On October 28, I will make a presentation to the Faculty of Environmental Studies' (FES) incoming Master's of Environmental Studies (MES) class. The idea of the coordinator of the seminar series, Roger Keil, is to present the diversity of what goes on at our Faculty and what all we do. He asked me to present a little slice of this in the form of a talk on Geographic Information Systems (GIS) and Mathematical Modeling. And 10 minutes to do it in! Here is the talk I will be giving. Needless to say, it will be accompanied by a set of slides - attached here in powerpoint format (though prepared in OpenOffice!)

Sidetrack: GIS and Mathematical Modeling

Justin Podur, FES
October 28, 2008

This is a movie from NASA's scientific visualization studio that shows the border of Haiti and the Dominican Republic from space. You can see that the political border that was somewhat arbitrarily drawn, like all political borders, here has a definite physical form in the shape of deforestation on the Haitian side and forest on Dominican side.

If you've used google earth you are familiar with how the world looks from this angle. It is not a perspective on the world that has been around for that many decades. To be able to see something like this on any old computer is due to relatively recent technological changes – in remote sensing, in aerial photography, in data processing.

At the end of the day though, this is just an image. A map is much more than just an image. And a Geographic Information System is more than a map. It is a system.

A system is made up of components. An information system is a database, with some kind of catalogue or index that makes it easy for a user to find information she needs.

In a geographic information system that index is spatial – you can search for data on some thing – incidences of health problems, or populations of species, or occurrence of habitat or soil or concentrations of chemicals – based on space.

Then, you can turn around and represent that data visually.

This is hurricane Ike, which just devastated the Caribbean and the Gulf Coast of the US, also from the scientific visualization studio. The red, green, and blue colors represent levels of precipitation that are being dumped onto the Gulf of Mexico and the Caribbean. The levels of precipitation were gathered from weather stations and sensors. The raw numbers the sensors gather correspond to specific points in space. From there analysts do what's called 'interpolation', where they guess at what the values are in between the weather stations where precipitation was measured, using equations about the way that masses of air move and also using the first rule of geography: that things that are close together are similar. Then, they turn those spatially-referenced numbers into an image. We go from raw numbers collected some way, to equations about the system, to an image.

Here is another example. A visualization of the carbon dioxide being emitted from the US and its environs.

This process also describes how we model the climate: we use measurements of weather and equations governing the transfer of mass and energy in the atmosphere, add some statistical and spatial analysis, and make predictions about how the climate will evolve, as, say, emissions of carbon dioxide increase, or other feedbacks influence the climate.

But I am not here as a salesman for GIS or mathematical modeling or anything else. It would be easy to present to you a series of cliches like: “GIS is a powerful tool”, or “GIS is the way of the future”.

But I could just as easily make a list of all the things a GIS can't do and won't ever be able to do. I can show you the deforestation of Haiti by an aerial photograph but I cannot show you the complex causes of it: I can't show you how the indemnity the French forced the Haitians to pay through the 19th century devastated their chances of development.

I can't show you in an aerial photograph how the US forced Haiti to destroy its population of creole pigs and in a stroke destroyed the only cash reserve that Haitian peasant families could have had.

I can't show you how Haitians haven't been allowed to have any tariff protections for their economy and consequently are utterly dependent on imports and aid for even the most basic commodities.

I can't show you how when Haitians tried to organize against this and built a popular movement that movement was crushed by external force, including the US and Canada and France. Nor how the country is currently occupied by nearly every country under the sun through the United Nations nor how hurricanes have just re-devastated the country, partly because it has not been allowed the sovereignty to develop its own systems of survival.

There is no reason to romanticize technology or science or what they can do. Knowing what they can't do is key to knowing what they can.

But nor am I going to tell you that something like GIS or mathematical modeling is evil, or is inherently biased towards the status quo, or is used on behalf the powerful.

I won't tell you that even though all of these these are true.

I studied physics as an undergraduate. A great deal of the mechanics that we learn was worked out in Europe by people who wanted to figure out how to hit an enemy boat or an outpost with artillery fire. Trial and error has been useful in warfare but Europe especially developed real science and used it navigation, communication, artillery, and other military purposes.

The original GIS software, GRASS, was developed by the US Navy.

A whole field of mathematical modeling, systems analysis and operations research, was developed to make the development of high-tech weapons systems maximally efficient during WWII. Later on the same mathematics was used in economic planning by the government and then by corporations.

Global Positioning Systems are now indispensable for political assassinations conducted by the US and its allies, including several that happened this year. Cellular phones can be used to track where people are, all day, and listen in on their conversations.

We don't like military technology. But the process of making these technologies “civilian” actually involves making them commercial, which, in the realm of information, involves creating barriers and restrictions on what people are allowed to come to know without paying for it.

I am not telling you this to make you scared any more than my opening was intended to make you excited about technology or methods or even science. What I would prefer is if every person here had the confidence to be able to look at a technology like this, whether it is GIS or GPS or any of the mathematical models that run our computers and our systems every minute of the day, and think about its implications.

Because whether you know it or not you are actually running this technology and you are running these systems, and there is a way to function in this system by just doing it and being very good at doing it and never thinking about what it means. There is also a way to think about what it means without necessarily trying to understand even conceptually the ideas that underpin the technology, and reject it all because of the negative implications. But those are both, in my view, different ways of repudiating our responsibilities to ourselves and each other.

You cannot avoid models, even if you try to avoid technology. Because a model is just an abstract representation of reality. I use mathematical models and computer models, but that is a choice of language. Some ideas are better suited to mathematical language. Other ideas are better suited to prose. And in general, the greater the breadth of a scientific field, the harder it is to make mathematical, the harder it is to go deep, the harder it is to make predictions. Perhaps you could put physics on one end of the spectrum of inquiry and history on the other. But in all inquiry you have to abstract some. You have to assume some. Better to be explicit about those abstractions. Better to be explicit about those assumptions. The funny thing about mathematical models is that on the one hand they actually force you to be explicit about your assumptions and abstractions and make things clearer, but on the other hand they take a bit of work to learn and consequently they can be used to obscure things. But that is part of what research is about, is doing that work, dispelling those obscurities.

Today you heard about development. Economic models and systems based on them are used to make decisions about who will get investment and aid and food and whose resources will be taken and when. To intervene in society it's necessary to understand its organization and some of its technological basis.

I see two trends occurring in this society. On the one hand, systems are getting more and more complex and we're able to do more and more things with technology, which is changing society in unpredictable ways. On the other hand, markets, private property, and corporate organizations are running down the planet's resources, destroying its ecosystems, depriving peoples of the means of survival, and generating conflicts and wars that could end very badly. I don't know how these two trends are going to combine but I do think the more of us that are willing to face them with open eyes, refusing to be intimidated by either the science or the politics, the better chance we'll have.