API for grasslands for habitat suitability final project

[hanried]

In case anyone isn't sure what API to use to get the grassland data, I used this one: https://data-usfs.hub.arcgis.com/datasets/usfs::national-grassland-units-feature-layer/api

And here's the code I used to use said API:

Set up the grassland URL

grasslands_url = ("https://apps.fs.usda.gov/arcx/rest/services"
"/EDW/EDW_NationalGrassland_01/MapServer/0"
"/query?where=1%3D1&outFields=*&geometry=&geometry"
"Type=esriGeometryEnvelope&inSR=4326&spatialRel=esriSpatialRelIntersects&outSR=4326&f=json")

Set up a path to save the data on your machine

grasslands_dir = os.path.join(data_dir, 'grasslands_dir')

Make the grasslands directory

os.makedirs(grasslands_dir, exist_ok=True)

Join grasslands shapefile path

grasslands_path = os.path.join(grasslands_dir, 'S_USA.NationalGrassland.shp')

Only download once

if not os.path.exists(grasslands_path):
grasslands_gdf = gpd.read_file(grasslands_url)
grasslands_gdf.to_file(grasslands_path)

Check that the URL is correct

grasslands_gdf = gpd.read_file(grasslands_path)

Display grasslands_gdf and plot grasslands to check download

display(grasslands_gdf)
grasslands_gdf.plot()

I haven't gotten past this step of the project yet but hopefully it's the right API!

[eculler]

Cool, @hanried ! This looks great. I also put a direct link in the assignment description if you prefer to download a single shapefile with all the grasslands rather than to go through the API (or if this doesn't end up working out for you)

[BioNCphilic]

Thanks! A bit ahead of where I am, but nice to have a guidepost.

[BioNCphilic]

@eculler - good to know it is ok to download a single shapefile. I'm doing this a bit differently because I want to compare two areas which can support native rivercane which both the Eastern band of the Cherokee & the Cherokee Nation use for a variety of purposes. It's actually pretty cool that rivercane can grow as far west as Oklahoma. However, when I looked at the grassland data the area in Oklahoma was not anywhere near the area where Cherokee people live.

So - I hope it's ok if I got the shape files for Cherokee County OK and Jackson County NC from a US Census Source. I downloaded the zip files and was going to use python code to extract the .shp files, but it became a mess with different directories for data and another for the project, so I just used my file manager to extract them and moved them into the correct directory. I'm sure I could have made it work, but this was much easier.

[BioNCphilic]

Well - actually - extracting files manually did not work apparently even though I can see the file on github and in my terminal.

Now what? I guess I have to get VScode/python to do the extraction?

[BioNCphilic]

[BioNCphilic]

OK - after many tries, I finally have a shapefile for US Counties from the US Census. I'm doing this because I want to compare a county in Oklahoma with one in North Carolina as my two locations. Here's the code if you want to use it:

Define download URL and file paths

base_url = "https://www2.census.gov/geo/tiger/TIGER2024/COUNTY/tl_2024_us_county.zip"

Create the 'counties' directory if it doesn't exist

os.makedirs(county_dir, exist_ok=True)
county_dir = os.path.join(data_dir, "counties")

Download county data (if not already downloaded)

if not os.path.exists(os.path.join(county_dir, "tl_2024_us_county.zip")):
county_response = requests.get(base_url)
county_response.raise_for_status() # Raise exception on download errors

Save the downloaded data (compressed) to the specified path

with open(os.path.join(county_dir, "tl_2024_us_county.zip"), "wb") as county_file:
county_file.write(county_response.content)

Extract the downloaded shapefile

with zipfile.ZipFile(os.path.join(county_dir, "tl_2024_us_county.zip"), "r") as county_zip:
county_zip.extractall(county_dir)

print("Shapefile extracted successfully!")

[BioNCphilic]

Actually - use this code! And here is the link to figure out locations - specifically a county in a US State.

Define the download path (replace with your desired location)

download_path = "\GitHubr\ivercane_data"

Define the URL of the shapefile

url = "https://www2.census.gov/geo/tiger/TIGER2023/COUNTY/tl_2023_us_county.zip"

Download the shapefile

response = requests.get(url)
response.raise_for_status()

with open("tl_2023_us_county.zip", "wb") as f:
f.write(response.content)

Extract the shapefile

with zipfile.ZipFile("tl_2023_us_county.zip", "r") as zip_ref:
zip_ref.extractall()

Load the shapefile into a GeoDataFrame

county_data = gpd.read_file("tl_2023_us_county.shp")

Filter for a specific counties

cherokee_county = county_data[(county_data['STATEFP'] == '40') & (county_data['COUNTYFP'] == '021')]
jackson_county = county_data[(county_data['STATEFP'] == '37') & (county_data['COUNTYFP'] == '099')]

Plot the shapefile

cherokee_county.plot()