Evaluación - parte 1¶

In [347]:
import os
import geopandas as gpd
from  fiona import listlayers
import matplotlib.pyplot as plt
import pandas as pd

Exercise 1¶

1. Use two maps of points.¶

In [348]:
peru_health = "https://github.com/IntroSpatialDataScience/MiningGDF_Project1/raw/main/data/peru_health_sities.csv"
ports = "https://github.com/IntroSpatialDataScience/MiningGDF_Project1/raw/main/data/UpdatedPub150.csv"
peruMaps = "https://github.com/IntroSpatialDataScience/geodfEv3/raw/main/maps/peru_24891.gpkg"
In [349]:
listlayers(peruMaps)
Out[349]:
['country', 'cities', 'rivers']
In [350]:
peru = gpd.read_file(peruMaps,layer="country")
rivers = gpd.read_file(peruMaps,layer="rivers")
healthsites =pd.read_csv(peru_health)
portsFile =pd.read_csv(ports)
In [351]:
portsFile
Out[351]:
World Port Index Number Region Name Main Port Name Alternate Port Name UN/LOCODE Country Code World Water Body Sailing Direction or Publication Publication Link Standard Nautical Chart ... Supplies - Fuel Oil Supplies - Diesel Oil Supplies - Aviation Fuel Supplies - Deck Supplies - Engine Repairs Dry Dock Railway Latitude Longitude
0 7950.0 United States E Coast -- 6585 Maurer United States North Atlantic Ocean U.S. Coast Pilot 2 - Atlantic Coast: Cape Cod ... https://nauticalcharts.noaa.gov/publications/c... 12331 ... Yes Yes Unknown Yes Yes Moderate Unknown Unknown 40.533333 -74.250000
1 52235.0 Sulawesi -- 51970 Mangkasa Oil Terminal Indonesia Teluk Bone; Banda Sea; South Pacific Ocean Sailing Directions Pub. 163 (Enroute) - Borneo... https://msi.geo.nga.mil/api/publications/downl... ... No No Unknown No No NaN Unknown Unknown -2.733333 121.066667
2 47620.0 Madagascar -- 47350 Iharana Madagascar Indian Ocean Sailing Directions Pub. 171 (Enroute) - East A... https://msi.geo.nga.mil/api/publications/downl... 61560 ... No No Unknown No No Emergency Only Unknown Unknown -13.350000 50.000000
3 47360.0 Madagascar -- 47350 Andoany Madagascar Mozambique Channel; Indian Ocean Sailing Directions Pub. 171 (Enroute) - East A... https://msi.geo.nga.mil/api/publications/downl... 61420 ... No No Unknown No No Emergency Only Unknown Unknown -13.400000 48.300000
4 47020.0 Tanzania -- 46965 Chake Chake Tanzania Indian Ocean Sailing Directions Pub. 171 (Enroute) - East A... https://msi.geo.nga.mil/api/publications/downl... 61200 ... No No Unknown No No Unknown Unknown Unknown -5.250000 39.766667
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3819 51560.0 Sarawak -- 51550 Kuching MY KCH Malaysia South China Sea; North Pacific Ocean Sailing Directions Pub. 163 (Enroute) - Borneo... https://msi.geo.nga.mil/api/publications/downl... 71350 ... Unknown Yes Unknown Yes Yes Limited Small Small 1.566667 110.350000
3820 51635.0 Brunei -- 51610 Lumut BN LUM Brunei South China Sea; North Pacific Ocean Sailing Directions Pub. 163 (Enroute) - Borneo... https://msi.geo.nga.mil/api/publications/downl... 71330 ... Unknown Yes Unknown Unknown Unknown Emergency Only Unknown Unknown 4.716667 114.450000
3821 48475.0 Iran -- 48410 Bandar-E Pars Terminal Assaluyeh Iran Persian Gulf; Indian Ocean Sailing Directions Pub. 172 (Enroute) - Red Se... https://msi.geo.nga.mil/api/publications/downl... ... Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown 27.533333 52.533333
3822 48264.0 United Arab Emirates -- 48260 Mina Saqr Ras Al Khaimah AE MSA United Arab Emirates Persian Gulf; Indian Ocean Sailing Directions Pub. 172 (Enroute) - Red Se... https://msi.geo.nga.mil/api/publications/downl... 62458 ... Yes Yes Unknown Unknown Unknown Moderate Unknown Unknown 25.983333 56.050000
3823 48480.0 Iran -- 48410 Jazireh-Ye Lavan Oil Terminal Lavan Island IR LVP Iran Persian Gulf; Indian Ocean Sailing Directions Pub. 172 (Enroute) - Red Se... https://msi.geo.nga.mil/api/publications/downl... 62481 ... Yes Yes Unknown No No NaN Unknown Unknown 26.783333 53.333333

3824 rows × 107 columns

In [352]:
portsFile.rename(columns={'Main Port Name':'portName'},inplace=True)
portsFile=portsFile.loc[:,['portName', 'Country Code','Latitude', 'Longitude']]
In [353]:
seaports=gpd.GeoDataFrame(data=portsFile.copy(),
                           geometry=gpd.points_from_xy(portsFile.Longitude,
                                                       portsFile.Latitude), 
                          crs=4326)
In [354]:
seaports[seaports['Country Code']=='Peru']
Out[354]:
portName Country Code Latitude Longitude geometry
919 La Pampilla Oil Terminal Peru -11.933333 -77.133333 POINT (-77.13333 -11.93333)
946 Iquitos Peru -3.750000 -73.233333 POINT (-73.23333 -3.75000)
952 Chancay Peru -11.583333 -77.250000 POINT (-77.25000 -11.58333)
991 Puerto Supe Peru -10.800000 -77.750000 POINT (-77.75000 -10.80000)
1004 Salaverry Peru -8.233333 -78.983333 POINT (-78.98333 -8.23333)
1502 Conchan Oil Terminal Peru -12.166667 -77.000000 POINT (-77.00000 -12.16667)
1655 Bahia San Nicolas Peru -15.250000 -75.233333 POINT (-75.23333 -15.25000)
1910 Punta Lobitos (Bahia De Huarmey) Peru -10.100000 -78.166667 POINT (-78.16667 -10.10000)
1976 Puerto De Chimbote Peru -9.083333 -78.616667 POINT (-78.61667 -9.08333)
1987 Pimental Peru -6.833333 -79.933333 POINT (-79.93333 -6.83333)
2188 Bahia De Matarani Peru -17.000000 -72.116667 POINT (-72.11667 -17.00000)
2203 Bahia De Ancon Peru -11.750000 -77.183333 POINT (-77.18333 -11.75000)
2204 Caleta Lobitos Peru -4.450000 -81.283333 POINT (-81.28333 -4.45000)
2216 Puerto Ilo Peru -17.633333 -71.366667 POINT (-71.36667 -17.63333)
2219 Puerto Cabo Blanco Peru -4.250000 -81.233333 POINT (-81.23333 -4.25000)
2221 Puerto De Huacho Peru -11.116667 -77.616667 POINT (-77.61667 -11.11667)
2223 Puerto Del Callao Peru -12.050000 -77.150000 POINT (-77.15000 -12.05000)
3596 Talara Peru -4.583333 -81.283333 POINT (-81.28333 -4.58333)
3633 Puerto Bayovar Peru -5.833333 -81.050000 POINT (-81.05000 -5.83333)
3634 Paita Peru -5.083333 -81.116667 POINT (-81.11667 -5.08333)
In [355]:
seaports_pe=seaports[seaports['Country Code']=='Peru'].copy()
seaports_pe
Out[355]:
portName Country Code Latitude Longitude geometry
919 La Pampilla Oil Terminal Peru -11.933333 -77.133333 POINT (-77.13333 -11.93333)
946 Iquitos Peru -3.750000 -73.233333 POINT (-73.23333 -3.75000)
952 Chancay Peru -11.583333 -77.250000 POINT (-77.25000 -11.58333)
991 Puerto Supe Peru -10.800000 -77.750000 POINT (-77.75000 -10.80000)
1004 Salaverry Peru -8.233333 -78.983333 POINT (-78.98333 -8.23333)
1502 Conchan Oil Terminal Peru -12.166667 -77.000000 POINT (-77.00000 -12.16667)
1655 Bahia San Nicolas Peru -15.250000 -75.233333 POINT (-75.23333 -15.25000)
1910 Punta Lobitos (Bahia De Huarmey) Peru -10.100000 -78.166667 POINT (-78.16667 -10.10000)
1976 Puerto De Chimbote Peru -9.083333 -78.616667 POINT (-78.61667 -9.08333)
1987 Pimental Peru -6.833333 -79.933333 POINT (-79.93333 -6.83333)
2188 Bahia De Matarani Peru -17.000000 -72.116667 POINT (-72.11667 -17.00000)
2203 Bahia De Ancon Peru -11.750000 -77.183333 POINT (-77.18333 -11.75000)
2204 Caleta Lobitos Peru -4.450000 -81.283333 POINT (-81.28333 -4.45000)
2216 Puerto Ilo Peru -17.633333 -71.366667 POINT (-71.36667 -17.63333)
2219 Puerto Cabo Blanco Peru -4.250000 -81.233333 POINT (-81.23333 -4.25000)
2221 Puerto De Huacho Peru -11.116667 -77.616667 POINT (-77.61667 -11.11667)
2223 Puerto Del Callao Peru -12.050000 -77.150000 POINT (-77.15000 -12.05000)
3596 Talara Peru -4.583333 -81.283333 POINT (-81.28333 -4.58333)
3633 Puerto Bayovar Peru -5.833333 -81.050000 POINT (-81.05000 -5.83333)
3634 Paita Peru -5.083333 -81.116667 POINT (-81.11667 -5.08333)
In [356]:
# reprojecting
seaports_pe_24891=seaports_pe.to_crs(24891) # projected crs
seaports_pe_24891.rename(columns={'portName':'name'},inplace=True)
In [357]:
# reset indexes
seaports_pe_24891.reset_index(drop=True, inplace=True)
In [358]:
seaports_pe_24891
Out[358]:
name Country Code Latitude Longitude geometry
0 La Pampilla Oil Terminal Peru -11.933333 -77.133333 POINT (589113.575 768847.884)
1 Iquitos Peru -3.750000 -73.233333 POINT (1031485.913 1672619.329)
2 Chancay Peru -11.583333 -77.250000 POINT (576836.026 807768.749)
3 Puerto Supe Peru -10.800000 -77.750000 POINT (523049.750 895072.078)
4 Salaverry Peru -8.233333 -78.983333 POINT (389344.884 1179950.206)
5 Conchan Oil Terminal Peru -12.166667 -77.000000 POINT (603329.260 742815.069)
6 Bahia San Nicolas Peru -15.250000 -75.233333 POINT (788623.266 397331.204)
7 Punta Lobitos (Bahia De Huarmey) Peru -10.100000 -78.166667 POINT (478020.130 972927.354)
8 Puerto De Chimbote Peru -9.083333 -78.616667 POINT (429289.610 1085734.797)
9 Pimental Peru -6.833333 -79.933333 POINT (284873.321 1335036.499)
10 Bahia De Matarani Peru -17.000000 -72.116667 POINT (1117489.143 191320.549)
11 Bahia De Ancon Peru -11.750000 -77.183333 POINT (583900.850 789222.475)
12 Caleta Lobitos Peru -4.450000 -81.283333 POINT (135318.377 1598546.647)
13 Puerto Ilo Peru -17.633333 -71.366667 POINT (1194737.080 116863.191)
14 Puerto Cabo Blanco Peru -4.250000 -81.233333 POINT (140845.825 1620666.732)
15 Puerto De Huacho Peru -11.116667 -77.616667 POINT (537309.775 859873.537)
16 Puerto Del Callao Peru -12.050000 -77.150000 POINT (587138.310 755945.627)
17 Talara Peru -4.583333 -81.283333 POINT (135334.239 1583803.519)
18 Puerto Bayovar Peru -5.833333 -81.050000 POINT (161344.217 1445613.961)
19 Paita Peru -5.083333 -81.116667 POINT (153876.998 1528536.133)
In [359]:
healthsites.rename(columns={'X':'Longitude','Y':'Latitude','#loc +name':'name','#loc+amenity':'tipo'},inplace=True)
healthsites = healthsites[["name","tipo","Latitude","Longitude"]]
In [360]:
healthsites.head()
Out[360]:
name tipo Latitude Longitude
0 Patacancha doctors -11.895442 -75.506386
1 Ayancocha Alta doctors -10.247465 -76.125374
2 Kamanquiriato doctors -12.525074 -73.034027
3 C.S. Humay clinic -13.723504 -75.890124
4 Farmacia Mafer pharmacy -12.072449 -75.215294
In [361]:
healthsites=gpd.GeoDataFrame(data=healthsites.copy(),
                           geometry=gpd.points_from_xy(healthsites.Longitude,
                                                       healthsites.Latitude), 
                          crs=4326)
In [362]:
healthsites=healthsites.to_crs(24891)
In [363]:
hospital_24891 = healthsites[healthsites.tipo=="hospital"]
hospital_24891= hospital_24891 .dropna()
hospital_24891.reset_index(drop=True, inplace=True)
hospital_24891
Out[363]:
name tipo Latitude Longitude geometry
0 Clinica Angeles hospital -7.164819 -78.499376 POINT (443236.154 1297938.682)
1 Puesto de Salud Tunan hospital -10.566427 -77.759120 POINT (522280.907 920940.930)
2 E.E.S.S José Sabogal hospital -7.249858 -78.037710 POINT (494209.135 1288279.166)
3 Hospital Orurillo hospital -14.721034 -70.519466 POINT (1301668.655 438648.812)
4 Hospital - MINSA: Julio C. de Marini --- Puerta 4 hospital -11.063388 -75.334201 POINT (787310.657 862398.526)
... ... ... ... ... ...
177 Hospital de Tocache hospital -8.189700 -76.513832 POINT (661762.904 1182912.096)
178 Hospital de apoyo de Rioja hospital -6.062290 -77.170810 POINT (590937.038 1419196.623)
179 Centro de Salud hospital -12.621540 -72.407222 POINT (1104040.248 681285.661)
180 Hospital de Yunguyo hospital -16.247167 -69.095064 POINT (1448158.733 259405.694)
181 Puesto de Salud Tingua hospital -9.224351 -77.686054 POINT (531539.845 1069459.666)

182 rows × 5 columns

In [364]:
base = peru.boundary.plot()
hospital_24891.plot(ax=base,color='red',marker="^",markersize=4)
seaports_pe_24891.plot(ax=base,color = 'black',alpha=0.8,markersize=15)
plt.show()

2. Compute the distance matrix for both maps.¶

In [365]:
seaports_pe_24891.head()
Out[365]:
name Country Code Latitude Longitude geometry
0 La Pampilla Oil Terminal Peru -11.933333 -77.133333 POINT (589113.575 768847.884)
1 Iquitos Peru -3.750000 -73.233333 POINT (1031485.913 1672619.329)
2 Chancay Peru -11.583333 -77.250000 POINT (576836.026 807768.749)
3 Puerto Supe Peru -10.800000 -77.750000 POINT (523049.750 895072.078)
4 Salaverry Peru -8.233333 -78.983333 POINT (389344.884 1179950.206)
In [366]:
hospital_24891.head()
Out[366]:
name tipo Latitude Longitude geometry
0 Clinica Angeles hospital -7.164819 -78.499376 POINT (443236.154 1297938.682)
1 Puesto de Salud Tunan hospital -10.566427 -77.759120 POINT (522280.907 920940.930)
2 E.E.S.S José Sabogal hospital -7.249858 -78.037710 POINT (494209.135 1288279.166)
3 Hospital Orurillo hospital -14.721034 -70.519466 POINT (1301668.655 438648.812)
4 Hospital - MINSA: Julio C. de Marini --- Puerta 4 hospital -11.063388 -75.334201 POINT (787310.657 862398.526)
In [367]:
# Distancia entre Hospital Clinica Angeles y La pampilla oil terminal en km
hospital_24891.iloc[0].geometry.distance(seaports_pe_24891.iloc[0].geometry)/1000
Out[367]:
548.8326654754006
In [368]:
DF_distance = seaports_pe_24891.geometry.apply\
(lambda g: hospital_24891.geometry.distance(g)/1000)
DF_distance
Out[368]:
0 1 2 3 4 5 6 7 8 9 ... 172 173 174 175 176 177 178 179 180 181
0 548.832665 166.129167 528.030027 785.344618 219.166160 649.574040 533.272390 11.759705 330.068993 367.870217 ... 18.115392 209.331106 783.926409 651.071969 416.689917 420.389221 650.351296 522.318506 998.744176 306.075445
1 697.440583 907.915279 660.593444 1263.203053 846.214693 1191.850135 624.617462 1013.991516 812.298576 639.669705 ... 1014.276875 950.690337 1423.713145 507.778441 1090.909225 613.602703 508.238507 993.985198 1473.359772 783.420481
2 508.050675 125.635200 487.562779 813.407566 217.448805 678.053401 493.309225 52.531291 289.298057 341.594572 ... 58.127177 228.005272 818.920359 612.313301 445.939409 384.636329 611.590454 542.164425 1029.517023 265.582153
3 410.696616 25.880274 394.263357 902.535095 266.273145 768.275668 403.947184 154.189602 189.865474 315.283875 ... 160.319773 310.080441 917.882565 529.232180 538.474248 319.520289 528.502812 619.075594 1122.451951 174.594137
4 129.713336 291.131914 150.770271 1175.526444 509.132425 1046.345560 184.980610 468.813039 133.389813 394.308393 ... 474.798578 592.148482 1217.184609 313.481300 826.920920 272.434122 312.854990 871.467609 1403.028498 180.076557
5 577.747374 195.697874 556.271771 761.705338 219.429618 625.786602 560.813582 18.000268 359.414107 384.170223 ... 12.150505 194.541303 756.233173 677.213157 392.765099 443.959327 676.495066 504.477315 973.355774 334.440448
6 964.565232 587.456720 938.332538 514.706437 465.069174 397.762047 937.701529 409.990963 749.806256 690.489294 ... 403.856356 353.909379 418.470311 1041.496679 255.030783 795.758060 1040.811597 424.403118 673.802997 719.616951
7 326.867386 68.275945 315.767081 981.758858 328.446726 848.676265 330.286820 244.062212 106.293288 316.214397 ... 250.245523 389.085256 1004.677712 461.059993 621.349023 279.025087 460.333020 690.620059 1204.276575 110.375937
8 212.661691 189.220506 212.694066 1086.170093 421.969382 955.196664 236.213643 366.643172 47.936287 343.410688 ... 372.694568 497.679849 1120.134216 371.275326 732.233053 251.966783 370.576146 786.681338 1311.836147 103.537387
9 162.650038 477.323269 214.494129 1355.501250 689.804270 1228.102081 248.733875 654.461843 319.802620 546.970433 ... 660.613938 777.134130 1403.062389 317.728139 1011.633830 406.432764 317.423889 1048.057543 1584.365671 362.457485
10 1295.847516 941.604346 1261.664050 308.372116 747.906066 303.789767 1253.325130 771.694760 1094.925405 960.346668 ... 764.703963 644.709118 92.645244 1336.631337 422.057891 1091.302067 1336.015260 490.149654 337.606227 1055.682191
11 527.805585 145.419286 507.052447 798.806932 216.171884 663.238647 512.459489 32.707401 309.143282 353.120837 ... 38.319951 217.160224 801.268553 630.735047 430.699811 401.315359 630.013440 531.220622 1013.729505 285.086931
12 430.323722 780.313724 474.413834 1644.912143 983.365644 1520.546466 496.293101 957.390225 621.532951 816.870808 ... 963.570625 1075.576466 1702.099619 489.492929 1309.107274 670.742812 489.647623 1334.087621 1875.326183 661.002636
13 1399.890338 1048.207191 1364.901018 339.087522 849.599450 373.139010 1355.688094 878.827991 1200.658144 1058.546553 ... 871.819110 748.285502 140.615533 1436.099805 521.582235 1191.856427 1435.495336 571.663047 290.759178 1160.719870
14 442.259319 796.937212 485.125883 1656.706373 996.437379 1533.018490 505.675131 974.286471 637.199832 826.652511 ... 980.400812 1089.748691 1716.226644 492.937803 1322.885359 680.429073 493.125039 1345.429409 1887.352286 675.626410
15 448.052360 62.889533 430.568285 872.739805 250.013632 737.922610 439.113692 116.420110 227.538580 327.539773 ... 122.685659 282.180483 883.296621 562.616260 506.909641 346.182744 561.888065 594.202877 1090.966294 209.665538
16 560.771167 177.284892 540.383964 781.812563 226.718301 645.929842 545.892792 6.862382 341.585956 380.066182 ... 13.200631 210.609910 777.076547 663.982880 412.920438 433.438804 663.261875 522.265952 993.935649 318.405783
17 420.145564 767.537971 464.893358 1634.538245 972.367428 1509.816064 487.521709 944.465266 609.259013 807.719157 ... 950.676259 1063.989554 1690.493815 484.292380 1297.703603 661.695604 484.426816 1323.982559 1864.815742 649.252248
18 318.231130 636.833639 368.175626 1521.288479 855.554924 1393.831014 397.667203 812.227011 483.476208 708.447244 ... 818.651666 941.942480 1567.272404 430.477235 1176.615357 565.182388 430.404307 1213.620022 1750.137610 527.765911
19 370.005278 710.558492 416.592575 1582.807651 919.226583 1457.191838 441.492992 887.206965 553.314926 759.821044 ... 893.466633 1009.389998 1635.744246 450.468277 1243.475766 614.332213 450.529252 1273.044997 1812.693378 594.458096

20 rows × 182 columns

In [369]:
seaPortName = seaports_pe_24891.set_index('name')
seaPortName
Out[369]:
Country Code Latitude Longitude geometry
name
La Pampilla Oil Terminal Peru -11.933333 -77.133333 POINT (589113.575 768847.884)
Iquitos Peru -3.750000 -73.233333 POINT (1031485.913 1672619.329)
Chancay Peru -11.583333 -77.250000 POINT (576836.026 807768.749)
Puerto Supe Peru -10.800000 -77.750000 POINT (523049.750 895072.078)
Salaverry Peru -8.233333 -78.983333 POINT (389344.884 1179950.206)
Conchan Oil Terminal Peru -12.166667 -77.000000 POINT (603329.260 742815.069)
Bahia San Nicolas Peru -15.250000 -75.233333 POINT (788623.266 397331.204)
Punta Lobitos (Bahia De Huarmey) Peru -10.100000 -78.166667 POINT (478020.130 972927.354)
Puerto De Chimbote Peru -9.083333 -78.616667 POINT (429289.610 1085734.797)
Pimental Peru -6.833333 -79.933333 POINT (284873.321 1335036.499)
Bahia De Matarani Peru -17.000000 -72.116667 POINT (1117489.143 191320.549)
Bahia De Ancon Peru -11.750000 -77.183333 POINT (583900.850 789222.475)
Caleta Lobitos Peru -4.450000 -81.283333 POINT (135318.377 1598546.647)
Puerto Ilo Peru -17.633333 -71.366667 POINT (1194737.080 116863.191)
Puerto Cabo Blanco Peru -4.250000 -81.233333 POINT (140845.825 1620666.732)
Puerto De Huacho Peru -11.116667 -77.616667 POINT (537309.775 859873.537)
Puerto Del Callao Peru -12.050000 -77.150000 POINT (587138.310 755945.627)
Talara Peru -4.583333 -81.283333 POINT (135334.239 1583803.519)
Puerto Bayovar Peru -5.833333 -81.050000 POINT (161344.217 1445613.961)
Paita Peru -5.083333 -81.116667 POINT (153876.998 1528536.133)
In [370]:
hospName = hospital_24891.set_index('Latitude')
hospName
Out[370]:
name tipo Longitude geometry
Latitude
-7.164819 Clinica Angeles hospital -78.499376 POINT (443236.154 1297938.682)
-10.566427 Puesto de Salud Tunan hospital -77.759120 POINT (522280.907 920940.930)
-7.249858 E.E.S.S José Sabogal hospital -78.037710 POINT (494209.135 1288279.166)
-14.721034 Hospital Orurillo hospital -70.519466 POINT (1301668.655 438648.812)
-11.063388 Hospital - MINSA: Julio C. de Marini --- Puerta 4 hospital -75.334201 POINT (787310.657 862398.526)
... ... ... ... ...
-8.189700 Hospital de Tocache hospital -76.513832 POINT (661762.904 1182912.096)
-6.062290 Hospital de apoyo de Rioja hospital -77.170810 POINT (590937.038 1419196.623)
-12.621540 Centro de Salud hospital -72.407222 POINT (1104040.248 681285.661)
-16.247167 Hospital de Yunguyo hospital -69.095064 POINT (1448158.733 259405.694)
-9.224351 Puesto de Salud Tingua hospital -77.686054 POINT (531539.845 1069459.666)

182 rows × 4 columns

In [371]:
seaPortName.geometry.apply(lambda g: hospName.geometry.distance(g)/1000)
Out[371]:
Latitude -7.164819 -10.566427 -7.249858 -14.721034 -11.063388 -14.314151 -7.151624 -12.030624 -9.139774 -9.036817 ... -12.060909 -12.063551 -16.387885 -6.055780 -13.532035 -8.189700 -6.062290 -12.621540 -16.247167 -9.224351
name
La Pampilla Oil Terminal 548.832665 166.129167 528.030027 785.344618 219.166160 649.574040 533.272390 11.759705 330.068993 367.870217 ... 18.115392 209.331106 783.926409 651.071969 416.689917 420.389221 650.351296 522.318506 998.744176 306.075445
Iquitos 697.440583 907.915279 660.593444 1263.203053 846.214693 1191.850135 624.617462 1013.991516 812.298576 639.669705 ... 1014.276875 950.690337 1423.713145 507.778441 1090.909225 613.602703 508.238507 993.985198 1473.359772 783.420481
Chancay 508.050675 125.635200 487.562779 813.407566 217.448805 678.053401 493.309225 52.531291 289.298057 341.594572 ... 58.127177 228.005272 818.920359 612.313301 445.939409 384.636329 611.590454 542.164425 1029.517023 265.582153
Puerto Supe 410.696616 25.880274 394.263357 902.535095 266.273145 768.275668 403.947184 154.189602 189.865474 315.283875 ... 160.319773 310.080441 917.882565 529.232180 538.474248 319.520289 528.502812 619.075594 1122.451951 174.594137
Salaverry 129.713336 291.131914 150.770271 1175.526444 509.132425 1046.345560 184.980610 468.813039 133.389813 394.308393 ... 474.798578 592.148482 1217.184609 313.481300 826.920920 272.434122 312.854990 871.467609 1403.028498 180.076557
Conchan Oil Terminal 577.747374 195.697874 556.271771 761.705338 219.429618 625.786602 560.813582 18.000268 359.414107 384.170223 ... 12.150505 194.541303 756.233173 677.213157 392.765099 443.959327 676.495066 504.477315 973.355774 334.440448
Bahia San Nicolas 964.565232 587.456720 938.332538 514.706437 465.069174 397.762047 937.701529 409.990963 749.806256 690.489294 ... 403.856356 353.909379 418.470311 1041.496679 255.030783 795.758060 1040.811597 424.403118 673.802997 719.616951
Punta Lobitos (Bahia De Huarmey) 326.867386 68.275945 315.767081 981.758858 328.446726 848.676265 330.286820 244.062212 106.293288 316.214397 ... 250.245523 389.085256 1004.677712 461.059993 621.349023 279.025087 460.333020 690.620059 1204.276575 110.375937
Puerto De Chimbote 212.661691 189.220506 212.694066 1086.170093 421.969382 955.196664 236.213643 366.643172 47.936287 343.410688 ... 372.694568 497.679849 1120.134216 371.275326 732.233053 251.966783 370.576146 786.681338 1311.836147 103.537387
Pimental 162.650038 477.323269 214.494129 1355.501250 689.804270 1228.102081 248.733875 654.461843 319.802620 546.970433 ... 660.613938 777.134130 1403.062389 317.728139 1011.633830 406.432764 317.423889 1048.057543 1584.365671 362.457485
Bahia De Matarani 1295.847516 941.604346 1261.664050 308.372116 747.906066 303.789767 1253.325130 771.694760 1094.925405 960.346668 ... 764.703963 644.709118 92.645244 1336.631337 422.057891 1091.302067 1336.015260 490.149654 337.606227 1055.682191
Bahia De Ancon 527.805585 145.419286 507.052447 798.806932 216.171884 663.238647 512.459489 32.707401 309.143282 353.120837 ... 38.319951 217.160224 801.268553 630.735047 430.699811 401.315359 630.013440 531.220622 1013.729505 285.086931
Caleta Lobitos 430.323722 780.313724 474.413834 1644.912143 983.365644 1520.546466 496.293101 957.390225 621.532951 816.870808 ... 963.570625 1075.576466 1702.099619 489.492929 1309.107274 670.742812 489.647623 1334.087621 1875.326183 661.002636
Puerto Ilo 1399.890338 1048.207191 1364.901018 339.087522 849.599450 373.139010 1355.688094 878.827991 1200.658144 1058.546553 ... 871.819110 748.285502 140.615533 1436.099805 521.582235 1191.856427 1435.495336 571.663047 290.759178 1160.719870
Puerto Cabo Blanco 442.259319 796.937212 485.125883 1656.706373 996.437379 1533.018490 505.675131 974.286471 637.199832 826.652511 ... 980.400812 1089.748691 1716.226644 492.937803 1322.885359 680.429073 493.125039 1345.429409 1887.352286 675.626410
Puerto De Huacho 448.052360 62.889533 430.568285 872.739805 250.013632 737.922610 439.113692 116.420110 227.538580 327.539773 ... 122.685659 282.180483 883.296621 562.616260 506.909641 346.182744 561.888065 594.202877 1090.966294 209.665538
Puerto Del Callao 560.771167 177.284892 540.383964 781.812563 226.718301 645.929842 545.892792 6.862382 341.585956 380.066182 ... 13.200631 210.609910 777.076547 663.982880 412.920438 433.438804 663.261875 522.265952 993.935649 318.405783
Talara 420.145564 767.537971 464.893358 1634.538245 972.367428 1509.816064 487.521709 944.465266 609.259013 807.719157 ... 950.676259 1063.989554 1690.493815 484.292380 1297.703603 661.695604 484.426816 1323.982559 1864.815742 649.252248
Puerto Bayovar 318.231130 636.833639 368.175626 1521.288479 855.554924 1393.831014 397.667203 812.227011 483.476208 708.447244 ... 818.651666 941.942480 1567.272404 430.477235 1176.615357 565.182388 430.404307 1213.620022 1750.137610 527.765911
Paita 370.005278 710.558492 416.592575 1582.807651 919.226583 1457.191838 441.492992 887.206965 553.314926 759.821044 ... 893.466633 1009.389998 1635.744246 450.468277 1243.475766 614.332213 450.529252 1273.044997 1812.693378 594.458096

20 rows × 182 columns

In [372]:
MatrizDistance = seaPortName.geometry.apply(lambda g: hospName.geometry.distance(g)/1000)#.\
#sort_index(axis=0).sort_index(axis=1)
MatrizDistance.head()
Out[372]:
Latitude -7.164819 -10.566427 -7.249858 -14.721034 -11.063388 -14.314151 -7.151624 -12.030624 -9.139774 -9.036817 ... -12.060909 -12.063551 -16.387885 -6.055780 -13.532035 -8.189700 -6.062290 -12.621540 -16.247167 -9.224351
name
La Pampilla Oil Terminal 548.832665 166.129167 528.030027 785.344618 219.166160 649.574040 533.272390 11.759705 330.068993 367.870217 ... 18.115392 209.331106 783.926409 651.071969 416.689917 420.389221 650.351296 522.318506 998.744176 306.075445
Iquitos 697.440583 907.915279 660.593444 1263.203053 846.214693 1191.850135 624.617462 1013.991516 812.298576 639.669705 ... 1014.276875 950.690337 1423.713145 507.778441 1090.909225 613.602703 508.238507 993.985198 1473.359772 783.420481
Chancay 508.050675 125.635200 487.562779 813.407566 217.448805 678.053401 493.309225 52.531291 289.298057 341.594572 ... 58.127177 228.005272 818.920359 612.313301 445.939409 384.636329 611.590454 542.164425 1029.517023 265.582153
Puerto Supe 410.696616 25.880274 394.263357 902.535095 266.273145 768.275668 403.947184 154.189602 189.865474 315.283875 ... 160.319773 310.080441 917.882565 529.232180 538.474248 319.520289 528.502812 619.075594 1122.451951 174.594137
Salaverry 129.713336 291.131914 150.770271 1175.526444 509.132425 1046.345560 184.980610 468.813039 133.389813 394.308393 ... 474.798578 592.148482 1217.184609 313.481300 826.920920 272.434122 312.854990 871.467609 1403.028498 180.076557

5 rows × 182 columns

3. 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 [373]:
MatrizDistance.mean(axis=1).sort_values(ascending=True)
Out[373]:
name
Chancay                              448.143117
Bahia De Ancon                       449.625592
La Pampilla Oil Terminal             452.819991
Conchan Oil Terminal                 455.602180
Puerto De Huacho                     455.714610
Puerto Supe                          457.084584
Puerto Del Callao                    457.596527
Punta Lobitos (Bahia De Huarmey)     476.164537
Puerto De Chimbote                   520.391670
Salaverry                            579.275404
Bahia San Nicolas                    605.486088
Pimental                             727.716116
Bahia De Matarani                    786.740363
Puerto Ilo                           874.688745
Puerto Bayovar                       882.304410
Iquitos                              909.292370
Paita                                940.145080
Talara                               990.956609
Caleta Lobitos                      1001.373271
Puerto Cabo Blanco                  1013.599111
dtype: float64
In [374]:
RawMD = MatrizDistance.loc[['Puerto Supe']]
RawMD
Out[374]:
Latitude -7.164819 -10.566427 -7.249858 -14.721034 -11.063388 -14.314151 -7.151624 -12.030624 -9.139774 -9.036817 ... -12.060909 -12.063551 -16.387885 -6.055780 -13.532035 -8.189700 -6.062290 -12.621540 -16.247167 -9.224351
name
Puerto Supe 410.696616 25.880274 394.263357 902.535095 266.273145 768.275668 403.947184 154.189602 189.865474 315.283875 ... 160.319773 310.080441 917.882565 529.23218 538.474248 319.520289 528.502812 619.075594 1122.451951 174.594137

1 rows × 182 columns

In [375]:
SomeStats=pd.DataFrame()
SomeStats['min']=RawMD.min(axis=1)
SomeStats['max']=RawMD.max(axis=1)

# see some
SomeStats
Out[375]:
min max
name
Puerto Supe 25.880274 1143.444191
In [376]:
Dmin = SomeStats.iloc[0,0]
Dmin
Out[376]:
25.880274006481745
In [377]:
RawMD.columns[RawMD.eq(Dmin).any()]
Out[377]:
Index([-10.5664266712342], dtype='float64', name='Latitude')
In [378]:
hospmin = (RawMD == Dmin).idxmax(axis=1)[0]
hospmin
Out[378]:
-10.5664266712342
In [379]:
pointHosp = hospName.loc[[hospmin]].reset_index()
pointHosp
Out[379]:
Latitude name tipo Longitude geometry
0 -10.566427 Puesto de Salud Tunan hospital -77.75912 POINT (522280.907 920940.930)
In [380]:
pointSea = seaports_pe_24891[seaports_pe_24891['name'].str.contains('Puerto Supe', case=False)]
pointSea
Out[380]:
name Country Code Latitude Longitude geometry
3 Puerto Supe Peru -10.8 -77.75 POINT (523049.750 895072.078)
In [381]:
# Eligiré Puerto Supe,la distancia minima es 25.880274
base=peru.boundary.plot()
pointHosp.plot(ax=base,alpha=0.8,markersize=55)
pointSea.plot(ax=base,alpha=0.8,markersize=55)

base.set_xlim([0.45*10**6,0.65*10**6])  
base.set_ylim([0.7*10**6,1*10**6]) 
plt.show()
In [382]:
base=peru.boundary.explore()
pointHosp.explore(m=base,color='green',marker_kwds=dict(radius=5))
pointSea.explore(m=base,color='blue',marker_kwds=dict(radius=5))
Out[382]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Exercise 2¶

1. Use a map of points and a map of lines.¶

In [383]:
rivers
Out[383]:
NAME SYSTEM geometry
0 Purus Amazon LINESTRING (1131054.221 886360.832, 1176563.29...
1 Amazon Amazon MULTILINESTRING ((1354163.098 1665135.135, 135...
2 Putamayo Amazon MULTILINESTRING ((802419.846 2076530.727, 8388...
3 Ucayali Amazon LINESTRING (1172614.703 369928.966, 1206823.39...
4 Rio Maranon Amazon LINESTRING (661246.427 945690.747, 659626.587 ...
In [384]:
rivers[rivers.NAME.str.contains('Amazon')]
Out[384]:
NAME SYSTEM geometry
1 Amazon Amazon MULTILINESTRING ((1354163.098 1665135.135, 135...

2. Compute the distance matrix for both maps.¶

In [385]:
#Distancia del rio Amazonas a cada puerto maritimo
rivers[rivers.NAME.str.contains('Amazon')].iloc[0].geometry.distance(seaPortName.geometry)/1000
Out[385]:
name
La Pampilla Oil Terminal             938.685854
Iquitos                               25.999630
Chancay                              910.333608
Puerto Supe                          865.348486
Salaverry                            761.349308
Conchan Oil Terminal                 955.494755
Bahia San Nicolas                   1226.257348
Punta Lobitos (Bahia De Huarmey)     831.937014
Puerto De Chimbote                   786.604283
Pimental                             785.261583
Bahia De Matarani                   1412.619401
Bahia De Ancon                       923.148166
Caleta Lobitos                       888.469431
Puerto Ilo                          1493.779814
Puerto Cabo Blanco                   883.161887
Puerto De Huacho                     886.381690
Puerto Del Callao                    951.045501
Talara                               888.613747
Puerto Bayovar                       876.296463
Paita                                872.906597
Name: geometry, dtype: float64
In [386]:
MatrizDistanceRiver=rivers.set_index('NAME').geometry.apply\
(lambda g: seaPortName.geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)
MatrizDistanceRiver
Out[386]:
name Bahia De Ancon Bahia De Matarani Bahia San Nicolas Caleta Lobitos Chancay Conchan Oil Terminal Iquitos La Pampilla Oil Terminal Paita Pimental Puerto Bayovar Puerto Cabo Blanco Puerto De Chimbote Puerto De Huacho Puerto Del Callao Puerto Ilo Puerto Supe Punta Lobitos (Bahia De Huarmey) Salaverry Talara
NAME
Amazon 923.148166 1412.619401 1226.257348 888.469431 910.333608 955.494755 25.999630 938.685854 872.906597 785.261583 876.296463 883.161887 786.604283 886.381690 951.045501 1493.779814 865.348486 831.937014 761.349308 888.613747
Purus 555.709161 691.852305 596.999947 1224.213424 559.762917 546.899461 726.298010 554.534901 1169.300835 957.774488 1119.420187 1232.768357 729.536529 594.334959 559.332320 763.999198 608.066874 658.746755 797.701361 1215.682765
Putamayo 1146.814623 1619.645834 1451.011736 820.666285 1133.409318 1180.427332 156.870694 1162.853518 849.061777 904.250018 899.463133 803.425271 987.084802 1106.522808 1175.347781 1693.782465 1083.624801 1044.001778 947.120669 829.327022
Rio Maranon 174.541279 881.607516 562.959188 335.726038 161.702174 210.980897 72.202547 190.988341 293.570363 191.834800 275.920587 341.164953 173.915096 150.747760 203.703765 985.681183 147.175307 182.885655 171.609286 329.527196
Ucayali 347.376265 186.921893 319.062679 722.557738 351.923915 339.159054 72.202547 346.120585 689.196274 535.528437 672.352845 723.366590 445.476261 389.798011 351.086943 254.030877 406.889982 446.376363 446.679764 718.827705
In [387]:
AmazonDistance = MatrizDistanceRiver.loc[['Amazon']]
AmazonDistance
Out[387]:
name Bahia De Ancon Bahia De Matarani Bahia San Nicolas Caleta Lobitos Chancay Conchan Oil Terminal Iquitos La Pampilla Oil Terminal Paita Pimental Puerto Bayovar Puerto Cabo Blanco Puerto De Chimbote Puerto De Huacho Puerto Del Callao Puerto Ilo Puerto Supe Punta Lobitos (Bahia De Huarmey) Salaverry Talara
NAME
Amazon 923.148166 1412.619401 1226.257348 888.469431 910.333608 955.494755 25.99963 938.685854 872.906597 785.261583 876.296463 883.161887 786.604283 886.38169 951.045501 1493.779814 865.348486 831.937014 761.349308 888.613747
In [388]:
AmazonD=pd.DataFrame()
AmazonD['min']=AmazonDistance.min(axis=1)
AmazonD['max']=AmazonDistance.max(axis=1)

# see some
AmazonD
Out[388]:
min max
NAME
Amazon 25.99963 1493.779814

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

In [389]:
dmin = AmazonD.iloc[0,0]
dmax = AmazonD.iloc[0,1]
In [390]:
rivMin = (AmazonDistance == dmin).idxmax(axis=1)[0]
In [391]:
rivMax = (AmazonDistance == dmax).idxmax(axis=1)[0]
In [392]:
pointRivMin  = seaPortName.loc[[rivMin]]
pointRivMin
Out[392]:
Country Code Latitude Longitude geometry
name
Iquitos Peru -3.75 -73.233333 POINT (1031485.913 1672619.329)
In [393]:
pointRivMax  = seaPortName.loc[[rivMax]]
pointRivMax
Out[393]:
Country Code Latitude Longitude geometry
name
Puerto Ilo Peru -17.633333 -71.366667 POINT (1194737.080 116863.191)
In [394]:
base=rivers[rivers.NAME.str.contains('Amazon')].explore()
#el puerto mas cercano al Amazonas
pointRivMax.explore(m=base,color='red',marker_kwds=dict(radius=10))
# el puerto mas lejano al Amazonas
pointRivMin.explore(m=base,color='red',marker_kwds=dict(radius=10))
Out[394]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Exercise 3¶

In [395]:
rivers[~rivers.SYSTEM.isna()]
Out[395]:
NAME SYSTEM geometry
0 Purus Amazon LINESTRING (1131054.221 886360.832, 1176563.29...
1 Amazon Amazon MULTILINESTRING ((1354163.098 1665135.135, 135...
2 Putamayo Amazon MULTILINESTRING ((802419.846 2076530.727, 8388...
3 Ucayali Amazon LINESTRING (1172614.703 369928.966, 1206823.39...
4 Rio Maranon Amazon LINESTRING (661246.427 945690.747, 659626.587 ...
In [396]:
system=rivers.dissolve(by='SYSTEM')
system
Out[396]:
geometry NAME
SYSTEM
Amazon MULTILINESTRING ((1131054.221 886360.832, 1176... Purus
In [397]:
system.reset_index(drop=False,inplace=True)
system.drop(columns='NAME',inplace=True)

system
Out[397]:
SYSTEM geometry
0 Amazon MULTILINESTRING ((1131054.221 886360.832, 1176...
In [398]:
SystemMatrix = system.set_index('SYSTEM').geometry.apply\
(lambda g: seaPortName.geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)

SystemMatrix
Out[398]:
name Bahia De Ancon Bahia De Matarani Bahia San Nicolas Caleta Lobitos Chancay Conchan Oil Terminal Iquitos La Pampilla Oil Terminal Paita Pimental Puerto Bayovar Puerto Cabo Blanco Puerto De Chimbote Puerto De Huacho Puerto Del Callao Puerto Ilo Puerto Supe Punta Lobitos (Bahia De Huarmey) Salaverry Talara
SYSTEM
Amazon 174.541279 186.921893 319.062679 335.726038 161.702174 210.980897 25.99963 190.988341 293.570363 191.8348 275.920587 341.164953 173.915096 150.74776 203.703765 254.030877 147.175307 182.885655 171.609286 329.527196
In [399]:
PortSystem = seaports_pe_24891.dissolve()
PortSystem
Out[399]:
geometry name Country Code Latitude Longitude
0 MULTIPOINT (135318.377 1598546.647, 135334.239... La Pampilla Oil Terminal Peru -11.933333 -77.133333

1. Create a set of points and a set of polygons¶

In [400]:
system.convex_hull
Out[400]:
0    POLYGON ((1172614.703 369928.966, 661246.427 9...
dtype: geometry
In [401]:
system.convex_hull.plot()
Out[401]:
<Axes: >
In [402]:
PortSystem.convex_hull
Out[402]:
0    POLYGON ((1194737.080 116863.191, 788623.266 3...
dtype: geometry
In [403]:
PortSystem.convex_hull.plot()
Out[403]:
<Axes: >

2. Compute the distance matrix for both sets.¶

In [404]:
systems_hulls=system.convex_hull.to_frame()
systems_hulls['system']=['Amazon']
systems_hulls.rename(columns={0:'geometry'},inplace=True)
systems_hulls=systems_hulls.set_geometry('geometry')
systems_hulls.crs="EPSG:24891"
systems_hulls
Out[404]:
geometry system
0 POLYGON ((1172614.703 369928.966, 661246.427 9... Amazon
In [405]:
DistancePol =systems_hulls.set_index('system').geometry.apply\
(lambda g: seaPortName.geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)

DistancePol
Out[405]:
name Bahia De Ancon Bahia De Matarani Bahia San Nicolas Caleta Lobitos Chancay Conchan Oil Terminal Iquitos La Pampilla Oil Terminal Paita Pimental Puerto Bayovar Puerto Cabo Blanco Puerto De Chimbote Puerto De Huacho Puerto Del Callao Puerto Ilo Puerto Supe Punta Lobitos (Bahia De Huarmey) Salaverry Talara
system
Amazon 161.733929 186.921893 268.906036 335.213423 154.700335 178.024956 0.0 171.366412 293.570363 186.495242 275.920587 338.682657 155.388624 150.74776 181.41114 254.030877 146.857156 156.506963 153.764797 329.489885

3. Select one polygon of the distance matrix, and plot the closests and the farthest point to that polygon.¶

In [406]:
mins=DistancePol.idxmin(axis="columns")
mins
Out[406]:
system
Amazon    Iquitos
dtype: object
In [407]:
maxs=DistancePol.idxmax(axis="columns")
maxs
Out[407]:
system
Amazon    Puerto Cabo Blanco
dtype: object
In [408]:
base=systems_hulls.explore(color='yellow')
rivers.explore(m=base)
seaports_pe_24891[seaports_pe_24891.name.isin(mins)].explore(m=base,color='red',marker_kwds=dict(radius=10))
seaports_pe_24891[seaports_pe_24891.name.isin(maxs)].explore(m=base,color='blue',marker_kwds=dict(radius=10))
Out[408]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Exercise 4¶

1. Create a set of points and a set of lines¶

In [409]:
MatrizRiverHosp=rivers.set_index('NAME').geometry.apply\
(lambda g: hospName.geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)
MatrizRiverHosp
Out[409]:
Latitude -18.017590 -16.435019 -16.432789 -16.427053 -16.414922 -16.405231 -16.401872 -16.400836 -16.395251 -16.395170 ... -5.754284 -5.713447 -5.316950 -5.083226 -4.650864 -4.600688 -3.774880 -3.750730 -3.745417 -3.475285
NAME
Amazon 1560.135049 1358.458726 1358.313858 1358.162054 1356.341802 1355.259696 1355.234525 1355.103074 1354.165240 1354.283360 ... 591.939828 629.594560 758.332705 873.025216 368.156442 509.531152 32.031674 28.070593 26.913785 364.567238
Purus 824.298307 628.975497 628.760817 628.291028 626.764729 625.677748 625.424161 625.302246 624.565276 624.600240 ... 886.307128 920.183217 1057.437516 1169.407561 836.445311 929.000133 725.987557 727.062631 727.185928 937.656180
Putamayo 1735.115736 1558.800166 1558.585263 1558.109287 1556.589092 1555.502121 1555.244730 1555.123084 1554.389562 1554.423562 ... 715.213495 731.740203 781.529593 849.145876 480.623080 572.710184 161.585903 157.751673 156.799257 392.571622
Rio Maranon 1093.063962 868.084336 868.291376 869.682771 866.754491 865.872554 866.940396 866.784611 865.042842 865.536194 ... 9.679506 27.633293 173.081491 293.689248 14.688167 0.325360 69.096702 71.944730 72.615585 136.933000
Ucayali 333.005225 118.660214 118.496015 118.298231 116.516087 115.431774 115.372998 115.242169 114.332314 114.435367 ... 384.947355 425.107659 568.585182 689.316500 241.799511 361.531300 69.096702 71.944730 72.615585 332.485142

5 rows × 182 columns

In [410]:
MatrizRiverHosp
Out[410]:
Latitude -18.017590 -16.435019 -16.432789 -16.427053 -16.414922 -16.405231 -16.401872 -16.400836 -16.395251 -16.395170 ... -5.754284 -5.713447 -5.316950 -5.083226 -4.650864 -4.600688 -3.774880 -3.750730 -3.745417 -3.475285
NAME
Amazon 1560.135049 1358.458726 1358.313858 1358.162054 1356.341802 1355.259696 1355.234525 1355.103074 1354.165240 1354.283360 ... 591.939828 629.594560 758.332705 873.025216 368.156442 509.531152 32.031674 28.070593 26.913785 364.567238
Purus 824.298307 628.975497 628.760817 628.291028 626.764729 625.677748 625.424161 625.302246 624.565276 624.600240 ... 886.307128 920.183217 1057.437516 1169.407561 836.445311 929.000133 725.987557 727.062631 727.185928 937.656180
Putamayo 1735.115736 1558.800166 1558.585263 1558.109287 1556.589092 1555.502121 1555.244730 1555.123084 1554.389562 1554.423562 ... 715.213495 731.740203 781.529593 849.145876 480.623080 572.710184 161.585903 157.751673 156.799257 392.571622
Rio Maranon 1093.063962 868.084336 868.291376 869.682771 866.754491 865.872554 866.940396 866.784611 865.042842 865.536194 ... 9.679506 27.633293 173.081491 293.689248 14.688167 0.325360 69.096702 71.944730 72.615585 136.933000
Ucayali 333.005225 118.660214 118.496015 118.298231 116.516087 115.431774 115.372998 115.242169 114.332314 114.435367 ... 384.947355 425.107659 568.585182 689.316500 241.799511 361.531300 69.096702 71.944730 72.615585 332.485142

5 rows × 182 columns

In [411]:
minMts1=MatrizRiverHosp.loc['Ucayali'].min()*1000
In [412]:
minMts2=MatrizRiverHosp.loc['Amazon'].min()*1000

2. Get the buffer for the lines, select different values for the distance.¶

In [413]:
bufferAroundAmazon=rivers[rivers.NAME=='Amazon'].buffer(distance = minMts2)
bufferAsBaseAmazon=bufferAroundAmazon.explore(color='skyblue')
rivers[rivers.NAME=='Amazon'].explore(m=bufferAsBaseAmazon,color='blue',style_kwds={'weight':0.5})
hospital_24891.explore(m=bufferAsBaseAmazon,color='black')
Out[413]:
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In [414]:
clinic = healthsites[healthsites.tipo=="clinic"]
clinic = clinic.dropna()
clinic.reset_index(drop=True, inplace=True)
clinic.head()
Out[414]:
name tipo Latitude Longitude geometry
0 C.S. Humay clinic -13.723504 -75.890124 POINT (721265.335 568293.953)
1 Bellavista clinic -8.392582 -74.536877 POINT (880039.288 1157649.393)
2 Psicóloga clinic -12.140289 -76.962673 POINT (607436.252 745685.071)
3 House Bienestor y Salud clinic -11.930253 -76.690799 POINT (637403.079 768563.384)
4 Buenos Aires clinic -11.915073 -76.673163 POINT (639351.074 770219.269)
In [415]:
bufferAroundUcayali=rivers[rivers.NAME=='Ucayali'].buffer(distance = minMts1*2)
bufferAsBaseUcayali=bufferAroundUcayali.explore(color='red')
rivers[rivers.NAME=='Ucayali'].explore(m=bufferAsBaseUcayali,color='blue',style_kwds={'weight':0.5})
clinic.explore(m=bufferAsBaseUcayali,color='black')
Out[415]:
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3. Keep the points that are within the buffer (as in point 2, you need to play with differn distances until you show something interesting.¶

In [416]:
healthsites.head()
Out[416]:
name tipo Latitude Longitude geometry
0 Patacancha doctors -11.895442 -75.506386 POINT (766825.252 770369.321)
1 Ayancocha Alta doctors -10.247465 -76.125374 POINT (701875.869 954271.274)
2 Kamanquiriato doctors -12.525074 -73.034027 POINT (1035681.433 694088.414)
3 C.S. Humay clinic -13.723504 -75.890124 POINT (721265.335 568293.953)
4 Farmacia Mafer pharmacy -12.072449 -75.215294 POINT (798273.742 750124.695)
In [417]:
riversWithinBufferUcayali=clinic.clip(mask=bufferAroundUcayali)
riversWithinBufferUcayali.head()
Out[417]:
name tipo Latitude Longitude geometry
805 Espinar - Essalud clinic -14.792545 -71.412932 POINT (1203962.071 434805.652)
1032 Yauri clinic -14.790478 -71.404141 POINT (1204928.248 434997.733)
56 El Descanso clinic -14.533539 -71.306776 POINT (1216703.848 463310.984)
620 Techo Obrero clinic -14.288580 -71.224611 POINT (1226774.571 490360.500)
1175 Pampaphalla clinic -14.253414 -71.222321 POINT (1227182.993 494286.131)
In [418]:
bufferAsBaseUcayali=bufferAroundUcayali.explore(color='red')
rivers[rivers.NAME=='Ucayali'].explore(m=bufferAsBaseUcayali,color='blue',style_kwds={'weight':0.5})
riversWithinBufferUcayali.explore(m=bufferAsBaseUcayali,color='black')
Out[418]:
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In [419]:
riversWithinBufferAmazon=clinic.clip(mask=bufferAroundAmazon)
riversWithinBufferAmazon.head()
Out[419]:
name tipo Latitude Longitude geometry
1467 Yanashi clinic -3.587722 -72.255566 POINT (1141251.383 1689772.555)
549 Centro de salud Pevas clinic -3.319647 -71.860558 POINT (1185885.117 1719339.312)
1356 San Pablo de Ramon Castilla clinic -4.019810 -71.104614 POINT (1270104.114 1640096.809)
410 Centro de Atencion Primaria Ii Caballococha clinic -3.908630 -70.516872 POINT (1336454.296 1651785.629)
194 Clinica Naval de Iquitos clinic -3.731837 -73.241211 POINT (1030620.667 1674651.022)
In [420]:
bufferAsBaseAmazon=bufferAroundAmazon.explore(color='red')
rivers[rivers.NAME=='Amazon'].explore(m=bufferAsBaseAmazon,color='blue',style_kwds={'weight':0.5})
riversWithinBufferAmazon.explore(m=bufferAsBaseAmazon,color='black')
Out[420]:
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In [421]:
riversAll_buf=rivers.buffer(distance = minMts1*3)
allRiversWithinBuffs=clinic.clip(riversAll_buf)

base=riversAll_buf.explore(color='green')
allRiversWithinBuffs.explore(m=base, color='blue')
Out[421]:
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In [ ]: