Uneven Intraurban Growth in Chinese
Cities: A Study of Nanjing
Yehua Dennis Wei
Department of Geography and
Institute of Public and International Affairs
University of Utah
Jun Luo
Department 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, theories
growth machines
development/entrepreneur states
globalization, globalizing cities …
Markusen: evidences, methodology…

Neoclassical/modeling approaches
Land use/land cover change
Location factors, growth determinants
Statistics, GIS/RS, landscape metrics…
Positivism, theory?
1.2 Modeling urban growth
Statistical
models:
global
models
underlying 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?
1.4 Objective
 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) Local analysis/perspectives
Explore spatially varying relationships between
urban 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 Delta
 From
1988 to 2000
Population: 4.88 million to 5.45 million
Built-up area: 392 km2 to 512 km2
 Study area: the majority of built-up
areas, 1128.89 km2
Population
density
Xuanwu
Lake
Zhongshan
Mountain
Ya
ng
tze
Ri
ve
r
2000
Person/10,000 sq.meter
7 - 27
27- 96
96 - 191
191 - 308
308 - 572
540
A
B
C
D
E
F
1000
3000
5000
7000
9000 11000
2000
4000
6000
8000 10000
1000
3000
5000
7000
9000 11000
2000
4000
6000
8000 10000
1000
3000
5000
7000
9000 11000
2000
4000
6000
8000 10000
distance (m)
distance (m)
distance (m)
Popul ation Density
480
420
360
300
240
180
120
60
540
Popul ation Density
480
420
360
300
240
180
120
60
Urban growth in
Nanjing: 1988-2000
3. Data and Methodology
3.1 Data
 Census
data
 Landsat TM imageries: 1988 and 2000
Image processing
Classification: built-up, agriculture, forest and
water body
 GIS: transportation, plan scheme, topographic
and land use survey
3.2 Land use data sampling

Sampling: 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 conversion
2682 land use sample points
3.3 Variables inputs
 Dependent
variable: Probability of non-urban to
urban land conversion
 Explanatory variables:
Proximity
factors: proximity to economic nodes
Neighborhood factors
Variables
Type
Descriptions
Dependent variable
ChangeProb
Continuous
Probability of land use conversion
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
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
Explanatory variable
Proximity
Neighborhood
Water body
Agriculture
Land
Forest land
4. Logistic GWR model
4.1 Global logistic regression model
( C
ChangeProbi 
e
 k X i )
k
( C
1 e
 k X )
k
Findings: All explanatory variables are significant
 road infrastructure development
local roads: more important than highways
Land use constraints: forest, water
City centers more important than subcenters
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
Explanatory variables
-2 Log likelihood
PCP
1873.536
70.1%
4.2 Logistic GWR model
( Ci 
Change Pr obi 
e
 ki X ki )
k
( Ci 
1 e
 ki X ki )
k
Weighting scheme: Fixed kernel vs Adaptive kernel
2 2
  dij  
wij  1  

b
 
 
if j   N nearest neighbour points
dij is the distance from j to i
b is the distance from Nth nearest neighbour to i
 0 otherwise
N=138, Chosen by minimizing an AIC score
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 variability
All parameters with p-value below 0.01
Significant spatial variability
Summary statistics for GWR parameters estimates
Min
Max
1.266
Mean
Std.D
%Positive
%Negative
-1.275
1.045
8.24
91.76
Dis2Hwy
-5.569
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
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 city
Suburban centers: weak, local influence
GWR parameter t-statistic surfaces
GWR parameter surfaces:
Neighborhood: varied effectiveness
GWR parameter t-statistic surfaces
Urban growth probabilities
6. Conclusions
Findings:
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?