We all learned the order of planets in school. In my case using the mnemonic, My Very Excellent Mother Just Served Us Nine Pizzas (MVEMJSUNP) for Mercury, Venus, Earth, Mars Jupiter Saturn, Uranus, Neptune, and Pluto. Since Pluto has been demoted to a dwarf planet, you could change the Nine Pizzas to Noodles or something else.
And in terms of distances, Venus’s orbit (0.72 AU, or Astronomical Units (i.e. 1 AU is the distance from the Earth to the Sun) is closer to Earth’s orbit (1 AU by definition) than Mercury’s (varies between 0.31 and 0.47 AU because of it’s more elliptical orbit) or Mars’ (1.5 AU).
However, I saw an article, stating that Mercury might in fact be the closest planet to Earth (on average) so I thought I’d whip up a visualization that shows which planet is closest as a function of the planetary orbits around the sun.
Because of where the planets are on these orbital paths, and specifically the time it takes Mercury to orbit the sun, Mercury is the planet that is closest to Earth more often and has an average distance to Earth that is lower than the other 2 inner planets. Mars is occasionally the closest as well, but on average much further than Mercury or Venus. Also interesting is that Mercury is, on average, about 1 AU away from Earth, which is the same as the distance to the Sun.
This simulation shows how the planet positions vary each day over a 30 year period and the regularity with which the distance between Earth and the other varies over time. Mercury has the shortest period while Mars has the longest. You can change the speed of the simulation to speed up or slow down the orbits of the planets.
Data and Tools:
This map shows the electoral outcome of the 2016 US Presidential Election and is color coded red if the state was won by Donald Trump (R) and blue if the state was won by Hilary Clinton. When looking at the map, red states tend to be larger in area than blue states, but also generally have lower populations. This gives a misleading impression that the electoral share is “redder” than it actually is. For 2016, we can see that Trump won 306 electoral votes or (57% of the total electoral votes), but the map is shaded such that 73% of the area of the US is colored red. Similarly, Clinton won 232 electoral votes, but the map is shaded such that only 27% of the map is colored blue.
The map shrinks the states with low electoral votes relative to its area and increases the size of states with large numbers of electoral votes relative to its area. On average blue states grow as they are under-represented visually, while red states tend to shrink quite a bit because they are over-represented visually. Alaska is the state that shrinks the most and DC and New Jersey are the areas that grow the most in the new map.
I think this gives a more accurate picture of how the states voted because it also gives a sense of the relative weight of those states votes. Even with the change in sizes, the map is still mostly red, but gives a better sense of how close the electoral vote totals are.
Data and Tools:
Electric vehicles are any vehicle that can be plugged in to recharge a battery that provides power to move the vehicle. Two broad classes are battery electric vehicles (BEVs) which only have batteries as their power source and plug-in hybrid electric vehicles (PHEVs) which have an alternative or parallel power source, typically a gasoline engine. PHEVs are built so that when the battery is depleted, the car can still run on gasoline and operate like a hybrid vehicle similar to a regular Toyota Prius (which is not plugged in at all).
Electric vehicles (EVs) have been sold in the US since 2011 (a few commercial models were sold previous to that but not in any significant numbers) and some conversions were also available. Since then, the number of EVs sold has increased pretty significantly. I wanted to look at the distribution of where those vehicles were located. What is interesting is that California accounts for around 50% of the electric vehicles sold in the United States. Other states have lower rates of EV adoption (in some cases much, much lower). There are many reasons for this, including beneficial policies, public awareness, a large number of potential early adopters and a mild climate. Even so, the EV heatmap of California done early shows that sales are mostly limited to the Bay Area, and LA areas.
The map shows data for total electric vehicle sales by state for years 2016, 2017 or 2018 and also the number of EV sales per 1000 licensed drivers (this is all people in the state with a drivers license, not drivers of EVs). If you hover over a state, you can see both data points for that state.
It will be interesting to see how the next generation of electric vehicles continues to improve, lower in price and become more popular with drivers outside of early adopters.
Data and Tools:
These statistics can be sorted from small to large or vice versa to get a view of the globe and its constituent countries in a unique and interesting way. It’s a bit hypnotic to watch as the countries appear and add to the world one by one.
You can use this map to display all the countries that have higher life expectancy than the United States:
select “Life expectancy”, sort from “high to low” and use the scroll bar to move to the United States and you’ll get a picture like this:
or this map to display all the countries that have higher population density than the United States:
select “Population density, sort from “high to low” and use the scroll bar to move to the United States and you’ll get a picture like this:
I hope you enjoy exploring the countries of the world through this data viz tool. And if you have ideas for other statistics to add, I will try to do so.
Data and tools: Data was downloaded primarily from Wikipedia: Life expectancy from World Health Organization (2015) | GDP from United Nations (2017) | Population from United Nations (2017) | Land Area from CIA factbook (2016)
This visualization is one of a series of visualizations that present US household spending data from the US Bureau of Labor Statistics. This one looks at the age of the primary resident.
This visualization focuses on the age of the primary resident. This is defined in the BLS documentation as the person who is first mentioned when the survey respondent is asked who in the household rents or owns the home.
I obtained data from the US Bureau of Labor Statistics (BLS), based upon a survey of consumer households and their spending habits. This data breaks down spending and income into many categories that are aggregated and plotted in a Sankey graph.
One of the key factors in financial health of an individual or household is making sure that household spending is equal to or below household income. If your spending is higher than income, you will be drawing down your savings (if you have any) or borrowing money. If your spending is lower than your income, you will presumably be saving money which can provide flexibility in the future, fund your retirement (maybe even early) and generally give you peace of mind.
As stated before, one of the keys to financial security is spending less than your income. We can see that on average, income tends to increase with the household primary age up to the 45-54 group, then declines from there.
The youngest group (under 25) tends to borrow or draw down on savings to live their lifestyle, while the same is true of the over 75 age group. This is probably because seniors tend to draw down savings that were built up specifically for this purpose, and college students borrow to go to school. Social security also makes up a big portion of income for the older age groups.
How does your overall spending compare with those in your income group? How about spending in individual categories like housing, vehicles, food, clothing, etc…?
Probably one of the best things you can do from a financial perspective is to go through your spending and understand where your money is going. These sankey diagrams are one way to do it and see it visually, but of course, you can just make a table or pie chart or whatever.
The main thing is to understand where your money is going. Once you’ve done this you can be more conscious of what you are spending your money on, and then decide if you are spending too much (or too little) in certain categories. Having context of what other people spend money on is helpful as well, and why it is useful to compare to these averages, even though the income level, regional cost of living, and household composition won’t look exactly the same as your household.
Here is more information about the Consumer Expenditure Surveys from the BLS website:
The Consumer Expenditure Surveys (CE) collect information from the US households and families on their spending habits (expenditures), income, and household characteristics. The strength of the surveys is that it allows data users to relate the expenditures and income of consumers to the characteristics of those consumers. The surveys consist of two components, a quarterly Interview Survey and a weekly Diary Survey, each with its own questionnaire and sample.
Data and Tools:
This interactive map shows how population is distributed by latitude or longitude. It animates the creation of a bar graph by shifting population from its location on the map to aggregate population levels by latitude or longitude increments. Each “block” of the bar graph represents 1 million people. Population is highest in the northern hemisphere at 25-26 degrees North latitude and 77-78 degrees East Longitude.
It should be relatively explanatory. Press the “Aggregate Population by Latitude” button to make a plot of population by line of latitude (i.e. rows of the map).
Press the “Aggregate Population by Longitude” button to make a plot of population by line of longitude (i.e. columns of the map). To see the population distributed across the map, press the “Show Population Grid” button.
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