Ghana Social Science JoumaJ I 11 
Urban sprawl and sustainability: A comparative Analysis of Accra and Kumasi 
urban regions. 
Gerald Albert Baeribameng ¥rran 
Department of Geography & Resource Development, University of Ghana, Legon. 
gyiran@ug.edu.gh 
Abstract Keywords: 
One of the important developmental topics of global concern is how Shannon's Entropy; 
to achieve sustainable urban development in developing countries urban sprawl; sustainable 
where urban sprawl is very rapid. This is because urban sprawl development; Accra; 
Kumasi 
have adverse consequences on the environment and human 
welfare. Indicator-based methods have become popular among 
the approaches developed to determine sustainability of urban 
environments and have been widely used to measure sustainable 
development of cities. This paper uses Shannon's entropy to 
examine intra-city and inter-city land-use change patterns and 
the intensity and level of urban sprawl and then discusses the 
sustainability of the urban areas under study. The results show 
high growth rates of the urban regions. All entropy values were also 
very large near the upper limits, and thus reveal that both urban 
regions are sprawling at high rates. but the Accra urban region is 
sprawling faster than that of Kumasi. The results also show a high 
rate of densification (compactness) of built· up areas at the same 
time. Although both regions exhibit unsustainable development, the 
problem is worse in the Accra urban region, highlighting a difference 
in the development patterns of the regions. With the current pattern 
and form of development, it is not possible to achieve the pillars of 
sustainable development: to protect the environment while realizing 
economic and social goals. Thus, the sustalnability of the growth of 
the regions is far from reality. It is recommended that an all-inclusive 
sustainable development planning framework be developed for the 
regions, but the plans should be region specific since each region 
exhibits different patterns. 
Introduction 
Many cities all over the globe continue to urbanize at a rapid rate, resulting in the excessive conversion 
of natural landscapes into residential uses. The resulting land-use changes have severe consequences 
on the environment and human life. Thus, mitigating the adverse effects of urbanization has been 
at the centre of global engagements, leading to the coining of the concept of sustainable urban 
development. Sustainable urban development (SUD) is a new type of development that emerged 
after the popularization of the concept of sustain ability and is made up of words with different 
121 Volume 17, Number I. June 2020 
meanings (yigitcanlar et aI., 2015). SUD is coined by marrying the sustainability and urban 
development concepts. Sustainability, according to Goonetilleke et al. (2014), connotes conserving 
ecosystems and their services, and at the same time providing for the needs of humans. In contrast, 
urban development represents any activity that advances the quality of human life through the 
use of natural resources. The use of natural resources in this regard will eventually deplete and/ 
or degrade them. This means that sustainability and urban development contradict each other. 
Therefore putting these two contradictory terms toge ther to form SUD amounts to setting the stage 
for an intellectual debate (Yigitcanlar et aI. , 2015). 
However, urban development/growth in the developing world, particularly Asia and sub-Saharan 
Africa (SSA), is proceeding at a fast rate (UN-DESA, 2014, 2018), with dire consequencies for 
human wellbeing. The consequences of rapid urban development are said to be many and complex 
(Hiremath et aI ., 2013). One of the major challenges to SUD is urban sprawl (Bovet et aI. , 2018; 
Henniget aI., 2016; OECD. 2018; Wei, 2018). Although there is no agreed definition of urban sprawl, 
it is generally described as haphazard. poorly planned/ unplanned, uncontrolled, uncoordinated 
expansion oflow-densi ty buildings occurring at the peripheries of urban centres (Akubia & Bruns. 
2019; Altieri et a1., 2014; Bhatta, 2010; Sinha, 2018). Urban sprawl often generates environmental , 
social and economic costs stemming from inefficien t utili'l.ation of natural resources (Bovet et aI. , 
2018). Studies have shown that urban sprawl might lead to debilitating effec ts on human wellbeing 
in terms of lack of potable water, food shortage and hunger, flooding. settlement segregation and 
increased urban warming. and thereby contribute to global climate change (Akubia & Bruns. 2019; 
Duque eta!', 2019; Moclain et aI., 2018; Rain et ai., 2011; Sulemana & Yiran, 2018). Some of these 
challenges, particularly the need to ensure human we ll being while sustaining the environment in 
urban areas, have generated a lot of research interest in sustainable urban planning. 
Sustainable urban planning can be considered as the purposeful interven tion by city planners 
in urban development using various tools including regula tions, stakeholder engagement and 
collective choice (Hopkins, 2001 cited in Cobbinah & Darkwah, 2017). Unfortunately, sustainable 
urban planning in SSA countries like Ghana is a mirage, as plann.ffs are unable to manage the 
environmental, economic and social consequences that arise due to urban sprawl (Cobbinah et 
a1., 2015). For SUD to be achieved, there is the need for a balance between conservation of the 
environment and the development of urban areas thaI promotes equity in the provision of goods 
and services in such areas (Hiremath et aI. , 2013). 
According to Yigitcanlar and Teriman (2015), sustainable urban planning is undermined by the lack 
of complete and accurate information on cities. Comprehensive and precise spatial data is critical 
for spatial planning. The need for accu rate data in Ghana is vital as many cities across the country 
are rapidly sprawling, characterised by scattered or leapfrog deVelopment, increas ing vast expanses 
of single-use or low.density development and a paucity of functional open spaces, among others 
(Amoako & Cobbinah, 2014; Cobbinah & Aboagye, 20 17; Jaeger et aI .. 2010). Several approaches 
including the use of indicators have been developed to provide informat ion or assess sustainable 
development of cities. According to Guy and Kibert (1998), "indicators are parameters or values 
that prOvide information about a phenomenon" (ci ted in Hiremath et aI., 2013: 556). Indicator-
based approaches are popular and are being increasingly used to assess the sustainability of urban 
growth around the world as the information necessary to building a sustainable development system 
that incorporates development and environmental protection (Hiremath et al.. 2013). Thus, this 
Ghana Social Science Journal I 13 
paper extends this knowledge by carrying out a comparative analysis of urban development of two 
urban regions. This will be achieved by accurately quantifying the urban footprint and detecting 
the trend of urban sprawl. However, urban areas are unique in terms of characteristics, activities 
and resources, and therefore, a comparative_a nalysis will highl ight the factors contributing to the 
different patterns of growth. Knowledge of this information will be used to assess the sustainability 
of the development of the cities. The study therefore undertakes a comprehensive examination of 
land-use change using time series data to quantify urban growth/sprawl by means ofs patial metrics 
(Shannon's index) in the two largest cities in Ghana. 
The paper uses Remote Sensing techniques to collect spatial data and bring them into the GIS 
environment to quantify and analyse the trend of urban expansion. The integration of Remote 
Sensing (RS) and Geographic [nformation Systems (GIS) technologies has gained popularity, and 
the tools have generally been used for research into urban growth and land-use changes (Bhatta et 
aI., 2010a; Chong, 2017; Nkeki, 2016). GIS procedures were used to determine spatial measures 
(metrics) and statistics using temporal data extracted from satellite imagery (Moghadarn & Helbich, 
2013; Nkeki, 2016; Taubenb6ck et aI., 2009). The use of spatial metrics (indices) to quantify urban 
growth/sprawl from rnulti·date satellite data has increased in recent times (Bhatta, 2010; Bhatta 
et aI., 2010a; Jaeger et aI., 2010; Mosammam et aI., 2017; Ramachandra et aI., 2012; Shaw & Das, 
2018). The methods involved are described in detail in the next section. 
Description of methodology 
Choice of study areas 
The two largest urban regions in Ghana, Accra urban region and Kumasi urban region are chosen 
for this study. as they are currently the most rapidly expanding regions, a trend primarily driven 
by population growth from rural-urban migration (Awumbila et aI., 2014; Yiran et aI., 2015). The 
influence of these cities has grown beyond their core towns into adjoining districts and regions. 
Thus catchment areas of the cities considered in this study are described as Accra Urban Region 
(AUR) and Kumasi Urban Region (KUR). AUR is located on the coast of Ghana (Figure 1). The 
region is mostly plain with gently undulating topography and has the Akuapim range to the north 
and the sea to the south limiting growth in some directions. This region (AUR) covers almost 
the entire area of Greater Accra Metropolitan Area (GAMA) and parts of Central and Eastern 
Regions where many people live and commute daily to Accra to work or transact business. KUR, 
on the other hand. is located close to the middle of Ghana (see Figure 1) and has no natural feature 
constraining development, although the topography is more undulating than AUR. 
14 I Volume 17. Number I. June 2020 
Figure 1 Map of Ghana showing the Study areas 
This enables the growth of KUR to spread radially in all directions and cover areas beyond the 
former Kumasi Metropolitan Area where people live and commute daily to work and do business 
in Kumasi. These two urban regions, AUR and KUR, were chosen for th is study because they are 
the most extensive and very fast-growing urban regions. The reason for their fast growth is because 
most of the jobs in Ghana, especially those termed 'white-colour: are located in these regions. 
They also house the largest market centres in the country, offering a Jot of job opportunit ies in 
the informal sector, especially to those engaged in trading and other services (Yiran et aI., 2015). 
These cities also host the best social amenities such as the best hospitals, most of the good schools 
across all levels of the educational ladder, the best entertainment centres, the best infrastructure 
and utility services, among others. These and many others const itute the pulJ factors attracting 
many people, especially from the hinterlands. Also, Songsore (2003) chronicled urban growth for 
the pre-structural adjustment period, which indicates natural increase as an essential factor in the 
growth of urban areas. However, the rate of growth of these cities outpaces the rate of infrastructure 
provision, creating huge housing deficits annually. Governments, in a bid to br idge the housi nggap, 
liberalized the housing sector, leading to p rivate and individual part icipation in housing provision 
(Fuseini et al., 2017). To meet the housing demand, more land has to be converted from other 
uses in and around these cities into residential land. But the land governance systems, especially 
land tenure in the two cities, are different (Ubink & Quan, 2008). The comparative analysis will 
highlight the role land governance plays in the growth of each region. 
Ghana Soc", Science JoumaJ I 15 
Satellite image acquisition and pre-processing 
Three Landsat images for AUR and !CUR were downloaded for classification from earth-explorer 
and the United States Geological Survey (USGS) website. Information about the bands is shown in 
Table I. The initial intent was to start from around 1990, as those years marked the beginning of the 
implementation of the structural adjustment Program (Fuseini et al., 2017) but due to cloud cover, 
especially in KUR area, the images that met the less than 10% cloud cover criteria were between 
2001 and 2020. So the images were downloaded within these time limits. The bands in Table 1 were 
chosen because they give an excellent contrast between built up and non-built up areas. 
Table 1 Landsat imagery used for classification 
Accra urban region Kumasi urban region 
Date Landsat l Bands Bounding Date Landsat Band Bounding 
coordinates coordinates 
26/12/2002 LE07 7,4,2 Upper left 02/0412001 LE07 7,4,2 Upper left 
17/01/2011 LE07 7,4,2 5' 52' 06/0212010 LE07 7,4,2 6' 55' 47"N, 
47"N,O' 1'50' 19"W 
34'06"W 
02/01/2020 LCOS 7,5,3 Lower right 09/0112020 LCOS 7,5,3 Lower right 
5' 20' 5S"N, 6' 30' 15"N, 
0'04' 09"E I' 24' 09"W 
The LE07 images had gaps, and these were corrected using the gap-fill extension in ENVl 5.3 
before calibration. Exoatmospheric calibration was performed on all images because they came 
from different sensors and dates and thus needed to be on a common scale to enable comparison. 
The images were then subset using the bounding coordinates of the upper left and lm. .... er right 
coordinates for the respective regions shown in Table 1 during the band combination procedures. 
Due to cloud cover, it was not possible to get the same dates, as can be seen in Table 1, but these 
slight differences in dates did not affect the work. 
Image classification 
Supervised classification algorithm in ENV! 5.3 was used to classify the images into five classes 
(Table 1). Due to difficulty in defining urban areas, especially in relation to satellite-derived data 
(M0ller-Jensen et al., 2020), this study limited the urban areas to impervious surfaces (buildings and 
some roads) referred to as built-up areas. The ma.x.imum likelihood classifier was used to perform 
the classification. The ma.ximum Likelihood classifier was chosen because it is parametric and also a 
popular method for classifying Landsat images (see for example, Osmanet al., 2016; Ramachandra 
et al., 2012; Yiran et al., 2012). 
1 LE07 is Landsat 7 Enhanced Thematic Mapper (ETM+) and Leos is Landsat S 
16 Volume 17. Number I. June 2020 
The training samples or regions of interest (ROls), explained in Table 2, were selected using Google 
Earth in conju nction with unsupervised classification. 
Table 2 Definition of land use classes 
Class Description 
Built-up area Buildings, road network, and other impervious surfaces in the 
urban area 
Vegetation Forest, grassland, farms with crops, shrubs, bushes, etc. 
Soil Bare land, gravel roads, burnt areas, farms wi th dead or withered 
crops, excavated areas, exposed soils, bare playgrounds/school 
parks etc. 
Water bodies Lakes, rivers, lagoons, ponds, darns, sea 
Clouds I Cloud cover 
shadows Shadows of clouds 
This was done by taking coordinates of unique and large areas on the unsupervised classification 
in ENVI and searching for these locations in Google Earth. The time slide bar on Google earth was 
slid to the year corresponding to the year the image was taken or a little after it was taken (whichever 
is available) to see what featu re that was. Though pixel -based classification usually produces a "salt" 
and "pepper" effect, smoothing was not applied because some land uses , especiaUy in the peri-urban 
areas, are even smaller than the size of a pL'XeP and would be lost if smoothing and aggregation were 
applied. As with many land cover classification studies, post-classification accuracy assessment was 
done, and this was found to be within what many studies have reported (e.g., Nkeki, 2016; Yiran 
et aI., 2012). After classification, three of the land cover classes (Vegetation, soil and water bodies) 
were combined into one class called non-built-up. This resulted in only two classes; built-up and 
non-built-up areas. The resultant classified images for each region were up loaded in ArcMap and 
converted to shapefiles amd the areas of the classes calculated. 
However, the sea and lake Bosumtwi, which are at the lower boundaries (Figure 1) of the subsets, 
were deleted before the areas of the classes were calculated. There were a few clouds and cloud 
shadows on the 2001 and 2010 images of KUR, but those over/ near built-up were selected and 
added to the built-up class while the rest were added to the non-built-up class. This was done in 
ArcMap after the classified images were converted into shape fi les. 
I Clouds and shadows were found on the KUR images 
2 Pi."I:el is picture element of size 30m x 30m 
Ghana Social Scierx:e Journal 117 
Shannon's entropy 
Shannon's entropy (HJ is an index which measures the amount of in-till/concentration 
(compactness) or dispersion (sprawl) of a geographical feature among many zones (Bhatta et aI., 
20lDa; Yeh & Li, 2001a). This measure was~hosen because it is a most widely used measure for 
studying urban sprawl (e.g. Chong, 2017; Nkeki, 2016; Rahimiet aI., 2019; Vani & Prasad, 2020; Yeh 
& Li, 200lb). Hn was calculated to quantify urban sprawl developments in the two urban regions. 
Shannon's entropy is calculated using equation 1 taken from Chong (2017): 
Ho = - 1: P, log" (P, ). .... ..1 
Where Pi is the proportion of built-up area in the jth zone and n is the total number of zones in each 
region. 
The minimum and maximum values of Hn are zero (0) and laglo (n), respectively. Values close to 
zero (0) indicate highly clustered (very compact) built-up areas while values closer to log" (n) show 
dispersed (sprawl) areas (Chong, 2017; Deka et aI., 2011; Yeh & Li, 2001b). Half of the value of 
log" (n) is usually taken as the threshold, and when Ho is larger than the threshold value, the region 
is considered to be sprawling. Determining the threshold is robust and is easily computed for any 
study area mathematically (Bhatta et aI., 2010b). 
In calculating Ho for this study, the multiple ring buffer tool in ArcGIS Analysis ToolBox was used 
to generate a series of concentric buffer zones at 1 Km intervals around points used to represent the 
city centres. The 1 Km buffer was chosen because of potential information loss due to aggregation 
effects when larger size buffers are used (Yeh & Li, 2001b). For AUR, Accra was used as the centre 
where AUR originated and started to expand, while Kumasi was used as the centre of growth for 
KUR. These points were obtained from the town/village shapefile data of Ghana. All buffers were 
dissolved to eliminate overlaps in the zones. The Tabulate Intersection tool in Statistics in the 
Analysis Toolbox in ArcGIS was used to compute the intersection of the land~use classes and the 
buffer zones. The tool generates a table that presents the areas of the two classes (the built-up and 
the non-built-up areas) in each zone. The area values of the intersecting layers were used to do 
the entropy calculation. All the tables (i.e. the tables generated with the tabulate intersection tool) 
were exported to Excel to enable easy calculation of the entropy. In Excel, the ratio (R) of the area 
of the built-up area to the total area in the ith zone was calculated using equation 2 from which the 
proportion (Pi) needed for equation 1 was obtained by equation 3 (see Chong, 2017). 
R, = (Area ofbuilt-up),1 (area of built-up +Area of non-built-up), ....................... 2 
P, = R,Isum R, ...................................... 3 
The values obtained from equation 3 were then substituted into equation 1 to compute the 
Shannon's entropy for each year in each region. Finally. the change in entropy (dE) from one time 
to another was calculated using equation 4. With the change in entropy. a decrease in entropy (i.e. 
a negative value) over time means the region's development is compacting or densifying while an 
increase in entropy (a positive value) means the region's development is spreading or dispersing 
(Chong, 2017). 
181 Volume 17. Number I. June 2020 
dE = Ho' " ' " 
Where tl and t2 are the first and second reference years respectively. 
Results 
This section presents the results of the study by fi rst describing urban growth in the regions as seen 
from the classified images and then comparing them using the Shannon's entropy indices. 
Urban growth in AUR and KUR 
The AUR has grown over the years in terms of filling the open spaces withi n it and expanding into 
surrounding areas. As can be seen in Figure 2, over the past two decades. the region has grown 
tremendously. 
--,-..., .... ~ 
Figure 2 Urban growth of AUR from satellite imagery (2002 to 2020) 
In 2002, there were a lot of greens (non-built-up areas) within AUR, but some of these were fi lled 
up by 2011 with the reds (buit-up areas) while at the same time the red spots expanded into the 
hinterlands, and those which existed increased in size and new ones appearing. In  effect. the 
built-up area has increased in size by in-filling and expansion. The in-filling and expansion of the 
built-up area continued into 2020. The extent of built-up areas in each year was extracted from the 
classified images to quantitatively assess the rate of growth of the urban region (Figure 3). From 
Figure 3, the average growth rate of built-up areas between 2002 and 20U is 28.2 square Km/ 
annum while that between 2011 and 2020 is 31.3 square KmJannum. This translates to 5.6% per 
annum and 4.2% per annum respectively, even though the last decade had a nominal increase in 
the growth rate of about 3 square Km/year. This means that AUR is growing averagely at 4.9% per 
annum. The growth in AUR is constrained in the south by the sea and in the north by the Akuapem 
range, leading to the growth in the soulh-west to north-east orientation as in Figure 2. 
Ghana Social Science JoumaJ 119 
1200 
1032.67 
1000 
~ 800 
a~- 600 
c 
• Built-yp 
j 400 
200 
2002 2011 2020 
Year 
Figure 3 Changes in built-up extent in AUR 
KUR, on the other hand, has a radial or concentric growth (Figure 4). The radial growth is due 
to the fact that, though the land has hills/mountains, they are not elongated and therefore do not 
constrain development very much in any direction. As shown in Figure 4, the built-up class (red) 
increased immensely from 2001 to 2010. 
Legend + 
L ' '4:4:. 
Figure 4: Urban growth of KUA from 2001 to 2020 
20 Volume I 7, Number I, June 2020 
Similar toAVR, the built-up areas consumed the non-built-up within KUR as well as its periphery. 
The expansion and in-fill also continued into 2020, as was the case in AUR (Figure 5). The growth 
rates ofKUR computed from Figure 5 are 23.6 square km/ annum between 2001 and 2010 and 14,2 
square Km/annum between 2010 and 2020. These growth rates translate to 20% per annum and 
4.3% per annum, respectivel y, The phenomenal growth of20% per annum between 2001 and 2010 
can be described as abnormal and requires further investigation. Thus KUR is growing averagely 
at about 12.2% per annum. From growth rates deduced from both Figs, 3 and 5, it is clear AUR is 
grov,,ring faster than KVR, 
SOD 
4SO 
400 
~3SO 
~300 
.5r2S0 
i 200 • Built-up 
e 
< 1SO 
so I 
2001 2010 2020 
Year 
Figure 5: Area of land use classes in KUR (square Km) 
Comparing urban sprawl in AUR and KUR 
After exploring the growth pattern in the two urban regions, it is necessary to investigate the degree 
of sprawl in each region to ascertain the level and nature of sprawl and determine whether or 
not they are developing sustainably. Urban sprawl is being assessed because it is known to pose a 
challenge to SUO, Shannon's entropy is a good indicator for determining urban sprawl or urban 
growth. Thus, entropies (Table 3) were calculated to aid in assess ing the sustainability of urban 
growth in the regions. The number of zones obtained from the multiple ring buffer analysis for 
AUR was 50, giving a maximum entropy of 1.70 and a threshold entropy of 0.85. The number of 
zones for KUR was 34, giving a maximum entropy of 1.53 and a threshold of 0.77. From Table 3, 
all the entropies for AUR for the three periods are bigger than 0.85, which means AUR has been 
sprawling over the study period. 
Ghana Social Science Journal I 21 
Table 3: Shannon's entropy calculations 
[ Accra (AUR) Kumasi (KUR) 
Year Entropy Relative Entropy Year Entropy Relative Entropy 
2002 1.529 0.90 2001 1.051 0.686 
2011 1.570 0.92 2010 1.292 0.844 
2020 1.601 0.94 2020 1.277 0.834 
The entropies are also closer to the maximum entropy of 1.70. with relative entropies ranging 
between 0.90 and 0.94. showing a strong dispersed development or sprawling. A similar observation 
can be made for KUR, albeit with lower values. This indicates that the sprawling and its degrees or 
intensities are lower in KUR compared to AUR. Whereas the changes in entropies for AUR are both 
positive for the periods of 2002-2011 and 2011-2020. the entropy for KUR is positive for 2001-2010 
but negative for 2010-2020 (Table 4). 
Table 4: Change in entropy over time 
C Accra Kumasi 
Period Change in entropy Period Change in entropy 
2002-2011 0.041 2001-2010 0.241 
2011-2020 0.032 2010-2020 -0.015 
As stated earlier, a positive change shows a sprawling development, while a negative value shows 
compacting development. Thus, while AUR is constantly sprawling, KUR saw densification or 
compact development between 2010 and 2020. The sprawling and densification observed between 
the two regions are visually illustrated in Fig. 6. 
2~ I VOlume I). Number I. June 2020 
AUR 
+ 
Legend 
~2010. 2020 
2001·2010 
• Up to 2001 
Fig. 6: Spatial pattern of sprawl in AUR and KUR 
It can be seen in Fig. 6 that the pink colour whch represents the development in KUR between 
2010 and 2020 is more of filling in the spaces while in AUR, it is both filling and expansion. Thus, 
whereas AUR is sprawling and densifying at the same time, KUR sprawled ve ry fast between 2001 
and 2010 and then compacted between 2010 and 2020. 
Discussion 
As noted by Yigitcanlar et al. (201S), sustain ability assessment tools such as indicators link past and 
present-day activities to future development goals. Thus urban growth rate, as well as Shannon's 
entropy index, were used to assess SUD. The results show that the growth rates in the two regions 
are high, which conforms to the findings of Toure et al. (2020). AUR is growing faster than KUR, 
and this is not surprising because Accra, as the national capital , receives more migrants than Kumasi 
due to more diversified employment and livelihood activities in Accra (GSS, 20.14). Also, whereas 
KUR sprawled in the first decade and compacted in the second decade, AUR's growth continued 
Ghana Social Science Journal I 23 
Fig. 7: BUilt-up development near Abokobi in the Ga East District of the Greater Accra Region 
In 2008. the Abokobi area in Fig. 7 was occupied by trees and shrubs with a few isolated buildings. 
The 2020 image, however. shows almost an entirely built environment within a decade. Although the 
western portion of the 2020 image is not built-up, it has been cleared in readiness for development. 
The inference here is that more open space lands used for agricultural purposes, forests, grasslands 
and bushes are being cleared for other uses such as residential, commercial and industrial uses. This 
compact and sprawl development could be attributed to the involment of numerous private estate 
agencies in housing provision and land tenure insecurity arising mostly from sales to multiple users 
(Barry & Danso. 2014; Danso & Manu. 2013) which make people develop properties immediately 
after initiating the process of acquiring the land. The estate agencies acquire large tracts of land, 
develop them into single occupancy units and sell or rent them out to people. Another factor that 
propelled the sprawl is that the urban regions were surrounded by independent satellite towns 
which also grew rapidly. In AUR these satellite towns include Abokobi. Pukuasi. Amasaman. Kasoa. 
Dodowa, Ashaiman, Adenta) and Dawenya, just to mention a few. In KUR also, some of these 
satellite towns include Ejisu, Abuakwa, Ofinso, Kwabre, Asokore-Mampong, Nkawie) and Atwima. 
This has resulted in the regions having many centres of growth, which is similar to the polycentric 
cities in Europe (Westerink et al .• 2013). According to Westerink et al. (2013). polycentrism is 
proposed as a sustainable urban form because it is aimed at combining the demand for urban 
living with preservation of peri-urban land. But the case of the two regions presented here cannot 
be described as sustainable though they exhibit polycentrism. This is because the satellite towns, 
especially in AUR. have chiefs and family heads who dispose off land. and therefore they grew 
rapidly outward. meeting the growth from the city. The case of KUR is slightly different because 
of the centralized land sale system as the Asantehene has to endorse alIlaods leased/sold (Ubink 
& Quan. 2008). So instead of containing development. they spread out rapidly in areas without 
approved land-use plans. 
The sprawl from both cities and their satellite towns is believed to be fueled by inadequate housing 
provision by the formal sector in developing countries (Jiboye. 2011; Owusu-Ansah et al.. 2019) 
which led to the liberalization of the housing sector (Fuseini et al.. 2017; Vidyarthi. 2018). The results 
tend to support this post-liberalization concept. as the sprawling is higher after the 2000s. with the 
proliferation of many estate developers. The private/infonnal housing providers maximize the use 
of the land for building and in some cases pave the remaining part of the plot in the compound (Fig. 
7). This leads to developments that undermine green growth of cities and therefore unsustainable 
development, as it is argued that the development of green compact cities (i.e. urban containment 
and densification) can lead to SUD (e.g .• AJawadi. 2017; Nilsson et al.. 2014). This is because the 
core strategies of the compact development plan are sustainable transportation, mL'{ed land use, 
241 Volume 17. Number I. June 2020 
vertical development, densificalion, green space and diversity, thereby making it able to contribute 
to environmental , social and economic goals of sustainabil ity (Bibri et al., 2020). But scholars like 
Westerink et aJ. (2013) and Nress (2014) have shown that there still exist some envrionmental 
issues such as air pollution and urban heat that could militate against compact development and 
have proposed a balanced compact urban development. The pro-balanced compact development 
scholars argue that compact development, if not checked, could lead to many socio-economic 
consequences, such as increased urban inequality, income segregation (Andersen, 2019; Guo et 
al. 2019; Malik et aI., 2020) and public health concerns like cardiovascular diseases, obesity, etc. 
(Kabisch et ai., 2016; Restivo et aI .• 2019). Balanced. compact development is very critical as the 
kind of sprawl and densification (see Fig. 7) taking place in both AUR and KUR is the low density 
type which consumes large spans ofland within a short time. This is largely because the majority 
of the housing units in these areas are not high rising buildings and also house single families. and 
thus are not able to reduce the housing deficit. 
Sprawl development also results in settlemen t segregation. and in some cases, high class and low-
class settlements develop side by side, as is the case of Roman Ridge and Maamobiin AUR (Fig. 8). 
The road (Achimota road) in the middle of Fig. 8 separates the low-class settlement (Maamobi) from 
a high-class settlement (Roman Ridge). These settlement patterns have resulted in the development 
of slums which are dotted aU over in the two regions and have become part of the urban footprint 
in many developing countries. 
As can be seen, the streets in Maamobi are narrow or non-existent and do not make provisions for 
pedestrians and cycling, yet the majority of the residents do not own cars and rely on walking as a 
means of transport. It is also crowded with buildings and other structures and has no green space. 
The high-class area. on the other hand, is well planned and has some green spaces and a weU laid-
out street network which supporl walking and cycling. This is clear evidence that land-use planning 
can support green growth. Roman Ridge was a sett lement for the colonial masters and inherited its 
Ghana Soc ..i  Science Journal I 25 
beautiful plan from then. It has been maintained as a dwelling place for the high income class even 
after independence. Maamobi on the other hand evolved as a migrant settlement which attracts 
migrants from all over the country and beyond and has grown to assume the characteristics of a 
slum. Both regons have these types of slums at many locations within them. However, the slowing 
of sprawl in KUR is a positive sign, as it canf uake city authorities, if properly resourced, move in 
quickly to resolve land use planning issues in the peri-urban areas. 
The growth of many cities in developing countries, in particular, is taking place outside planning 
zones and thus makes it difficult to implement control measures (Arnoako & Cobbinah, 2014; 
Cobbinah & Aboagye, 2017). This means that the sprawl and densification of the regions do 
not follow a sustainable development planning framework. This has serious environmental and 
livelihood consequences (Cobbinah et al., 2017; Huang et a!., 2018; Luo & Lau, 2019; SuJemana 
& Yiran. 2018). It calls for control of urban growth, especially the expansion of cities towards rural 
areas, through sustainable urban planning. Cobbinah & Darkwah (2017) noted that sustainable 
urban planning needs to be all-inclusive, that is, bring all stakeholders together to develop a land-
use plan. This is particularly critical given the kind ofland tenure system practised in Ghana where 
individuals, families and stools, own and cUspense land at will (Kuusaana & Eledi. 2015; Mireku 
et a!., 2016). So when the stakeholders are involved, they will know how to allot their lands for 
development according to the agreed plan by resolving some of the conflicts. Yigitcanlar & Teriman 
(2015) proposed an integrated urban planning and development process to aid in achieving SUD 
of cities, which is similar to the all-inclusive approach stated above. Such an integrated/ inclusive 
process will produce a sustainable development plan which can resolve most of the environmental 
and socio-economic challenges (Chenet a!., 2019; Dhiman et a!., 2019; Gavrilidiset a!., 2019; Jenks, 
2019). 
But the high rate of urban sprawl in Africa, which is projected to increase into 2100 (UN-DESA, 
2018), poses a serious challenge to sustainable development planning. From the results, it can be 
seen that even though KUR shows compact development over the last decade, both regions show 
high. unsustainable rates of sprawl. As the areas develop without land-use plans, their densification 
or compact development will not follow proper procedures, resulting in several socio-economic 
and environmental challenges that do not foster sustainable development. The reasons for 
unsustainable development, through sprawl and densification, in these unplanned areas are known 
and almost the same across many developing countries. These include the lack ofland-use plans, 
population growth, poor institutional arrangements and inefficient enforcement of regulations, 
lack of resources (both human and financial), improved transportation network. low land cost, 
changing lifestyle and rising incomes, among others (Bhatta, 2010; Denis, 2020; Koprowskaet 
a!., 2020; Rallimi et a!., 2019; Yigitcanlar & Teriman, 2015). It is observed that the sprawl and 
compact development taking place in these urban regions and many others in developing countries 
barely follow green compact development protocols, neither do they resolve the issues of land-use 
conflicts and other socio-economic effects expounded above. Thus, from all indications. the t\VO 
regions are not on the path to SUD. 
26 I Volume 17, Number I. June 2020 
Conclusion 
The study attempted to provide an understanding of the nature of sprawl in areas around the two 
big cities of Ghana. Accra and Kumasi, and how it can be a potential challenge to sustainable 
development. This was done using growth rates and spatial metrics such as Shannon's entropy 
to quantify urban growth (sprawl and compact development) in the cities. Landsat images were 
classified and used for the entropy calculations. The growth rates indicate very fast-growing 
regions. All entropy values were also substantial near the upper limits, and thus reveal that both 
regions (AUR and KUR) are sprawling. The rates of sprawl were higher for AU R than KUR. and 
over the last decade, KUR saw compact development while AUR continued to sprawl at a higher 
rate. This mea ns that the nature of urban development in the two regions is different. However. the 
sprawl is taking place outside zones that have proper planning schemes, signifying that the negative 
effects of urban sprawl identified in the li terature are present in the Areas, Thus. the conclusion is 
that the rates of sprawling in both regions are unsustainable and need to be checked. The compact 
development witnessed in the last decade for KUR should be monitored and sustained, but should 
incorporate the tenets of sustainable urban planning to achieve sustainable development It is 
recommended that an all-inclusive sustainable development planning framework be developed 
for the regions. This will ensure a compact green growth as v,'ell as minimise the socio-economic 
challenges that accompany compact developments. The sustainable development plan should also 
be region·specific. as each region shows different development characteristics. 
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