Should neighborhood effect be stable in urban geosimulation model? A case study of Tokyo - Yaolong...

Should neighborhood effect be stable in urban geosimulation model? A case study of Tokyo

Yaolong Zhao, Fei Dong and Hong Zhang

School of geographySouth China Normal University

Guangzhou, P.R.China2010.03.23

2

Background of the research

Two motivations:• 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.

• GISystems need such powerful functions of modeling spatial processes.

Research on the theories and technologies of modeling spatial processes is an vital issue in GIScience and Systems.

3

Urban modeling is one important branch in this field.

Three principal roles of urban modeling (Batty 1971):• to help in refining and experimenting with hypotheses about the

structure of cities; they form an essential part of theory development in urban research

• to provide methods for educating planners in urban theory • to help predict the likely consequences of planning or not planning the

future of cities in practical planning studies


4


t t+∆t t+n∆t

Observed data Observed data Predicted

Learning StageHistorical pattern

Prediction StageFuture pattern

Urban modeling: conceptual framework

5


• Cellular Automata (CA) – based urban geosimulation has been in popular use

• The core of CA modeling is neighborhood effect

6

Background of the researchDarling, how about we construct our

house in this parcel?

Nearly in all the CA-based urban geosimulation models the neighborhood effect keeps stable through the period of urban evolution

?

7

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.

Objective of this study

8

Methodology – study area

9

Methodology –data set

Data set used: Detailed Digital Information (10m grid land-use)

Metropolitan Area of Tokyo in 1974, 1979, 1984, 1989, and 1994

10

Methodology – procedure

1974

1979

1984

1989

1994

5 years

5 years

5 years

5 years

Neighborhood effect model

11

Methodology – data processing

12

Methodology – data processing

The changes in the area with distance less than 600m to the boundary of study area were deleted to eliminate the effect of boundary.

To eliminate the effect of spatial dependence in urban land-use pattern for the dynamic analysis of neighborhood effect:

• systematic sampling effective to better reduce spatial dependence but may lose some important information

• random sampling efficient in representing land-use pattern but low in efficiency in reducing spatial dependence

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


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

Modificatory Reilly’s Model

Impact index: great

Distance from developable cells: far

Scheme of impact gradient

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:

2ji

jkhkh d

AGf =

Aj: area of the cell j, (here in square meters) ;

dji: the Euclidean distance between the cell j in the neighborhood area and the developable cell i, and

Gkh: constant of the effect of land use k on the transition to land use h. + stands for positive, – repulsive.


16

The aggregated effect of the cells in the neighborhood can be expressed as:


∑=

=m

jkj

ji

jkhkh I

d

AGF

12

m: number of the cells in certain distance to cell i Ikj index of cells. Ikj=1, if the state of cell j is equal to k; Ikj=0, otherwise.

17

The neighborhood effect contribution to the probability of conversion to land use h of a cell (Pi) is described as a function of a set of aggregated effect from different land use types using logistic regression:


As Gkh is a constant, let:

mkm mi

m

kkhikhoi

kikhikhoi

ih

ih Id

AGF

P

PLog ∑∑∑ +=+=

− 2)

1( ββββ

khikhikhii Gββββ == '0

'0 ,

Then:

mkm mi

m

kikhi

ih

ih Id

A

P

PLog ∑∑+=

− 2''

0)1

( ββ

the effect of different land-use types in the neighborhood on the change of transformation odds Pih/(1-Pih) of central cell i to land-use type k.

21

0.9380.9140.9310.924ROC

86.684.887.084.1PCP (%)

0.6870.6280.6830.649Nagelkerke R2

3300.62415206.6631768.0199967.621-2 log likelihood

Test:

-2.816-3.064-1.979-2.592β0h Constant

0.2640.2290.164 **0.234βkh Public

0.5170.3880.287βkh Road

1.8110.2740.6420.367βkh Commercial

0.1970.5700.160βkh Residential

0.4570.2201.4170.359βkh Industrial

0.1270.1791.108βkh Vacant

498220286264413844Sample size

CommercialResidentialIndustrialVacant

PCP: percentage correctly predicted. ROC: relative operating characteristic. **: significant at p<0.05. Others significant at p<0.001.

Results Neighborhood effect during 1984-1989

22

Effect of urban land-use on change of vacant land

-0.5

0

0.5

1

1.5

2

vacant industrial residential commercial road public

Urban land-use category

Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94

Effect of urban land-use on change of industrial land

-0.5

0

0.5

1

1.5

2



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94

Effect of urban land-use on change of residential land

-0.5

0.0

0.5

1.0

1.5

2.0



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94

Effect of urban land-use on change of commercial land

-0.5

0.0

0.5

1.0

1.5

2.0



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94

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

23


-0.5

0

0.5

1

1.5

2



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94


-0.5

0

0.5

1

1.5

2



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94


-0.5

0.0

0.5

1.0

1.5

2.0



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94


-0.5

0.0

0.5

1.0

1.5

2.0



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94

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


-0.5

0

0.5

1

1.5

2



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94


-0.5

0

0.5

1

1.5

2



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94


-0.5

0.0

0.5

1.0

1.5

2.0



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94


-0.5

0.0

0.5

1.0

1.5

2.0



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94

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


-0.5

0

0.5

1

1.5

2



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94


-0.5

0

0.5

1

1.5

2



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94


-0.5

0.0

0.5

1.0

1.5

2.0



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94


-0.5

0.0

0.5

1.0

1.5

2.0



Reg

ress

ion

coef

fici

ent

1974-79

1979-84

1984-89

1989-94

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.

26

Conclusions• 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 empirically keep relative stable on the whole during the period of 20 years.

• 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.

27

Thank you!

Should neighborhood effect be stable in urban geosimulation model? A case study of Tokyo - Yaolong...

Documents

Transcript of Should neighborhood effect be stable in urban geosimulation model? A case study of Tokyo - Yaolong...