Identification of urban sprawl dynamics in a rapid growing city … · 2017-12-12 · Urban sprawl...

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 3, No 3, 2013

© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0

Research article ISSN 0976 – 4380

Submitted on September 2012published on March 2013 486

Identification of urban sprawl dynamics in a rapid growing city using GIS Srimanta Gupta, Moupriya Roy, Arpan Sarkar

Department of Environmental Science, The University of Burdwan, Burdwan-713104 West

Bengal, India

[email protected]

ABSTRACT

Urban development is a phenomenon in developing countries. The measurement and

monitoring of urban sprawl is crucial for government officials and planners who urgently

need updated information and proper planning tools. In this study attempts have been made to

identify, characterize and quantify the sprawl by the use of Geographical Information System

and Remote Sensing techniques. The Remote Sensing software PCI Geomtica V10.1 and

satellite image (IRS-P6, LISS-4MX, Date: 7th

January, 2008) is used in this work. Calculation

of Shannon’s Entropy from the remotely sensed data can efficiently identify and characterize

the urban sprawl. In this study, using the population data of Bardhaman Planning Area in

2011, the α-population density, β-population density and the distance (2, 4, 6, 8, 10, 12 km)

from the urban centre are chosen as influencing factors of urban sprawl. These analyses find

the individual influence of α and β-population density and the distance from the urban centre

on the sprawl phenomenon. The equations involving the distance term clearly show that the

percentage of built-up area decreases as the distance from the urban core increases. These

regression analyses only reveal the individual effects of a single influencing factor on sprawl.

To sum up their influences on sprawl, a multivariate regression analysis has to be undertaken.

The study finds a moderately dispersed built-up land development in Bardhaman Planning

Area in 2011. It is predicted to be getting more dispersed in 2021 with a 24.6% increase in

built-up land.

Keywords: Urban Sprawl, Shannon’s Entropy, urban growth, GIS and RS, Sprawl dynamics.

1. Introduction

Urban sprawl is outgrowth of urban area caused by uncontrolled, unplanned haphazard urban

growth. Rapid constructions are often seen in the urban areas, in suburbs and at the rural-

urban fringes. Particularly concretization in the rural-urban fringe is resulted from extreme

loss of agricultural and forest lands, water bodies. This puts a tremendous pressure on the

environment by causing resource depletion, energy loss, and loss of habitats. It is important

to identify and characterize the urban sprawl in an area having urban, rural and rural-urban

fringe areas in it. Recognition and characterization of urban sprawl in an area helps in

planning and decision making for sustainable development. The need for understanding urban

sprawl is already stressed (Sierra Club, 1998; The Regionalist, 1997) and attempted in the

developed countries (Batty et al., 1999; Torrens and Alberti, 2000; Barnes et al., 2001, Yeh

and Li, 2001; Hurd et al., 2001; Epstein et al., 2002). Typically conditions in environmental

systems with gross measures of urbanization are correlated with population density with

built-up area (The Regionalist, 1997). Various issues concerned with quantifying urban

sprawl phenomenon are addressed (Torrens and Alberti, 2000; Barnes et al., 2001) to arrive

at a common platform for defining the process. Most of these studies quantify sprawl

considering the impervious or the built-up as the key feature of sprawl. The Shannon’s

Identification of Urban Sprawl Dynamics in a Rapid Growing City Using GIS

Srimanta Gupta, Moupriya Roy, Arpan Sarkar

International Journal of Geomatics and Geosciences

Volume 3 Issue 3, 2013 487

entropy index reflects the dispersion of spatial variable in a specified area. Types of sprawl

are useful in quantifying the urban sprawl (Yeh and Li, 2001).

1.1 Objective

In this study, the main objective is to identify and characterize the urban sprawl of

Bardhaman Planning Area in 2011 and its future fate by 2021.

2. The study area

The study area of this work is Bardhaman Planning Area (BPA) in the district Bardhaman,

West Bengal, India (Figure1). The BPA consists of both town and some mouja areas. The

BPA area includes Bardhaman Municipality Area (BMA) having 35 wards, and the parts of

two blocks (CD Block 1 and CD Block 2). Block 1 and Block 2 contain 29 and 23 moujas

respectively. The total area under the BPA is approximately 164.4Km2 in which Bardhaman

Municipal area contains approximately 31.22Km2; CD.Block.1 contains near about

90.70Km2 and the CD Block 2 contains approximately 41.97Km

2 of area.

Figure 1: Study Area

3. Materials and methodology

Satellite image (IRS-P6, LISS-4MX, and Date: 7th

January, 2008) of BPA (Fig 2), PCI

Geomatica V10.1 software, population data of BPA (2011). Expected population data of

2011 and 2021 are collected from Burdwan Development Authority, created by Department

of Architecture and Regional Planning, IIT, Kharagpur.





Figure 2: Satellite Image of BPA (IRS-P6, LISS-4MX, and Date: 7th

January, 2008)

3.1 Land Cover Classification and Development of Land Cover Map by GIS/RS

Approach

A study on multi-spectral satellite image (IRS-P6, LISS-4MX, and Date: 7th

January, 2008)

of BPA and field survey reports suggests that BPA comprises of urban built-up lands,

agricultural lands, water bodies, wetlands, sand lands, range lands and some forest areas.

Total land use data of the BPA is classified in the following classes and related subclasses

and shown in Table 1. Multi-spectral satellite image of BPA were analyzed by unsupervised

classification and finally the land cover map was developed using PCI Geomatica V10.1

software.





Table 1: Land cover classes of BPA

Class Sub Class

1.Urban or Built-up Land 1.1 Residential

1.2 Commercial and Services

1.3 Transportation, Communication and Utilities

1.4 Industrial

1.5 Other Urban or Built-up Land

2. Agriculture and

Vegetation

2.1 Cropland and Pasture

2.2 Farms

2.3 Dense Evergreen Natural Vegetation along

with Urban or Built-up Land

2.4 Dense Evergreen Natural Vegetation

2.5 Spar Vegetation

2.6 Forest

3.Rangeland

4.Wetland

5.Water Bodies

5.1 Ponds

5.2 Streams and Canals

5.3 Artificial Canals

6. Barren Land

6.1 Sand Land

3.2 Creation of buffer Zones

Circular buffer zones according to Table 2 are drawn around a point chosen at Curzon Gate

More, Bardhaman as shown in Fig 3. Built-up Land (including the following land cover

classes: Commercial and services, Transportation, communication and utilities, Other urban

built-up land, Residential Area, Industrial Area) in each Zone (Area in Km2) is calculated

(Table 3 and Table 4) to determine relative Shannon’s Entropy for detection of urban sprawl.

Table 2: Buffer Zones

Origin of Buffer Zones Buffer Zone

Number (n).

Distance from the Centre

(in Km)

Circular buffer zones were drawn

around a point chosen at Curzon

Gate More, Bardhaman.

1 2

2 4

3 6

4 8

5 10

6 12





Figure 3: Buffer Zones Drawn From Centre of BPA

Table 3: Land Cover Data of Each Buffer Zone

Land Cover Class Area in Each Buffer Zones in Km

2

Zone1 Zone 2 Zone3 Zone4 Zone5 Zone6

Artificial canals 0.000 0.304 0.358 0.147 0.066 0.020

Commercial and services 0.457 0.228 0.170 0.030 0.000 0.000

Crop land and pasture 0.045 15.382 32.687 35.10 12.881 2.678

Dense Ever green vegetation

along with urban built up land 0.090 2.417 2.535 0.419 0.094 0.000

Dense Evergreen Vegetation

cover 0.030 0.196 0.024 0.161 0.177 0.000

Farms 0.000 0.002 0.246 0.006 0.006 0.000

Forest 0.003 0.173 0.000 0.000 0.000 0.000

Industrial Area 0.085 0.303 0.145 0.181 0.005 0.000

Other urban built-up land 0.042 0.022 0.026 0.125 0.000 0.000

Ponds 0.817 1.550 0.987 0.826 0.496 0.031

Rangeland 0.000 0.644 1.602 0.087 0.000 0.000

Residential Area 9.209 11.094 2.084 1.194 0.480 0.176

Sand Land 0.000 1.021 0.981 0.000 0.000 0.000

Spar Vegetation 0.813 1.056 0.989 1.103 0.181 0.057

Streams or canals 0.244 0.529 0.335 0.173 0.053 0.000

Transportation, communication

and utilities 0.475 0.845 0.510 0.386 0.116 0.019

Wetland 0.203 0.273 0.416 0.308 0.102 0.000

Total Area Under Each Buffer

Zone in Km2 12.513 36.039 44.095 40.246 14.657 2.981





Table 4: Built-up Land in Each Zone (Area in Km2)

Urban or Built-up Land Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 6

Commercial and services 0.457 0.228 0.170 0.030 0.000 0.000

Industrial Area 0.085 0.303 0.145 0.181 0.005 0.000

Other urban built-up land 0.042 0.022 0.026 0.125 0.000 0.000

Residential Area 9.209 11.094 2.084 1.194 0.480 0.176

Transportation, communication

and utilities 0.475 0.845 0.510 0.386 0.116 0.019

Total Built-up Land(xi) 10.268 12.492 2.935 1.916 0.601 0.195

Percentage of Built-up Area 82.060 34.667 6.667 4.760 4.100 6.540

Identification and Characterization of Urban Sprawl: Shannon’s Entropy Approach

Various approaches have been directed through the years to identify and characterize the

urban sprawl, but none of them stood for long for the lack of versatility. Shannon’s Entropy

calculation incorporation with GIS/RS data is a very popular and well appreciated approach

in this field. Calculation of Shannon’s Entropy from the remotely sensed data can efficiently

identify and characterize the urban sprawl. (Yeh and Li. 1999, Torrens and Alberti. 2000,

Yeh and Li. 2001, Hurd et.al., 2001, Lata et.al, 2001, Sudhira et.al, 2003, 2004, Li. 2009)

The Shannon’s entropy En can be used to measure the degree of spatial concentration or the

dispersion of a geospatial variable (Xi) and is given by:

] …1

Where, Pi is the value of each type of land development or the geospatial variable in the ith

zone (Xi say) divided by the total land area in that zone. The letter n denotes the total zone

numbers.

= …2

The value of En varies from a minimum 0 to maximum log (n). If the distribution of the

geospatial variables is concentrated in one zone, then the minimum entropy value is obtained

and the entropy value approaches to the maximum when the distribution of the geospatial

variables is dispersed among all the zones. It is more convenient to scale the entropy value in

the range between 0 and 1. It can be done by calculating the Relative entropy (Thomas,

1981) which is given by:

= ] …3

The land cover data are used to calculate the Shannon’s Entropy. The value of entropy is

independent of the size and number of buffer zones. Calculation of Relative Shannon’s

Entropy ( ) is shown in table 5.

Table 5: Calculation of Relative Shannon’s Entropy ( )

Zone No. Value of Pi = ]

1 0.821

0.65 2 0.347

3 0.067

4 0.048

5 0.041

6 0.065





4. Results and discussions

4.1 Land cover data analysis

The LULC data of BPA, CD Block1, CD Block2 and BMA are tabulated in Table 6 and 7

and maps are presented in Fig 4, 5, 6 and 7 respectively. BPA covers 164.38 Km2 areas in

which 18.87% is covered with urban or built-up land, cropland and pasture has covered

72.79% area, water bodies have covered 4.61% area and range land, wetland and sand land

has covered 1.55%, 0.86% and 1.33% area respectively. 16.10% of total BPA and 85.34% of

total built-up area is residential area. Industrial area occupies only 0.48% of BPA and 2.50%

of total built-up land (Table 6). The land cover data as it is given in Table 3 reveal that the

two constituting blocks of BPA are the intensively farmed zones. 80.19% area of CD block 1

and 72.55% area of CD block 2 is cultivated land. Where as in the municipal area, almost

60.12% area is built-up land in which 54.07% is residential area. Almost 13.75% of total

municipal area is cultivated land. The LULC maps of BPA, CD Block1, CD Block2 and

BMA are presented in Fig 4, 5, 6 and7.

Table 6: Land Cover Data of BPA

Land Cover Class Area.Under.Each

Class in Km2

Total Area. in

BPA.in Km2

%.of Occupancy.of

Each Class

Residential 26.467

164.386

16.101

Commercial and Services 0.966 0.588

Transportation,.Communication.an

d Utilities

2.563 1.559

Industrial 0.783 0.476

Other Urban or Built-up Land 0.235 0.143

Urban or Built-up Land

31.014 18.867

Cropland and Pasture 107.879 65.625

Farms

0.282 0.172

Dense Evergreen Natural

Vegetation along with Urban or

Built-up Land

6.064 3.689


Vegetation

0.642 0.391

Spar Vegetation 4.586 2.790

Forest 0.195 0.119

Agriculture and Vegetation

119.648 72.785

Rangeland 2.548 1.550

Wetland 1.420 0.864

Ponds 5.138 3.126

Streams and Canals 1.455 0.885

Artificial Canals 0.977 0.594

Water Bodies 7.570 4.605

Sand land 2.186 1.330





Table 7: Land Cover Data of CD Block 1, CD Block 2, and Bardhaman Municipal Area

(BMA)

Land Cover Classes Area occupied by the classes

in Km2 in

Percentage of Occupancy of

the Class

Block 1 Block2 BMA Block 1 Block 2 BMA

Residential 7.024 2.561 16.881 7.744 6.102 54.068

Commercial and Services

0.231 0.036 0.698 0.255 0.086 2.236

Transportation,.Communicatio

n.and Utilities

1.047 0.664 0.851 1.154 1.582 2.726

Industrial 0.242 0.278 0.263 0.267 0.662 0.842

Other Urban or Built-up Land 0.157 0.000 0.078 0.173 0.000 0.250

Urban or Built-up Land

8.701 3.539 18.771 9.593 8.432 60.121

Cropland and Pasture 72.732 30.449 4.293 80.191 72.548 13.750

Farms 0.280 0.000 0.002 0.309 0.000 0.006


Vegetation along with Urban

or Built-up Land

1.102 2.600 2.361 1.215

6.195

7.562


Vegetation

0.025 0.518 0.098 0.028 1.234 0.314

Spar Vegetation 1.822 1.012 1.748 2.009 2.411 5.599

Forest 0.000 0.000 0.195 0.000 0.000 0.625

Agriculture and Vegetation

75.961 34.579 8.697 83.752 82.388 27.855

Rangeland

1.533 0.250 0.765 1.690 0.596 2.450

Wetland

0.148 0.942 0.330 0.163 2.244 1.057

Ponds 1.931 1.313 1.887 2.129 3.128 6.044

Streams and Canals 0.480 0.495 0.469 0.529 1.179 1.502

Artificial Canals 0.495 0.356 0.124 0.546 0.848 0.397

Water Bodies

2.906 2.164 2.48 3.204 5.155 7.943

Sand land 1.449 0.497 0.179 1.598 1.184 0.573

Total Land Area in the

Blocks and BMA

90.698

41.971 31.222

4.2 Shannon’s Entropy Analysis

Shannon’s Entropy value from the remotely sensed data can efficiently identify and

characterize the urban sprawl. (Yeh and Li. 1999, Torrens and Alberti. 2000, Yeh and Li.

2001, Hurd et.al., 2001, Lata et.al, 2001, Sudhira et.al, 2003, 2004, Li. 2009). The relative

Shannon’s entropy value calculated in the study (Table 5) is 0.65 (maximum is 1) which

indicates moderately dispersed built-up land development. Most of the land development is

concentrated in the urban core, but the process of urbanization is spreading the land

development outside the core of the urban area.





Figure 4: LULC Map of BPA

Figure 5: LULC Map of CD Block 1





Figure 6: LULC Map of CD Block 2

Figure 7: LULC Map of BMA

4.3 Dynamics of sprawl and future prediction

In this study some factors are considered which influence the urban growth rate effectively

and the urban sprawl as well. Population always plays a vital role in urbanization. A large

population always induces a higher urbanization rate. In this study along with the population

in BPA in 2011, the α-population density, β-population density or simply the population

density and the distance from the urban centre are chosen as the influencing factors of urban





sprawl (Sudhira et.al. 2003). α-population density is the population in a zone divided by the

total residential area in that zone. β-population density or simply the population density is

simply the ratio between the population in a zone and the total area in that zone. Population

statistics in Each Buffer Zone in 2011 is given in Table 8.

In order to make a relevant relationship between the percentage of built-up land and the

causal factors or the influencing factors of urban sprawl, regression analyses were undertaken

(Sudhira et.al. 2003, Batty et al., 1999). Linear, quadratic, logarithmic regression analyses

figure out the nature of dependence of the sprawl on the individual causal factors. The

regression analyses are given in Table 9 and the corresponding regression statistics is given in

Table 10. The linear regression analyses undertaken reveal that β-population density has a

significant contribution on sprawl in BPA. The quadratic regression analyses find out the

influence of population, α-population density, β-population density and the distance from the

urban centre on the sprawl phenomenon. The equations following the power law can easily

be represented in logarithmic way and vice-versa. These analyses find the individual

influence of α and β-population density and the distance from the urban centre on the sprawl

phenomenon.

The equations involving the distance from Urban Centre in Table 9 clearly shows that the

percentage of built-up area decreases as the distance from the urban core increases. These

regression analyses only reveal the individual effects of a single influencing factor on sprawl.

To sum up their influences on sprawl, a multivariate regression analysis is to be undertaken.

The problem is that the ways in which the causal factors are affecting the sprawl are exactly

not known. In that cause only a multivariate linear regression analysis can be done to obtain

the cumulative effects of the causal factors on sprawl. The equation established from the

multivariate linear regression analysis is

Pbuilt-up = -7.95+1.87×10-7

POP+5.37×10-4

α-POPden+4.95×10-3β-POPden -0.12Dist ...4

By using the equation 4, future percentage of built-up area in BPA can be estimated if the

chances of occurrence of major changes like set up of heavy industrial plants, sudden

migrations or natural disasters in BPA are neglected. Changes in the socio-economic

conditions may also affect in urbanization but these are not considered for simplicity. The α-

Population density of a zone is a function of the built-up area in a particular time in that zone.

To get the value of α-Population density of that particular year for which the percentage of

built-up area is to be estimated, a factor is to be multiplied with the present α-Population

density. Thus,

α-POPden |for 2021(say) = α-POPden 2011× × % of residential

area .…5

Population Statistics in Each Buffer Zone in 2021 is tabulated in Table 11. Zone wise

estimation of percentage of built-up area in BPA in the year 2021 by applying equation 5 is

given in Table 12. Total built-up land cover in entire BPA in the year 2021 could increase by

24.6% and that could result in 38.66 Km2 urban cover in BPA. In 2021 almost 23.52% area

of BPA will be covered by urban built-up land. Urbanization rate will be higher in Zone 1

and 2 and it suggests that a huge portion of land in and near the BPA will be converted to

built-up land within next 10 years.





Table 8: Population Statistics in Each Buffer Zone in 2011.

Zone No. 1 2 3 4 5 6

Population in

2011

199013 231832 41166 23854 9231 3336

α-Pop Den2011 21610 20897 19753

19978 19231 19062

β-Pop Den2011 15904 6433 934 593 630 1119

Table 9: Relation between the Causal Factor of Sprawl (x) and the Percentage of built-up

Land (y)

Causal

Factor

Relation Type

Linear

(y=ax+b)

Quadratic

(y=ax2+bx+c)

Logarithmic

(ln y=aln x+ ln b)

Power

(y=axb)

1.P

op

ula

tio

n

20

11

y = 2.48×10-4

x+ 2.11

R² = 0.667

y= -2.74×10-9

x2-8.89×10

- 4x-

8.50

R2=0.747

ln y=13.72ln x-119.8

R2=0.545

y=0.022x0.6

R2=0.674

2.α

-

Po

pu

lati

on

Den

sity

y=0.03x-552.4

R2=0.826

y=2×10-5

x2-0.727x+7113

R2=0.996

ln y=576.9ln x -5692

R2=0.812

y=9×10-

10x

23.3

R2=0.851

3.β

-

Po

pu

lati

on

Den

sity

y=0.005x+1.4

R2=0.999

y= -10-8

x2+0.005x+1.2

R2=0.999

ln y=20.60ln x-139.3

R2=0.900

y=0.013x0.9

R2=0.994

4.D

ista

nce

fro

m

Urb

an

Cen

tre

y= -

6.73x+70.24

R2=0.653

y=1.6x2-29.14x+130

R2=0.969

ln y= - 43.6ln x

+101.3

R2=0.863

y=251.9x-1.7

R2=0.862

Table 10: Regression Statistics

Coefficients Standard Error

Multiple R 0.999992697 Intercept -7.95 13.33

R2

0.999985393 Distance -0.12 0.107

Adjusted R2

0.999926965 Population 1.87×10-7

2.97×10-7

Standard Error 0.266213386 α-POPden 5.37×10-4

6.5×10-4

Observations 6 β-POPden 4.95×10-3

4.41×10-5

Table 11: Population Statistics in Each Buffer Zone in 2021.

Zone No. 1 2 3 4 5 6

Population in 2021 235795

286366

57764 34451 13235 4799

β-Pop Den2021 18844 7946 1310 856 903 1610





Table 12: Estimated Percentage of Built-up Area in 2021 in Each Zone of BPA

Zone No. Pbuilt-up in 2011 Pbuilt-up in 2021 Change in Pbuilt-up Urban cover in

Km2 in 2021

1 82.06% 97.44% +15.38% 12.19

2 34.66% 43.17% +8.51% 15.56

3 6.66% 8.91% +2.25% 3.93

4 4.76% 6.15% +1.39% 2.48

5 4.10% 6.32% +2.22% 0.93

6 6.54% 10.46% +3.92% 0.31

5. Conclusion

Identification, characterization and quantification of sprawl are very important for better

urban planning and land resource management. In this study attempts have been made to find

the present land cover status in BPA to identify, characterize and quantify the sprawl by the

use of GIS and RS techniques. The study finds a moderately dispersed built-up land

development in BPA in 2011. It is predicted to be getting more dispersed in 2021 with a

24.6% increase in built-up land. This is going to put pressure on the existing agricultural land,

water bodies, and natural vegetations in and around the municipal area by next 10 years.

Acknowledgement

Authors would like to express gratitude to the department of environmental science, the

University of Burdwan for providing GIS laboratory facilities and also like to express sincere

gratitude to Prof. Jayanta Kumar Dutta, Dr Apurba Ratan Ghosh and Dr. Naba Kumar

Mondal for their constant moral support and valuable comments.

6. References

1. Barnes K.B., Morgan III JM., Roberge M.C., et.al. (2001), Sprawl Development: Its

patterns, consequences and measurement. Towson University, Towson, available at

http://www.chesapeake.towson.edu/landscape/urbansprawl/download/Sprawlwhite

paper.pdf, accessed during January 2013.

2. Batty M., Xie Y., Sun Z., et.al. (1999). The dynamics of urban sprawl. Working Paper

Series, Paper 15, Centre for Advanced Spatial Analysis, University College, London,

available at http://www. casa.ac.uk/working papers/, accessed during December 2012.

3. Epstein J., Payne K., Kramer E., et.al., (2002), Techniques for mapping suburban

sprawl, Photogrammetry and Remote Sensing, 63(9), pp 913–918.

4. Hurd J.D., Wilson E.H., Lammey S.G., et.al. (2001), Characterisation of forest

fragmentation and urban sprawl using time sequential Landsat Imagery. In:

Proceedings of the ASPRS Annual.Convention, St..Louis,.MO,.April.23–27,.2001.

available at http://www.resac.uconn.edu/publications/tech papers/ index.html,

accessed during January 2013.

5. Lata K.M., Sankar R.C.H., Krishna P.V., et.al., (2001), Measuring urban sprawl: a

case study of Hyderabad, GIS development, 5(12), pp 8-13.





6. Li F., (2009), Applying remote sensing and GIS on monitoring and measuring urban

sprawl. A case study of China. Revista Internacional Sostenibilidad, Tecnología y

Humanismo, 4, pp 47-56.

7. Sierra Club., (1998), The Dark Side of the American Dream: The Costs and

Consequences of Suburban Sprawl, available at http://www. sierraclub.org/

sprawl/report98, accessed during December 2012.

8. Sudhira H.S., Ramachandra T.V., Jagadish K.S., et.al., (2003), Urban sprawl pattern

recognition and modeling using GIS, Proceedings of Map India 2003, New Delhi.

9. Sudhira H.S., Ramachandra T.V., Jagadish K.S., et.al., (2004), Urban sprawl: metrics,

dynamics and modelling using GIS, International Journal of Applied Earth

Observation and Geoinformation, 5, pp 29–39.

10. The Regionalist, (1997), Debate on Theories of David Rusk, 2(3) available at

http://scpp.ubalt.edu/public/regional/detail.htm#issue3v2, accessed during December

2012.

11. Thomas R. W., (1981), Information Statistics in Geography. Norwich, Geo Abstracts

31.

12. Torrens P.M., & Alberti M., (2000). Measuring sprawl. Working paper no. 27, Centre

for Advanced Spatial Analysis, University College London, available at

http://www.casa.ac.uk/working papers/, accessed during December 2012.

13. Yeh A.G.O, and Li X., (1999), Measurement of Urban Sprawl in a Rapid Growing

Region Using Entropy. Towards Digital Earth, Proceedings of the International

Symposium on Digital Earth Science Press, 1999.

14. Yeh,A.G.O., and Li X., (2001), Measurement and monitoring of urban sprawl in a

rapidly growing region using entropy, Photogrammetry and Remote Sensing, 67(1),

pp 83.

Date post:	09-Jul-2020
Category:	Documents
Upload:	others
View:	3 times
Download:	0 times

Identification of urban sprawl dynamics in a rapid growing city … · 2017-12-12 · Urban sprawl...

Documents