National Atlas - Indian Lands of the United States dataset
Ty Tuff, ESIIL Data Scientist 2023-05-21
The “National Atlas - Indian Lands of the United States” dataset is a comprehensive geographical resource that presents the locations and extents of lands associated with Native American tribes recognized by the U.S. government. It encompasses lands managed by these tribes under the oversight of the U.S. Bureau of Indian Affairs, lands held in trust by the U.S. for specific tribes, as well as other types of recognized tribal lands. https://www.loc.gov/resource/g3701e.ct003648r/
This dataset offers vital spatial information for educators, policymakers, researchers, and others interested in learning about and engaging with Native American tribal lands. Its intent is to facilitate enhanced awareness and understanding of the rich cultural heritage and sovereignty of these Indigenous and Tribal Nations.
As such, it serves not only as a tool for geographical reference, but also as a resource that contributes to a deeper understanding of the history, diversity, and contemporary life of Native American tribes across the United States.
R: In R, we’ll use the ‘sf’ package to read the Shapefile and the ‘dplyr’ package to process the data.
R code:
# Install and load necessary libraries
library(sf)
library(dplyr)
library(USAboundaries)
library(ggplot2)
# Download the Indian Lands dataset
url <- "https://prd-tnm.s3.amazonaws.com/StagedProducts/Small-scale/data/Boundaries/indlanp010g.shp_nt00968.tar.gz"
temp_file <- tempfile(fileext = ".tar.gz")
download.file(url, temp_file, mode = "wb")
untar(temp_file, exdir = tempdir())
# Read the Shapefile
shapefile_path <- file.path(tempdir(), "indlanp010g.shp")
indian_lands <- read_sf(shapefile_path)
states <- us_states(map_date = NULL) # for contemporary boundaries
counties <- us_counties(map_date = NULL) # for contemporary boundaries
bbox <- st_bbox(indian_lands)
# Extract the xmin, xmax, ymin, and ymax values
xmin <- bbox[1]
xmax <- bbox[3]
ymin <- bbox[2]
ymax <- bbox[4]
ggplot(data= indian_lands) +
geom_sf(data=states, fill=NA, color="grey70", size=0.2) +
geom_sf(data=counties, fill=NA, color="grey90", size=0.1) +
geom_sf(data=indian_lands, fill="cornflowerblue", color="darkblue") +
coord_sf(xlim=c(xmin, xmax), ylim=c(ymin, ymax)) +
theme_minimal() + # Use minimal theme
theme(legend.position = "none")
Python: In Python, we’ll use the ‘geopandas’ and ‘pandas’ libraries to read the Shapefile and process the data.
Python code:
import geopandas as gpd
import matplotlib.pyplot as plt
import contextily as ctx
from urllib.request import urlretrieve
import os
import tarfile
import tempfile
# Download the Indian Lands dataset
url = "https://prd-tnm.s3.amazonaws.com/StagedProducts/Small-scale/data/Boundaries/indlanp010g.shp_nt00968.tar.gz"
temp_file, _ = urlretrieve(url)
tar = tarfile.open(temp_file, "r:gz")
tar.extractall(path=tempfile.gettempdir())
tar.close()
# Read the Shapefile
shapefile_path = os.path.join(tempfile.gettempdir(), "indlanp010g.shp")
indian_lands = gpd.read_file(shapefile_path)
# Set CRS
indian_lands = indian_lands.set_crs("EPSG:4326", allow_override=True)
# Get the US map with state boundaries
us_map = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
us_map = us_map[us_map.name == 'United States of America'].set_crs("EPSG:4326")
# Plot
fig, ax = plt.subplots(figsize=(10,10))
us_map.boundary.plot(ax=ax, color='black')
indian_lands.plot(ax=ax, color='blue')
plt.axis('equal')
plt.show()
