Mapping Tasiyagnunpa (Western Meadowlark) migration

Introduction to vector data operations

Tasiyagnunpa (or Western Meadowlark, or sturnella neglecta) migrates each year to next on the Great Plains in the United States. Using crowd-sourced observations of these birds, we can see that migration happening throughout the year.

Read more about the Lakota connection to Tasiyagnunpa from Native Sun News Today

Set up your reproducible workflow

Import Python libraries

We will be getting data from a source called GBIF (Global Biodiversity Information Facility). We need a package called pygbif to access the data, which is not included in your environment. Install it by running the cell below:

#for gbif (globzl diversity info facility) biology data
%%bash
pip install pygbif

  Cell In[8], line 3
    pip install pygbif
        ^
SyntaxError: invalid syntax

!pip install pygbif

Requirement already satisfied: pygbif in /opt/conda/lib/python3.11/site-packages (0.6.4)
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Your Task: Import packages

Add imports for packages that will help you:

1. Work with tabular data
2. Work with geospatial vector data
3. Make an interactive plot of tabular and/or vector data

#Copied from  Textbook unit 3 solutions tab for needed items
#needed imports for intereative maps, occurance data, spatial joins and other mapping/ spatial data stuffz
import calendar
import os
import pathlib
import requests
import time
import zipfile
from getpass import getpass
from glob import glob

import cartopy.crs as ccrs
import geopandas as gpd
import hvplot.pandas
import pandas as pd
import panel as pn
import pygbif.occurrences as occ
import pygbif.species as species

Create a folder for your data

For this challenge, you will need to save some data to your computer. We suggest saving to somewhere in your home folder (e.g. /home/username), rather than to your GitHub repository, since data files can easily become too large for GitHub.

Warning

The home directory is different for every user! Your home directory probably won’t exist on someone else’s computer. Make sure to use code like pathlib.Path.home() to compute the home directory on the computer the code is running on. This is key to writing reproducible and interoperable code.

Your Task: Create a project folder

The code below will help you get started with making a project directory

1. Replace 'your-project-directory-name-here' and 'your-gbif-data-directory-name-here' with descriptive names
2. Run the cell
3. (OPTIONAL) Check in the terminal that you created the directory using the command ls ~/earth-analytics/data

# Create data directory in the home folder
data_dir = os.path.join(
    # Home directory
    pathlib.Path.home(),
    # Earth analytics data directory
    'earth-analytics',
    'data',
    # Project directory
    'spceies_distributions_esiil',
)
os.makedirs(data_dir, exist_ok=True) #ensures only happens once

# Define the directory name for GBIF data
gbif_dir = os.path.join(data_dir, 'tasiyagnunpa_data')

Define your study area – the ecoregions of North America

Track observations of Taciyagnunpa across the different ecoregions of North America! You should be able to see changes in the number of observations in each ecoregion throughout the year.

Download and save ecoregion boundaries

Your Task

1. Find the URL for for the level III ecoregion boundaries. You can get ecoregion boundaries from the Environmental Protection Agency (EPA)..
2. Replace your/url/here with the URL you found, making sure to format it so it is easily readable.
3. Change all the variable names to descriptive variable names
4. Run the cell to download and save the data.

# Set up the ecoregions level III boundary URL
ecoregion3_url = ("https://gaftp.epa.gov/EPADataCommons/ORD/Ecoregions/"
                  "cec_na/NA_CEC_Eco_Level3.zip")
# Set up a path to save the dataon your machine
ecoregion3_path = os.path.join(data_dir, 'NA_CEC_Eco_Level3.zip')

# Don't download twice
if not os.path.exists(ecoregion3_path):
    # Download, and don't check the certificate for the EPA
    a_response = requests.get(ecoregion3_url, verify=False)
    # Save the binary data to a file
    with open(ecoregion3_path, 'wb') as a_file:
        a_file.write(a_response.content)

# Open up the ecoregions boundaries
ecoregions_gdf = (
    gpd.read_file(ecoregion3_path)
    .rename(columns={
        'NA_L3NAME': 'name',
        'Shape_Area': 'area'})
    [['name', 'area', 'geometry']]
)

# We'll name the index so it will match the other data
ecoregions_gdf.index.name = 'ecoregion'

# Plot the ecoregions to check download
ecoregions_gdf.plot()

<Axes: >

Create a simplified GeoDataFrame for plotting

Plotting larger files can be time consuming. The code below will streamline plotting with hvplot by simplifying the geometry, projecting it to a Mercator projection that is compatible with geoviews, and cropping off areas in the Arctic.

Your task

Download and save ecoregion boundaries from the EPA:

1. Make a copy of your ecoregions GeoDataFrame with the .copy() method, and save it to another variable name. Make sure to do everything else in this cell with your new copy!
2. Simplify the ecoregions with .simplify(1000), and save it back to the geometry column.
3. Change the Coordinate Reference System (CRS) to Mercator with .to_crs(ccrs.Mercator())
4. Use the plotting code in the cell to check that the plotting runs quickly and looks the way you want, making sure to change gdf to YOUR GeoDataFrame name.

# Make a copy of the ecoregions
ecoregions_plot_gdf = ecoregions_gdf.copy()

# Simplify the geometry to speed up processing
ecoregions_plot_gdf.geometry = ecoregions_plot_gdf.simplify(1000)

# Change the CRS to Mercator for mapping
ecoregions_plot_gdf = ecoregions_plot_gdf.to_crs(ccrs.Mercator())

# Check that the plot runs
ecoregions_plot_gdf.hvplot(geo=True, crs=ccrs.Mercator())

Access locations and times of Tasiyagnunpa encounters

For this challenge, you will use a database called the Global Biodiversity Information Facility (GBIF). GBIF is compiled from species observation data all over the world, and includes everything from museum specimens to photos taken by citizen scientists in their backyards.

Your task: Explore GBIF

Before your get started, go to the GBIF occurrences search page and explore the data.

Contribute to open data

You can get your own observations added to GBIF using iNaturalist!

Register and log in to GBIF

You will need a GBIF account to complete this challenge. You can use your GitHub account to authenticate with GBIF. Then, run the following code to save your credentials on your computer.

Tip

If you accidentally enter your credentials wrong, you can set reset_credentials=True instead of reset_credentials=False

reset_credentials = False
# GBIF needs a username, password, and email
credentials = dict(
    GBIF_USER=(input, 'enter_user_name'),
    GBIF_PWD=(getpass, 'enter_password'),
    GBIF_EMAIL=(input, 'enter_email'),
)
for env_variable, (prompt_func, prompt_text) in credentials.items():
    # Delete credential from environment if requested
    if reset_credentials and (env_variable in os.environ):
        os.environ.pop(env_variable)
    # Ask for credential and save to environment
    if not env_variable in os.environ:
        os.environ[env_variable] = prompt_func(prompt_text)

# Query speciesr
species_info = species.name_lookup('Sturnella neglecta', rank='SPECIES')

# Get the first result
first_result = species_info['results'][0]

# Get the species key (nubKey)
species_key = first_result['nubKey']

# Check the result
first_result['species'], species_key

('Sturnella neglecta', 9596413)

Download data from GBIF

Your task

1. Replace csv_file_pattern with a string that will match any .csv file when used in the glob function. HINT: the character * represents any number of any values except the file separator (e.g. /)

2. Add parameters to the GBIF download function, occ.download() to limit your query to:

   • Sturnella Neglecta observations
   • in north america (NORTH_AMERICA)
   • from 2023
   • with spatial coordinates.

3. Then, run the download. This can take a few minutes.

# Only download once (cell needs to be run even after download, because. Code later in this cell is needed)
gbif_pattern = os.path.join(gbif_dir, '*.csv')
if not glob(gbif_pattern):
    # Submit query to GBIF
    gbif_query = occ.download([
        "continent = NORTH_AMERICA",
        "speciesKey = 9596413",
        "hasCoordinate = TRUE",
        "year = 2023",
    ])
    download_key = gbif_query[0]

    # Wait for the download to build
    if not 'GBIF_DOWNLOAD_KEY' in os.environ:
        os.environ['GBIF_DOWNLOAD_KEY'] = gbif_query[0]

        # Wait for the download to build
        wait = occ.download_meta(download_key)['status']
        while not wait=='SUCCEEDED':
            wait = occ.download_meta(download_key)['status']
            time.sleep(5)

    # Download GBIF data
    download_info = occ.download_get(
        os.environ['GBIF_DOWNLOAD_KEY'], 
        path=data_dir)

    # Unzip GBIF data
    with zipfile.ZipFile(download_info['path']) as download_zip:
        download_zip.extractall(path=gbif_dir)

# Find the extracted .csv file path (take the first result)
gbif_path = glob(gbif_pattern)[0]

Load the GBIF data into Python

Your task

1. Look at the beginning of the file you downloaded using the code below. What do you think the delimiter is?
2. Run the following code cell. What happens?
3. Uncomment and modify the parameters of pd.read_csv() below until your data loads successfully and you have only the columns you want.

You can use the following code to look at the beginning of your file:

!head $gbif_path

gbifID	datasetKey	occurrenceID	kingdom	phylum	class	order	family	genus	species	infraspecificEpithet	taxonRank	scientificName	verbatimScientificName	verbatimScientificNameAuthorship	countryCode	locality	stateProvince	occurrenceStatus	individualCount	publishingOrgKey	decimalLatitude	decimalLongitude	coordinateUncertaintyInMeters	coordinatePrecision	elevation	elevationAccuracy	depth	depthAccuracy	eventDate	day	month	year	taxonKey	speciesKey	basisOfRecord	institutionCode	collectionCode	catalogNumber	recordNumber	identifiedBy	dateIdentified	license	rightsHolder	recordedBy	typeStatus	establishmentMeans	lastInterpreted	mediaType	issue
4726720538	4fa7b334-ce0d-4e88-aaae-2e0c138d049e	URN:catalog:CLO:EBIRD:OBS1679802956	Animalia	Chordata	Aves	Passeriformes	Icteridae	Sturnella	Sturnella neglecta		SPECIES	Sturnella neglecta Audubon, 1844	Sturnella neglecta		US	Long Canyon--middle	Nebraska	PRESENT	8	e2e717bf-551a-4917-bdc9-4fa0f342c530	41.515686	-103.94431							2023-04-08	8	4	2023	9596413	9596413	HUMAN_OBSERVATION	CLO	EBIRD	OBS1679802956				CC_BY_4_0		obsr240738			2024-04-17T09:31:02.556Z		CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_TAXON_CONCEPT_ID_IGNORED
4727158102	4fa7b334-ce0d-4e88-aaae-2e0c138d049e	URN:catalog:CLO:EBIRD:OBS1681211292	Animalia	Chordata	Aves	Passeriformes	Icteridae	Sturnella	Sturnella neglecta		SPECIES	Sturnella neglecta Audubon, 1844	Sturnella neglecta		US	Klickitat Wildlife Area-Canyon Creek, Washington, US (45.879, -121.032)	Washington	PRESENT	5	e2e717bf-551a-4917-bdc9-4fa0f342c530	45.8792	-121.03192							2023-04-08	8	4	2023	9596413	9596413	HUMAN_OBSERVATION	CLO	EBIRD	OBS1681211292				CC_BY_4_0		obsr1602841			2024-04-17T09:31:03.211Z		CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_TAXON_CONCEPT_ID_IGNORED
4834728050	4fa7b334-ce0d-4e88-aaae-2e0c138d049e	URN:catalog:CLO:EBIRD:OBS1742803682	Animalia	Chordata	Aves	Passeriformes	Icteridae	Sturnella	Sturnella neglecta		SPECIES	Sturnella neglecta Audubon, 1844	Sturnella neglecta		CA	Range Road 251	Alberta	PRESENT	2	e2e717bf-551a-4917-bdc9-4fa0f342c530	50.958218	-113.37701							2023-05-27	27	5	2023	9596413	9596413	HUMAN_OBSERVATION	CLO	EBIRD	OBS1742803682				CC_BY_4_0		obsr1363203			2024-04-17T09:31:49.066Z		CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_TAXON_CONCEPT_ID_IGNORED
4668925094	4fa7b334-ce0d-4e88-aaae-2e0c138d049e	URN:catalog:CLO:EBIRD_CAN:OBS1766846197	Animalia	Chordata	Aves	Passeriformes	Icteridae	Sturnella	Sturnella neglecta		SPECIES	Sturnella neglecta Audubon, 1844	Sturnella neglecta		CA	Cedoux BBS Stop 48	Saskatchewan	PRESENT	3	e2e717bf-551a-4917-bdc9-4fa0f342c530	49.931126	-103.71253							2023-06-18	18	6	2023	9596413	9596413	HUMAN_OBSERVATION	CLO	EBIRD_CAN	OBS1766846197				CC_BY_4_0		obsr144843			2024-04-17T09:32:00.137Z		CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_TAXON_CONCEPT_ID_IGNORED
4786905120	4fa7b334-ce0d-4e88-aaae-2e0c138d049e	URN:catalog:CLO:EBIRD:OBS1625422653	Animalia	Chordata	Aves	Passeriformes	Icteridae	Sturnella	Sturnella neglecta		SPECIES	Sturnella neglecta Audubon, 1844	Sturnella neglecta		US	Kern Water Bank B	California	PRESENT	5	e2e717bf-551a-4917-bdc9-4fa0f342c530	35.31122	-119.34128							2023-02-07	7	2	2023	9596413	9596413	HUMAN_OBSERVATION	CLO	EBIRD	OBS1625422653				CC_BY_4_0		obsr51532			2024-04-17T10:10:07.351Z		CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_TAXON_CONCEPT_ID_IGNORED
4723003520	4fa7b334-ce0d-4e88-aaae-2e0c138d049e	URN:catalog:CLO:EBIRD:OBS1667298003	Animalia	Chordata	Aves	Passeriformes	Icteridae	Sturnella	Sturnella neglecta		SPECIES	Sturnella neglecta Audubon, 1844	Sturnella neglecta		CA	Lake Country--Beaver Lake Road--KM 1-5	British Columbia	PRESENT	1	e2e717bf-551a-4917-bdc9-4fa0f342c530	50.026264	-119.36027							2023-03-27	27	3	2023	9596413	9596413	HUMAN_OBSERVATION	CLO	EBIRD	OBS1667298003				CC_BY_4_0		obsr131954			2024-04-17T10:10:36.812Z		CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_TAXON_CONCEPT_ID_IGNORED
4751558999	4fa7b334-ce0d-4e88-aaae-2e0c138d049e	URN:catalog:CLO:EBIRD:OBS1732588615	Animalia	Chordata	Aves	Passeriformes	Icteridae	Sturnella	Sturnella neglecta		SPECIES	Sturnella neglecta Audubon, 1844	Sturnella neglecta		US	Eureka Reservoir	Montana	PRESENT	1	e2e717bf-551a-4917-bdc9-4fa0f342c530	47.88029	-112.31076							2023-05-15	15	5	2023	9596413	9596413	HUMAN_OBSERVATION	CLO	EBIRD	OBS1732588615				CC_BY_4_0		obsr197150			2024-04-17T10:11:22.488Z		CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_TAXON_CONCEPT_ID_IGNORED
4762562259	4fa7b334-ce0d-4e88-aaae-2e0c138d049e	URN:catalog:CLO:EBIRD:OBS1776201323	Animalia	Chordata	Aves	Passeriformes	Icteridae	Sturnella	Sturnella neglecta		SPECIES	Sturnella neglecta Audubon, 1844	Sturnella neglecta		US	23959–23997 SD-240, Wall US-SD 43.92673, -102.23886	South Dakota	PRESENT	7	e2e717bf-551a-4917-bdc9-4fa0f342c530	43.926735	-102.23885							2023-07-05	5	7	2023	9596413	9596413	HUMAN_OBSERVATION	CLO	EBIRD	OBS1776201323				CC_BY_4_0		obsr441430			2024-04-17T10:11:51.214Z		CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_TAXON_CONCEPT_ID_IGNORED
4635193877	4fa7b334-ce0d-4e88-aaae-2e0c138d049e	URN:catalog:CLO:EBIRD:OBS1860461976	Animalia	Chordata	Aves	Passeriformes	Icteridae	Sturnella	Sturnella neglecta		SPECIES	Sturnella neglecta Audubon, 1844	Sturnella neglecta		US	Barr Lake SP--South End	Colorado	PRESENT	1	e2e717bf-551a-4917-bdc9-4fa0f342c530	39.93179	-104.76997							2023-10-26	26	10	2023	9596413	9596413	HUMAN_OBSERVATION	CLO	EBIRD	OBS1860461976				CC_BY_4_0		obsr248020			2024-04-17T10:12:41.864Z		CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_TAXON_CONCEPT_ID_IGNORED

# Load the GBIF data
gbif_df = pd.read_csv(
    gbif_path, 
    delimiter='\t',
    index_col='gbifID',
    usecols=['gbifID', 'decimalLatitude', 'decimalLongitude', 'month']
)
gbif_df.head()

	decimalLatitude	decimalLongitude	month
gbifID
4726720538	41.515686	-103.94431	4
4727158102	45.879200	-121.03192	4
4834728050	50.958218	-113.37701	5
4668925094	49.931126	-103.71253	6
4786905120	35.311220	-119.34128	2

Convert the GBIF data to a GeoDataFrame

To plot the GBIF data, we need to convert it to a GeoDataFrame first.

Your task

1. Replace your_dataframe with the name of the DataFrame you just got from GBIF
2. Replace longitude_column_name and latitude_column_name with column names from your `DataFrame
3. Run the code to get a GeoDataFrame of the GBIF data.

gbif_gdf = (
    gpd.GeoDataFrame(
        gbif_df, 
        geometry=gpd.points_from_xy(
            gbif_df.decimalLongitude, 
            gbif_df.decimalLatitude), 
        crs="EPSG:4326")
    # Select the desired columns
    [[ 'month', 'geometry']]
)
gbif_gdf.geometry

gbifID
4726720538    POINT (-103.94431 41.51569)
4727158102    POINT (-121.03192 45.87920)
4834728050    POINT (-113.37701 50.95822)
4668925094    POINT (-103.71253 49.93113)
4786905120    POINT (-119.34128 35.31122)
                         ...             
4659909364    POINT (-117.78588 33.65364)
4767048893     POINT (-93.20390 46.77750)
4803174022    POINT (-122.44387 37.45439)
4831633599    POINT (-108.23702 35.04276)
4808227896    POINT (-119.50544 35.00428)
Name: geometry, Length: 249046, dtype: geometry

Count the number of observations in each ecosystem, during each month of 2023

Identify the ecoregion for each observation

You can combine the ecoregions and the observations spatially using a method called .sjoin(), which stands for spatial join.

Further reading

Check out the geopandas documentation on spatial joins to help you figure this one out. You can also ask your favorite LLM (Large-Language Model, like ChatGPT)

Your task

1. Identify the correct values for the how= and predicate= parameters of the spatial join.
2. Select only the columns you will need for your plot.
3. Run the code.

gbif_ecoregion_gdf = (
    ecoregions_gdf
    # Match the CRS of the GBIF data and the ecoregions
    .to_crs(gbif_gdf.crs)
    # Find ecoregion for each observation
    .sjoin(
        gbif_gdf,
        how='inner', 
        predicate='contains')
    # Select the required columns
    [['month','name']]
)
gbif_ecoregion_gdf

	month	name
ecoregion
57	6	Thompson-Okanogan Plateau
57	9	Thompson-Okanogan Plateau
57	6	Thompson-Okanogan Plateau
57	4	Thompson-Okanogan Plateau
57	8	Thompson-Okanogan Plateau
...	...	...
2545	6	Eastern Cascades Slopes and Foothills
2545	6	Eastern Cascades Slopes and Foothills
2545	5	Eastern Cascades Slopes and Foothills
2545	5	Eastern Cascades Slopes and Foothills
2545	4	Eastern Cascades Slopes and Foothills

248063 rows × 2 columns

Count the observations in each ecoregion each month

Your task:

1. Replace columns_to_group_by with a list of columns. Keep in mind that you will end up with one row for each group – you want to count the observations in each ecoregion by month.
2. Select only month/ecosystem combinations that have more than one occurrence recorded, since a single occurrence could be an error.
3. Use the .groupby() and .mean() methods to compute the mean occurrences by ecoregion and by month.
4. Run the code – it will normalize the number of occurrences by month and ecoretion.

occurrence_df = (
    gbif_ecoregion_gdf
    # For each ecoregion, for each month...
    .groupby(['ecoregion', 'month'])
    # ...count the number of occurrences
    .agg(occurrences=('name', 'count'))
)
display(occurrence_df) #Not needed to run this cell. The display(x_y_z) can be used for identifying bugs, which was occuring with this code.
# Get rid of rare observations (possible misidentification?)
occurrence_df = occurrence_df[occurrence_df.occurrences>1]

# Take the mean by ecoregion
mean_occurrences_by_ecoregion = (
    occurrence_df
    .groupby(['ecoregion'])
    .mean()
)
display(mean_occurrences_by_ecoregion) #Not needed to run this cell. The display(x_y_z) can be used for identifying bugs, which was occuring with this code.
# Take the mean by month
mean_occurrences_by_month = (
    occurrence_df
    .groupby(['month'])
    .mean()
)
display(mean_occurrences_by_month) #Not needed to run this cell. The display(x_y_z) can be used for identifying bugs, which was occuring with this code. 
# Normalize the observations by the monthly mean throughout the year
occurrence_df['norm_occurrences'] = (
    occurrence_df
    / mean_occurrences_by_ecoregion
    / mean_occurrences_by_month
)
occurrence_df

		occurrences
ecoregion	month
57	2	1
	3	132
	4	397
	5	660
	6	481
...	...	...
2545	8	76
	9	63
	10	78
	11	45
	12	61

1120 rows × 1 columns

	occurrences
ecoregion
57	234.555556
59	7.714286
60	799.416667
61	495.083333
62	270.100000
...	...
2538	30.833333
2540	21.125000
2541	3.000000
2544	19.272727
2545	186.333333

154 rows × 1 columns

	occurrences
month
1	222.380282
2	199.342466
3	186.316832
4	364.680000
5	569.465909
6	396.635294
7	221.263889
8	134.344262
9	129.160494
10	155.305263
11	176.628205
12	196.833333

		occurrences	norm_occurrences
ecoregion	month
57	3	132	0.003020
	4	397	0.004641
	5	660	0.004941
	6	481	0.005170
	7	182	0.003507
...	...	...	...
2545	8	76	0.003036
	9	63	0.002618
	10	78	0.002695
	11	45	0.001367
	12	61	0.001663

983 rows × 2 columns

Plot the Tasiyagnunpa observations by month

Your task

1. If applicable, replace any variable names with the names you defined previously.
2. Replace column_name_used_for_ecoregion_color and column_name_used_for_slider with the column names you wish to use.
3. Customize your plot with your choice of title, tile source, color map, and size.

# non functioning code to
# #Join the occurrences with the plotting GeoDataFrame
occurrence_gdf = ecoregions_plot_gdf.join(occurrence_df)

# Get the plot bounds so they don't change with the slider
xmin, ymin, xmax, ymax = occurrence_gdf.total_bounds

# Plot occurrence by ecoregion and month
migration_plot = (
    occurrence_gdf
    .hvplot(
        c='norm_occurrences',
        groupby= 'month',
        # Use background tiles
        geo=True, crs=ccrs.Mercator(), tiles='CartoLight',
        title="Meadowlark Occurrence per Month",
        xlim=(xmin, xmax), ylim=(ymin, ymax),
        frame_height=600,
        widget_location='bottom'
    )
)

# Save the plot
migration_plot.save('migration.html', embed=True)

# Show the plot
migration_plot

WARNING:bokeh.core.validation.check:W-1005 (FIXED_SIZING_MODE): 'fixed' sizing mode requires width and height to be set: figure(id='p6652', ...)

BokehModel(combine_events=True, render_bundle={'docs_json': {'f1c3f18b-2da0-4b86-98ee-55e5b40134d0': {'version…

# Join the occurrences with the plotting GeoDataFrame
occurrence_gdf = ecoregions_plot_gdf.join(occurrence_df)

# Get the plot bounds so they don't change with the slider
xmin, ymin, xmax, ymax = occurrence_gdf.total_bounds

# Define the slider widget
slider = pn.widgets.DiscreteSlider(
    name='month', 
    options={calendar.month_name[i]: i for i in range(1, 13)}
)

# Plot occurrence by ecoregion and month
migration_plot = (
    occurrence_gdf
    .hvplot(
        c='norm_occurrences',
        groupby='month',
        # Use background tiles
        geo=True, crs=ccrs.Mercator(), tiles='CartoLight',
        title="Tasiyagnunpa migration",
        xlim=(xmin, xmax), ylim=(ymin, ymax),
        frame_height=600,
        colorbar=False,
        widgets={'month': slider},
        widget_location='bottom'
    )
)

# Save the plot
migration_plot.save('migration.html', embed=True)

# Show the plot
migration_plot

WARNING:bokeh.core.validation.check:W-1005 (FIXED_SIZING_MODE): 'fixed' sizing mode requires width and height to be set: figure(id='p11601', ...)

BokehModel(combine_events=True, render_bundle={'docs_json': {'2d7f44ee-3b70-4efa-89a0-6ecc95b8be75': {'version…

::: {.content-visible when-format=“html”} :::

Want an EXTRA CHALLENGE?

Notice that the month slider displays numbers instead of the month name. Use pn.widgets.DiscreteSlider() with the options= parameter set to give the months names. You might want to try asking ChatGPT how to do this, or look at the documentation for pn.widgets.DiscreteSlider(). This is pretty tricky!

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jupyter nbconvert *.ipynb --to html