Agent Teams and Evolutionary Computation: Optimizing Semi- Parametric Spatial Autoregressive Models

Classical spatial autoregressive models share the same weakness as the classical linear regression models, namely it is not possible to estimate non-linear relationships between the dependent and independent variables. In the case of classical linear regression a semi-parametric approach can be used to address this issue. Therefore an advanced semi- parametric modelling approach for spatial autoregressive models is introduced. Advanced semi-parametric modelling requires determining the best configuration of independent variable vectors, number of spline-knots and their positions. To solve this combinatorial optimization problem an asynchronous multi-agent system based on genetic-algorithms is utilized. Three teams of agents work each on a subset of the problem and cooperate through sharing their most optimal solutions. Through this system more complex relationships between the dependent and independent variables can be derived. These could be better suited for the possibly non-linear real-world problems faced by applied spatial econometricians.

Of cells and cities: a comparative Econometric and Cellular Automata approach to Urban Growth Modeling

This paper presents a comparative assessment of two distinct urban growth modeling approaches. The first urban model uses a traditional Cellular Automata methodology, based on Markov transition chains to prospect probabilities of future urban change. Drawing forth from non-linear cell dynamics, a multi-criteria evaluation of known variables prospects the weights of variables related to urban planning (road net- works, slope and proximity to urban areas). The latter model, frames a novel approach to urban growth modeling using a linear Logit model (LLM) which can account for region specific variables and path depen- dency of urban growth. Hence, the drivers and constraints for both models are used similarly and the same study area is assessed. Both models are projected in the segment of Faro-Olh ̃ao for 2006 and a comparative assessment to ground truth is held. The calculation of Cohenââ¬â¢s Kappa for both projections in 2006 allows for an assessmentof both models. This instrumental approach illuminates the differ- ences between the traditional model and the new type of urban growth model which is used. Both models behave quite differently: While the Markov Cellular Automata model brings an over classification of ur- ban growth, the LLM responds in the underestimation of urban sprawl for the same period. Both excelled with a Kappa calculation of over 89%, and showed to have fairly good estimations for the study area. One may conclude that the Markov CA Model permits a riper un- derstanding of urban growth, but fails to analyze urban sprawl. The LLM model shares interesting results within the possibility of identi- fying urban sprawl patterns, and is therefore an interesting solution for some locations. Another advantage of the LLM is directly linked to the possibility of establishing probability for urban growth. Thus, while the traditional methodology shared better results, LLM can be also an interesting estimate for urban patterns from an econometric perspective. Hence further research is needed in exploring the utility of spatial econometric approaches to urban growth.