American Indian and Alaska Native Areas
Ty Tuff, ESIIL Data Scientist 2023-05-21
The National American Indian/Alaska Native/Native Hawaiian (AIANNH) Areas Shapefile is a dataset that provides a comprehensive view of the geographic, demographic, and cultural landscape of Indigenous communities in the United States. This unique resource comprises diverse legal entities, encompassing federally recognized American Indian reservations, off-reservation trust lands, Alaska Native regional corporations, and Native Hawaiian homelands.
These entities hold a rich tapestry of diverse cultures, languages, and histories, each with unique stories to tell and invaluable wisdom to impart. The AIANNH Areas Shapefile is more than just a collection of data; it is a detailed map of vibrant communities and proud nations, embodying the resilience and enduring spirit of the Indigenous peoples of America.
For researchers, policy makers, educators, and anyone with a vested interest in understanding these communities and their contributions to the broader cultural fabric of the United States, this dataset serves as a vital resource. Its depth and breadth offer unprecedented insights into the geographic boundaries that define these communities, the demographic characteristics that diversify them, and the cultural aspects that unify and distinguish them.
From the wild expanses of Alaska to the lush tropical landscapes of Hawaii, and across the vast American mainland, the AIANNH Areas Shapefile traces the footsteps of the First Nations. By diving into this dataset, you are not only exploring data, but also appreciating the rich tapestry of cultures that form an integral part of the United States’ historical, present, and future narrative.
LSAD
Legal/Statistical Area Description (LSAD) is a classification that the U.S. Census Bureau uses to define the legal and statistical entities for geographic areas. It is a code that represents the type, function, or status of a geographic entity.
For example, in the context of American Indian, Alaska Native, and Native Hawaiian Areas (AIANNHA), the LSAD can represent the following classifications:
“RS” for Reservation (Federal) “OK” for Oklahoma Tribal Statistical Area “SD” for State Designated Tribal Statistical Area “RC” for Alaska Native Regional Corporation, and others.
R code:
# Install and load necessary libraries
library(sf)
library(dplyr)
library(knitr)
library(ggplot2)
# Download dataset from source
url <- "https://www2.census.gov/geo/tiger/TIGER2020/AIANNH/tl_2020_us_aiannh.zip"
temp_file <- tempfile(fileext = ".zip")
download.file(url, temp_file, mode = "wb")
unzip(temp_file, exdir = tempdir())
# Read the Shapefile
shapefile_path <- file.path(tempdir(), "tl_2020_us_aiannh.shp")
aiannh <- read_sf(shapefile_path)
# Plot the count per LSAD
ggplot() +
geom_sf(data = aiannh, aes( fill="steelblue")) +
theme_minimal() +
labs(title = "AIANNH Areas")+ theme(legend.position = "none")
# Count the number of AIANNH per legal statistical area description (LSAD)
state_counts <- aiannh %>%
group_by(LSAD) %>%
summarize(count = n())
# Plot the count per LSAD
ggplot(state_counts, aes(x = reorder(LSAD, -count), y = count)) +
geom_bar(stat = "identity", fill = "steelblue") +
theme_minimal() +
labs(x = "Legal Statistical Area Description (LSAD)",
y = "Count",
title = "Number of AIANNH per LSAD") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Python code:
# Import necessary libraries
import os
import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import urllib.request
import zipfile
from tempfile import TemporaryDirectory
# Download dataset from source
url = "https://www2.census.gov/geo/tiger/TIGER2020/AIANNH/tl_2020_us_aiannh.zip"
with TemporaryDirectory() as tmpdirname:
zip_path = os.path.join(tmpdirname, "tl_2020_us_aiannh.zip")
shapefile_path = os.path.join(tmpdirname, "tl_2020_us_aiannh.shp")
# Download and unzip the file
urllib.request.urlretrieve(url, zip_path)
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(tmpdirname)
# Read the Shapefile
aiannh = gpd.read_file(shapefile_path)
# Create a map of the AIANNH areas
aiannh.plot(color="blue", edgecolor="darkblue")
plt.title("AIANNH Areas")
plt.axis("off") # to turn off the axis
plt.show()
# Count the number of AIANNH per LSAD
state_counts = aiannh['LSAD'].value_counts().reset_index()
state_counts.columns = ['LSAD', 'Count'] # rename columns
# Create a bar plot of the count per LSAD
state_counts = state_counts.sort_values(by='Count', ascending=False) # sort by Count
state_counts.plot(x='LSAD', y='Count', kind='bar', legend=None, color="steelblue")
plt.title("Number of AIANNH per LSAD")
plt.xlabel("Legal Statistical Area Description (LSAD)")
plt.ylabel("Count")
plt.xticks(rotation=90)
plt.show()
