Available with Spatial Analyst license.
A set of conceptual steps can be used to help you build a model.
Step 1: State the problem
- To solve your spatial problem, start by clearly stating the problem you are trying to solve and the goal you are trying to achieve.
Step 2: Break the problem down
- Once the goal is understood, you must break the problem down into a series of objectives, identify the elements and their interactions that are needed to meet your objectives, and create the necessary input datasets to develop the representation models.
- With your objectives defined, you can now begin to develop the steps necessary to reach your goal. By arranging the objectives in order, you will begin to get a better understanding of problem you are ultimately trying to solve.
- For example, if your goal was to find the best sites for spotting moose, your objectives might be to find out where moose were recently spotted, what vegetation types they feed on most, and so on.
- Once you have established your objectives, you need to identify the elements and the interactions between these elements that will meet your objectives. The elements will be modeled through representation models and their interactions through process models.
- In the moose spotting example, known sitings and vegetation types will be only a few of the elements necessary for identifying where moose are most likely to be. The location of humans and the existing road network will also influence the moose. The interactions between the elements are that moose prefer certain vegetation types, and they avoid humans, who can gain access to the landscape through roads. A series of process models might be needed to ultimately find the locations with the greatest chance of spotting a moose.
- During this step, you should also identify the necessary input datasets. Once you have identified them, they need to be represented as a set of data layers (a representation model). To do this, you should have a good understanding of how raster data is represented in Spatial Analyst.
- Input datasets might contain sightings of moose in the past week, vegetation type, and the location of human dwellings and roads.
- The overall model (made up of a series of objectives, process models, and input datasets) provides you with a model of reality, which will help you in your decision-making process.
Step 3: Explore input datasets
- It is useful to understand the spatial and attribute properties of the individual objects in the landscape and the relationships between them (the representation model). To understand these relationships, you need to explore your data. A variety of tools and mechanisms are available in ArcGIS with which to explore your data. These include: mapping quantitative data, Graphs, Reports and the interactive tools of the Spatial Analyst toolbar.
Step 4: Perform analysis
- At this stage, you need to identify the tools to use to build the overall model. Spatial Analyst provides a wide variety of tools to serve this purpose.
- In the moose spotting example, you may need to identify the tools necessary to select and weight certain vegetation types, buffer houses and roads, and weight them appropriately.
Step 5: Verify the model result
- Check the result from the model in the field. Should certain parameters be changed to give you a better result?
- If you created several models, determine which model you should use. You need to identify which model is best. Does one particular model clearly meet your initial goal better than the rest?
Step 6: Implement the result
- Once you have conceptually solved your spatial problem, and verified that the results from the most a particular ideal model meets your initial expectations, you can then implement your result.
- When you visit the locations with the greatest chance of spotting moose, do you in fact see any?
- Many times, there are conflicting objectives and evaluation criteria that must be resolved before a result can be agreed on. See GIS and Multicriteria Decision Analysis by Jacek Malczewski for more information.