ACS Demo

Introduction

This notebook demonstrates how to load US Census American Community Survey (ACS) 5-year data detailed tables and do demographic analysis on it. The process is very much parallel to how we loaded and used US Census redistricting data in the SoMa DIS Demo and Seeing White notebooks.

Imports and configuration

# So we can run from within the censusdis project and find the packages we need.
import os
import sys

sys.path.append(
    os.path.join(os.path.abspath(os.path.join(os.path.curdir, os.path.pardir)))
)

import censusdis.data as ced
import censusdis.states

import divintseg as dis

# Set your API key here.
CENSUS_API_KEY = None

YEAR = 2019
DATASET = "acs/acs5"

# Feel free to try other states.
STATE = censusdis.states.STATE_NJ

# A group of variables counting people by race and ethnicity.
# See https://api.census.gov/data/2019/acs/acs5/groups/B03002.html
GROUP = "B03002"

Load the data and compute diversity and integration

df_acs5 = ced.download(
    DATASET,
    YEAR,
    leaves_of_group=GROUP,
    state=STATE,
    block_group="*",
    api_key=CENSUS_API_KEY,
)

df_di = dis.di(
    df_acs5,
    by=["STATE", "COUNTY", "TRACT"],
    over="BLOCK_GROUP",
).reset_index()

df_di.head()

	STATE	COUNTY	TRACT	diversity	integration
0	34	001	000100	0.799079	0.742759
1	34	001	000200	0.774784	0.701973
2	34	001	000300	0.781342	0.726768
3	34	001	000400	0.765627	0.687127
4	34	001	000500	0.695903	0.685162

Add geography to our dataframe

gdf_di = ced.add_inferred_geography(df_di, YEAR)

Plot the data for the state

import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = (8, 8)

ax = gdf_di.plot(
    "diversity",
    cmap="hot",
    legend=True,
    vmin=0.0,
    vmax=1.0,
)

ax.set_title(f"Diversity in {censusdis.states.NAMES_FROM_IDS[STATE]}")

ax.tick_params(
    left=False,
    right=False,
    bottom=False,
    labelleft=False,
    labelbottom=False,
)