Grain, Extent, and 8-bit Cities

A friend sent me a link to this cool website a couple of days ago: a guy named Brett Camper has coded it up to show zoomable maps of several major cities, pixellated à la old video game world maps. It’s visually neat, and it also illustrates how observed patterns change with changing scale of observation. What exactly does “scale of observation” mean? The “scale of observation” is not just one value—it comes in several parts. These parts go by several different names; one of the common frustrations with trying to use scale as an organizing concept is that the terminology is often vague and varied. For the purposes of this blog post, I’ll use the terms “grain” and “extent.”

“Grain” refers to the size of the most fundamental sampling unit: a pixel in a satellite image, a net tow on an oceanographic cruise, or a single quadrat in an ecological field study. Values for those examples might be 1 square km of the earth’s surface, 1000 cubic meters of seawater filtered, and a single square meter of a prairie soil, respectively. Any variation within the grain will be averaged out: you can’t tell where the squashed creatures in your net’s cod end came from with any more specificity than that they came from somewhere in the net’s 1000-odd meter track. (There is also the issue of how far apart the sampled units are. In the satellite image, they are contigous, while the net tows might be tens of kilometers apart, and the quadrats separated by tens of meters.)

“Extent” is the total area or distance over which measurements are collected. A satellite image might span a couple thousand kilometers from one side to the other. A long cruise could likewise cover thousands of kilometers, while the prairie grass study would probably only span a few kilometers, at most. Thinking about the grain and extent of your sampling, you can start to imagine the types of patterns and phenomena you would be capable of detecting. To illustrate, look at this clip from 8-Bit Cities, centered above Seattle’s University District—specifically, my office in the UW’s Fishery Science Building.

At this zoom level, you can’t actually see the Fishery Science Building, but if you click the “+” button on the map four times, it will show up. Being zoomed in, the real-world, geographical size of each tile is now smaller that it was zoomed out, and the geographical distance from one side of the map to the other is smaller as well. Both the grain and extent have decreased, and as a result, finer-scale patterns are visible: surface streets and buildings, for example. If you let your eyes go blurry and just look at the patterns as you zoom in and out, you will notice that they look quite different.

You may also notice that as you zoom in, the number of tiles doesn’t actually change. There are always about 26 of them across the map. Though the grain and extent have both changed, their ratio has not. This ratio is known as the “scope,” and it is also an interesting one to ponder. Scope is the extent divided by the grain, and characterizes how densely information is packed inside the boundaries of your measurements. As an example, look at the image below, from Google Maps, showing the same area as the 8-bit map, but with a much smaller grain, and, therefore, a much larger scope (clicking the “+” button four times here will also zoom you in to the same area as above):


View Larger Map

Looks pretty different, huh? We are fairly used to looking at maps, so the loss of relevant information with the 8-bit map is obvious. But for some other space, or measurement, or phenomenon—say, the variance of fish density in space and time in some region—and it is not immediately obvious what the appropriate sampling scales should be to measure the things we’re interested in.

This is just scratching the surface of all the ways you can look at grain, extent, and scope. You can consider them in space, as I did here, or in time, as I tend to do in my thesis work, or in both space and time, or in terms of spectral resolution, or taxonomy…the list goes on. As Camper puts it on 8-Bit Cities:

Maps offer us visual architectures of the world, encouraging us to think about and interact with space in particularly constrained ways. Take some time to think about your surroundings a little differently. Set out on a quest. Be an adventurer.

And as Chuang Chou put it circa 300 B.C., in a truly awesome quote I found at the top of this paper [pdf]:

“This being so,” asked the Earl of the River, “may I take heaven and earth as the standard for what is large, and the tip of a downy hair as the standard for what is small?”

“No,” said the Overlord of the North Sea. “Things are limitless in their capacities, incessant in their occurrences, inconstant in their portions, uncertain in their beginning and ending. For this reason, great knowledge observes things at a relative distance; hence it does not belittle what is small or make much of what is big, knowing that their capacities are limitless.”

2010/07/10

Filed under: Uncategorized — Tags: , , , , , — Sam @ 12:57 am

Methods of sampling and analysis and our concepts of ocean dynamics

This post was chosen as an Editor's Selection for ResearchBlogging.orgI read a paper today (actually, more like an essay) by Peter Wangersky, a longtime chemical oceanographer. Titled “Methods of sampling and analysis and our concepts of ocean dynamics,” it is essentially a personable ramble through six decades of marine science, reflecting on the technical capabilities and sampling methods over time and the way those capabilities and methods influenced the assumptions that were made and the questions that were posed—in essence, the working mental picture we have of the ocean. Things have indeed changed a lot since he began during World War II. The paper is full of quotable nuggets:

Adding machines did exist, and some could even be made to multiply, after a fashion. They were strictly mechanical, however, and the wear and tear on various joints, machine and human, coupled with the high frequency of random inputs, discouraged us from the use of any but the most necessary statistical tools. Perhaps this is why most analytical chemists of this vintage believe that if one does the analyses right, there’s no need for statistics. This is a self-reinforcing fallacy; if you don’t do the statistics, you never discover your limitations or the limitations of your methods and the universe you are sampling.

He also talks about the analysis of water samples for salinity, in the days before CTDs could measure it in-situ with electrical conductivity:

A group of female technicians, the salinity girls, ran the Mohr-Knudsen silver nitrate titrations to a chromate endpoint for eight hours a day. Needless to say, there was a considerable turnover in this group. There was always a shortage of salinity girls, and the sample bottles kept coming in from the ships and stacking up in the hallways.

Those were different times…

The most interesting aspect of the paper is his discussion of the shift in emphasis starting to occur from ship-based sampling to automated sampling with collections of stationary instruments. My thesis research is using a stationary echosounder, so I’ve done a fair amount of thinking about the differences between the traditional ship-based approach and the more recent ocean observatory approach. I appreciated hearing about this shift from the perspective of someone who has seen the entire evolution of modern oceanography firsthand. He doesn’t discuss it in the same vocabulary I might, but he comes to essentially similar conclusions.

Wangersky’s point is that our perception of the ocean and its dynamics is hugely dependent on the tools we have available to study it. It will be a very interesting time in the coming years, as more types of sensors and instruments are deployed long-term at more locations around the world. Just by virtue of observing the ocean at a new spatio-temporal scale, we’re likely to find stuff going on that we missed before. In the old days, oceanographers generally assumed that the ocean was in a steady state. When hydrographic samples were few and far between, and you had to bring them back to shore for the “salinity girls” to analyze before you knew what they meant, this assumption was kind of necessary. These assumptions were also kind of wrong, which is the point that Stommel made in his famous 1963 paper (remember Stommel?).

As instruments, methods, and computing power have increased, these types of oversimplifications have retreated, to the point where we now embrace variability and dynamic changes, and try to understand them in and of themselves, rather than as unwanted noise on top of some supposed equilibrium or steady state that isn’t really there. Every advance in instrumentation and every expansion of the scope of our observations has yielded a new perspective on the oceans, adding on to an understanding that is slowly and gradually becoming more complete.

Peter J. Wangersky (2005). Methods of sampling and analysis and our concepts of ocean dynamics Scientia Marina, 69 (S1), 75-84 : 10.3989/scimar.2005.69s175

2010/06/12

Filed under: Research Blogging — Tags: , — Sam @ 3:33 am

Varieties of Oceanographic Experience

This post was chosen as an Editor's Selection for ResearchBlogging.org

I’ve been thinking a lot lately about issues of scale in ecology, both because I’m taking a fascinating seminar on the topic this quarter, and because my particular research is conducive to thinking about them. “Scale” came to the fore as a topic of interest starting in the late 70′s, and is tied up with other concepts that were on the rise at that time, like chaos theory, fractal geometry, and nonlinear dynamics. An example of one of these ties is Benoit Mandelbrot’s famous question, “how long is the coast of Britain?” The answer, as he shows, depends on how long your ruler is. This turns out to be a recurring conundrum: the pattern detected by an ecological study depends very strongly on the scale of the measurements.

In 1963, fifteen years before most other people started thinking about this stuff, a short paper was published in Science that, in just four pages, managed to lay out how and why we need to consider the scale of our measurements when designing an experiment to measure oceanographic phenomena. It was written by Henry Stommel, which is not at all surprising if you know who he was. For those who don’t, Stommel was one of the original badass physical oceanographers. The kind of guy who could sit down with a pen and paper and demonstrate why the upper ocean circulates the way it is observed to. Or correctly derive the circulation of the deep ocean before it was observed. No big deal. But thanks to his 1963 paper, titled “Varieties of Oceanographic Experience,” he is cited far and wide to this day as one of the first scientists to explicitly consider the importance of scale to experimental design.

To illustrate why scale is important, take one of Stommel’s examples: say we want to measure changes in the height of the sea surface from month to month. At first glance, it would appear that only 12 measurements are necessary: go out once a month for a year, and you’re set. At second glance, however, this is ridiculous on several levels. The ocean has tides, for one. Measuring sea level once a month would give you a near-random sample of different parts of the tidal cycle, and you wouldn’t be able to detect any long-term trends. There are also shorter- and longer-term fluctuations: regular wind-driven waves, and sea-level deviations due to large oceanic eddies. To resolve sea-level changes from month to month, you will actually need to measure it every hour or so. Do it less frequently, and you will get results that are inconclusive or just plain wrong.

Choosing the right scale of measurement for your question of interest is very important. It is also not trivial, especially when the measurement is not as straightforward as the water level on a ruler nailed to the dock. Even today some people are prone to testing hypotheses using data collected at a scale inappropriate to the question. If Stommel had stopped with this message, the paper might still have found a fair number of readers. But the coup de grace was the graphic he came up with to make the message explicit:

Figure 1 from Stommel (1963).

Figure 1 from Stommel (1963).

It’s a three-dimensional surface, with time along the x-axis and distance along the y. Both are shown on a logarithmic scale, so that each tick mark is a factor of 10 larger than the one before it. Various phenomena that make the sea level go up and down are located on this surface based on their typical size and duration. The height of the surface at each point represents how much the sea level goes up or down—that is, how much energy or variability is concentrated at that space and time scale.

Gravity waves (aka wind waves, the normal ones that crash on the beach and make you seasick on boats) are typically several meters long and perturb the sea surface for a few seconds. They can therefore be placed in the lower left corner. Tides happen every 12-13 hours and affect the entire ocean basin, so they are located near the middle of the time axis, stretching from about a kilometer up to 10,000 km. And every ten thousand years or so, we hit an ice age that lowers the surface of the ocean everywhere about 100 meters, allowing humans to do things like cross the Bering Strait into North America. This figure is an elegant summary of all the different processes that perturb the sea surface, and of the spatio-temporal scales at which they all take place.

This kind of diagram (now known as a Stommel diagram) has found its way over the years into all kinds of different contexts. One direct descendent near and dear to my own heart is the one drawn up by Haury et al. in 1978. It is along similar lines, but shows variability in zooplankton abundance, not sea level. The possibilities for these diagrams are nearly endless, and not limited to the ocean, or even the natural sciences. Any system that has stuff going on over short and long distances and time spans can be clarified by sketching up a Stommel diagram. Drawing a picture like this can help make it clear how to approach your research question, and will hopefully help you avoid screwing it up by choosing the wrong scale of measurements. The hope, as Stommel put it in the last line of the paper, is to “look forward to a time when theory and observation will at last advance together in a more intimately related way.” Amen.

Stommel, H. (1963). Varieties of Oceanographic Experience: The ocean can be investigated as a hydrodynamical phenomenon as well as explored geographically Science, 139 (3555), 572-576 DOI: 10.1126/science.139.3555.572

2010/05/16

Filed under: Research Blogging — Tags: , , — Sam @ 9:50 pm

Two Views of the Ocean

A significant part of my thesis research deals with scale. “Scale” is a very vague word, but in ecology, it connotes some very profound truths and problems about the natural world and our understanding of it. I hope to write about the topic of scale in ecology in greater depth some time, but for now, I present two pictures I’ve seen online in the past couple of days. The first shows the oceanic water column from top to bottom:

The Pacific Ocean, from top to bottom over the Marianas Trench. Click to see full water column.

The Pacific Ocean, from top to bottom over the Marianas Trench. Click to see full water column.

And the second shows the Atlantic Ocean basin from west to east:

The Atlantic Ocean, from North America to Europe, drawn to scale.  Click for full-size image

The Atlantic Ocean, from North America to Europe, drawn to scale. Click for full-size image

It’s kind of humbling to look at one and then the other. The first awes you with the immense depth of the ocean at its deepest point, and the second with the relative thinness of the ocean on the surface of the planet. That thin wet film on our ball of rock has so much going on in it: Currents, ecosystems, the lives and deaths as large as whales and as small as photosynthetic archaea. That’s deep, man…but also shallow. You dig?

2010/02/19

Filed under: Uncategorized — Tags: , , — Sam @ 4:59 am

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