Explore the US Census 2015

Data from Kaggle : US Census Demographic Data

Explore Poverty Rates by Race and Gender (US Census 2015)

Dashboard to answer the question "How does the poverty rate vary by race and gender?"

Tableau Link: Poverty Rates by Race and Gender Across US Counties (US Census 2015)

Introduction:

The "Explore Poverty Rates by Race and Gender" Tableau viz aims to answer the question of how poverty rates vary by race and gender across US counties. It includes several visualizations, such as a map and packed bubble chart, and filters to provide a targeted analysis.

Visualizations:

The dashboard consists of a map that displays poverty rates at the county level using color coding and tooltips to provide more detailed information. The packed bubble chart focuses on race, showing how poverty rates vary across the country. The dashboard includes filters that allow users to customize the data by state, county, race, poverty rate ranges, and child poverty ranges.

Key Findings:

The data reveals significant disparities in poverty rates among different racial and gender groups across the US. Puerto Rico has the highest overall poverty rate among states, and counties with a poverty rate below the national average are predominantly Asian populated. The findings also highlight the need for tailored interventions and policies to address the unique challenges faced by each demographic.

• Poverty rates for children are generally higher than overall poverty rates in all racial groups and both genders.
• Poverty rates for Native Americans are the highest among all racial groups, with 18.87% overall poverty rate and 28.24% child poverty rate.
• Poverty rates for females are generally higher than those for males in all racial groups, except for Asian Americans.
• The highest poverty rate counties are located in the South and Southwest regions of the U.S., with New Mexico having the highest number of counties with poverty rates over 40%.

Reasonings:

The dashboard with a map, packed bubble chart, and filters was chosen as it provides an easy-to-understand visualization of poverty rates across US counties by race and gender. The visualizations allow for targeted analysis and provide insights into the disparities among different racial and gender groups.

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Employment Rates by Work Type (US Census 2015)

A story to answer the question "How does the employment rate vary by work type (professional, service, office, construction, production)?"

Tableau Link: Employment Rate Variations by Work Type and Gender in the United States

Introduction:

The "Employment Rates by Work Type" Tableau story explores variations in employment rates across US counties by work type, including professional, service, office, construction, and production. The story includes two slides with several visualizations, such as maps, scatter plots, and bar charts, and allows users to filter the data by state, work type, and county.

Visualizations:

The first dashboard includes a map, stacked bar chart, and filters to explore variations in employment rates by work type and state. The second dashboard includes a map, scatter plot, symbol map, and filters that allow users to customize the data by work type and state.

Key Findings:

The visualizations reveal that the majority of people are employed in the Professional work type, and there are disparities in median household income across different counties and states. The data also indicates that employment rates may not be the only factor influencing income levels, and further investigation is necessary to understand the underlying causes of these patterns.

• The Professional work type has the highest employment rate among all work types, with over 36% of the employed population.
• The Construction work type has the lowest employment rate among all work types, with less than 5% of the employed population.
• The West region has the highest employment rate overall, while the South region has the lowest employment rate overall.
• The scatter plot shows a strong positive correlation between employment rates and median household income, indicating that areas with higher employment rates also tend to have higher incomes.

Reasonings:

The use of multiple visualizations and filters provides a comprehensive analysis of employment rates across different work types and helps identify patterns and disparities. The inclusion of

both maps and scatter plots allows for the exploration of geographic trends as well as identifying correlations between employment rates and other factors. The dashboard allows for a deeper understanding of employment rates by work type and highlights the unique challenges faced by different regions and demographics.