This visualization shows the current location of the International Space Station (ISS), actually the point above the Earth that the station is closest to. It is approximately 260 miles (420 km) above the Earth’s surface The station began construction in 1998 and had its first long term residents in 2000.
The visualization can also show the animated future orbital path of the ISS using ephemeris calculations, which makes a nice, cool pattern over an approximately 3.9 day cycle, where it starts to repeat. The animation allows you to view the orbital patterns on the globe (orthographic projection) or a mercator or equirectangular projection.
One of the cooler features is to drag and rotate the globe view while the orbital paths are being drawn. You can also adjust the speed of the orbit as well as keep the ISS centered in your view while the globe spins around underneath it. If you select the “rotate earth” checkbox, it becomes apparent that the ISS is in a circular orbit around the earth and that the pattern being made is simply a function of the earth’s rotation underneath the orbit.
This visualization only shows the approximate location of the ISS as there are several confounding factors that are not represented here. The speed of the ISS changes somewhat over time as the station experiences a small amount of atmospheric drag, which slows the station over time. But it still goes over 7000 meters per second or about 17000 miles per hour. As it slows, its orbit decays so it falls closer to earth and it experiences even more atmospheric drag. Occasionally, the station is boosted up to a higher orbit to counteract this decay. Secondly the earth is not a perfect sphere and this also causes the calculations to be only approximately correct.
Some other cool facts about the International Space Station:
Other cool space-related orbital art can be seen at the inner planet spirographs.
Here are a couple of images showing the final pattern made by the ISS on different map projections.
Sources and Tools:
I used the satellite.js javascript package and the ISS TLE file to calculate the position of the ISS.
The visualization was made using the d3.js open source graphing library and HTML/CSS/Javascript code for the interactivity and UI.
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 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.
Sources and Tools:
The equations for average daily solar insolation come from online lecture notes from University of Albany. The equations for number of hours of daylight comes from Wikipedia. The visualizations are made using the javascript d3 data visualization library and the interface and animation are made using javascript.
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.
These are combined to get eight different distinct phases:
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.
Earth is known as the blue planet, because it’s covered with quite a bit of water. But do you know where all that water on Earth is located? This interactive visualization will show the various amounts of water in its many forms on Earth: Oceans, Lakes, Rivers, Ice, Groundwater, etc….
If you hover over a part of the circular, sunburst graph, it will show you the amount of water that is in each of the various forms shown. If the label for that form is bolded, you can click on it and see further subdivisions beyond that broad category. For example, if you click on Oceans, it will show you how the water in the oceans is distributed among the five main oceans on Earth. As you move towards more focused views on the graph, you can click on the center of the circle to move back out to larger categories and see the big picture again.
As you can see, most of the water on Earth is found in a salty form, and most of that is in the oceans. It can be hard to click through to see freshwater lakes and rivers, as you have to be very precise to expand the “Surface Water” wedge, when you are looking at all “Freshwater”.
Even smaller, on that same visualization, is the “Living things” wedge is basically invisible. You can further explore the details of the living things category by clicking on the button that appears on the freshwater graph.
Checking the Group Rivers and Lakes checkbox will group rivers by continent and lakes by major groups.
It is interesting to see how much water there is on Earth (about 1.4 billion cubic kilometers of water), but how little of it is non-salty, liquid freshwater at the surface (about 100,000 cubic kilometers, though that is still quite a lot) but it only makes up about 0.008% of all water on Earth. That means for every 10,000 gallons of water on Earth, only one of those gallons is freshwater in a lake or river that we can easily access.
It is also believed that there is more water deep in the Earth’s interior (i.e. the mantle) than on the surface or near-subsurface, but estimates of that are highly uncertain and are not included in this graph.
If you click out past Earth’s water to look at water in the solar system, the estimates shown in this visualization are only including liquid water and do not include estimates of ice (which I haven’t been able to find estimates of). The amount of water in living things is estimated assuming that the ratio of organic carbon to liquid water is more or less the same across all different types of living things (i.e. viruses, bacteria, fungi, plants, animals, etc.). This isn’t a great assumption but the estimates, which come from estimates of the dry carbon weight of these organisms, vary across many orders of magnitude so being off in liquid water weight/volume by a factor of two or so isn’t a huge problem.
Tools and Data Sources
The sunburst chart is made using the open source, javascript Plot.ly graphing library. Data on water distributions is primarily from Wikipedia – Distribution of Water – List of Rivers by Discharge – List of Lakes – Weight of Living Biomass – Extra-terrestrial water estimates
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:
I thought about simulating the planets but there are plenty of tools out there that generate this orbital data so instead just downloaded ephemeris data (data related to positions of astronomical bodies) from NASA website.. I processed the data using javascript and drew the picture using HTML canvas tools and made the distance vs time plot with the Plotly open source plotting javascript library.
I’ve been playing (and watching) alot of soccer recently with the kids and it got me thinking about how hard the pros can kick the ball compared to us. This got me thinking about how much energy athletes can impart to a soccer ball and how that compares to balls and projectiles in other sports. This is not a scientific study, as I just googled the fastest pitch, shot, serve, kick, throw etc. from a variety of sports and the weight of the respective balls/projectiles to calculate their kinetic energy and momentum. I added in the stats for a (sort of) human projectile for comparison as well (Usain Bolt).
The graph is color coded so orange refers to projectiles that require no additional equipment, while the blue requires a bat or racket or club to aid in hitting the ball. You can toggle between log and linear scale on the x-axis to better see the differences between different projectiles.
The hammer throw is interesting because it far exceeds the kinetic energy and momentum of the other balls. If you watch a video of olympic hammer throws, you’ll see how much energy these very large, strong athletes are able to put into the throw. I think another aspect is that the top kinetic energy projectiles are all throws where there is significant acceleration of the projectile over a longer period of time rather than an instantaneous kick or hit.
Switching to the speed tab, all of the fastest projectiles are aided by equipment to achieve their very high speeds, but generally these projectiles have lower weights. This is also seen in the momentum tab, where the heavier projectiles are mostly unaided by equipment, probably because of the challenge of imparting enough momentum onto a heavy ball/projectile would require accelerating an even heavier racket/bat.
The equation for kinetic energy is \(E = {1\over2} mv^2\),
where E is kinetic energy (expressed in joules or kilojoules), m is mass and v is velocity (or speed).
The equation for momentum is \(P = mv\), where P is momentum.
The difference between momentum and kinetic energy is slightly tricky. The momentum rankings seem to prioritize the mass of the projectile while kinetic energy is a balance between speed (velocity) and mass. In kicking, throwing or hitting a ball/projectile, the player needs to put impart the energy into the ball. In a collision, total momentum of the system (player and ball) is conserved but kinetic energy is not, although total energy is (some energy may be “lost” as heat, sound, etc). In terms of being “hit” by the projectile, I believe that kinetic energy is probably more important than momentum for gauging the overall effect of the impact, but the total energy is not the only concern.The area over which the impact would occur is also important. Honestly, the table tennis (ping-pong) ball is the only one I think I’d be okay getting hit by (at least at these world record speeds).
Data sources and tools:
Mostly google for ball weights and trying to find some mention of the “fastest” throw or kick or whatever. Calculations are made using the equations above and plotted using Plot.ly javascript library.
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