Spatial modeling

Models abstract and simplify complex systems in order to make them easier to understand. Many types of models are used in GIS, including process models like those that model soil erosion or measure spatial interaction between customers and retail outlets. The most common models, however, are those that help you to locate something. These are suitability models.

A GIS model can be presented as a procedural flowchart like the one below. The boxes represent GIS data while the connection lines represent a GIS analysis function.

Here a soil raster was created from a soil vector layer and a slope raster was derived from an elevation surface. Both were reclassified to a common scale and then combined into a map of suitable locations.

In a broad sense, a model is a filter that helps extract information from volumes of complex data. For example, as a farmer you may decide where to apply fertilizer on your crop based on previous harvest yields, soil moisture, and soil pH.

The level of difficulty depends on the nature of the problem. Some models (e.g., finding conflicts between a general plan, zoning restrictions, and actual land use) are quite simple, requiring only a day or so to design and implement—provided you have the data, of course. Other models (e.g., siting a nuclear power plant) may require many months to design and implement. Because models often require specialized knowledge, they are typically a team effort.

It is important to note that your model is only as good as your data, your design, and your implementation. While you will always have to contend with some amount of errors in your data, with proper planning and some simple skills, you should be able to minimize errors in design and eliminate errors in implementation.

Regardless, your model is an abstraction of reality, so error will always be present. Models, however, do provide you with a way to better understand a problem and to test alternatives.