Uneven Intraurban Growth in Chinese Cities: A Study of Nanjing

Yehua Dennis WeiDepartment of Geography and

Institute of Public and International AffairsUniversity of Utah

Jun LuoDepartment of Geography, Geology and Planning

Missouri State University

Outline

1. Introduction

2. Study area and growth patterns

3. Data and Methodology

4. Logistic GWR model

5. Spatial variations of urban growth

6. Conclusion

1. Introduction 1.1 Research on urban growth in China

Two broadly defined groups: Institutional/political economy perspectives

Process, mechanisms, theoriesgrowth machinesdevelopment/entrepreneur statesglobalization, globalizing cities …Markusen: evidences, methodology…

Neoclassical/modeling approachesLand use/land cover change Location factors, growth determinantsStatistics, GIS/RS, landscape metrics…Positivism, theory?

1.2 Modeling urban growthStatistical models:

global modelsunderlying forces

1.3 Urban growth Local, non-stationary process over the space Same set of factors have different influences on different areas of a city

Context-sensitive theory?

Theories: Regional Development

Industrial agglomeration (RS), remaking the Wenzhou model (EG) Methodology: GIS local analysis, LISA, ESDA, GWR,

spatial regression…Regional development (PiRS)Urban growth/structure (EPB)

1.4 Objective

1) Local analysis/perspectives

Explore spatially varying relationships betweenurban land expansion and influential factors

Modeling: Logistic geographically weighted

regression (GWR), a local regression technique

2) Socio-economic factors

2. Study area and Growth Patterns

2.1 Nanjing: coastal, Yangtze DeltaFrom 1988 to 2000

Population: 4.88 million to 5.45 millionBuilt-up area: 392 km2 to 512 km2

Study area: the majority of built-up areas, 1128.89 km2

Person/10,000 sq.meter

7 - 27

27- 96

96 - 191

191 - 308

308 - 572

ZhongshanMountain

XuanwuLake

-Population density

2000

A B C

D E F

60

120

180

240

300

360

420

480

540

Pop

ulat

ion

Den

sity

10002000

30004000

50006000

70008000

900010000

11000

distance (m)

60

120

180

240

300

360

420

480

540

Pop

ulat

ion

Den

sity

10002000

30004000

50006000

70008000

900010000

11000

distance (m)

10002000

30004000

50006000

70008000

900010000

11000

distance (m)

Urban growth in

Nanjing: 1988-2000

3.1 Data Census dataLandsat TM imageries: 1988 and 2000

Image processingClassification: built-up, agriculture, forest and

water body GIS: transportation, plan scheme, topographic

and land use survey

3. Data and Methodology

3.2 Land use data samplingSampling: combined systematic and random scheme

Systematic sampling: extract regularly spaced points with 300m interval

Extract all 1332 points with non-urban to urban land use conversion

Randomly select 1350 points without land use conversion2682 land use sample points

3.3 Variables inputsDependent variable: Probability of non-urban

to urban land conversionExplanatory variables:

Proximity factors: proximity to economic nodesNeighborhood factors

Variables Type Descriptions

Dependent variable

ChangeProb Continuous Probability of land use conversion

Explanatory variable

Proximity

Dis2Hwy Continuous Distance to inter-city highway

Dis2Lard Continuous Distance to local artery roads

Dis2Rail Continuous Distance to railways

Dis2YRiver Continuous Distance to Yangtze River

Dis2YBrid Continuous Distance to Yangtze bridge

Dis2MCen Continuous Distance to major city centers

Dis2MNCen Continuous Distance to suburban centers

Dis2Induc Continuous Distance to industrial centers

Neighborhood

AgriDen Continuous Density of agriculture land

BuiltDen Continuous Density of built-up land

WaterDen Continuous Density of water body

ForeDen Continuous Density of forest land

AgricultureLand

Water body

Forest land

4.1 Global logistic regression model

1

k ik

kk

( C X )

i ( C X )

eChangeProb

e

4. Logistic GWR model

Findings: All explanatory variables are significant road infrastructure developmentlocal roads: more important than highwaysLand use constraints: forest, waterCity centers more important than subcenters

Explanatory variables B S.E. t value Exp(B)

Constant 5.453 0.472 11.552 233.564

Dis2Hwy -0.269 0.021 -12.744 0.764

Dis2Lard -1.369 0.100 -13.698 0.254

Dis2Rail 0.034 0.016 2.091 1.035

Dis2YRiver -0.100 0.020 -4.942 0.905

Dis2YBrid 0.115 0.024 4.703 1.122

Dis2MCen -0.192 0.022 -8.573 0.825

Dis2MNCen -0.073 0.018 -4.039 0.930

Dis2Induc 0.087 0.024 3.653 1.091

AgriDen -2.125 0.404 -5.262 0.119

BuiltDen 4.039 0.653 6.181 56.745

WaterDen -4.812 0.803 -5.994 0.008

ForeDen -5.360 0.517 -10.369 0.005

Sample size 2682

-2 Log likelihood 1873.536

PCP 70.1%

Weighting scheme: Fixed kernel vs Adaptive kernel

22

1 if j nearest neighbour points

is the distance from to

is the distance from th nearest ne

ijij

ij

dw Nb

d j i

b N

ighbour to

0 otherwise

i

N=138, Chosen by minimizing an AIC score

1

i ki kik

i ki kik

( C X )

i ( C X )

eChange Pr ob

e

4.2 Logistic GWR model

4.3 Model comparison

Global logistic model Logistic GWR

PCP 70.1% 85.6%

RSS 450.842 297.648

Moran’s I of residuals

0.74 0.48

Significance test for spatial variabilityAll parameters with p-value below 0.01Significant spatial variability

Min Max Mean Std.D %Positive %Negative

Dis2Hwy -5.569 1.266 -1.275 1.045 8.24 91.76

Dis2Lard -17.231 -3.156 -7.677 2.333 0 100

Dis2Rail -1.351 4.090 0.428 1.003 61.26 38.74

Dis2YRiver -7.135 10.198 -0.105 1.823 36.73 63.27

Dis2YBrid -2.263 9.408 1.308 1.736 72.97 27.03

Dis2MCen -13.435 1.949 -2.307 2.225 16.48 83.52

Dis2MNCen -8.160 2.056 -1.438 1.570 20.95 79.05

Dis2Induc -2.919 10.317 0.223 1.354 59.02 40.98

AgriDen -35.034 14.586 -14.529 7.182 2.46 97.54

BuildDen -11.746 150.900 24.482 17.812 89.37 10.63

WaterDen -94.201 52.989 -26.648 16.928 7.53 92.47

ForeDen -77.013 -17.955 -41.671 13.483 0 100

Summary statistics for GWR parameters estimates

5. Spatial variations of urban growth pattern Parameters vary across space: local process All the variables except for Dis2Lard and ForeDen

have both positive and negative parameter values Dis2Lard: significant all over the city (-) Other parameters have certain parts in the study

area where they are non-significant Use inverse distance weighted (IDW) interpolation

to generate parameter and t-statistic surfaces (30×30m)

GWR parameter surfaces: Roads: more negative effective in the north

GWR parameter t-statistic surfaces

GWR parameter surfaces: Centers: more effective in the north

Influence of major centers: compact citySuburban centers: weak, local influence

GWR parameter t-statistic surfaces

GWR parameter surfaces: Neighborhood: varied effectiveness

GWR parameter t-statistic surfaces

Urban growth probabilities

6. ConclusionsFindings:

1. Logistic GWR can significantly improve the global logistic regression for urban growth modeling:

2. Effects of determining factors have significant spatial variation

3. Interpretation of spatial process should be careful with spatial context; need for local analysis

Limitations:

1) Data: socio-economic variables

Discussion:

1) The nature of theory: Theoretical statements

2) Local analysis vs. generalization

3) Representativeness, sampling bias

Thank You and Questions?