S-130 Sea Area', 'Sailing Direction or Publication',
       'Publication Link',
       ...
       'Supplies - Fuel Oil', 'Supplies - Diesel Oil',
       'Supplies - Aviation Fuel', 'Supplies - Deck', 'Supplies - Engine',
       'Repairs', 'Dry Dock', 'Railway', 'Latitude', 'Longitude'],
      dtype='object', length=108)
In [40]:
airports=gpd.read_file(australia_link,layer='airports')
ports.rename(columns={'Main Port Name':'portName'},inplace=True)
ports=ports.loc[:,['portName', 'Country Code','Latitude', 'Longitude']]
ports.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 [41]:
seaports=gpd.GeoDataFrame(data=ports.copy(),
                           geometry=gpd.points_from_xy(ports.Longitude,
                                                       ports.Latitude),
                          crs=4326)
seaports_aus=seaports[seaports['Country Code']=='Australia'].copy()
seaports_aus.reset_index(drop=True, inplace=True)
seaports_aus_3112=seaports_aus.to_crs(3112)
In [42]:
seaports_aus_3112.head()
Out[42]:
portName Country Code Latitude Longitude geometry
0 Dover Australia -43.316667 147.033333 POINT (1087783.364 -4930315.345)
1 Burnie Australia -41.050000 145.950000 POINT (1022145.796 -4664513.364)
2 Ballina Australia -28.866667 153.566667 POINT (1878859.725 -3411689.117)
3 Beauty Point Australia -41.150000 146.816667 POINT (1094875.552 -4683076.964)
4 Lucinda Australia -18.516667 146.333333 POINT (1298466.288 -2193115.291)
In [43]:
largeAirports=airports[airports['kind']=='large_airport']
largeAirports.reset_index(drop=True, inplace=True)
largeAirports.head()
Out[43]:
name kind latitude_deg longitude_deg elevation_ft municipality geometry
0 Brisbane International Airport large_airport -27.384199 153.117004 13.0 Brisbane POINT (1860613.688 -3244612.198)
1 Melbourne Airport large_airport -37.661548 144.836432 434.0 Melbourne POINT (959922.488 -4276694.309)
2 Adelaide International Airport large_airport -34.947512 138.533393 20.0 Adelaide POINT (412864.26 -3941270.535)
3 Darwin International Airport / RAAF Darwin large_airport -12.414970 130.881850 103.0 Darwin POINT (-345664.176 -1452918.314)
4 Perth International Airport large_airport -31.940300 115.967003 67.0 Perth POINT (-1684432.427 -3723348.148)
In [44]:
seaports_aus_3112.set_index('portName').geometry.apply\
(lambda g: largeAirports.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)
Out[44]:
name Adelaide International Airport Brisbane International Airport Darwin International Airport / RAAF Darwin Melbourne Airport Perth International Airport Sydney Kingsford Smith International Airport
portName
Albany 1878.175154 3416.684096 2815.472432 2434.582964 384.225804 3039.604161
Ardrossan 80.019657 1631.980784 2529.626327 719.069619 2049.116205 1217.362816
Ballina 1558.717159 168.070267 2963.998927 1262.014116 3576.895625 601.710321
Batemans Bay 1058.736672 955.463941 3212.250154 524.799195 3166.158422 214.750284
Beauty Point 1007.678609 1629.581473 3536.817723 428.204614 2940.345598 892.228278
... ... ... ... ... ... ...
Weipa 2479.501024 2002.647513 1216.555606 2773.920364 3383.577441 2527.090502
Welshpool 823.014372 1392.130776 3284.246655 184.831298 2859.408875 676.801398
Western Port 709.393357 1413.245949 3195.164448 84.528120 2746.504332 724.749851
Whyalla 228.614239 1602.636024 2373.292327 834.052445 2016.826775 1259.082988
Wyndham 2379.967832 2883.121141 458.109525 2942.479143 2184.598814 3067.981537

66 rows × 6 columns

In [45]:
distanceMatrixKM_sea_air= seaports_aus_3112.set_index('portName').geometry.apply\
                          (lambda g: largeAirports.set_index('name').geometry.distance(g)/1000).\
                          sort_index(axis=0).sort_index(axis=1)
In [46]:
port_point = seaports_aus_3112[seaports_aus_3112['portName'] == 'Albany']
closest_point1=largeAirports[largeAirports['name']== 'Perth International Airport']
closest_point2=largeAirports[largeAirports['name']== 'Adelaide International Airport']
In [47]:
base = border.plot(facecolor="white",
                      edgecolor='black',
                      linewidth=0.1,
                      figsize=(6,6))
port_point.plot(facecolor='green',linewidth=0,marker="^",
            ax=base, markersize=30, zorder=5)
closest_point1.plot(facecolor='blue',linewidth=0,
            ax=base, markersize=20, zorder=5)
closest_point2.plot(facecolor='red',linewidth=0,
            ax=base, markersize=20, zorder=5)
Out[47]:
<Axes: >
No description has been provided for this image

EJERCICIO 2¶

In [48]:
eletricity_lines=gpd.read_file('https://github.com/Tracy-12/TAREA_GRUPAL_3/raw/refs/heads/main/Electricity_Transmission_lines/Electricity_Transmission_Lines.shp')
liquid_fluel_terminals=gpd.read_file('https://github.com/Tracy-12/TAREA_GRUPAL_3/raw/refs/heads/main/Liquid_fuel_terminals/Liquid_Fuel_Terminals.shp')
countries=gpd.read_file('https://github.com/Tracy-12/TAREA_GRUPAL_3/raw/refs/heads/main/World_Countries/World_Countries.shp')
In [49]:
australia=countries[countries.COUNTRY=='Australia']
liquid_fluel_terminals=liquid_fluel_terminals.set_crs(epsg=4326, allow_override=True)
liquid_fluel_terminals_clipped=gpd.clip(liquid_fluel_terminals,australia)
liquid_fluel_terminals_3112=liquid_fluel_terminals_clipped.to_crs(3112)
decommissioned_terminals=liquid_fluel_terminals_3112[liquid_fluel_terminals_3112.operationa=='Decommissioned']
In [50]:
eletricity_lines=eletricity_lines.set_crs(epsg=4326, allow_override=True)
eletricity_lines_clipped=gpd.clip(eletricity_lines,australia)
eletricity_lines_3112=eletricity_lines_clipped.to_crs(3112)
In [51]:
#Solo se trabaja con una linea de un estado estado
single_line = eletricity_lines_3112[eletricity_lines_3112['name'] == 'Sydney West to Bannaby']
distanceMatrixKM_elec_term=single_line.set_index('name').geometry.apply\
(lambda g: decommissioned_terminals.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=0)
distanceMatrixKM_elec_term
Out[51]:
name Newport Botany Birkenhead Newcastle Bundaberg Townsville Karumba Fremantle
name
Sydney West to Bannaby 597.794519 38.694289 1055.338487 130.229928 1003.238753 1642.864355 2044.071122 3177.302682
In [71]:
#!pip install folium matplotlib mapclassify
In [54]:
closest_terminal=decommissioned_terminals[decommissioned_terminals['name']=='Botany']
farthest_terminal = decommissioned_terminals[decommissioned_terminals['name'] == 'Fremantle']
In [55]:
base=single_line.explore()
closest_terminal.explore(m=base,color='red',marker_kwds=dict(radius=10))
farthest_terminal.explore(m=base, color='purple', marker_kwds=dict(radius=10))
Out[55]:
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EJERCICIO 3

In [56]:
#Se junta la data de lineas por estado
electricity_lines_by_state=eletricity_lines_3112.dissolve(by='state')
electricity_lines_by_state.reset_index(drop=False,inplace=True)
electricity_lines_by_state.drop(columns=['length_m','objectid','featuretyp','descriptio','class','name','name','spatialcon','revised','ga_guid','comment_','st_lengths'],inplace=True)
electricity_lines_New_South_Wales=electricity_lines_by_state[electricity_lines_by_state['state']=='New South Wales']
In [57]:
electricity_lines_New_South_Wales.convex_hull
Out[57]:
1    POLYGON ((1414735.513 -4265409.239, 967413.224...
dtype: geometry
In [58]:
electricity_lines_New_South_Wales.convex_hull.plot()
Out[58]:
<Axes: >
No description has been provided for this image
In [59]:
lines_SWales_hulls=electricity_lines_New_South_Wales.convex_hull.to_frame()
lines_SWales_hulls['State']=['New South Wales']
lines_SWales_hulls.rename(columns={0:'geometry'},inplace=True)
lines_SWales_hulls=lines_SWales_hulls.set_geometry('geometry')
lines_SWales_hulls
Out[59]:
geometry State
1 POLYGON ((1414735.513 -4265409.239, 967413.224... New South Wales
In [60]:
distanceMatrixKM_linesHull_fluel=lines_SWales_hulls.set_index('State').geometry.apply\
(lambda g: decommissioned_terminals.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=1)
distanceMatrixKM_linesHull_fluel
Out[60]:
name Birkenhead Botany Bundaberg Fremantle Karumba Newcastle Newport Townsville
State
New South Wales 324.421223 0.0 384.478317 2388.472819 1476.642103 0.0 185.535614 1073.945797
In [61]:
closest_terminal_1=decommissioned_terminals[decommissioned_terminals['name']=='Botany']
closest_terminal_2=decommissioned_terminals[decommissioned_terminals['name']=='Newcastle']
farthest_terminal_1= decommissioned_terminals[decommissioned_terminals['name'] == 'Fremantle']
In [62]:
base=lines_SWales_hulls.explore()
mins = distanceMatrixKM_linesHull_fluel.idxmin(axis=1)
closest_terminal_1.explore(m=base,color='red',marker_kwds=dict(radius=10))
closest_terminal_2.explore(m=base,color='red',marker_kwds=dict(radius=10))
farthest_terminal_1.explore(m=base, color='purple', marker_kwds=dict(radius=10))
Out[62]:
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EJERCICIO 4¶

In [63]:
airports
Out[63]:
name kind latitude_deg longitude_deg elevation_ft municipality geometry
0 Argyle Downs Airport small_airport -16.508604 128.922485 407.0 Argyle Downs POINT (-544268.865 -1917793.807)
1 Angus Downs Airport small_airport -25.032500 132.274800 1724.0 Angus Downs Station POINT (-172087.264 -2846597.417)
2 Hopetoun Airfield closed -33.908501 120.147003 NaN Hopetoun POINT (-1271965.498 -3889204.632)
3 Dwellingup Airstrip small_airport -32.692699 116.074997 NaN Dwellingup POINT (-1662649.636 -3803821.329)
4 Tjukayirla Roadhouse Airstrip small_airport -27.155199 124.584999 1425.0 Cosmo Newbery POINT (-920899.555 -3112269.058)
... ... ... ... ... ... ... ...
2749 Yarrawonga Airport small_airport -36.028900 146.029007 423.0 None POINT (1082617.757 -4105488.7)
2750 Mugarinya Airport small_airport -21.285426 118.385282 439.0 Yandeyarra Station POINT (-1604526.014 -2534299.095)
2751 Zanthus Airport small_airport -31.036351 123.567889 NaN None POINT (-984908.515 -3543899.565)
2752 Churchable Airstrip small_airport -27.429915 152.378454 NaN Churchable POINT (1788526.725 -3238837.645)
2753 Zeehan Airport small_airport -41.888804 145.350768 NaN None POINT (962454.411 -4754487.352)

2754 rows × 7 columns

In [64]:
small_airport=airports[airports['kind']=='small_airport']
small_airport.reset_index(drop=True, inplace=True)
small_airport.head()
Out[64]:
name kind latitude_deg longitude_deg elevation_ft municipality geometry
0 Argyle Downs Airport small_airport -16.508604 128.922485 407.0 Argyle Downs POINT (-544268.865 -1917793.807)
1 Angus Downs Airport small_airport -25.032500 132.274800 1724.0 Angus Downs Station POINT (-172087.264 -2846597.417)
2 Dwellingup Airstrip small_airport -32.692699 116.074997 NaN Dwellingup POINT (-1662649.636 -3803821.329)
3 Tjukayirla Roadhouse Airstrip small_airport -27.155199 124.584999 1425.0 Cosmo Newbery POINT (-920899.555 -3112269.058)
4 Langley Park Airstrip small_airport -31.961229 115.867867 11.0 Perth POINT (-1693297.64 -3726957.208)
In [65]:
electricity_lines_by_state
Out[65]:
state geometry operationa capacitykv
0 Australian Capital Territory MULTILINESTRING ((1365976.343 -4064288.851, 13... Operational 132
1 New South Wales MULTILINESTRING ((997652.205 -4045143.527, 997... Operational 66
2 Northern Territory MULTILINESTRING ((-17246.151 -2719395.622, -17... Operational 22
3 Queensland MULTILINESTRING ((1632865.141 -3271177.923, 16... Operational 132
4 South Australia MULTILINESTRING ((602142.421 -4271382.784, 602... Operational 132
5 Tasmania MULTILINESTRING ((1094650.002 -4897241.888, 10... Operational 110
6 Victoria MULTILINESTRING ((966851.434 -4352817.646, 966... Operational 66
7 Western Australia MULTILINESTRING ((-1494032.542 -3993308.124, -... Operational 132
In [66]:
electricity_lines_Victoria=eletricity_lines[eletricity_lines['state']=='Victoria']
In [67]:
single_line_proyected = electricity_lines_Victoria.to_crs(epsg=3112)
bufferAroundNSWalles=single_line_proyected.buffer(distance = 9000)
bufferAsBase=bufferAroundNSWalles.explore(color='red')
single_line_proyected.explore(m=bufferAsBase,color='blue',style_kwds={'weight':3})
small_airport.explore(m=bufferAsBase,color='black')
Out[67]:
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In [68]:
linesWithinBuffer=small_airport.clip(mask=bufferAroundNSWalles)
linesWithinBuffer
Out[68]:
name kind latitude_deg longitude_deg elevation_ft municipality geometry
1912 Tyabb Airport small_airport -38.266826 145.179406 88.0 Tyabb POINT (984219.277 -4346691.366)
299 Bellbrae private airstrip small_airport -38.326970 144.236209 NaN BellBrae POINT (900826.518 -4346318.876)
1895 Torquay Airport small_airport -38.299999 144.365005 9.0 Torquay POINT (912384.013 -4344241.845)
743 Barwon Heads Airport small_airport -38.256145 144.427895 50.0 Connewarre POINT (918313.694 -4339812.929)
1115 George Town Airport small_airport -41.079491 146.839061 131.0 George Town POINT (1097595.898 -4675332.653)
... ... ... ... ... ... ... ...
1684 Prairie Airport small_airport -36.307201 144.139008 NaN None POINT (910430.525 -4121278.018)
1467 Moama Airstrip small_airport -36.074459 144.764485 310.0 None POINT (968672.377 -4100203.665)
1696 Pyramid Hill Airport small_airport -36.060618 144.126763 NaN None POINT (911537.012 -4093902.272)
861 Cohuna Airport small_airport -35.826478 144.213812 NaN None POINT (921464.156 -4068646.543)
503 Mildura (Sunraysia Gliding Club) Airfield small_airport -34.262146 142.057185 180.0 Koorlong POINT (738292.397 -3881742.818)

94 rows × 7 columns

In [69]:
base=bufferAroundNSWalles.plot(color='lightblue')
linesWithinBuffer.plot(ax=base,color='red',markersize=4)
Out[69]:
<Axes: >
No description has been provided for this image
In [70]:
single_line_proyected = electricity_lines_Victoria.to_crs(epsg=3112)
bufferAroundNSWalles=single_line_proyected.buffer(distance = 9000)
bufferAsBase=bufferAroundNSWalles.explore(color='red')
single_line_proyected.explore(m=bufferAsBase,color='blue',style_kwds={'weight':3})
linesWithinBuffer.explore(m=bufferAsBase,color='black')
Out[70]:
Make this Notebook Trusted to load map: File -> Trust Notebook