Transition Rule Elicitation Methods for Urban Cellular Automata Models

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

Transition Rule Elicitation Methods for

UrbanCellular Automata

ModelsJunfeng Jiao¹ and Luc Boerboom2

¹ Texas A&M University, USA, [email protected] ITC, Enschede, the Netherlands, [email protected]

Distance Education Course on Spatial Decision Support Systems 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


Content

CA models Transition rules Data vs. knowledge driven

elicitation of transition rules Knowledge elicitation methods to

gain understanding


Cellular automata

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


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).


Content


elicitation of transition rules Rule elicitation methods to gain

understanding


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


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


Transition to what?


we see the need to explicitly differentiate transition rules and consider transition potential and conflict resolution rules


Classification of transition rules


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.


Content



understanding


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).

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artificial neural network


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)

Predicted land use

Influence factors


Visual observation (trial-error)


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


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.


Most CA models are data driven, few are knowledge driven

Little empirical basis for many assumed spatial relations


Content



understanding


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



(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)

Documents

Transition Rule Elicitation Methods for Urban Cellular Automata Models