Posts for Tag: visualization

Antipodes map: What’s on the other side of the Earth?

Posted In: Fun | Maps
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What is an antipode?

An antipode is a point that is on the exact opposite side of the earth (or other sphere) from a given location. If you drew a line (vector) from your location to the center of the earth and continued that line until it emerged from the other side of the earth’s surface, that point of intersection on the other side is the antipode. When I was a kid, people occasionally mentioned “digging a hole to China”. While this is currently impossible for many reasons1Earth’s core is about 6000 degrees C, China is not the antipode for North America (where I grew up). If you grew up in Argentina or Chile, then maybe that would make a little more sense.

The antipodes for most of North America and Europe are in the Indian and South Pacific oceans respectively.

Other examples of antipodes that are both on land:

Instructions:

It should be relatively explanatory, but you find your location by dragging the globe on the left side so that your location is in the center crosshair. The other globe (on the right) will show you the antipode to your location.
You can zoom in and out with the +/- buttons or pinch to zoom on mobile. If you zoom in enough, it will look like a normal two-dimensional web map (like google maps).

Tools:
This interactive visualization is made using the awesome webglearth javascript library. I just discovered this recently after making a number of 2D maps.


Footnotes   [ + ]

1. Earth’s core is about 6000 degrees C

Scaling the physical size of States in the US to reflect population size (animation)

Posted In: Maps
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States sized to New Jersey’s population density


Choropleth maps are a pretty useful kind of map that colors distinct areas of the map (e.g. states, counties or countries) to reflect different numerical or categorical values. It is useful to show differences across geographic regions. I’ve been making a bunch of these recently (stressed states, bitcoin electricity consumption, college admissions). One of the issues that can be problematic with these maps is that some regions can be very large but only have very few people. If the choropleth map is tracking a intensity value (like CO2 emissions per capita), a large area with a high color value might visually indicate that total emissions (emissions per capita x # of people) is also high. In the US this is reflected in states like Alaska, Montana and Wyoming, which are large but have very few people.

I decided to make a modified choropleth map (updated after learning that it’s called a cartogram) that scales the size of the states to be the proportional to the state’s population. States with larger populations show up as larger. This is equivalent to making each state have the same population density. Since New Jersey has the highest population density of any state in the US (1200 people/square mile), it stays the same size in this map and all the other states shrink, to reflect their lower population density. For example, California has a larger population than NJ (4.4x), but its physical size is about 20x larger. So California is shrunk to about 20% its original size to make its physical size 4.4x the size of NJ.

The states are also colored to show population as well (darker redder colors reflect larger population while yellow/beige reflects small populations).

States sized to California’s population density

Living in California, I decided to make another animation, this time with scaled to the density of California, so some states that are less dense will shrink, while others that are denser will grow. New Jersey grows quite a bit. Because many of the dense Northeast states grow a bit, I had to space them out (manually) so you could still see them otherwise they’d overlap too much.

Data Sources and Tools:
2015 population and population density data comes from Wikipedia and leaflet.js open source mapping library was used to create the maps. State outlines in geoJSON format come from leaflet. Javascript code was used to scale the coordinates of the geoJSON polygons to the appropriate size and animate the map.

state size scaled by population

Speed and Kinetic Energy of Sports Pitches, Shots and Kicks

Posted In: Science | Sports
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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.

Equations and stuff

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.

Heatmap of Electric Vehicle (EV) Sales in California (Animation)

Posted In: Energy | Transportation
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College Admissions By State

Posted In: College
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Real Country Sizes Shown on Mercator Projection

Posted In: Maps
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I remember as a child thinking that Alaska was as large as 1/2 of the continental US. Later, however, I learned that while it is the largest state, it is actually only about 1/5 the size of the lower 48 states. My son has also remarked that Greenland is very big. And while it is very big, it’s nowhere near the size of the continent of Africa.

The map above shows the distortion in sizes of countries due to the mercator projection. Pressing on the button animates the country ‘shrinking’ to its actual size or ‘growing’ to the size shown on the mercator projection. It was inspired by a similar animation that I saw on reddit and decided I wanted to try to build the same thing.

The mercator projection is a commonly used projection on computer maps because it has perpendicular latitude and longitude lines (forming rectangles). It is formed by projecting the glob onto a cylinder A variant of the was adopted by Google maps, which helped establish it as the informal standard for web-based maps (although Google maps now uses a globe view, instead of a map projection when zooming out to a very wide view).

Areas far from the equator are distorted in terms of their distances and are shown much larger than they actually are. This is one of the major issues with a projection of a globe onto a cylinder area. This is why Greenland, Russia and Canada shrink so much in the animation, they are fairly high in latitude in the Northern Hemisphere.

This next graph shows each country plotted with their actual land area and apparent land area as shown on a Mercator projection. The further the countries are from the 1:1 line the greater the overestimate of their size from the Mercator (also color coded to be red). It is a logarithmic plot showing many different orders of magnitude in country size. The table also shows the top 10 countries whose size is overestimated (and the difference in land area in square kilometers or as a percentage reduction from the size in the Mercator projection).

As it shows, Greenland is the country that has the largest percent difference between its apparent size in a Mercator projection and it’s real size (it’s only about 1/4 of the apparent size). And Russia is the country with the largest absolute difference between these two sizes.

mercator projection real size

Data and tools: This visualization was made using the Leafletjs javascript mapping library and country shapefiles (converted to geojson). I calculated the area in two ways, one assuming latitude and longitude are rectangular coordinates (i.e. Mercator projection) and the other was the actual area. Then I calculated the latitude and longitude coordinates for the outline of the “real” size by modifying the original latitude and longitude by the ratio of these two areas to draw the new smaller, “real” country size.