Housing in the richest American cities is increasingly becoming unaffordable to the American middle class occupying these cities. Income tends to remain stagnant while home prices are on a steady increase.
The article (https://www.theatlantic.com/business/archive/2014/10/why-are-liberal-cities-so-unaffordable/382045/) attempts to establish a relationship between the median house hold income and the percentage of home affordability by the middle class in the metropolitan cities
The problems with the graph:
- Line graphs are good at showing trends; they declutter the graph and provide a visual that emphasises on the trend as opposed to individual data points. The consequence of this property is that the contribute to loss of visual information when the inspection of data points are actually necessary. In the graph under consideration, the aim of the author is to match the increasing pay trend in the richest metros against the decreasing affordability of housing. What the author ends up losing in the graph is actual information about median household income and percentage of homes reachable to the middle class.
- For the sake of comparing trends in median household income and affordability of homes, the author normalises two very different quantities and presents them on the same y-scale. In an attempt to make a point about trends in opposite directions, the author forces a visual perspective on the user for two correlated but independent quantities.
- The graph is not easily comprehensible for comparable data points. For example, it is hard to say which of the two cities, Bethesda or Washington, DC has higher % of homes accessible to the middle class. Underemphasis on labelling data points leads to comprehension issues with the graph.
- While the aim of the author is to present trends, the data is essentially discrete. The author presents discrete data in a continuous graph format and makes no attempt to visualise the discreteness of the data. Both the median household income and the percentage of affordability are discrete data points corresponding to each city and the author has created a continuous line graph without explicitly marking the data points.
- The author has ordered the top 25 cities in order of median income. What is unclear is if the cities are also ordered by their richness.
How have I Improved the graph?https://public.tableau.com/views/medianincome_affordability/Sheet1?:embed=y&:display_count=yes
- Implementation of a dual axis graph: When multiple quantities are being compared on the same graph, especially when the quantities are on different scales, the best approach is to plot them on a dual axis chart. The modified graph presents on the left side of the y-scale, median household income and on the right side of the y-scale, affordability for the middle class. This way, both quantities are represented in their own units and a visual perspective is not forced onto the user by modifying their values to fit a scale.
- Explicit Labelling: Labelling of data points is important when visualising discrete data. It enables the reader to perceive differences in data points especially data points that have small differences. In the modified graph, the user can now easily tell that the city of Bethesda is 1% less in affordable homes for the middle class when compared to Washington DC.
- Discretization of data: The author wants to present a trend using discrete data. How can we present the trend and at the same time not lose the discrete quality of data in the visualisations? We do this by presenting one discrete quantity in the standard format for discrete data, i.e. bar chart and the other discrete quantity in the author’s continuous line format, but we explicitly add markers for the discrete data points for clarity. This contributes to us being able to observe the intended trends while still being able to visualise the discrete data.
- Color Coding: The bar chart and the line chart are color coded with complementing colors. Color coding visually brings out the difference in scales and trends of the two axes which makes it easy for the user to interpret the graph.
https://www.usatoday.com/story/money/business/2014/05/13/housing-affordability-worsens/9034185/