Improving Urban Simulation Accuracy through Analysis of Control

Factors: A Case Study in the City Belt along the Yellow River in

Ningxia, China

Rongfang Lyu, Jianming Zhang, Mengqun Xu and Jijun Li

College of Earth Environmental Sciences, Lanzhou University, Tianshui South Road 222, Lanzhou, China

Keywords: Urban Simulation, Spatial Heterogeneity, Macro-control Influence, SLEUTH-3r Model, City Belt along

Yellow River in Ningxia.

Abstract: Spatial heterogeneity of urban expansion and macro-scale influence of socioeconomic development are the

two main problems in urban-expansion modelling. In this study, we used the SLEUTH-3r model to simulate

urban expansion at a fine scale (30 m) for a large urban agglomeration (22000 km

) in north-western China.

Multiple spatial constraint factors were integrated into the model through Ordinary Least Regression and

Binary Logistic Regression to simulate the spatial heterogeneity in urban expansion. A critical

parameter—the diffusion multiplier (D

)—was used to simulate the macro-scale influence of socioeconomic

development in the urban model. These two methods have greatly enhanced the ability of the SLEUTH-3r

model to simulate urban expansion with high heterogeneity, and adapt to urban growth driven by

socioeconomic development and government policy.

1 INTRODUCTION

Urbanization, an unprecedented global phenomenon,

has significantly altered natural landscapes and

human lives (Zhang et al., 2012). Urban expansion,

a significant performance of urbanization, has

brought numerous threats to ecosystem, such as loss

of natural resources (Delphin et al., 2016), climate

change (Singh et al., 2017), and biodiversity

decrease (Haase et al., 2012). Therefore, it is critical

to predict urban expansion patterns for sustainable

development, especially in metropolitan areas,

which form the basic unit in future socioeconomic

development (Poyil and Misra, 2015).

Urbanization is a dynamic process influenced by

geophysical, environmental, demographic, and

social factors at multiple scales (Akın et al., 2014).

Complicated interactions between these factors, and

associated temporal changes lead to spatial and

temporal heterogeneity in urban expansion (Li et al.,

2017). A number of techniques have been developed

to simulate urban expansion, ranging from static

models based on gravity theory and optimization

mathematics to dynamic models (Berling-Wolff and

Wu, 2004). In particular, the cellular automata (CA)

model is widely used in urban simulation for its

simplicity, flexibility, intuitiveness, and transparency

in modeling complex systems (Santé et al., 2010).

However, the CA model often fails to capture the

change magnitude of urban expansion driven by

political and economic strategies (Qi et al., 2004).

Despite its successful application in many cities, the

SLEUTH model is also a CA model that fails to

consider the macro-scale driving influence of

socioeconomic development (Berberoğlu et al., 2016,

Chaudhuri and Clarke, 2013). Since urbanization in

China is highly driven by government policies, it is

essential to integrate these macro-scale control

factors into urban model.

The SLEUTH model has been always used to

simulate urban land distribution in a single city at

coarse resolution (Chaudhuri and Clarke, 2013), but

not for large urban agglomerations consisting of

several cities with high spatial heterogeneity (Jat et

al., 2017). Several approaches have been developed

to evaluate the effects of driving forces on urban

expansion, such as binary (Haregeweyn et al., 2012),

multiple linear (Gao and Li, 2011), and

geographically-weighted regressions (Su et al.,

2012), analytic hierarchy process (Thapa and

Murayama, 2012), and logistic regression (Long et

al., 2012). Among them, multiple linear and binary

Lyu, R., Zhang, J., Xu, M. and Li, J.

Improving Urban Simulation Accuracy through Analysis of Control Factors: A Case Study in the City Belt along the Yellow River in Ningxia, China.

DOI: 10.5220/0006627201590166

In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2018), pages 159-166

ISBN: 978-989-758-294-3

159

regression, both reliable and easy to manipulate,

were selected to integrate multiple factors into the

SLEUTH model to simulate urban spatial expansion

with high heterogeneity (Liu et al., 2014).

To date, most of urban studies in China focused

on fast-growing coastal and major interior cities;

however, urban growth in inner northwestern China,

especially in large urban agglomerations, has not

been well described. Our study will help to bridge

the gap, as the study area is a large city belt in

northwestern China. The main objectives of our

study were to: (1) identify factors that control urban

expansion, and quantify their impacts, (2) simulate

urban expansion with high spatial heterogeneity, and

(3) integrate the macro-scale driving influence of

socioeconomic development into model to simulate

urban expansion with proper magnitude.

2 STUDY AREA AND METHODS

2.1 Study Area

The City Belt along the Yellow River in Ningxia

(CBYN), located in northwestern China, is a large

urban agglomeration consisting of four cities:

Shizuishan, Yinchuan, Wuzhong and Zhongwei

(Fig. 1). The study area, with Tengger desert in the

west, the Maowusu desert in the east, and the Ulan

Buh desert in the north, is one of the core areas of

the west Longhair-Lanxin xian economic belt. Since

2000, socioeconomic development in this area has

been deliberately enhanced by the government

through West Development Project. Gross Domestic

Product (GDP) increased from 5045.93 million Yuan

in 1990 to 223,550.29 million Yuan in 2013, with an

annual growth rate of 188.27%, while population

increased at an annual rate of 2.75 %. (Ningxia

Statistical Yearbook, 1990-2014). Growing industry

and commerce in the urbanized areas provide more

work opportunities, and attract population from the

rural areas, further promoting urbanization.

2.2 Data Collection and Processing

Twelve scenes of Landsat MSS/TM/ETM+/OLI

images, covering the study area in 1989, 1999, 2006

and 2016, were used as the primary resource data

(involving path/row of 129/33, 129/34 and 130/34).

Images were preprocessed in ENVI 5.3, including

geographical registration, radiometric calibration

and atmospheric correction, and then were

exportedinto eCognition 8.7 for an object-based

classification. Reference samples were identified

in Google Earth and field survey to examine

classification accuracy. The Kappa coefficients

Figure 1: Location and administrative division of the study area—Shizuishan, Yinchuan, Wuzhong and Zhongwei:a) the

study area in China; b) the study area in Ningxia Hui Autonomous Region; c) topography and the city center of Shizuishan,

Yinchuan, Wuzhong and Zhongwei.

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

160

(consistency test between classification results and

reference samples) reached 0.93, 0.89, 0.91 and 0.87

in 1989, 1999, 2006 and 2016, respectively, thus the

results were reliable.

The ASTER DEM data (version 4.1)

(https://search.earthdata.nasa.gov/) was resampled to

30 m in ArcGIS 10.3, and used to generate slope and

hillshade layers. Transportation layers were

extracted from satellite images and by visual

interpretation using Google Earth. All the input

layers were resampled for 30 m in ArcGIS 10.3, and

then imported into Photoshop CS6 to be exported in

GIF format. Socioeconomic data, such as population

and GDP, was obtained from Ningxia Statistical

Yearbook (1990-2014), compiled by the statistical

bureau of Ningxia Hui Autonomous Region and

Ningxia Survey Office of National Statistical Bureau,

and published by China Statistics Press.

2.3 Overview of the SLEUTH Model

The SLEUTH model (Clarke et al., 1997) is

designed to simulate urban growth and land use

change. The name includes the first letters of the

input layers: slope, land cover, excluded, urban,

transportation, and hillshade. The model simulates

urban expansion with four rules: spontaneous

growth that simulates the random urbanization, new

spreading center growth that establishes new urban

centers, edge growth and road influenced growth.

The model behavior are controlled by five growth

coefficients (diffusion, breed, spread, road gravity,

and slope) that range from 0 to 100, indicating the

relative contribution of each growth types for whole

urban growth. Moreover, self-modification is applied

to better predict rapid or depressed urban growth.

Model calibration allows users to obtain parameters

describing past urban expansion, while prediction

helps forecast urban growth and land use change

under different scenarios.

Due to the large amounts of input data, we

selected the 3r-version of the SLEUTH model

(SLEUTH-3r) for our study; it has more efficient

utility of computer memory and higher simulation

accuracy of dispersed settlements (Jantz et al., 2010).

Two new accuracy parameters—area fractional

difference (AFD) and clusters fractional difference

(CFD)—were designed in SLEUTH-3r model to

compare urban pixels and clusters between

simulated and real maps. Besides that, Lee-Sallee

metric, the shape index of spatial fit between actual

urban map and predicted one, has also been used in

our study to examine the simulation accuracy.

2.4 Simulating Spatial Heterogeneity

To address spatial heterogeneity in urban expansion,

we first established a suitability system of factors

driving urban growth from past studies (details in

2.4.1 below). Second, we detected the spatial

relationships between factors and urban expansion

through the Ordinary Least Square (OLS) regression

model in ArcGIS 10.3 (details in 2.4.2 below).

Finally, suitability for urban expansion was

calculated and mapped through Binary Logistic

Regression with weighted factors derived from the

former step (details in 2.4.3 below). Then the

suitability map was transformed into the excluded

layer for the SLEUTH-3r model.

2.4.1 Suitability-Factor System

Different types of explanatory variables have been

identified (Gao and Li, 2011, Su et al., 2012), and

categorized based on physical conditions, ecological

protection, and socio-economic development (Table

1). Ecological factors are protected from urban

expansion and are assigned value of 100 in the

excluded layer. Slope factor is not included in the

system, as it is already in SLEUTH-3r model. All

variables were first normalized into the range of 0-1

to eliminate the effect of magnitude. Based on

correlation analysis, multicollinearity did not exist

among the explanatory variables in the subsequent

regression analysis.

Table 1: Factors influencing urban development.

Type

Factor

Code

Physical

Elevation

Geomorphic type

Ecological

Water areas

National natural reserves

Socio-economic

Growth rate of GDP

Growth rate of population

Distance to city centers

Distance to county centers

2.4.2 Weights Estimation

OLS, which could minimize the sum of squared

vertical distance between observed variables and

simulation values (Gao and Li, 2011), was used to

explore the relationships between urban expansion

and its driving factors, as follows:

Z=C+



+er

(1)

Where Z was the dependent variable, C was the

constant parameter; w

was the parameter of

independent variable X

; er was the error term.

Improving Urban Simulation Accuracy through Analysis of Control Factors: A Case Study in the City Belt along the Yellow River in

Ningxia, China

161

Because non-urbanized area greatly surpassed

urbanized area in CBYN, we randomly selected

5,000 points in each area, with a distance between

each point > 300 m to minimize the impacts of

spatial autocorrelation. The “extract multi values to

points” tool in ArcGIS 10.3 was used to obtain the

values of driving parameters and urban expansion (0

for non-urbanized area and 1 for urbanized area) at

each point. They were then used to establish the

OLS model in ArcGIS 10.3.

2.4.3 Generating Suitability Maps

If the probability of a cell suitable for urbanization

followed the logistic curve described in Eq. (2), the

possibility of a cell being urbanized was estimated

with Eq. (3):

1-p

=C+



i=1

(2)

1+exp(-C-



)

(3)

Where p

was the probability of a cell becoming

urbanized, X

was the driving factor for urban

expansion, w

was the coefficient of each factor

derived from OLS, and C was a constant.

2.5 Socioeconomic Factors in the Model

In SLEUTH-3r model, spontaneous urban growth

was the foundation of other growth types, and

mainly determined by a diffusion multiplier (D

diffusion coefficient (D

), and the size of input

images (Jantz et al., 2010). Thus D

could generally

determine the simulation magnitude of urban growth

in model, and allowed the integration of

socioeconomic development into the model.

The D

value was 0.005 in the original version,

and 0.015 in the 3r version of the SLEUTH model,

and neither could generate enough urban growth

(AFD ranging from -0.847 to -0.06). Thus, the first

problem was obtaining an appropriate D

. As

discussed above, D

was related to simulation

magnitude, so we explored the relationship between

and simulation magnitude of urban area and

cluster (AFD and CFD) to find appropriate D

We selected the annual growth rates of GDP and

population as the representatives for socioeconomic

development, and generated an indicator (SE) using

factor analysis in SPSS 22.0. Then we explored the

relationship between SE and D

through regression

analysis in SPSS 22.0, to use D

representing

different socioeconomic development conditions.

3 RESULTS

3.1 Urban Expansion Suitability Map

Multiple linear regression analysis processed in

SPSS 22.0 had the same results as OLS in ArcGIS

10.2 (Eq. (4)). The six factors had different effects

on urban expansion, indicated by the coefficients of

each factor. And the influence of geophysical factors

was greater than that of socioeconomic factors. The

regression model was as follows (Eq. (4)):

ln 

1-p

=1.53-1.32×X

-0.4×X

-0.51×

-0.53×X

+0.05×X

+0.02×X

(4)

Where p

was the urbanization probability of each

cell.

Based on binary logistic regression, a probability

map for urban suitability was generated (Fig. 2a).

Then, we converted it to an excluded layer that

contained areas ranging from unsuitable for

urbanized (value=100) to suitable (value=0) in

SLEUTH-3r model using the “map algebra” tool in

ArcGIS 10.3 (Fig. 2b). The transformation equation

was as follows (Eq. (8)):

MAX



suit



-R

suit

MAX



suit



-MIN



suit



)×100]

(5)

Where R

and R

suit

were the raster maps of excluded

layer and suitability map, respectively.

Figure 2: Suitability map for urbanization probability (a)

and excluded map for SLEUTH-3r model (b).

3.2 Determination of D

We explored the relationships between AFD/CFD

and D

in the calibration mode of the model with

the five growth coefficients ranging from 0 to 100

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

162

and an increment of 50. We found that the minimum

values of AFD and CFD were almost the same

(-0.847 and -0.73) under different D

, while the

maximum value increased with an increase in D

The relationships between the maximum values of

AFD/CFD and D

were established through

regression analysis in SPSS 22.0. The equations and

simulated curves were as follows (Eq (6) with R

0.975, Eq (7) with R

of 0.997, and Fig. 3)):

AFD

max

=3.323+0.79×ln(D

)

(6)

CFD

max

=2.69+202.67×D

-188.45×D

+71.49×D

(7)

Figure 3: Maximum and minimum values of AFD and

CFD over increasing D

From the testing data shown in figure 3, three

values of D

—0.03/0.04/0.05—were considered to

have the largest opportunity to simulate sufficient

amount of urban area with fewer clusters. We

calibrated the model with the three D

values (Table

2), and 0.04 was the most suitable value for D

our study. Under D

of 0.04, the maximum value of

AFD was 0.783. As discussed in Section 2.5, 0.783

of the maximum value of AFD was appropriate for

determination.

Table 2: Coarse calibration performance of the model under

different D

AFD

CFD

Lee-Sallee

0.03

0.002

6.9

0.301

0.04

0.001

5.162

0.351

0.05

0.001

6.283

0.309

3.3 The Socioeconomic Factor

The socioeconomic development indicator (SE) was

generated with the following equation (Eq. (8),

Section 2.5):

SE=8.23×10

-7

×GDP

+2.74×10

-5

×P

-0.94

(8)

We obtained 30 values of D

through the method

discussed in Section 3.2 for the five different areas

(4 cities and the whole region) in the six periods

(1989-1999, 1999-2006, 2006-2016, 1989-2006,

1999-2016, and 1989-2016). The relationship

between D

and SE was estimated with regression

analysis in SPSS 22.0 (Eq. 9) with a R

of 0.981).

Therefore, SLEUTH-3r model could predict urban

expansion driven by different socioeconomic

development conditions by setting the D

value.

D

=0.083×SE+0.043×SE

-0.011×SE

+0.056

(9)

3.4 Simulation Accuracy of the Model

The SLEUTH-3r model was calibrated to find a

combination of coefficients that best simulated

historical urban expansion through the “brute-force”

method (Silva and Clarke, 2002). The selection

criterion used the minimum absolute value of CFD

and AFD of < 0.05. Then the model was initialized

in 1989 and ran in predict mode to 2016, with the

coefficients derived from calibration. In the

prediction mode, we utilized two scenarios, in which

one (S

) came from the suitability map, and the other

) coded water with 100 and other land with 50 as

comparison.

To evaluate the simulation accuracy, we

calculated the Kappa metric and spatial topology for

the predicted maps (Table 3). The Kappa metric

(consistency between predicted and real maps) in

2016 under S

reached 0.77, while the one under S

was 0.56, indicating that S

could significantly

improve model accuracy. Urban spatial topology can

further describe the simulation accuracy

(Kantakumar et al., 2016), and was classified based

on proportion of built-up area (using 30% and 50%

as a boundary) within the neighborhood of 3×3 cells

through “block statistics” tool in ArcGIS 10.3.

Prediction under S

accurately simulated the area of

the urban core, 74.82% of the real urban fringe, but

172.86% of the scattered settlement; this indicated

that most of the simulation error occurred in

scattered settlements. Under S

, the main error

occurred in simulating the urban core (at 78.29%)

and urban fringe (at 62.38%). Overall, integrating the

effects of multiple drivers into the model can greatly

enhance the ability to simulate urban expansion with

high spatial heterogeneity.

Table 3: Urban spatial pattern predicted in 2016 under

different scenarios.

Urban

area

(km

)

Kappa

Urban topology type

Urban

core

(km

)

Urban

fringe

(km

)

Scatter

settlement

(km

)

1205.81

0.77

1165.67

146.25

349.05

931.51

0.56

911.66

121.94

212.81

Real

1182.123

—

1164.51

195.48

201.93

Improving Urban Simulation Accuracy through Analysis of Control Factors: A Case Study in the City Belt along the Yellow River in

Ningxia, China

163

4 DISCUSSION

Documentation and source code of the SLEUTH

model have been publicly available, thus interested

researchers were able to modify and improve it.

Several successful efforts reduced computation time

and increased model efficiency, including OSM

(Charles Dietzel, 2007), pSLEUTH (Guan and

Clarke, 2010), SLEUTH-3r (Jantz et al., 2010), and

SLEUTH-GA (Shan et al., 2008), among others.

These modifications helped to overcome some of the

limitations, enhance model applicability, and provide

suggestions for more accurate simulation (Chaudhuri

and Clarke, 2013). Using the SLEUTH-3r model, we

simulated urban expansion in CBYN during

1989-2016. We confronted three main problems.

First, the determination methods for D

were not

appropriate for our study as they could not generate

sufficient urban growth area. Second, urban growth

in China, largely driven by socioeconomic

development at macro-scale, could not be effectively

expressed in this model. Third, spatial heterogeneity

in urban growth, such as city and villages in a large

urban agglomeration, was an important source of

simulation error that needed to be addressed.

4.1 Parameters Driving Urban Growth

Similar to most studies that analysed urban

expansion, the factor system we built in this study

was incomplete, due to lack of data and the presence

of unknown urban-growth driving factors (Hietel et

al., 2007). For example, urban planning has been

shown to greatly affect urban expansion (Long et al.,

2012), however, it has not been included in this

study due to lack of data. The incomplete picture of

the factors driving urbanization was one source of

simulation error.

In 1989-2016, physical factors impacted urban

expansion more than socioeconomic conditions did

at spatial scale. Elevation and morphology exhibited

significantly negative effects on urban expansion in

CBYN, while low elevation and flat areas were more

suitable for urban growth. Previous studies

suggested that the effects of elevation on urban

expansion depended on the topography (Li et al.,

2013). Positive effects of elevation on urban

expansion have been shown in Lagos and Nigeria,

where low elevation areas necessitated drainage,

possibly increasing the cost of building construction

(Dewan and Yamaguchi, 2009). In CBYN, areas of

high elevation were more likely to be situated in the

mountains, where costs of development were higher

than at low elevations.

The significant relationships between urban

expansion and social factors of proximity to urban

centers (negatively correlated), and growth rate of

GDP and population (positively correlated) were

consistent with previous findings (Luo and Wei,

2009, Poelmans and Rompacy, 2009). Moreover, the

effects of proximity exceeded those of economic

development and population growth. This was

mainly due to the coarser resolution of census data

compared with other factors. The spatial

heterogeneity of urban and suburban areas could not

be expressed by GDP or population data, indicating

that data at finer-scales were needed.

Previous studies on megacities in China and

USA have shown that positive relationships existed

between socioeconomic development and urban

expansion, especially in developing countries

(Kuang et al., 2014), and that the socioeconomic

factors would play an increasingly important role in

urbanization. For example, studies in Beijing (Liu et

al., 2014) suggested that the importance of

urbanization drivers varied over time, and the effects

of physical and neighborhood factors decreased with

increasing socioeconomic factors. Compared with

Beijing, CBYN developed at a slower pace in the

past thirty years, as indicated by urban population

rate of 67.56% in CBYN in 2015, and 86% in

Beijing in 2010 (Liu et al., 2014). As a result, the

impacts of socioeconomic development were less

important than those of geophysical conditions, but

would increase in the future.

4.2 Implications of Model Simulation

Chinese megacities are in a stage of development at

which population growth, economic development,

and policy significantly influence urban expansion

patterns and rates. This is unlike megacities in

developed countries where population and economic

conditions are not important forces of urban growth

(Kuang et al., 2014). The effects of socioeconomic

development on urban expansion were classified in

this study into two categories: spatial heterogeneity

and temporal dynamics; the former was expressed in

the excluded layer from the suitability map, and the

latter was reflected in the changing value of D

Spatial differences in physical conditions,

cultural background, socioeconomic development,

and human preferences were responsible for the high

heterogeneity in urban distribution and expansion

(Lin et al., 2014); this was also reflected in D

with

value ranging from 0.008 to 0.38 among different

cities. This heterogeneity improved the difficulty in

precise urban simulation, and can be an important

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

164

source of simulation error. Linear or logistic

regression-based models cannot calculate

heterogeneous urban expansion due to their

dependability on weights (Hu and Lo, 2007).

Artificial neural network models also have limited

capacity for accurate modeling of spatial

heterogeneity (Almeida et al., 2008).

The SLEUTH model can simulate urban growth

at coarse resolution well, and has been successfully

applied to cities all over the world (Akın et al., 2014,

Al-shalabi et al., 2012, Bihamta et al., 2014).

However, the SLEUTH model is still inadequate for

simulating urban growth with high heterogeneity, or

at high resolution at large-scales (Jat et al., 2017). In

our study, integrating various spatial factors into the

model greatly enhanced the simulation accuracy in

an urban agglomeration. The influence of

socioeconomic growth on urban expansion, and the

fundamental function of D

in controlling the

magnitude of urbanization (suggested by Eq. (9)),

allowed D

to exert temporal influence in the model.

The high correlation between D

and SE further

supports this conclusion. Future research needs to

focus on predicting urban expansion under different

socioeconomic growth scenarios, and on

comparing the effects of government policies on

urbanization.

5 CONCLUSIONS

Urban expansion is unavoidable and has significant

impacts on ecosystem services and functions. The

successful application of the SLEUTH-3r model in

the City Belt along the Yellow River in Ningxia at a

resolution of 30 m has shown its utility in simulating

urban expansion in a large area with high precision.

In the past 27 years, the effects of elevation and

geomorphology on urban expansion exceeded those

of socioeconomic development. We quantitatively

integrated these factors into the model to simulate

urban expansion with high heterogeneity across a

large area with high accuracy.

The influence of socioeconomic development

was introduced into model with D

, which can be

set interactively. Both of these actions improve

model accuracy in simulating urban expansion in

urban agglomerations. However, the excessive

amounts of scatter settlements in the simulation

indicated the need for further research.

ACKNOWLEDGEMENTS

This research was supported by the National Natural

Science Foundation of China under grant No.

41371176 and the Fundamental Research Funds for

the Central Universities under grant No.

lzujbky_2017_it91.

REFERENCES

Akın, A., Clarke, K. C. & Berberoglu, S. (2014). The

impact of historical exclusion on the calibration of the

SLEUTH urban growth model. International Int J

Geographical Inf. Sci, 27: 156-168.

Al-shalabi, M., Billa, L., Pradhan, B., Mansor, S. &

Al-Sharif, A. A. A. (2012). Modelling urban growth

evolution and land-use changes using GIS based

cellular automata and SLEUTH models: the case of

Sana’a metropolitan city, Yemen. Environmental Earth

Sciences, 70(1): 425-437.

Almeida, C. M., Gleriani, J. M., Castejon, E. F. & Soares

Filho, B. S. (2008). Using neural networks and cellular

automata for modelling intra-urban land-use dynamics.

Int J Geographical Inf. Sci, 22(9): 943-963.

Berberoğlu, S., Akın, A. & Clarke, K. C. (2016). Cellular

automata modeling approaches to forecast urban

growth for adana, Turkey: A comparative approach.

Landscape Urban Plan., 153: 11-27.

Berling-Wolff, S. & Wu, J. (2004). Modeling urban

landscape dynamics: a review. Ecol. Res., 19(1):

119-129.

Bihamta, N., Soffianian, A., Fakheran, S. & Gholamalifard,

M. (2014). Using the SLEUTH Urban Growth Model to

Simulate Future Urban Expansion of the Isfahan

Metropolitan Area, Iran. Journal of the Indian Society

of Remote Sensing, 43(2): 407-414.

Charles Dietzel, K. C. C. (2007). Toward optimal

calibration of the SLEUTH land use change model.

Transactions in GIS, 11(1): 29-45.

Chaudhuri, G. & Clarke, K. C. (2013). The SLEUTH land

use change model: A review. The International Journal

of Environmental Resources Research, 1(1): 88-104.

Clarke, K. C., Hoppen, S. & Gaydos, L. J. (1997). A

self-modifying cellular automaton model of historical

urbanization in the San Francisco Bay Area. Environ.

Plann. B, 24: 247-261.

Delphin, S., Escobedo, F. J., Abd-Elrahman, A. & Cropper,

W. P. (2016). Urbanization as a land use change driver

of forest ecosystem services. Land Use Policy, 54:

188-199.

Dewan, A. M. & Yamaguchi, Y. (2009). Land use and land

cover change in Greater Dhaka, Bangladesh: using

remote sensing to promote sustainable urbanization.

Appl. Geogr., 29(3): 390-401.

Gao, J. & Li, S. (2011). Detecting spatially non-stationary

and scale-dependent relationships between urban

landscape fragmentation and related factors using

Improving Urban Simulation Accuracy through Analysis of Control Factors: A Case Study in the City Belt along the Yellow River in

Ningxia, China

165

Geographically Weighted Regression. Appl. Geogr.,

31(1): 292-302.

Guan, Q. & Clarke, K. C. (2010). A general-purpose

parallel raster processing programming library test

application using a geographic cellular automata model.

Int J Geographical Inf. Sci, 24(5): 695-722.

Haase, D., Schwarz, N., Strohbach, M., Kroll, F. & Seppelt,

R. (2012). Synergies, Trade-offs, and Losses of

Ecosystem Services in Urban Regions: an Integrated

Multiscale Framework Applied to the Leipzig-Halle

Region, Germany. Ecology and Society, 17(3): 22.

Haregeweyn, N., Fikadu, G., Tsunekawa, A., Tsubo, M. &

Meshesha, D. T. (2012). The dynamics of urban

expansion and its impacts on land use/land cover

change and small-scale farmers living near the urban

fringe: A case study of Bahir Dar, Ethiopia. Landscape

Urban Plan., 106(2): 149-157.

Hietel, E., Waldhardt, R. & Otte, A. (2007). Statistical

modelling of land-cover changes based on key

socio-economic indicators. Ecol. Econ., 62: 496-507.

Hu, Z. & Lo, C. P. (2007). Modeling urban growth in

Atlanta using logistic regression. Comput. Environ.

Urban., 31(6): 667-688.

Jantz, C. A., Goetz, S. J., Donato, D. & Claggett, P. (2010).

Designing and implementing a regional urban

modeling system using the SLEUTH cellular urban

model. Comput. Environ. Urban., 34(1): 1-16.

Jat, M. K., Choudhary, M. & Saxena, A. (2017). Urban

growth assessment and prediction using RS, GIS and

SLEUTH model for a heterogeneous urban fringe. The

Egyptian Journal of Remote Sensing and Space Science.

http://dx.doi.org/10.1016/j.ejrs.2017.02.002.

Kantakumar, L. N., Kumar, S. & Schneider, K. (2016).

Spatiotemporal urban expansion in Pune metropolis,

India using remote sensing. Habitat Inter., 51: 11-22.

Kuang, W., Chi, W., Lu, D. & Dou, Y. (2014). A

comparative analysis of megacity expansions in China

and the U.S.: Patterns, rates and driving forces.

Landscape Urban Plan., 132: 121-135.

Li, C., Zhao, J. & Xu, Y. (2017). Examining

spatiotemporally varying effects of urban expansion

and the underlying driving factors. Sustainable Cities

and Society, 28: 307-320.

Li, X., Zhou, W. & Ouyang, Z. (2013). Forty years of urban

expansion in Beijing: What is the relative importance

of physical, socioeconomic, and neighborhood factors?

Appl. Geogr., 38: 1-10.

Lin, J., Huang, B., Chen, M. & Huang, Z. (2014). Modeling

urban vertical growth using cellular automata:

Guangzhou as a case study. Appl. Geogr., 53: 172-186.

Liu, R., Zhang, K., Zhang, Z. & Borthwick, A. G. (2014).

Land-use suitability analysis for urban development in

Beijing. J Environ Manage, 145: 170-179.

Long, Y., Gu, Y. & Han, H. (2012). Spatiotemporal

heterogeneity of urban planning implementation

effectiveness: Evidence from five urban master plans of

Beijing. Landscape Urban Plan., 108: 103-111.

Luo, J. & Wei, Y. H. D. (2009). Modeling spatial variations

of urban growth patterns in Chinese cities: The case of

Nanjing. Landscape Urban Plan., 91(2): 51-64.

Poelmans, L. & Rompacy, A. V. (2009). Detecting and

modellling spatial patterns of urban sprawl in highly

fragmented areas: a case study in the Flanders-Brussels

region. Landscape Urban Plan., 93(1): 10-19.

Poyil, R. P. & Misra, A. K. (2015). Urban agglomeration

impact analysis using remote sensing and GIS

techniques in Malegaon city, India. International

Journal of Sustainable Built Environment, 4(1):

136-144.

Qi, Y., Henderson, M., Xu, M., Chen, J., Shi, P., He, C. &

Skinner, W. (2004). Evolcing core-periphery

interactions in a rapidly expanding urban landscape: the

case of Beijing. Landscape Ecology, 19: 491-497.

Santé, I., García, A. M., Miranda, D. & Crecente, R. (2010).

Cellular automata models for the simulation of

real-world urban processes: A review and analysis.

Landscape Urban Plan., 96(2): 108-122.

Shan, J., Alkheder, S. & Wang, J. (2008). Genetic

algorithms for the calibration of cellular automata

urban growth modeling. Photogrammetric Engineering

and Remote Sensing, 74: 1267-1277.

Silva, E. A. & Clarke, K. C. (2002). Calibration of the

SLEUTH urban growth model for Lisbon and Porto,

Portugal. Comput. Environ. Urban., 26: 525-552.

Singh, P., Kikon, N. & Verma, P. (2017). Impact of land use

change and urbanization on urban heat island in

Lucknow city, Central India. A remote sensing based

estimate. Sustainable Cities and Society, 32: 100-114.

Su, S., Xiao, R. & Zhang, Y. (2012). Multi-scale analysis of

spatially varying relationships between agricultural

landscape patterns and urbanization using

geographically weighted regression. Appl. Geogr, 32(2):

360-375.

Thapa, R. B. & Murayama, Y. (2012). Scenario based urban

growth allocation in Kathmandu Valley, Nepal.

Landscape and Urban Planning, 105: 140-148.

Zhang, C., Tian, H., Chen, G., Chappelka, A., Xu, X., Ren,

W., Hui, D., Liu, M., Lu, C., Pan, S. & Lockaby, G.

(2012). Impacts of urbanization on carbon balance in

terrestrial ecosystems of the Southern United States.

Environ Pollut, 164: 89-101.

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

166