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Introduction to Geoinformatics

Professor: Gilberto Câmara

Course Objective

The course “Introduction to Geoinformatics” is focused on discussing the core concepts of Geoinformatics. We take a realist perspective, and ask ourselves the question: What are the different types of geographical data, and how are these types representable in computers?.

What we call “geographical data” includes different kinds of data. We observe the natural world when we get data about topography, landscapes, the oceans and the atmosphere. Sometimes we represent data from nature as a continuous variation, as when we build digital terrain models. In other situations, we give names to natural features, as when we say “Mont Blanc”. We also create geographical reality, as when we draw boundaries of countries and of land parcels. We also measure facts of the social world, when we take a census and locate crimes. We also build continuous distributions out of social reality, e.g., when we create maps of disease incidence in a country. We also observe and detect change in the geographical world, as when we map new deforested areas.

The beauty and the challenge of Geoinformatics is that there are a relatively small set of data structures that are able to represent different types of geographical data. This representational power has enabled software engineers to develop the technology of geographical information systems. The challenge is to understand both the data structures and the semantics of the information they represent. This course is then focused on discussing the semantics of geographical data, as well as the links between such semantics and the associated computer representation. When he completes the course, we expect that the student is able


“The biggest problem with models is the fact that they are made by humans who tend to shape or use their models in ways that mirror their own notion of what a desirable outcome would be.” (John Firor, formed director of NCAR, cited in Myanna Lahsen's paper “Seductive Simulations”.

There are certain similarities between a work of fiction and a model: Just as we may wonder how much the characters in a novel are drawn from real life and how much is artifice, we might ask the same of a model; How much is based on observation and measurement of accessible phenomena, how much is based on informed judgment, and how much is convenience? (Naomi Oreskes, professor of History of Science, also cited by Myanna Lahsen).

“A model is clear, decisive, and positive, but it is believed by no one but the man who created it. Observations, on the other hand, are messy, inexact things, which are believed by everyone except the man who did that work”. Harlow Shapley, American astronomer

Conclusion: to understand what models are, a scientist needs to be able to develop models himself. He needs to master computer programs that allow him to grasp the basics of modelling activity. He needs to be understand the different techniques used in modelling and their limitations.


To build the models, we will use the TerraME software. The software are supporting material are available at the TerraME site.

Main references

  • Complex Adaptive Systems: An Introduction to Computational Models of Social Life, John H. Miller & Scott Page, Princeton University Press, 2007.
  • Simulation for the Social Scientist, N Gilbert, K. Troitzsch. Open University Press, 2005. Wiley, 2004.
  • Agent-Based Models of Geographical Systems, A. J. Heppenstall, A. T. Crooks, L. M. See, Michael Batty (Editors). Springer-Verlag, 2011.
  • Growing Artificial Societies: Social Science from the Bottom Up, J. M. Epstein and R. L. Axtell. MIT Press, 1996.

Additional references

  • Geosimulation: Automata-based modeling of urban phenomena. I. Benenson, P. Torrens.
  • Rules, Games, and Common-Pool Resources. E. Ostrom, R. Gardner, J. Walker. University of Michigan Press, 1994.
  • The Construction of Social Reality, John R. Searle, Free Press, 2007.

Course 2013

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