Tag Archives | NASA

Following Earth Observatory’s Lead

Are you looking for inspiration? If you ask anyone who visualizes earth and environmental data what their favorite sites are, or where they go for inspiration, odds are they will list NASA’s Earth Observatory in their top 5. (And if it’s not their number 1 site, please tell me what is.)

The Earth Observatory is a great exemplar of what a public portal can be. It combines a number of really cool features like an Image of the Day, a Natural Hazards archive, global maps of data, and an awesome collection of feature articles.

Indeed, there are quite a few image-of-the-some-specified-period sites out there, but none has quite the caché of the Earth Observatory. Of course, no other site has quite the same level of resources behind it either (I’ve heard estimates that Earth Observatory has upwards of 70* people involved), but that doesn’t mean we can’t learn from their experience while aspiring to model the site in our own work.

To me, the site is successful because it effectively melds visualizations and text into compelling stories. The visualizations are effective, clear and demonstrative of best practices in almost every case (and I hope to highlight this in future posts), but while the engaging appeal of the visualizations, images and photos may lead readers in, it is up to the text to tell the full story, imparting knowledge upon the reader.

A few years ago, the text of many of the daily images was almost formulaic in nature. And while proscribed formulas rarely lead to effective prose, those entries were still of interest because they covered three elements essential to conveying the story shown by the visualizations.

  1. What event or subject does the visualization relate to?
  2. What does the visualization show, and how can one read and interpret it?
  3. How was the data collected and visualized?

In other words, what is the relevance, story and science behind the visualization.

The key point here is that Earth Observatory follows a traditionally journalistic flow in their narratives. They grab a reader’s attention by starting each story with why they should care about the subject at hand. This is then followed with details on the image and relevant science. A reader is a lot more likely to appreciate and understand an image of flooding if they know how much damage it caused. Unfortunately, scientists who attempt to push their research out into the public realm often take the reverse approach, leading off with the (far more boring) instrument or dataset, leaving the point of the story to the end, in the fashion of a scientific paper.

If you’re interested in visualization generally, developing your skills or learning from the best, I highly recommend subscribing to their weekly email lists.

And if we hope to improve how we share our observations and visualizations of Earth’s mysteries with its citizens, than we would be well advised to follow the Earth Observatory model.

*Update: According to @rsimmon the core team has only 7 members, but they do have a lot of contributors.


Painting Temperatures by Number

Today is the Solstice, and pretty soon the dog days of sumer will be here. (Though, for those of us in the Northeast, we’ve already seen our fair share.) As things begin to heat up, I figured it would be appropriate to highlight some of the ways oceanographers visualize sea surface temperature data.

Temperature is a scalar variable, and its values are represented by continuous real numbers. Other variables types include categorical, vector or arrays (like RGB images), but we’ll save those for another day.

Temperature data collected at a singular point can easily be represented as numerical value (i.e. 76 degrees C), or if collected over a period of time, a time-series graph displaying temperature values over time can be easily drawn. Satellites, though, collect data over a large spatial area, meaning the dataset is inherently 3-dimensional. Each data point consists of a latitude (X position), longitude (Y position) and temperature (Z) value for each data point. The visualization challenge is figuring out how to display this 3D dataset on a 2D graph.

The most common solution is to create a pseudo-color image based on the temperature data. The trick is choosing the best colors to represent temperature values. The following are just a few examples.

The Scientist’s Rainbow

Perhaps the most common colormap, the rainbow (know as Jet in Matlab parlance) is often a scientist’s default choice. Like a Swiss Army knife, it is a good all-purpose tool. It is especially handy in helping subtle features stand out.

Sea Surface Temperature for June 2, 2011 from RU COOL

This Mid Atlantic image demonstrates how the rainbow palette allows you to easily distinguish small features within the range of a degree, even though the temperatures in the entire image range from 41 to 80 degrees Fahrenheit.

As useful as it is, the rainbow does have some major weaknesses. It is practically useless for colorblind viewers. In addition, it is important to keep in mind that non-scientists often associate rainbow representations with temperature, and only temperature. There is something intuitive about the red=hot and blue=cold extremes. Therefore, when creating images for public audiences it is unwise to use rainbow coloring for anything other than temperature, unless you enjoy confusing users.

An Alternate Choice

Sea Surface Temperature for May 2011 from NASA Earth Observatory

Given an infinite number of available colors (or at least 4 billion on modern displays), it’s easy to create an alternative color map. But balancing the need to highlight nuances in a dataset with accessibility for colorblind users while also keeping an image aesthetically pleasing is no small task.

Luckily, the artists at NASA’s Earth Observatory have come up with good alternatives over the years.

Hurricane Ready Temperatures

Hurricane-Ready Waters in the Atlantic, July 28, 2007

There is also no reason that colors need to be evenly distributed. If there is a scientific need to represent a dataset in a particular way, then adjusting the color map to highlight the message that needs to be communicated is certainly appropriate.

For instance, Tropical Storms develop best over waters that are above 80 degrees Fahrenheit. This image from NASA’s Earth Observatory, clearly shows those portions of the Atlantic Ocean that are ripe for hurricane development. For an alternate take, check out this recent image from NOAA’s Environmental Visualization Lab.

Temperature Anomalies

Sea Surface Temperature Anomaly for May 2011 from NASA Earth Observatory

Finally, sometimes it’s not the temperature value you want to represent, but the difference between the current temperature and a historical average. This is referred to as an anomaly, and it is often graphed slightly differently as it is a divergent dataset. Anomalies are typically represented as positive and negative differences, colored using red and blue respectively. Differences close to zero are left white.

This SST Anomaly image is from May 2011. These images are used to track El Nino and La Nina patterns, among other processes.

Well, that’s a quick roundup of some common examples. Do you have your own favorite way of coloring spatial maps of temperature or other datasets?

For more, check out the excellent primer Use of Color in Data Visualization by Robert Simmon.


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