GeoPandas: paquete para agregar capacidades geoespaciales a pandas¶
GeoPandas es un proyecto de software libre que extiende los tipos de datos de pandas para incorporar objetos geométricos (puntos, líneas, polígonos, etc). GeoPandas se apoya en las bibliotecas Shapely para realizar las operaciones geométricas, Fiona para acceder a los datos (ej. en archivos) y Matplotlib para graficación.
El objetivo de GeoPandas es facilitar el trabajo con datos geoespaciales en el lenguaje Python, lo que se logra a través de la implementación de nuevas estructuras. Las dos estructuras principales de GeoPandas son:
GeoSeries: es un vector en el que cada elemento es un conjunto de una o varias geometrías correspondientes a una observación. Por ejemplo, el polígono (o multipolígono) que representa una provincia.
GeoDataFrame: es una estructura tabular (i.e. con filas y columnas) de datos geométricos y no geométricos (ej. textos, números). El conjunto de geometrías se implementa a través de GeoSeries.
Instalación¶
En una estación de trabajo¶
geopandas puede instalarse tanto mediante Conda como mediante pip.
# Instalación mediante Conda
conda install geopandas
# Instalación mediante pip
pip install geopandas
En Google Colab¶
# Instalación de bibliotecas de GDAL para Python
!apt install gdal-bin python-gdal python3-gdal
# Instalación de r-tree
!apt install python3-rtree
# Instalación de GeoPandas
!pip install git+git://github.com/geopandas/geopandas.git
# Instalación de Descartes
!pip install descartes
Carga¶
Para instalar contextily:
!conda install -c conda-forge contextily -y
import os
import requests
import zipfile
import pandas as pd
import geopandas as gpd
import contextily as cx
from geojson import dump
from owslib.wfs import WebFeatureService
import matplotlib
import matplotlib.pyplot as plt
# Versión de geopandas
gpd.__version__
'0.10.2'
Operaciones básicas¶
read_file() - carga de datos¶
# Lectura de datos de países de Natural Earth
paises = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
paises
| pop_est | continent | name | iso_a3 | gdp_md_est | geometry | |
|---|---|---|---|---|---|---|
| 0 | 920938 | Oceania | Fiji | FJI | 8374.0 | MULTIPOLYGON (((180.00000 -16.06713, 180.00000... |
| 1 | 53950935 | Africa | Tanzania | TZA | 150600.0 | POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... |
| 2 | 603253 | Africa | W. Sahara | ESH | 906.5 | POLYGON ((-8.66559 27.65643, -8.66512 27.58948... |
| 3 | 35623680 | North America | Canada | CAN | 1674000.0 | MULTIPOLYGON (((-122.84000 49.00000, -122.9742... |
| 4 | 326625791 | North America | United States of America | USA | 18560000.0 | MULTIPOLYGON (((-122.84000 49.00000, -120.0000... |
| ... | ... | ... | ... | ... | ... | ... |
| 172 | 7111024 | Europe | Serbia | SRB | 101800.0 | POLYGON ((18.82982 45.90887, 18.82984 45.90888... |
| 173 | 642550 | Europe | Montenegro | MNE | 10610.0 | POLYGON ((20.07070 42.58863, 19.80161 42.50009... |
| 174 | 1895250 | Europe | Kosovo | -99 | 18490.0 | POLYGON ((20.59025 41.85541, 20.52295 42.21787... |
| 175 | 1218208 | North America | Trinidad and Tobago | TTO | 43570.0 | POLYGON ((-61.68000 10.76000, -61.10500 10.890... |
| 176 | 13026129 | Africa | S. Sudan | SSD | 20880.0 | POLYGON ((30.83385 3.50917, 29.95350 4.17370, ... |
177 rows × 6 columns
info() - información general sobre un conjunto de datos¶
paises.info()
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 177 entries, 0 to 176
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 pop_est 177 non-null int64
1 continent 177 non-null object
2 name 177 non-null object
3 iso_a3 177 non-null object
4 gdp_md_est 177 non-null float64
5 geometry 177 non-null geometry
dtypes: float64(1), geometry(1), int64(1), object(3)
memory usage: 8.4+ KB
head(), tail(), sample() - despliegue de filas de un conjunto de datos¶
# Muestra de 10 filas
paises.head(10)
| pop_est | continent | name | iso_a3 | gdp_md_est | geometry | |
|---|---|---|---|---|---|---|
| 0 | 920938 | Oceania | Fiji | FJI | 8374.0 | MULTIPOLYGON (((180.00000 -16.06713, 180.00000... |
| 1 | 53950935 | Africa | Tanzania | TZA | 150600.0 | POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... |
| 2 | 603253 | Africa | W. Sahara | ESH | 906.5 | POLYGON ((-8.66559 27.65643, -8.66512 27.58948... |
| 3 | 35623680 | North America | Canada | CAN | 1674000.0 | MULTIPOLYGON (((-122.84000 49.00000, -122.9742... |
| 4 | 326625791 | North America | United States of America | USA | 18560000.0 | MULTIPOLYGON (((-122.84000 49.00000, -120.0000... |
| 5 | 18556698 | Asia | Kazakhstan | KAZ | 460700.0 | POLYGON ((87.35997 49.21498, 86.59878 48.54918... |
| 6 | 29748859 | Asia | Uzbekistan | UZB | 202300.0 | POLYGON ((55.96819 41.30864, 55.92892 44.99586... |
| 7 | 6909701 | Oceania | Papua New Guinea | PNG | 28020.0 | MULTIPOLYGON (((141.00021 -2.60015, 142.73525 ... |
| 8 | 260580739 | Asia | Indonesia | IDN | 3028000.0 | MULTIPOLYGON (((141.00021 -2.60015, 141.01706 ... |
| 9 | 44293293 | South America | Argentina | ARG | 879400.0 | MULTIPOLYGON (((-68.63401 -52.63637, -68.25000... |
Selección de columnas¶
paises[["name", "pop_est"]]
| name | pop_est | |
|---|---|---|
| 0 | Fiji | 920938 |
| 1 | Tanzania | 53950935 |
| 2 | W. Sahara | 603253 |
| 3 | Canada | 35623680 |
| 4 | United States of America | 326625791 |
| ... | ... | ... |
| 172 | Serbia | 7111024 |
| 173 | Montenegro | 642550 |
| 174 | Kosovo | 1895250 |
| 175 | Trinidad and Tobago | 1218208 |
| 176 | S. Sudan | 13026129 |
177 rows × 2 columns
Selección de filas¶
# Países con población estimada mayor o igual a mil millones
paises[paises["pop_est"] >= 1000000000]
| pop_est | continent | name | iso_a3 | gdp_md_est | geometry | |
|---|---|---|---|---|---|---|
| 98 | 1281935911 | Asia | India | IND | 8721000.0 | POLYGON ((97.32711 28.26158, 97.40256 27.88254... |
| 139 | 1379302771 | Asia | China | CHN | 21140000.0 | MULTIPOLYGON (((109.47521 18.19770, 108.65521 ... |
Selección de filas y columnas en la misma expresión¶
paises.loc[paises["pop_est"] >= 1000000000, ["name", "pop_est"]]
| name | pop_est | |
|---|---|---|
| 98 | India | 1281935911 |
| 139 | China | 1379302771 |
plot() - mapeo¶
# Mapa de coropletas
paises.plot(column = "pop_est",
cmap='OrRd',
scheme='quantiles',
figsize=(20, 20)
)
<AxesSubplot:>
# Mapa de coropletas con leyenda
from mpl_toolkits.axes_grid1 import make_axes_locatable
fig, ax = plt.subplots(1, 1, figsize=(20, 20))
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
paises.plot(column='pop_est', ax=ax, legend=True, cax=cax)
<AxesSubplot:>
# Mapa con múltiples capas
ciudades = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
base = paises.plot(color='white', edgecolor='black', figsize=(20, 20))
ciudades.plot(ax=base, marker='o', color='red', markersize=8)
<AxesSubplot:>
Ejemplos de uso¶
Análisis de distribución de especies de murciélagos en Costa Rica¶
1. Obtención de datos¶
Registros de presencia de murciélagos¶
# Descarga de archivo CSV comprimido en ZIP mediante solicitud tipo GET
response = requests.get('https://api.gbif.org/v1/occurrence/download/request/0105729-210914110416597.zip',
allow_redirects=True)
open('datos/murcielagos.zip', 'wb').write(response.content)
# Descompresión
with zipfile.ZipFile("datos/murcielagos.zip") as zipfile:
zipfile.extractall("datos/")
# Cambio de nombre del archivo CSV
os.rename("datos/0105729-210914110416597.csv", "datos/murcielagos.csv")
# Carga de registros de presencia de murciélagos en un dataframe
murcielagos = pd.read_csv("datos/murcielagos.csv", sep="\t")
murcielagos
| gbifID | datasetKey | occurrenceID | kingdom | phylum | class | order | family | genus | species | ... | identifiedBy | dateIdentified | license | rightsHolder | recordedBy | typeStatus | establishmentMeans | lastInterpreted | mediaType | issue | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3416142999 | 50c9509d-22c7-4a22-a47d-8c48425ef4a7 | https://www.inaturalist.org/observations/10118... | Animalia | Chordata | Mammalia | Chiroptera | Vespertilionidae | Rhogeessa | Rhogeessa io | ... | Yuzefovich Alexander | 2021-11-15T14:33:49 | CC_BY_NC_4_0 | jo22nfrog | jo22nfrog | NaN | NaN | 2021-12-08T05:34:32.772Z | StillImage | COORDINATE_ROUNDED |
| 1 | 3416092578 | 50c9509d-22c7-4a22-a47d-8c48425ef4a7 | https://www.inaturalist.org/observations/10131... | Animalia | Chordata | Mammalia | Chiroptera | Phyllostomidae | Uroderma | Uroderma bilobatum | ... | Merav Vonshak | 2021-11-17T06:37:32 | CC_BY_NC_4_0 | Merav Vonshak | Merav Vonshak | NaN | NaN | 2021-12-08T06:09:54.326Z | StillImage | COORDINATE_ROUNDED |
| 2 | 3415771696 | 50c9509d-22c7-4a22-a47d-8c48425ef4a7 | https://www.inaturalist.org/observations/88766024 | Animalia | Chordata | Mammalia | Chiroptera | Phyllostomidae | Artibeus | Artibeus jamaicensis | ... | Yuzefovich Alexander | 2021-07-27T06:38:03 | CC_BY_NC_4_0 | Alex Castelein | Alex Castelein | NaN | NaN | 2021-12-08T05:05:13.398Z | StillImage | COORDINATE_ROUNDED |
| 3 | 3415765386 | 50c9509d-22c7-4a22-a47d-8c48425ef4a7 | https://www.inaturalist.org/observations/36722404 | Animalia | Chordata | Mammalia | Chiroptera | Phyllostomidae | Uroderma | Uroderma bilobatum | ... | Jay | 2019-12-17T22:12:20 | CC_BY_NC_4_0 | Jay | Jay | NaN | NaN | 2021-12-08T04:59:02.428Z | StillImage | COORDINATE_ROUNDED |
| 4 | 3415753400 | 50c9509d-22c7-4a22-a47d-8c48425ef4a7 | https://www.inaturalist.org/observations/39440952 | Animalia | Chordata | Mammalia | Chiroptera | Emballonuridae | Rhynchonycteris | Rhynchonycteris naso | ... | Jakob Fahr | 2020-03-02T08:06:42 | CC_BY_NC_4_0 | Ben Zerante | Ben Zerante | NaN | NaN | 2021-12-08T05:48:47.800Z | StillImage | COORDINATE_ROUNDED |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 13184 | 45849434 | 847e2306-f762-11e1-a439-00145eb45e9a | urn:catalog:LSUMZ:Mammals:12726 | Animalia | Chordata | Mammalia | Chiroptera | Phyllostomidae | Anoura | Anoura geoffroyi | ... | NaN | NaN | CC0_1_0 | NaN | Gardner, Alfred L. | NaN | NATIVE | 2021-12-02T03:54:15.435Z | NaN | INSTITUTION_COLLECTION_MISMATCH |
| 13185 | 45849433 | 847e2306-f762-11e1-a439-00145eb45e9a | urn:catalog:LSUMZ:Mammals:12725 | Animalia | Chordata | Mammalia | Chiroptera | Phyllostomidae | Anoura | Anoura geoffroyi | ... | NaN | NaN | CC0_1_0 | NaN | Gardner, Alfred L. | NaN | NATIVE | 2021-12-02T03:54:15.435Z | NaN | INSTITUTION_COLLECTION_MISMATCH |
| 13186 | 45849432 | 847e2306-f762-11e1-a439-00145eb45e9a | urn:catalog:LSUMZ:Mammals:12724 | Animalia | Chordata | Mammalia | Chiroptera | Phyllostomidae | Anoura | Anoura geoffroyi | ... | NaN | NaN | CC0_1_0 | NaN | Gardner, Alfred L. | NaN | NATIVE | 2021-12-02T03:54:15.437Z | NaN | INSTITUTION_COLLECTION_MISMATCH |
| 13187 | 45849431 | 847e2306-f762-11e1-a439-00145eb45e9a | urn:catalog:LSUMZ:Mammals:12723 | Animalia | Chordata | Mammalia | Chiroptera | Phyllostomidae | Anoura | Anoura geoffroyi | ... | NaN | NaN | CC0_1_0 | NaN | Gardner, Alfred L. | NaN | NATIVE | 2021-12-02T03:54:15.436Z | NaN | INSTITUTION_COLLECTION_MISMATCH |
| 13188 | 45849415 | 847e2306-f762-11e1-a439-00145eb45e9a | urn:catalog:LSUMZ:Mammals:10637 | Animalia | Chordata | Mammalia | Chiroptera | Phyllostomidae | Anoura | Anoura geoffroyi | ... | NaN | NaN | CC0_1_0 | NaN | Arnold, Keith A. | NaN | NATIVE | 2021-12-02T03:54:15.457Z | NaN | COORDINATE_REPROJECTED;INSTITUTION_COLLECTION_... |
13189 rows × 50 columns
murcielagos = gpd.GeoDataFrame(murcielagos, geometry=gpd.points_from_xy(murcielagos.decimalLongitude, murcielagos.decimalLatitude))
murcielagos.plot(figsize=(20, 20))
<AxesSubplot:>
Capas geoespaciales de Costa Rica¶
Áreas silvestres protegidas (ASP)¶
wfs = WebFeatureService(url='http://geos1pne.sirefor.go.cr/wfs', version='1.1.0')
# Parámetros de la solicitud
params = dict(service='WFS',
version='1.1.0',
request='GetFeature',
typeName='PNE:areas_silvestres_protegidas',
srsName='urn:ogc:def:crs:EPSG::4326',
outputFormat='json')
# Solicitud
response = requests.Request("GET", "http://geos1pne.sirefor.go.cr/wfs", params=params).prepare().url
response
'http://geos1pne.sirefor.go.cr/wfs?service=WFS&version=1.1.0&request=GetFeature&typeName=PNE%3Aareas_silvestres_protegidas&srsName=urn%3Aogc%3Adef%3Acrs%3AEPSG%3A%3A4326&outputFormat=json'
# Leer datos del URL
asp = gpd.read_file(response)
asp.plot(figsize=(20, 20))
<AxesSubplot:>
2. Visualización de todas las capas¶
# Mapa con todas las capas
base = asp.plot(color='white', edgecolor='green', figsize=(20, 20))
ax = murcielagos.plot(ax=base, marker='o', color='black', markersize=8)
cx.add_basemap(ax, crs=asp.crs)
3. Conteo de especies en cada polígono¶
# Join espacial de las capas de ASP y registros de presencia de murciélagos
asp_contains_murcielagos = asp.sjoin(murcielagos, how="inner", op="contains")
/home/mfvargas/miniconda3/envs/pf3311/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3457: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.
exec(code_obj, self.user_global_ns, self.user_ns)
/home/mfvargas/miniconda3/envs/pf3311/lib/python3.10/site-packages/geopandas/geodataframe.py:1938: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.
Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.
Left CRS: EPSG:4326
Right CRS: None
return geopandas.sjoin(left_df=self, right_df=df, *args, **kwargs)
asp_contains_murcielagos.info()
<class 'geopandas.geodataframe.GeoDataFrame'>
Int64Index: 2221 entries, 13 to 170
Data columns (total 67 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 2221 non-null object
1 codigo 2221 non-null object
2 nombre_asp 2221 non-null object
3 cat_manejo 2221 non-null object
4 estatus 2221 non-null object
5 siglas_cat 2221 non-null object
6 nombre_ac 2221 non-null object
7 siglas_ac 2221 non-null object
8 descripcio 2221 non-null object
9 a_creacion 2221 non-null int64
10 n_creacion 2221 non-null object
11 normativa 0 non-null object
12 area 2221 non-null float64
13 area_km 2221 non-null float64
14 version 2221 non-null object
15 geometry 2221 non-null geometry
16 index_right 2221 non-null int64
17 gbifID 2221 non-null int64
18 datasetKey 2221 non-null object
19 occurrenceID 2170 non-null object
20 kingdom 2221 non-null object
21 phylum 2221 non-null object
22 class 2221 non-null object
23 order 2221 non-null object
24 family 2208 non-null object
25 genus 2206 non-null object
26 species 2145 non-null object
27 infraspecificEpithet 327 non-null object
28 taxonRank 2221 non-null object
29 scientificName 2221 non-null object
30 verbatimScientificName 2221 non-null object
31 verbatimScientificNameAuthorship 766 non-null object
32 countryCode 2221 non-null object
33 locality 1921 non-null object
34 stateProvince 1912 non-null object
35 occurrenceStatus 2221 non-null object
36 individualCount 489 non-null float64
37 publishingOrgKey 2221 non-null object
38 decimalLatitude 2221 non-null float64
39 decimalLongitude 2221 non-null float64
40 coordinateUncertaintyInMeters 1417 non-null float64
41 coordinatePrecision 12 non-null float64
42 elevation 266 non-null float64
43 elevationAccuracy 236 non-null float64
44 depth 6 non-null float64
45 depthAccuracy 6 non-null float64
46 eventDate 2185 non-null object
47 day 2040 non-null float64
48 month 2184 non-null float64
49 year 2185 non-null float64
50 taxonKey 2221 non-null int64
51 speciesKey 2145 non-null float64
52 basisOfRecord 2221 non-null object
53 institutionCode 1913 non-null object
54 collectionCode 2055 non-null object
55 catalogNumber 2089 non-null object
56 recordNumber 953 non-null object
57 identifiedBy 600 non-null object
58 dateIdentified 556 non-null object
59 license 2221 non-null object
60 rightsHolder 659 non-null object
61 recordedBy 1998 non-null object
62 typeStatus 0 non-null object
63 establishmentMeans 968 non-null object
64 lastInterpreted 2221 non-null object
65 mediaType 343 non-null object
66 issue 2137 non-null object
dtypes: float64(15), geometry(1), int64(4), object(47)
memory usage: 1.2+ MB
asp_contains_murcielagos
| id | codigo | nombre_asp | cat_manejo | estatus | siglas_cat | nombre_ac | siglas_ac | descripcio | a_creacion | ... | identifiedBy | dateIdentified | license | rightsHolder | recordedBy | typeStatus | establishmentMeans | lastInterpreted | mediaType | issue | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 13 | areas_silvestres_protegidas.14 | H13 | Humedal lacustrino Rio Canas | Humedal | Estatal | HH | Area de Conservacion Tempisque | ACT | Area terrestre protegida | 1994 | ... | Kristina Yamamoto, Andrea Caiozzi-Cofre (MVZ) | NaN | CC_BY_NC_4_0 | NaN | MALAGA-A., A. | NaN | NaN | 2021-12-02T04:04:03.547Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;BASIS_OF_RECORD_I... |
| 13 | areas_silvestres_protegidas.14 | H13 | Humedal lacustrino Rio Canas | Humedal | Estatal | HH | Area de Conservacion Tempisque | ACT | Area terrestre protegida | 1994 | ... | Kristina Yamamoto, Andrea Caiozzi-Cofre (MVZ) | NaN | CC_BY_NC_4_0 | NaN | MALAGA-A., A. | NaN | NaN | 2021-12-02T04:04:03.549Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;BASIS_OF_RECORD_I... |
| 13 | areas_silvestres_protegidas.14 | H13 | Humedal lacustrino Rio Canas | Humedal | Estatal | HH | Area de Conservacion Tempisque | ACT | Area terrestre protegida | 1994 | ... | Kristina Yamamoto, Andrea Caiozzi-Cofre (MVZ) | NaN | CC_BY_NC_4_0 | NaN | MALAGA-A., A. | NaN | NaN | 2021-12-02T04:04:03.548Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;BASIS_OF_RECORD_I... |
| 13 | areas_silvestres_protegidas.14 | H13 | Humedal lacustrino Rio Canas | Humedal | Estatal | HH | Area de Conservacion Tempisque | ACT | Area terrestre protegida | 1994 | ... | Kristina Yamamoto, Andrea Caiozzi-Cofre (MVZ) | NaN | CC_BY_NC_4_0 | NaN | MALAGA-A., A. | NaN | NaN | 2021-12-02T04:04:03.547Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;BASIS_OF_RECORD_I... |
| 13 | areas_silvestres_protegidas.14 | H13 | Humedal lacustrino Rio Canas | Humedal | Estatal | HH | Area de Conservacion Tempisque | ACT | Area terrestre protegida | 1994 | ... | Kristina Yamamoto, Andrea Caiozzi-Cofre (MVZ) | NaN | CC_BY_NC_4_0 | NaN | MALAGA-A., A. | NaN | NaN | 2021-12-02T04:04:03.548Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;BASIS_OF_RECORD_I... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 170 | areas_silvestres_protegidas.171 | P18 | Internacional La Amistad | Parque Nacional | Estatal | PN | Area de Conservacion La Amistad Pacifico | ACLAP | Area terrestre protegida | 1982 | ... | NaN | NaN | CC_BY_4_0 | Barcode of Life Datasystems | NaN | NaN | NaN | 2021-12-01T17:18:04.335Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84 |
| 170 | areas_silvestres_protegidas.171 | P18 | Internacional La Amistad | Parque Nacional | Estatal | PN | Area de Conservacion La Amistad Pacifico | ACLAP | Area terrestre protegida | 1982 | ... | NaN | NaN | CC_BY_4_0 | Barcode of Life Datasystems | NaN | NaN | NaN | 2021-12-01T17:19:01.362Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84 |
| 170 | areas_silvestres_protegidas.171 | P18 | Internacional La Amistad | Parque Nacional | Estatal | PN | Area de Conservacion La Amistad Pacifico | ACLAP | Area terrestre protegida | 1982 | ... | NaN | NaN | CC_BY_4_0 | Barcode of Life Datasystems | NaN | NaN | NaN | 2021-12-01T17:18:58.795Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84 |
| 170 | areas_silvestres_protegidas.171 | P18 | Internacional La Amistad | Parque Nacional | Estatal | PN | Area de Conservacion La Amistad Pacifico | ACLAP | Area terrestre protegida | 1982 | ... | NaN | NaN | CC_BY_4_0 | Barcode of Life Datasystems | NaN | NaN | NaN | 2021-12-01T17:19:49.491Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84 |
| 170 | areas_silvestres_protegidas.171 | P18 | Internacional La Amistad | Parque Nacional | Estatal | PN | Area de Conservacion La Amistad Pacifico | ACLAP | Area terrestre protegida | 1982 | ... | NaN | NaN | CC_BY_4_0 | NaN | NaN | NaN | NaN | 2021-12-02T15:35:03.172Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84 |
2221 rows × 67 columns
asp_contains_murcielagos.plot(figsize=(20, 20))
<AxesSubplot:>
# Conteo de especies en cada ASP
asp_count_especies = asp_contains_murcielagos.groupby("id").species.nunique()
asp_count_especies = asp_count_especies.reset_index() # para convertir la serie a dataframe
asp_count_especies.rename(columns = {'species': 'especies_murcielagos'}, inplace = True)
asp_count_especies.head(10)
| id | especies_murcielagos | |
|---|---|---|
| 0 | areas_silvestres_protegidas.101 | 2 |
| 1 | areas_silvestres_protegidas.103 | 27 |
| 2 | areas_silvestres_protegidas.107 | 1 |
| 3 | areas_silvestres_protegidas.110 | 2 |
| 4 | areas_silvestres_protegidas.121 | 31 |
| 5 | areas_silvestres_protegidas.124 | 1 |
| 6 | areas_silvestres_protegidas.132 | 1 |
| 7 | areas_silvestres_protegidas.137 | 6 |
| 8 | areas_silvestres_protegidas.139 | 1 |
| 9 | areas_silvestres_protegidas.14 | 1 |
# Join para agregar la columna con el conteo a la capa de ASP
asp_especies = asp.join(asp_count_especies.set_index('id'), on='id', rsuffix='_b')
asp_especies
| id | codigo | nombre_asp | cat_manejo | estatus | siglas_cat | nombre_ac | siglas_ac | descripcio | a_creacion | n_creacion | normativa | area | area_km | version | geometry | especies_murcielagos | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | areas_silvestres_protegidas.1 | A01 | Montes Submarinos | Area Marina de Manejo | Estatal | AMM | Marina Cocos | ACMC | Area Marina de Manejo | 2011 | DE_36452 | None | 9.592623e+09 | 9.592623e+07 | 2019-11-19-001 | POLYGON ((-87.00000 6.13333, -86.43333 5.73333... | NaN |
| 1 | areas_silvestres_protegidas.2 | A02 | Cabo Blanco | Area Marina de Manejo | Estatal | AMM | Area de Conservacion Tempisque | ACT | Area Marina de Manejo | 1963 | Ley_10 | None | 8.247806e+08 | 8.247806e+06 | 2019-11-19-001 | POLYGON ((-85.14336 9.59649, -85.14332 9.59643... | NaN |
| 2 | areas_silvestres_protegidas.3 | B01 | Alberto Manuel Brenes | Reserva Biologica | Estatal | RB | Area de Conservacion Central | ACC | Area terrestre protegida | 1993 | Ley_7354 | None | 7.800440e+07 | 7.800440e+05 | 2019-11-19-001 | POLYGON ((-84.63388 10.18202, -84.64167 10.177... | NaN |
| 3 | areas_silvestres_protegidas.4 | B03 | Isla Guayabo | Reserva Biologica | Estatal | RB | Area de Conservacion Tempisque | ACT | Isla | 1973 | DE_2858_A | None | 6.268092e+04 | 6.268092e+02 | 2019-11-19-001 | POLYGON ((-84.87710 9.91109, -84.87737 9.91086... | NaN |
| 4 | areas_silvestres_protegidas.5 | B04 | Isla Pajaros | Reserva Biologica | Estatal | RB | Area de Conservacion Pacifico Central | ACOPAC | Isla | 1976 | DE_ 5963_A | None | 3.211583e+04 | 3.211583e+02 | 2019-11-19-001 | POLYGON ((-84.99674 10.09266, -84.99696 10.092... | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 167 | areas_silvestres_protegidas.168 | Z31 | Quebrada Rosario | Zona Protectora | Estatal | ZP | Area de Conservacion Central | ACC | Area terrestre protegida | 1997 | DE_ 26297_MINAE | None | 4.292695e+05 | 4.292695e+03 | 2019-11-19-001 | POLYGON ((-84.07619 9.70425, -84.07689 9.70374... | NaN |
| 168 | areas_silvestres_protegidas.169 | P10 | Santa Rosa | Parque Nacional | Estatal | PN | Area de Conservacion Guanacaste | ACG | Isla | 1966 | Ley_3694 | None | 3.230258e+06 | 3.230258e+04 | 2019-11-19-001 | MULTIPOLYGON (((-85.87565 10.84304, -85.87571 ... | NaN |
| 169 | areas_silvestres_protegidas.170 | V25 | Bahia Junquillal | Refugio Nacional de Vida Silvestre | Estatal | RVS | Area de Conservacion Guanacaste | ACG | Isla | 1995 | DE_ 23867_MIRENEM | None | 1.836792e+05 | 1.836792e+03 | 2021-08-20-001 | MULTIPOLYGON (((-85.73360 10.95775, -85.73329 ... | NaN |
| 170 | areas_silvestres_protegidas.171 | P18 | Internacional La Amistad | Parque Nacional | Estatal | PN | Area de Conservacion La Amistad Pacifico | ACLAP | Area terrestre protegida | 1982 | DE_13324_A | None | 1.982896e+09 | 1.982896e+07 | 2019-11-19-001 | MULTIPOLYGON (((-82.85282 9.01273, -82.85414 9... | 11.0 |
| 171 | areas_silvestres_protegidas.172 | B09 | Bicentenario de la República - Pájaro Campana | Reserva Biologica | Estatal | RB | Area de Conservacion La Amistad Pacifico | ACLAP | Area terrestre protegida | 2021 | DE_42615_MINAE | None | 5.082743e+07 | 5.082743e+05 | 2021-08-21-001 | POLYGON ((-82.77351 8.94507, -82.81050 8.95856... | NaN |
172 rows × 17 columns
# Mapeo
fig, ax = plt.subplots(1, 1, figsize=(20, 20))
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
asp_especies.plot(column='especies_murcielagos', ax=ax, legend=True, cax=cax)
cx.add_basemap(ax, crs=asp_especies.crs, source=cx.providers.Stamen.TonerLite)
