nsheikh23 / d3-challenge

Constructing an interactive scatter plot displaying relationships between factors shaping people's lives, such as rates of income, obesity, poverty and more.

D3-challenge

-- Project Status: [Completed]

Project Objective

The purpose of this project is to analyze the current trends shaping people's lives, as well as creating charts, graphs, and interactive elements to help readers understand the findings.

The data set is based on 2014 ACS 1-year estimates from U.S. Census Bueau and the Behavioral Risk Factor Surveillance System: https://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml. The data set includes data on rates of income, obesity, poverty, etc. by state. MOE stands for "margin of error."

Methods Used

• Data Visualization

Technologies

• JavaScript
• D3
• HTML, CSS

Task

Constructed an interactive scatter plot that displayed relationships between all the factors recorded in the data provided. For details on each scatter point, a tooltip was added.

The plot can be accessed at: https://nsheikh23.github.io/D3-challenge/

Analysis

Examining the relationship between poverty and lacking healthcare, it is possible to see a positive correlation. As the poverty percentage increases, the higher the percentage of people lacking healthcare. This is as expected because the states that have a lower poverty rate, have a greater population that can work and afford healthcare.

Following up on the last point, if comparing median household income and lacking healthcare, it can be clearly seen that majority of the states that have high uninsured people also have low household income. Very few states actually have high household income as well as uninsured population percentage. That may be due to higher cost of living in the state, however in general it is an indirect relationship thus supporting the point made earlier.

In another interesting analysis, it can be seen from the Age vs. Smokes that majority of the states are clumped together in the middle of chart. This means, most states have relatively similar patterns when it comes to age and smoking. Less than a quarter of each state populate smokes between the median ages of 36-40.

As for obesity, there is a negative correlation with median household income. At first, it seems strange there is a higher obesity percentage with lower household income. However, diving deeper, it kind of makes sense. People with higher household income may be more self conscious about their health, their diet and fitness. They are able to afford eating healthier and joining fitness programs. Whereas, lower income households do not have that luxury, and they can only afford fastfood restaurants and lower quality foods that lead to obesity. Moreover, they may not have the time or financial means to think of fitness as they are always focused on surviving.