Transition Rule Elicitation Methods for Urban Cellular Automata Models

1. Transition Rule Elicitation Methods for UrbanCellular Automata Models Junfeng Jiao¹ and Luc Boerboom2 ¹ Texas A&M University, USA, hkujjf@gmail.com 2 ITC, Enschede, the Netherlands, boerboom@itc.nl

2. Several PhD and MSc projects on CA modeling • Always theoretical, not empirical • Expansion oriented rather than land use change and land use conflict • Academic studies • Similar problems in MAS? • Junfeng Jiao looked at • What are different approaches of rule formulation • How knowledge rich? • How to elicit knowledge? • How can we empirically enrich future research Distance Education Course on Spatial Decision Support Systems

3. Content • CA models • Transition rules • Data vs. knowledge driven elicitation of transition rules • Knowledge elicitation methods to gain understanding Distance Education Course on Spatial Decision Support Systems

4. Complex dynamic behaviors based on a relatively simple set of rules Applied to lattice of cells (i.e. spatial) interacting with their environment Cells interact over time through rules Cellular automata Distance Education Course on Spatial Decision Support Systems

5. Simulations done for different purposes or with different aspirations • Land use dynamics (White and Engelen,1993, White, Engelen, et al., 1997), • Regional scale urbanization (Semboloni,1997; White and Engelen, 1997), • Poly centricity (Wu, 1998; Cheng, 2003)), • Urban spatial development (Wu and Webster, 1998), • Urban growth and sprawl (Batty, Xie, et al., 1999; Clarke, Hoppen, et al., 1997). Distance Education Course on Spatial Decision Support Systems

6. Content • CA models • Transition rules • Data vs. knowledge driven elicitation of transition rules • Rule elicitation methods to gain understanding Distance Education Course on Spatial Decision Support Systems

7. Transition rules • The control component • Determines the future cell state as a function • Current state • States of surrounding cells. TPT+1 T = f(ST, NBT) • TPT+1Transition Potential of tested cell in time T + 1 • S cell state at time T • NB Neighborhood states at time T Distance Education Course on Spatial Decision Support Systems

8. Land use CA • More considerations than neighborhood such as • Access, • Suitability • … TPT+1 T = f(ST, NBT, AC, SU …) • TPT+1Transition Potential of tested cell in time T + 1 • S cell state at time T • NB Neighborhood states at time T • AC Accessibility effect • SU Suitability effect Distance Education Course on Spatial Decision Support Systems

9. Transition to what? Distance Education Course on Spatial Decision Support Systems

10. we see the need to explicitly differentiate transition rules and consider transition potential and conflict resolution rules Distance Education Course on Spatial Decision Support Systems

11. Classification of transition rules Distance Education Course on Spatial Decision Support Systems

12. Advantage of classification of transition rules • Suppose: Poor residential state convertible to • institutional or to • high quality residential • As function of current concentration of each of these three states. • Is this a neighborhood effect? • Or is it a conflict resolution effect? • Could be modeled as both, but semantics are different. • We seem to treat cells as agents, although we are certainly not talking about agent-based systems. Distance Education Course on Spatial Decision Support Systems

13. Content • CA models • Transition rules • Data vs. knowledge driven elicitation of transition rules • Rule elicitation methods to gain understanding Distance Education Course on Spatial Decision Support Systems

14. Regression analysis • Modeler identifies the possible influence factors of land use change (neighborhood effect, suitability effect, and accessibility effect) • Modeler uses some methods to measure these different effects. • Modeler overlays different land use maps and identifies change areas and selects random samples. • Modeler uses regression analysis to calculate future land demand based on past urban development. • Examples: Wu (2000) and Sui and Zeng’s (2001). Distance Education Course on Spatial Decision Support Systems

15. Influence factors Predicted land use artificial neural network • Modeler identifies the possible influence factors of land use change (neighborhood effect, suitability effect, and accessibility effect) • Modeler uses some methods to measure these different effects. • Modeler forms a neural network. • Modeler selects functions to link the neurons. • Modeler trains ANN with historic land use change • Poor insight of how influence factors relate to land use change • Examples: Li and Yeh (2001, 2002) Distance Education Course on Spatial Decision Support Systems

16. Visual observation (trial-error) • Modeler identifies the possible influence factors of land use change (neighborhood effect, suitability effect, and accessibility effect) • Modeler uses some methods to measure these different effects. • Unlike previously, trial-error to calibrate distance functions for predictive modeling. Or assumptions for scenario development (difficult to assess) • Uncertainty as to interaction of effects i.e. attraction as source of change or repulsion by others? • Can be knowledge driven • Examples: www.riks.nl Distance Education Course on Spatial Decision Support Systems

17. Analytical Hierarchy Process and Multi-Criteria Evaluation (AHP-MCE) • Modeler identifies the possible factors determing land use change (neighborhood effect, suitability effect, and accessibility effect) • Modeler defines hierarchy to represent relationship between these factors the simulation objective • Importance of factors is expressed by decision makers (i.e. normative/prescriptive or descriptive) • Example: Wu and Webster (1998): simplified this step and determined the factors’ weights according to possible planning policies and their own understanding of the urban development • Unlike evaluation practice, where focus is on decision maker. Distance Education Course on Spatial Decision Support Systems

18. Most CA models are data driven, few are knowledge driven • Little empirical basis for many assumed spatial relations Distance Education Course on Spatial Decision Support Systems

19. Content • CA models • Transition rules • Data vs. knowledge driven elicitation of transition rules • Rule elicitation methods to gain understanding Distance Education Course on Spatial Decision Support Systems

20. Brainstorming on examples of empirical enrichment with knowledge elicitation • Interview to define stakeholders • Document analysis to understand actual transitions and competition and possibly derive an idea of dominance of land uses • Free-listing and sorting to identify factors and arrive at transition rules for different stakeholders Distance Education Course on Spatial Decision Support Systems

21. Distance Education Course on Spatial Decision Support Systems

22. (personal) Conclusions • Could have more complicated conflict resolution rules than a weighted summation, e.g. as function to degree of demand – supply gap. • Interesting to look at knowledge-driven methods to come closer to understanding of land use changes. • Focus on empirical support for transition rules. • Focus on knowledge elicitation • Getting closer to MAS (but of course not really) Distance Education Course on Spatial Decision Support Systems