Socioeconomic Indicators in Chicago

This project is to showcase my SQL and Python skills working with real world dataset and how to use them together.

Objectives

Understand a dataset of selected socioeconomic indicators in Chicago
Read Dataset using Python and store data in an SQLite database.
Solve example problems to practice SQL skills

About the dataset

The city of Chicago released a dataset of socioeconomic data to the Chicago City Portal. This dataset contains a selection of six socioeconomic indicators of public health significance and a hardship index, for each Chicago community area, for the years 2008 2012.

Scores on the hardship index can range from 1 to 100, with a higher index number representing a greater level of hardship.

A detailed description of the dataset can be found on the city of Chicago's website, but to summarize, the dataset has the following variables:

Community Area Number (ca): Used to uniquely identify each row of the dataset
Community Area Name (community_area_name): The name of the region in the city of Chicago
Percent of Housing Crowded (percent_of_housing_crowded): Percent of occupied housing units with more than one person per room
Percent Households Below Poverty (percent_households_below_poverty): Percent of households living below the federal poverty line
Percent Aged 16+ Unemployed (percent_aged_16_unemployed): Percent of persons over the age of 16 years that are unemployed
Percent Aged 25+ without High School Diploma (percent_aged_25_without_high_school_diploma): Percent of persons over the age of 25 years without a high school education
Percent Aged Under 18 or Over 64:Percent of population under 18 or over 64 years of age (percent_aged_under_18_or_over_64): (ie. dependents)
Per Capita Income (per_capita_income_): Community Area per capita income is estimated as the sum of tract-level aggragate incomes divided by the total population
Hardship Index (hardship_index): Score that incorporates each of the six selected socioeconomic indicators

Questions & SQL Queries

Problem 1 - How many rows are in the dataset?

%sql SELECT COUNT(*) AS TOTAL_ROWS FROM chicago_socioeconomic_data;

Problem 2 - How many community areas in Chicago have a hardship index greater than 50.0?

%%sql

SELECT COUNT(community_area_name) AS COMMUNITY_AREAS_WITH_BETTER_HARDSHIP_INDEX
FROM chicago_socioeconomic_data
WHERE hardship_index > 50.0;

Problem 3 - What is the maximum value of hardship index in this dataset?

%%sql

SELECT MAX(hardship_index) AS MAX_HARDSHIP_INDEX
FROM chicago_socioeconomic_data;

Problem 4 - Which community area which has the highest hardship index?

%%sql

SELECT community_area_name AS COMMUNITY_WITH_HIGHEST_INDEX
FROM chicago_socioeconomic_data
ORDER BY hardship_index DESC
LIMIT 1;

Problem 5 - Which Chicago community areas have per-capita incomes greater than $60,000?

%%sql

SELECT community_area_name AS COMMUNITY_WITH_PCI_GT_$60000
FROM chicago_socioeconomic_data
WHERE per_capita_income_ > 60000;

Problem 6 - Create a scatter plot using the variables `per_capita_income_` and `hardship_index`. Explain the correlation between the two variables.

import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

perCapitaIncome_vs_hardshipIndex = %sql SELECT per_capita_income_, hardship_index FROM chicago_socioeconomic_data;
dfCopy = perCapitaIncome_vs_hardshipIndex.DataFrame()

plot = sns.jointplot(x ='per_capita_income_', y='hardship_index', data = dfCopy, height=10, ratio=2)

# Rename the axis labels
plot.set_axis_labels('Per Capita Income (USD)', 'Hardship Index')

# Adjust layout
plt.tight_layout()

# Display the plot
plt.show()