Should neighborhood effect be stable in urban geosimulation model? A case study of Tokyo - Yaolong Zhao, Fei Dong and Hong Zhang - School of geography South China Normal University Guangzhou, P.R.China

1. Should neighborhood effect be stable in urban geosimulation model? A case study of Tokyo Yaolong Zhao, Fei Dong and Hong Zhang School of geography South China Normal University Guangzhou, P.R.China 2010.03.23

4. Background of the research t t+ ∆ t t+n ∆ t Observed data Observed data Predicted Learning Stage Historical pattern Prediction Stage Future pattern Urban modeling: conceptual framework

6. Background of the research Nearly in all the CA-based urban geosimulation models the neighborhood effect keeps stable through the period of urban evolution ? Darling, how about we construct our house in this parcel?

8. Methodology – study area

10. Methodology – procedure 1974 1979 1984 1989 1994 5 years 5 years 5 years 5 years Neighborhood effect model

11. Methodology – data processing

13. Type one Type two Two typical neighborhood configurations in CA-based urban geosimulation models It is not enough to represent social systems. There are no theoretical justification to identify the weight for every cell. Methodology -- model

14. Methodology -- model An extended neighborhood pattern Tobler’s First Law of Geography: theoretical fundamentals Modificatory Reilly’s Law of Retail Gravity: theory expression Logistic Regression Approach: model constitution Scheme of impact gradient Modificatory Reilly’s Model Impact index: great Distance from developable cells: far

15. Contribution of one cell with land use k in the neighborhood to the conversion of the developable cell i to land use h for next stage: A j : area of the cell j , (here in square meters) ; d ji : the Euclidean distance between the cell j in the neighborhood area and the developable cell i , and G kh : constant of the effect of land use k on the transition to land use h . + stands for positive, – repulsive. Methodology -- model

16. The aggregated effect of the cells in the neighborhood can be expressed as: Methodology -- model m : number of the cells in certain distance to cell i I kj index of cells. I kj =1, if the state of cell j is equal to k ; I kj =0, otherwise.

17. The neighborhood effect contribution to the probability of conversion to land use h of a cell ( P i ) is described as a function of a set of aggregated effect from different land use types using logistic regression: Methodology -- model As G kh is a constant, let: Then: the effect of different land-use types in the neighborhood on the change of transformation odds P ih /(1- P ih ) of central cell i to land-use type k .

21. Neighborhood effect during 1984-1989 PCP: percentage correctly predicted. ROC: relative operating characteristic. **: significant at p<0.05. Others significant at p<0.001. Results 0.938 0.914 0.931 0.924 ROC 86.6 84.8 87.0 84.1 PCP (%) 0.687 0.628 0.683 0.649 Nagelkerke R 2 3300.624 15206.663 1768.019 9967.621 -2 log likelihood Test: -2.816 -3.064 -1.979 -2.592 β 0h Constant 0.264 0.229 0.164 ** 0.234 β kh Public 0.517 0.388 0.287 β kh Road 1.811 0.274 0.642 0.367 β kh Commercial 0.197 0.570 0.160 β kh Residential 0.457 0.220 1.417 0.359 β kh Industrial 0.127 0.179 1.108 β kh Vacant 4982 20286 2644 13844 Sample size Commercial Residential Industrial Vacant

22. Neighborhood shows different degree of effect on the growth pattern of different land-use category.

23. Neighborhood effect on the growth pattern of one land-use category keep relative stable on the whole during the period of 20 years. This point provides an essential empirical evidence to identify neighborhood effect in urban geosimulation, especially for predicting future urban growth pattern.

24. The neighborhood effect on some land-use categories changed a little at different stages of urban growth during the period. This phenomenon indicates that at different stages of urban growth, land-use change shows a bit different degree of dependence upon the neighborhood effect.

25. No matter in which stage, the effect value of regression coefficient of each active land-use type on its own transformation is always more than that of other land-use types, especially industrial and commercial land. This phenomenon represents the effect of spatial autocorrelation in the spatial process of urban growth in the Tokyo metropolitan area. This characteristic also kept relatively stable.

27. Thank you!