4 COVER On Scale and S T O R I E S Toward Alternative Futures Complexity continued from page 3 to say, either here, here, or here. The beginning assumptions in the scenario chain are the most important, because if you make the wrong first steps, you will end up wrong. If you make the right first steps, you still may end up wrong, but you have a better chance. (See figure 2.) Complexity My second theme, complexity, interacts with scale. It represents the level of complexity that the analytic methods underpinning any design must achieve, especially in its understanding of process models. I think there are six questions that must be asked in any design problem and at any scale. They are • How should the state of the landscape be described in content, space, and time? This question is answered by representation models, the data upon which the study relies. • How does the landscape operate? What are the functional and structural relationships among its elements? This question is answered by process models that provide information for the several assessments that are the content for the study. • Is the current landscape working well? This question is answered by evaluation models, which are dependent on the cultural knowledge of the decision-making stakeholders. • How might the landscape be altered, and by what policies and actions, where, and when? This question is answered by the change models that will be tested in the research. They are also data, as assumed for the future. • What diff erence might the changes cause? This question is answered by impact models, which are information produced by the process models under changed conditions. • How should the landscape be changed? This question is answered by decision models, which, like the evaluation models, are dependent on the cultural knowledge of the stakeholders and responsible decision makers. Etc. “Facts” “History” “Constants” “Contingencies” Try to “Capture” Major “Generating” Assumptions/Ranges Too Many! T=N Time Future T=0 Time Present Figure 2 Furthermore, I believe that there are eight levels of analytic complexity associated with process models. Each of the eight levels is organized to answer a cumulatively more complex set of questions. I think that the larger the scale and the consequent greater risk, the more the analytic methods should aim to achieve more complex levels. At smaller scales and with less risk, simpler analytic levels may suffice. • Direct models ask, What is here? They are based on direct personal experience. If your feet are wet, don’t build here. • Thematic models add, Where and how much? • Vertical models add, What else? • Horizontal models add, What size and shape? • Hierarchical models ask, What happens at different nested scales? • Temporal models add, What if . . . and when? • Adaptive models add, From what and where to what and where? • Behavioral models add, From whom and where and when to whom and where and when? Finally, the Bottom Line What are the spatial-analytic needs of designers? It all depends on scale and complexity. There cannot be one answer. At its simplest, and frequently at smaller scales, the direct personal experience of the designer may be sufficient, and without any formalized analysis. At the other, large-scale, extreme, it will frequently require a Expected to do the impossible? Geocortex transforms how you design, build, maintain and enhance ArcGIS Server applications. By leveraging Geocortex pre-built software components, you can do more – faster, cheaper and better than otherwise possible. Be a superhero all over again. Let’s talk. Please contact us for a personal demonstration. by Latitude Geographics® www.geocortex.com © 2011 Latitude Geographics Group Ltd. All Rights Reserved. Geocortex and Latitude Geographics are registered trademarks of Latitude Geographics Group Ltd. in the United States and Canada. Other companies and products mentioned herein are trademarks or registered trademarks of their respective trademark owners.