Should neighborhood effect be stable in urban geosimulation model? A case study of Tokyo  Yaolong Zhao, Fei Dong and Hong ...
Background of the research <ul><li>Two motivations: </li></ul><ul><li>Many spatial processes are not easily experimented w...
<ul><li>Urban modeling is one important branch in this field. </li></ul><ul><li>Three principal roles of urban modeling (B...
Background of the research t t+ ∆ t t+n ∆ t Observed data Observed data Predicted Learning Stage Historical pattern Predic...
Background of the research <ul><li>Cellular Automata (CA) – based urban geosimulation has been in popular use </li></ul><u...
Background of the research Nearly in all the CA-based urban geosimulation models the neighborhood effect keeps stable thro...
<ul><li>To empirically explore dynamics of the neighborhood effect in CA-based urban geosimulation model for checking its ...
Methodology – study area
Methodology –data set <ul><li>Data set used:  </li></ul><ul><li>Detailed Digital Information (10m grid land-use) Metropoli...
Methodology – procedure 1974 1979 1984 1989 1994 5 years 5 years 5 years 5 years Neighborhood effect model
Methodology – data processing
Methodology – data processing <ul><li>The changes in the area with distance less than 600m to the boundary of study area w...
Type one Type two Two typical neighborhood configurations in CA-based urban geosimulation models  It is not enough to repr...
Methodology -- model An extended neighborhood pattern Tobler’s First Law of Geography:  theoretical fundamentals Modificat...
Contribution of one cell with land use  k  in the neighborhood to the conversion of the developable cell  i  to land use  ...
The aggregated effect of the cells in the neighborhood can be expressed as:  Methodology -- model m : number of the cells ...
The neighborhood effect contribution to the probability of conversion to land use  h  of a cell ( P i ) is described as a ...
 
 
 
Neighborhood effect during 1984-1989 PCP: percentage correctly predicted.  ROC: relative operating characteristic.  **: si...
Neighborhood shows different degree of effect on the growth  pattern of different land-use category.
Neighborhood effect on the growth pattern of one land-use  category keep relative stable on the whole during the period of...
The neighborhood effect on some land-use categories changed a  little at different stages of urban growth during the perio...
No matter in which stage, the effect value of regression coefficient  of each active land-use type on its own transformati...
Conclusions <ul><li>Neighborhood shows different degree of effect on the growth pattern of different land-use category. </...
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Should neighborhood effect be stable in urban geosimulation model? A case study of Tokyo - Yaolong Zhao, Fei Dong and Hong Zhang

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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

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Should neighborhood effect be stable in urban geosimulation model? A case study of Tokyo - Yaolong Zhao, Fei Dong and Hong Zhang

  1. 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
  2. 2. Background of the research <ul><li>Two motivations: </li></ul><ul><li>Many spatial processes are not easily experimented with on the ground. Realistic but synthetic computer simulations by modeling based on GIS can be built as a laboratory for exploring ideas and plans that we would not otherwise be able to effect on the ground. </li></ul><ul><li>GISystems need such powerful functions of modeling spatial processes. </li></ul>Research on the theories and technologies of modeling spatial processes is an vital issue in GIScience and Systems.
  3. 3. <ul><li>Urban modeling is one important branch in this field. </li></ul><ul><li>Three principal roles of urban modeling (Batty 1971): </li></ul><ul><li>to help in refining and experimenting with hypotheses about the structure of cities; they form an essential part of theory development in urban research </li></ul><ul><li>to provide methods for educating planners in urban theory </li></ul><ul><li>to help predict the likely consequences of planning or not planning the future of cities in practical planning studies </li></ul>Background of the research
  4. 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
  5. 5. Background of the research <ul><li>Cellular Automata (CA) – based urban geosimulation has been in popular use </li></ul><ul><li>The core of CA modeling is neighborhood effect </li></ul>
  6. 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?
  7. 7. <ul><li>To empirically explore dynamics of the neighborhood effect in CA-based urban geosimulation model for checking its status during the period of urban evolution using Tokyo as a case study. </li></ul>Objective of this study
  8. 8. Methodology – study area
  9. 9. Methodology –data set <ul><li>Data set used: </li></ul><ul><li>Detailed Digital Information (10m grid land-use) Metropolitan Area of Tokyo in 1974, 1979, 1984, 1989, and 1994 </li></ul>
  10. 10. Methodology – procedure 1974 1979 1984 1989 1994 5 years 5 years 5 years 5 years Neighborhood effect model
  11. 11. Methodology – data processing
  12. 12. Methodology – data processing <ul><li>The changes in the area with distance less than 600m to the boundary of study area were deleted to eliminate the effect of boundary. </li></ul><ul><li>To eliminate the effect of spatial dependence in urban land-use pattern for the dynamic analysis of neighborhood effect: </li></ul><ul><li>systematic sampling </li></ul><ul><li> effective to better reduce spatial dependence but may lose some important information </li></ul><ul><li>random sampling </li></ul><ul><li> efficient in representing land-use pattern but low in efficiency in reducing spatial dependence </li></ul>
  13. 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. 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. 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. 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. 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 .
  18. 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
  19. 22. Neighborhood shows different degree of effect on the growth pattern of different land-use category.
  20. 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.
  21. 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.
  22. 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.
  23. 26. Conclusions <ul><li>Neighborhood shows different degree of effect on the growth pattern of different land-use category. </li></ul><ul><li>Neighborhood effect on the growth pattern of one land-use category empirically keep relative stable on the whole during the period of 20 years. </li></ul><ul><li>Due to the adjustment of land-use policy, the neighborhood effect on some land-use categories changed a little at different stages of urban growth during the period. How much degree of the effect of this change of neighborhood effect on urban geosimulation models would be a valuable extension to this research in the next step. </li></ul>
  24. 27. Thank you!

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