How does the Earth’s tilt affect sunlight and seasons by latitude?
This visualization looks at the variation in the amount of sunlight different latitudes receive over the different days of the year. The amount of sunlight can be classified in 3 different categories:
The number of hours of sunlight received each day
The average sunlight intensity (in watts per square meter)
The total amount of sunlight received across an entire latitude band (in gigawatt hours)
The default view is to see the number of hours of sunlight received by latitude on the current date, shown by the yellow bars. The sunlight hours range from 0 to 24 hours per day while most latitudes range from 9 to 15 hours.
If you hover over the yellow bars (or click on mobile), you will see the exact number of hours for that latitude band for that date.
Pressing the ‘Start Animation’ button, will change the angle of the sun relative to the Earth (as the earth rotates around the sun) and change the distribution of sunlight across the globe. You can view this animation with the earth fixed and the sun angle changing (the default view) or with the sun location fixed and the earth’s tilt changing.
This visualization helps to show how the seasons come about. When the Northern Hemisphere is tilted towards the sun, the amount of sunlight it receives increases (hours of daylight, average sun intensity and total amount of sunlight received). As the hemisphere tilts away from the sun, the amount of sunlight it receives decreases. The amount of sunlight a region receives causes the seasons that we experience.
Interestingly, when you are at the equator, the amount of sunlight per day does not really vary too significantly over the course of the year, whereas if you are near the poles, the difference between summer and winter is very dramatic. When looking at total sunlight received, the poles generally have lower sunlight because even in their summer, there is much lower land area relative to the middle latitudes (close to the equator)
The second visualization shown here shows how the tilt of the Earth’s axis is changed over the course of the Earth’s revolution around the sun. The Earth’s axis is tilted at 23.5 degrees relative to the plane of the Earth’s orbit around the sun. Like the last visualization, you can look at Earth the way we normally do (without the tilted axis) or from the perspective of the sun (with a tilted axis). This makes it a bit clearer why the tilt of the Earth’s axis can change from the north pole angled away to angled towards the sun.
This visualization shows the phases of the moon. It’s a fairly simple visualization that shows a photo of the moon and covers it with a shadow to show only the lit up portion of the moon. Half of the moon is always lit up (half of the sphere) but we can usually only see part of the lit up portion.
Controls
You can use the slider to control the opacity of the shadow. There is a button that lets you start and stop the animation and you can also step through the animation with the ‘Back’ and ‘Forward’ buttons or the left and right arrow keys.
Moon Phases
Full Moon – we can see the entire lit up portion of the moon
New Moon we can’t see any of the lit up part (moon is dark when viewing from Earth).
Waxing – refers to the moon when the lit up portion is getting bigger (i.e. we can see more of it each subsequent day)
Waning – refers to the moon when the lit up portion is getting smaller (i.e. we see less of it each subsequent day)
Crescent – is when the visible moon is less than half lit up
Gibbous – is when the visible portion of the moon is more than half lit
Quarter Moon – when the visible portion of the moon is exactly half of the circle
These are combined to get eight different distinct phases:
Waxing Crescent
Quarter Moon
Waxing Gibbous
Full Moon
Waning Gibbous
Quarter Moon
Waning Crescent
New Moon
The moon goes through these phases once every 29.5 days. Because it’s not exactly a whole number of days, the size of each crescent isn’t exactly the same each lunar cycle. The rate of change in the size of the lit up portion of the moon is fastest when the moon is close to a quarter moon and slowest when the moon is closest to a new moon or full moon. This has to do with the way the light is projected onto a 3D sphere but viewed as a 2D disc.
Data Sources and Tools:
The moon image was downloaded from unsplash.com and taken by Mike Petrucci representing Delaware. The code to determine the size and draw the shadow was written in javascript and d3 open source data visualization library.
Each state has two senators in the Senate, even though there is a great disparity in the populations of the states. This was a compromise that the framers of the Constitution dealt with in creating the framework of the US government. While the US House of Representatives is based on proportional representation, the Senate was designed to have two senators per state regardless of population. This leads to some interesting variations in the number of votes that some senators get relative to other senators (and how many people they represent).
Graph of Total Votes for Each Current Senator (2014, 2016 and 2018)
This graph is called a treemap and shows the total number of votes cast for the winner of each senate race of the current sitting senators. They are shown in order from largest to smallest vote totals, where the area of the rectangle is proportional to the number of votes. The treemap can be organized by party if desired. This graph does not show the number of votes that their opponents got.
If you hover over (click, on mobile) one of the boxes in the treemap, you can compare the number of votes received by that senator to the number of senators that received the same number of votes combined. This helps highlight the disparities in the representation of voters in large states in the Senate relative to that of voters in states with low populations.
For example, Kamala Harris, Democratic senator of my home state of California, received 7.5 million votes when she won her senate race in 2016. This large number of votes is larger than the combined votes for 22 of her Republican colleagues in small states. This is even more impressive since, as noted before, she ran against another Democrat Loretta Sanchez, in the election.
Note that some of the recently elected senators shown in the table are no longer serving in the Senate:
John McCain’s seat is currently held by Martha McSally
Johnny Isakson’s seat is currently held by Kelly Loeffler
Because of the large variation in population sizes and a tendency for more populous states to vote for democrats, Democratic Senators received many more votes in their elections than their Republican colleagues did, despite having fewer numbers. The 47 Democratic (and Independent) senators received a total of 67.5 million votes while the 53 Republican senators received 59.5 million votes.
Graph of Margin of Victory over Opposing Party for Each Current Senator (2014, 2016 and 2018)
This graph shows a slightly different set of data. Instead of total votes for the winning candidate, it shows the vote margin (i.e. the number of votes the winner received vs the opponent of a different party). The reason I specify it this way is that the two Democratic California senators defeated other democrats to win their elections (i.e. no republican was on the ballot in the general election because no republican got enough votes in the primary). This comparison is interesting because not only do some senators receive very few votes (because they live in small states), but they may only win by a small margin over their opponents. Comparing margins of victory, shows how few votes it would take to “flip” a Senate seat between the two parties.
If you take Kamala Harris’s margin of victory over Republicans to be her vote total (7.5 million votes) since there was no Republican running against her, her margin of victory is greater than the margin of victory of 43 of her Republican Senate colleagues combined.
The US coronavirus death rate is quite high compared to other countries (on a population-corrected basis)
US coronavirus deaths have surpassed 300,000. Many of these deaths could have been avoided if swift action had been taken in February and March, as many other countries did. This graph shows an rough estimate of the number of US deaths that could have been avoided if the US had acted similar to other countries.
This graph takes the rate of coronavirus deaths by country (normalized to their population size) and imagines what would happen if the US had had that death rate, instead of its own. It then applies that reduction (or increase) in death rate to the total number of deaths that the US has experienced. The US death rate is about 600/million people in September 2020 and if a country has a death rate of 60/million people, then 90% of US deaths (about 180,000 people) could have been avoided if the US had matched their death rate. The government response to the pandemic is one of several important factors that determine the number of cases and deaths in a country. This means proper messaging about the need to wear masks and socially distance as well as providing payments to citizens and business to help them during the economic shutdown. Other important factors can include the overall health of the population, the population structure (i.e. age distribution of population), ease of controlling borders to prevent cases from entering the country, presence of universal or low-cost health care system, and relative wealth and education of the population.
The graph lets you compare the potential reduction in US deaths when looking at 30 different countries. You can choose those 30 countries based on total population, GDP or GDP per capita. These give somewhat different sets of countries to compare death rates, which is an indication of the effectiveness of the coronavirus response.
A valid criticism of this graph is that testing and data collection is very different in each of the countries shown and the comparisons are not always valid. This is definitely a problem with all coronavirus data but for the most part, the very large differences between death rates would still exist even if data collection were totally standardized. Some of the data from the poorest countries is less reliable, because they have less testing capabilities.
On September 9th, 2020, the entire San Francisco Bay Area, we had a crazy combination of wildfire smoke and low clouds that darkened the sky and turned everything orange. At 9am, it looked like it was nighttime and at noon, it was so dark, that it looked like dusk.
Here is a plot of 8+ years of solar panel generation from our panels. If you click on the legend, you can toggle whether that data is shown. Total generation for the day was only 93 watt hours (as opposed to a summer median of 13300 watt hours, 13.3 kWh) and peak power was only 32 watts (vs a median summer peak of 2000 watts (2.0 kW)).
The solar generation was even worse than the next worst day in winter (typically when it rains all day). Clicking on the legend will toggle whether certain seasons are shown and you can view how solar generation varies by season.
Source and Tools:
Data on solar generation is downloaded from our solar panel inverter provider (enphase) and cleaned with a python script. Graph is made using the plotly open source javascript library.
2020’s stock market drop was unprecedented for the speed of the drop and also the speed of the recovery
This graph shows the stock market drops from the 2020 and other bear markets normalized so that the peak is at 100% at day 0. This lets you see the severity and duration of different bear markets from the Great Depression (1929), the Dot Com Bust (2000), and the Financial Crisis (2008) and other drops over 30%.
The coronavirus pandemic has significantly disrupted the global economy. Q2 GDP in the United States declined at an annualized rate of 32% and US unemployment reaching 15% due to coronavirus induced business shutdowns.
However, the stock market drop (represented by the S&P500 index) in late February and early March 2020 has somewhat surprisingly rebounded and reached a new all-time-high in August 2020, even as unemployment and GDP output has continued to falter. There certainly seems to be a disconnect between the fundamentals of the economy and the stock market.
Will the recovery in the stock markets continue or will it begin to align more closely with the fundamentals of the economy?
There are many proposed reasons why this disconnect is happening. The Federal Reserve actions to increase liquidity and prop up the stock market. The heavy weighting of tech in the S&P500 and the pandemic’s boost to many tech company’s business (i.e. Amazon, Zoom, Apple). Whatever the reason, the question of whether the market can continue at this pace or will have a correction is important and one to watch.
Data for the S&P500 price is daily from 1950 onward but before 1950, the data I had available was on a monthly basis. I interpolated this monthly data to create daily data, so not all the data is 100% accurate for any given day before 1950. Data for 2020 will continue to be updated daily.
Recent Comments