Posts for Tag: graphics

Planetary Art – Inner Planet Orbital Spirograph

Posted In: Fun
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Earlier, I had made a visualization showing that Mercury is the closest planet to Earth (on average) and not Venus or Mars. To make that, I downloaded a bunch of NASA ephemeris (orbital) data. I realized I could use the same data to make some cool orbital art inspired by a spirograph – a planetary spirograph.

Basically, you get to choose a planet and the visualization will draw a line connecting that planet and Earth every few days. These lines will then build up into a cool pattern over 40 earth years of orbital cycles. Each planet (Mercury, Venus and Mars) has a different orbital period around the sun than Earth does and as a result, interesting patterns emerges.

Orbital periods of the four inner rocky planets:

  • Mercury: 88 days
  • Venus: 225 days
  • Earth:365 days
  • Mars: 687 days

Also evident is that the orbits of some of the planets are not quite circular so the pattern isn’t quite centered on the sun. Venus has the most regular pattern, creating a distinctive 5-lobed design. The other planets also have visually stunning patterns, though they do not repeat perfectly over time.

You can change the planets using the drop down menu as well as change the speed of the spirograph, and hide the planets and the sun.

Data and Tools:
I had thought about simulating the planets but there are plenty of tools out there that generate this orbital data so instead just downloaded 40 years of 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.

Planetary Art Spirograph

Zip Code Map of the United States

Posted In: Maps
US zip code map
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This zip code map of the United States visualizes over 42,000 zip codes in the 50 states. Zip codes are five digit postal codes used for mail delivery in the US. The points on the map show the geographic center of each zip code. The interactive visualization lets you type in a zip code and will show you where that zip code lies on the map. As you begin to type in the zip code, the map will highlight all the zip codes that begin with those numbers.

For example, if you type in “0”, you will highlight all zip codes that start with the zero in the Northeastern US. This will represent about 10% of the zip codes in the US. When you type in another number, it will narrow down the zip codes that begin with those two digits (approximately 1% of zip codes). It will progressively narrow down the number of zip codes as you type in more numbers, until you get to a full 5 digit zip code that represents 1 out of almost 43,000 zip codes (0.002% of zip codes). The map will then tell you the name of the city that that zip code is in.

You can explore how zip codes are distributed across the US by typing in different 1 and 2 digit numbers. You can also click on the check box to show or hide the outlines of the states.

Sources and Tools:

Zip code data was downloaded from opendatasoft.com. And the visualization was created using javascript and the open source leaflet javascript mapping library.

US zip code map

Visualizing The Growth of Atmospheric CO2 Concentration

Posted In: Environment

Facebooktwittergoogle_plusredditpinterestlinkedinmailThe current CO2 concentration in the atmosphere is over 400 parts per million (ppm). This has grown about 46% since pre-industrial levels (~280 ppm) in the early 1800s. The growing concentration of CO2 is a big concern because it is the most prevalent greenhouse gas, which is increasing the temperature of the planet and leading to substantial changes in the Earth’s climate patterns.

This graph visualizes the growth in CO2 concentration in the atmosphere (mainly from CO2 emissions due to human activities, such as burning fossil fuels for energy production, deforestation and other industrial processes). The graph starts at 1980 when CO2 concentration in the atmosphere was around 340ppm. It has grown significantly since then.

One of the interesting aspects of CO2 concentration is that it is not identical all around the globe, as it takes awhile for the atmosphere to mix. The graph shows geographic differences in CO2 concentration as well as seasonal ups and downs, that underly an overall growing trend in annual average (mean) concentration.

Seasonal trends in CO2 concentration occur due to differences in the amount of plant growth across different months. Spring and summer plant growth in the northern hemisphere causes a significant amount of photosynthesis, and CO2 absorption, relative to the fall and winter. This plant growth causes a very large amount of CO2 to be absorbed by plants and a noticeable reduction in the amount of CO2 in the atmosphere. The southern hemisphere spring and summer (northern hemisphere fall and winter) aren’t as obvious because there is much less land in the southern hemisphere and the land that is there is close to the tropics and green all year round.

CO2 concentration can change by about 4-5 ppm due to the “breathing” of plants, which is pretty significant. The total weight of CO2 in the atmosphere is about 3 trillion tonnes of CO2, so 4-5 ppm is about 1% of this or 30 billion tons of CO2 removed by plant life each spring/summer.

Data and Tools:

Data comes from the US National Oceanic and Atmospheric Administration (NOAA). Data was downloaded using an automated python script and the graphs were made using javascript and the open-sourced Plot.ly javascript engine.

CO2 concentration graph

Assembling the World Country-By-Country

Posted In: Maps

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Watch the world assemble country-by-country based on a specific statistic




This map lets you watch as the world is built-up one country at a time. This can be done along the following statistical dimensions:

  • Country name
  • Population – from United Nations (2017)
  • GDP – from United Nations (2017)
  • GDP per capita
  • GDP per area
  • Land Area – from CIA factbook (2016)
  • Population density
  • Life expectancy – from World Health Organization (2015)
  • or a random order

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:
Countries with higher life expectancy than US

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:
Countries with higher population density than US

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)
The map was created with the help of the open source leaflet javascript mapping library



Estimating pi (π) using Monte Carlo Simulation

Posted In: Math

Facebooktwittergoogle_plusredditpinterestlinkedinmailThis interactive simulation estimates the value of the fundamental constant, pi (π), by drawing lots of random points to estimate the relative areas of a square and an inscribed circle.

Pi, (π), is used in a number of math equations related to circles, including calculating the area, circumference, etc. and is widely used in geometry, trigonometry and physics.

This app estimates the value of pi by comparing the area of a square and an inscribed circle. The areas are calculated by randomly placing dots into the square and then counting how many of them are also inside the circle. If you do this enough times, you will get a rough ratio of the relative areas of the two shapes. These points are plotted on the graph (red if the fall inside the circle and blue if the fall outside).

Also shown on the graph is the value of our estimate of pi as the simulation progresses, from a few points to many thousands, to millions of points. We can see that when we have only a few points, the value may not be very accurate but as the number of points increases the value of our estimate gets closer to the true value. Running the simulation will add and plot 1 million points. After the first 100 points are added, the rate at which points are added increases. You’ll notice this as the speed at which dots fills up the square increases and because the plot is shown with a logarithmic x-axis.

Here is the math:
Length of side of square: $2 \times r$
radius of circle: $r$
Area of square: $A_{square} = 4r^2$
Area of circle: $A_{circle} = \pi r^2$

The ratio of areas is $A_{circle}/A_{square} = \pi r^2 / 4r^2 = \pi / 4$
Solving for pi: $\pi = 4 \times A_{circle}/A_{square} \approx 4 \times N_{dots_{circle}}/N_{dots_{square}}$
So pi is estimated as 4 times the ratio of dots in the circle vs square

Tools:
This was programmed in javascript, canvas and plotted using the open source plotly javascript plotting library.
estimating pi using monte carlo

Current Bay Area Air Quality

Posted In: Environment | Health

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Fires are once again raging in California and air quality in one of the most populated metropolitan areas in the country (the San Francisco Bay Area) is quite poor. This map show current air quality in the Bay Area. For more information see the EPA’s Air Quality website.

AQI colors

EPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.

Air Quality Index
Levels of Health Concern
Numerical
Value
Meaning
Good 0 to 50 Air quality is considered satisfactory, and air pollution poses little or no risk.
Moderate 51 to 100 Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.
Unhealthy for Sensitive Groups 101 to 150 Members of sensitive groups may experience health effects. The general public is not likely to be affected.
Unhealthy 151 to 200 Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.
Very Unhealthy 201 to 300 Health alert: everyone may experience more serious health effects.
Hazardous 301 to 500 Health warnings of emergency conditions. The entire population is more likely to be affected.

For more information and additional maps see the EPA’s Air Quality website.