Quantifying the spatio-temporal patterns of settlement growth in a metropolitan region of Ghana

18 Pages·2016·1.68 MB·English

by  Ransford A. Acheampong

GeoJournal
DOI10.1007/s10708-016-9719-x
Quantifying the spatio-temporal patterns of settlement
growth in a metropolitan region of Ghana
. .
Ransford A. Acheampong Felix S. K. Agyemang
Mohammed Abdul-Fatawu
(cid:2)TheAuthor(s)2016.ThisarticleispublishedwithopenaccessatSpringerlink.com
Abstract Retrospective understanding of the mag- increaseoccurredinthelast13 yearsalone,withUEII
nitude and pace of urban expansion is necessary for valueof0.605indicatingamoderateintensityofurban
effectivegrowthmanagementinmetropolitanregions. expansion. Moreover, the metropolitan-core of the
Theobjective ofthispaper is toquantifythe spatial– sub-region, being the focal point of urban develop-
temporal patterns of urban expansion in the Greater ment and the historical origins of expansion,
Kumasi Sub-Region (GKSR)—a functional region accountedformorethanhalfofthetotalbuilt-upland
comprising eight administrative districts in Ghana, increaseoverthe28-yearperiod.Overthelastdecade
WestAfrica.TheanalysisisbasedonLandsatremote andhalfhowever,urbanexpansionhasspilledintothe
sensingimagesfrom1986,2001and2014whichwere neighbouring peripheral districts, with the highest
classifiedusingsupervisedmaximumlikelihoodalgo- intensity and fastest rate of expansion occurring in
rithm in ERDAS IMAGINE. We computed three districts located north and north east of the sub-
complementary growth indexes namely; Average regional core. We recommend a comprehensive
Annual Urban Expansion Rate, Urban Expansion regional growth management strategy grounded in
Intensity Index (UEII) and Urban Expansion Differ- effective strategic partnerships among the respective
entiationIndextoestimatetheamountandintensityof administrative districts to curb unsustainable urban
expansion over the 28-year period. Overall, urban expansion.
expansion in the GKSR has been occurring at an
average annualrateof5.6 %.Consequently,thesub- Keywords Urbanization(cid:2)Urbanexpansion(cid:2)Spatio-
region’s built-up land increased by 313 km2 from temporalchange(cid:2)Landsat(cid:2)GIS(cid:2)Greater-Kumasi(cid:2)
88 km2 in 1986 to 400 km2 in 2014. The analysis Ghana
furthershowthatabout72 %ofthetotalbuilt-upland
R.A.Acheampong(&)(cid:2)F.S.K.Agyemang Introduction
LabofInterdisciplinarySpatialAnalysis,Departmentof
LandEconomy,UniversityofCambridge,19Silver
Rapidandunfetteredurbanexpansionconstitutesone
Street,CambridgeCB39EP,UK
e-mail:[email protected] thevisiblemanifestationsoftheon-goingurbanization
process in cities of the Global South. The growth of
M.Abdul-Fatawu
urbanareas inthese regionshave beenfuelledbythe
FacultyofGeo-InformationScienceandEarth
demographic processes of natural population growth
Observation,UniversityofTwente,7500AEEnschede,
TheNetherlands and rural–urban migration as well as the
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GeoJournal
reclassification of previously rural settlements into satelliteplatforms,providingqualitylandcoverdataat
urbancentres(Potts2012;UN-Habitat2010). spatial and temporal resolution required for land-use
In broad terms, urban expansions could be cate- changestudies.Anumberofchallengesexistinusing
gorised into the three interrelated processes of infill- satelliteimagerytodetectlandusechange,including
ing, expanding, and outlying (Wilson et al. 2003). the many combinations of materials present and the
Infillingresultsinrelativelycompactandconsolidated variations in size or shape of urban features that can
physical development. In most cities in developing lead to different ‘mixtures’ within pixels (Schneider
countries however, rapid peri-urbanization, charac- 2012; Xian and Crane 2005). Notwithstanding, com-
terised by unconsolidated lateral physical expansion bined with Geographical Information Systems (GIS),
and sprawl, has resulted in existing built-up areas Landsat satellites have provided multi-spectral and
engulfingsmallertownsontheirperipheries(Webster multi-temporal data that have been used extensively
2002; Kombe 2005; Doan and Oduro 2012). As forlandcovermapping,environmentalmodellingand
contemporary form of urbanization, the uncontrolled landusemodelling(e.g.Schneider2012;Maseketal.
expansion of urban centres into their peripheries has 2000;Wakodeetal.2014).
been driven by the need to accommodate rapid In this paper, we use Landsat remote sensing
population growth and to meet the attendant space imagery from 1986, 2001 and 2014, GIS analytical
demandsofvarioussocio-economicactivitiesoftenat techniques and spatial metrics to quantify the spatio-
the expense of Greenfield land (Acheampong and temporal patterns of urban expansion within the
Anokye2013;Appiahetal.2014;Owusu-Ansahand Greater Kumasi Sub-Region (GKSR) in Ghana. The
O’Connor2006). GKSR is a newly designated, rapidly urbanizing
Althoughthereisemergingevidencethatsuggests metropolitanregioninGhana,comprisingtheKumasi
that urbanization particularly, in Sub-Saharan Africa metropolis,thecountry’ssecondlargestmetropolitan
hasbeenoccuringveryslowlyorevenstagnated(Potts area,andsevenperipheraldistricts.Inrecentyears,a
2012), there is a general consensus that its accumu- numberofempiricalstudieshavesoughttounderstand
latedimpactsovertheyears,posesvariouschallenges urban expansion trends in the sub-region. Using
to urban growth management and sustainable devel- OpenStreetMap and Google Earth, Oduro et al.
opment. The emerging challenges of climate change, (2014), analyzed drivers of urban growth in the sub-
environmental degradation and resource depletion region. Cobbinah and Amoako (2012), estimated
resultingfromdecadesofrapidurbanization(Watson historical land-use distribution in the Kumasi metro-
2009), pose serious threats to public health, the polis—the historical core of the sub-region between
continuous supply of essential eco-system services 1995 and 2010. Other studies have addressed urban
andfoodsecurity(Eigenbrodetal.2011;Baloyeand expansion and its implications in selected peri-urban
Palamuleni2015). townsinthesub-region(e.g.AcheampongandAnokye
Gainingaretrospectiveunderstandingofthespatio- 2013;Amoatengetal.2013;Appiahetal.2014).
temporalurbanland-usedynamicsandtheunderlying Whilesomeofthesepreviousstudieshaveprovided
driving-forces is crucial to managing growth to avert descriptiveanalysisofthereasonsdrivingrapidperi-
unstainable urban expansion and the associated neg- urbandevelopmentinthesub-regionbasedonsurvey
ative impacts. Adequate and reliable data through data (e.g. Acheampong and Anokye 2013; Owusu-
conventionalsurveyingandmappingtechniqueshave AnsahandO’Connor2006),othershavereliedondata
however, been either unavailable, limited in scope, fromgovernmentdepartmentsobtainedfromexisting
expensive or time consuming to acquire for accurate land-use plans and/or through conventional mapping
analysis of historical urban expansion at different techniquessuchasfield-updates(e.g.Amoatengetal.
spatialscales(Jatetal.2008). 2013; Cobbinah and Amoako 2012). Data from land
Inrecentyears,landusechangesinmanycountries use plans, for example, allows to estimate detailed
have been monitored and predicted at the national, land-use distributions in areas covered by planning
regionalandcityscalesusingsatelliteremotesensing schemes. However, relying on this alone could
imagery (e.g. Wakode et al. 2014; Xu et al. 2007). underestimate the quantum of built-up land in the
Among the commonly used satellite sensors is the studyareasinceinGhana,forexample,landuseplans
Thematic Mapper (TM) on board the Landsat series can provide data only for areas covered by planning
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schemes. Moreover, owning to the lack of frequent anincreaseinthequantumofbuilt-uplandofaspatial
updates of these land use plans by the physical unitthroughthecombinationofhorizontalandvertical
planningdepartments,dataderivedfromthemisoften development at varying densities. The expansion of
unreliableandfraughtwithaccuracyproblems. urbanareastakeplaceinsubstantiallydifferentforms
The current paper builds on the empirical insights in different contexts which makes it difficult to
accruedovertheyearsfromtheseinitialresearch.Our propose any single theoretical model, either descrip-
approach and contribution, however differs consider- tive or analytical to explain the phenomenon.
ably from these initial works in terms of the spatial Although classical models of urban spatial structure,
extent of the analysis, temporal scope, data sources grounded in urban micro-economics, offer useful
and methodology. This study, constitutes the first theoretical insights into how cities grow and the
attempt to apply Landsat satellite data and spatial generalemergentpatternsoflanduses(seee.g.Alonso
metrics to quantify historical urban expansion at the 1964; Burgess 1925), these models seem constrained
macro(i.e.sub-regional)andmicro(i.e.district-level) to their specific cities of origin. Moreover, they
scalesoveraperiodof28 years. address a broad range of urban growth phenomenon
Theremainderofthepaperisorganizedasfollows: and are unable to account for the different manifes-
Inthesectionthatfollows,wediscussthetypologyand tationsofphysicalurbanexpansion.
manifestations of urban expansion to set the frame- In the absence of a general theoretical model,
workforthestudy.Thisisfollowedwithadiscussion research has focused on understanding the processes
ofresearchmethodologyfocusingondataacquisition, ofurbanchangeandformsofurbanexpansion.Subur-
Landsat satellite image classification and accuracy banization,asaprocessofurbanchangeisacommon
assessment processes as well as the spatial metrics phenomenon in cities of the developed countries. In
applied in computing urban expansion indexes. The broad terms, suburbanization is associated with low-
results are presented in the penultimate section density, often fragmented and sprawling physical
followed with a discussion of the results and policy developmentonGreenfieldlandimmediatelysurround-
implications. ing existing built-up land of a city (Champion 2001;
Pacione2009).IncitiesoftheGlobalSouth,peri-urban
development typifies the unprecedented urbanization
Understandingandestimatingsettlement markedatthebeginningofthe21stcentury(UN-Habitat
expansion 2010). Consensus on a precise definition of the ‘peri-
urban’sometimesreferredtoas‘urbanfridge’islacking
Urbanization, in broad terms, is a process by which intheliterature.Twomaindefinitionalapproacheshave
settlements increase in population, physical size and beenadoptedinconceptualizingperi-urbanareas.The
economic activities over time. Agglomeration theory firstapproachconceptualizesperi-urbanareasinterms
posits that the concentration of population and ofdiscrete spatial limits. Basedon empirical observa-
economicactivitiesunderpintheurbanizationprocess, tion, leading exponents of this approach suggest a
and has historically been responsible for the emer- distanceofabout30–50 km,beyondtheexistingbuilt-
gence and growth of cities and large metropolitan- uplandofmajorcities,asareasonablegeneralizationof
regions(Jacobs1969;Henderson2002).Urbanization theextentoftheperi-urbanzone(seee.g.McGregorand
generatesexternaleconomiesofscale,whichnotonly Simon 2012; Webster 2002). Simon et al. (2004)
enhance productivity and growth but reinforces the estimates that the peri-urban zone of Kumasi, for
potential of existing cities as major attraction points example, stretches some 20 to 40 kilometres radius
foradditionalpopulationandactivities(Durantonand aroundthecity’smainbuilt-uparea.
Puga2004;Henderson2002). Theseconddefinitionalapproachforperi-urbanareas
Aside demographic change, urbanization shapes adoptsanintegratedandfunctionalviewbyconsidering
the form and structure of settlements through the the urban–rural continuum. Based on this, the urban-
physical expansion of existing built-up areas into peripheryisconceptualizedasatransitionzonebetween
Greenfieldlandorsurroundingruralsettlements(UN- fullyurbanizedlandincities,andareasinpredominantly
Habitat2010).Withinthecontextofthisstudy,urban agriculturaluse(McGregoretal.2011;Webster2002).
expansionrefersthephysicalprocesscharacterisedby As transition zones between the urban and rural, peri-
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urban areas are characterised by mixed land uses and area for purposes of strategic regional planning
indeterminateinnerandouterboundaries,andareoften and sustainablegrowthmanagement.Thefirstspatial
splitbetweenanumberofadministrativeareas(McGre- development plan for the sub-region, ‘The Compre-
goretal.2011;Webster2002).InGhanaforexample,the hensive Urban Development Plan for Greater
lackofeffectiveco-ordinationamonglocalgovernments Kumasi’ was adopted for implementation in 2013.
coupled with a generally weak development control The sub-region stretches between latitude 6.35(cid:3)–
system(AcheampongandIbrahim2015;Owusu-Ansah 6.40(cid:3)N and longitude 1.30(cid:3)–1.35(cid:3)W, with an eleva-
and O’Connor 2006) presents major challenges for tionrangingbetween250and300 mabovesealevel.
effective land use planning and development manage- It covers a contiguous area of approximately
mentinperi-urbanareas. 2850 km2ofurban,peri-urbanandruralland.
Different forms of physical expansion emerge Thesub-regioncomprisesonemetropolitanarea(i.e.
through suburbanization and peri-urbanization. In KMA) and seven immediate surrounding districts
anygivencityormetropolitanregion,urbanexpansion namely;Afigya-KwabreDistrict,KwabreEastDistrict,
can be compact through the infill of existing open Ejisu-Juaben Municipality, Bosomtwe District,
spacesinalreadybuilt-upareasandorredevelopment Atwima-Kwanwoma District and Atwima-Nwabiagya
ofbuilt-upareasathigherdensities(Angeletal.2005; District (Fig. 1). As the functional core of the sub-
Wilson et al. 2003). Furthermore, urban expansion region,KMAcoversatotallandareaofabout250 km2,
mayoccureitherascontiguousextensionstoexisting representingabout9 % ofGKSR’stotal landarea. At
built-up areas or spontaneous leapfrog development thecentreofthemetropolisislocatedthesub-region’s
away from main built-up land, leaving swaths of CentralBusinessDistrict(CBD),surroundedbyover50
undeveloped land that separate the new development settlementswithinitsinnerandouter-suburbanzones.
from existing built-up areas (Torrens and Alberti Accordingtothemostrecentcensusdataavailable,
2000). This form of expansion normally occurs in the GKSR has an estimated population of 2,764,091,
lineardirectionalongmajorroadnetworks(i.e.ribbon representingabout58 %ofthetotalpopulationofthe
development) and or in radial direction around an Ashanti Region and 11 % of Ghana’s population
alreadyestablishedbuilt-uparea(Sudhiraetal.2004). (Table 1).TheKMA1isthemostpopulousareawithin
Drawing on the processes and manifestations of the sub-region accounting for 74 % (i.e. over two
urban expansion, several studies have attempted to million)ofitstotalpopulation.
identifyandquantifythepace,amountandintensityof Overall, the sub-region’s population, based on the
urban expansion using spatial metrics and GIS intercensal estimates for 2000 and 2010, is estimated
analytical techniques. Among the commonly used tobegrowingatanannualrateof4.62 %comparedto
metrics are Landscape Expansion Index (LEI) (Liu, theaverageannualgrowthratesof2.84and2.69 %for
etal.(2010),UrbanExpansionIntensityIndex(UEII) the Ashanti Region and Ghana respectively. Among
(HUetal.2007;Lietal.2010)andUrbanExpansion therapidlygrowingdistrictswithinthesub-regionare
Differentiation Index (UEDI) (Lu et al. 2014). We theKumasimetropolis,AfigyaKwabreandBosomtwe
adoptasimilarapproachinthispapertoestimatethe districtswith annual population growth rates of5.69,
spatio-temporal patterns of urban expansion in the 4.30and3.47 %respectively.
GKSR. A detailed description of our approach are
discussedinthesectionthatfollows.
Landsatsatellitedataacquisitionandpreparation
Methods Forthisstudy,weusedrasterspatialdatacomprising
three Landsat satellite images (row 55, path 194) for
Studyarea
TheGKSRislocatedintheAshantiRegion,oneofthe 1 ThepopulationoftheKumasiMetropolisincludesthatofthe
Asokore-MampongMunicipality.Untilafterthe2010popula-
tenadministrativeregionsinGhana(Fig. 1).Thesub-
tioncensus,whenthelatterwascarvedoutoftheformerasa
region was designated in 2010 by the Town and
separate administrative unit, the two districts together consti-
CountryPlanningDepartmentasafunctionalplanning tutedtheKumasiMetropolitanArea.
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Fig.1 Locationofstudyarea
Table1 PopulationsizeandgrowthrateswithintheGKSR(1984–2010)Source:Basedon1984,2000and2010Populationand
HousingCensus,GhanaStatisticalService
Districts Populationsize Annualpopulationgrowthrate(%)
1984 2000 2010 1984–2000 2000–2010
AtwimaNwabiagya 56,352 127,809 149,025 5.25 1.55
Ejisu-Juaben 78,783 124,176 143,762 2.88 1.48
KwabreEast 42,044 101,100 115,556 5.64 1.35
AtwimaKwanmowa 44,437 79,240 90,634 3.68 1.35
Afigya-Kwabre 39,971 89,358 136,140 5.16 4.30
K.M.AandAsokore-Mampong 487,504 1,170,270 2,035064 5.63 5.69
Bosomtwe 41,283 66,788 93,910 3.05 3.47
GreaterKumasiSub-Region 734,022 1,758,741 2,764091 5.13 4.62
AshantiRegion 2,090,100 3,612,950 4,780380 3.48 2.84
Ghana 12,296,081 18,912,079 24,658,823 2.73 2.69
1986 (TM), 2001 (ETM?) and 2014 (OLI/TIRS) scale urban expansion studies (see e.g. Herold et al.
downloadedfromtheUnitedStatesGeologicalSurvey 2002; Wakode et al. 2014), the images had a spatial
websiteasstandardproducts.Aswithmostcity-region resolutionof30 m.
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The satellite images were first geometrically refer- residential, commercial and industrial purposes and
enced to the WGS 1984 UTM Zone 30 N coordinate their immediate surroundings, and other built-up
system.The2001imagehad20 %cloudcoverwhilethe lands.Thenon-built-upcategoryincludedfarmlands,
1986 and 2014 images had cloud cover\10 %. The grasslands,bare-land,forestsandothervegetation.
cloudcoveronthe2001imagewasthereforehighand Trainingsamplesforeachofthethreeclasseswere
neededtobecorrectedinordernottoaffecttheaccuracy selected by visual interpretation of (a) true colour
resultsoftheimageclassificationandinterpretation.In composites of the Landsat imagery (b) very high
order to overcome thischallenge, Google earth images resolution (VHR) images from Google Earth and
covering the same period (i.e. 2001) were downloaded (c) ground truthing/participatory mapping. Accuracy
and compared with the corresponding Landsat image. assessmentoftheclassifiedlandcovermapswasdone
Usingthisapproach,thespecificlandcovertypeswithin with the aid of 235 randomly selected validation
theareascoveredbycloudswereidentifiedandrecorded. points.Theactuallandcoverofeachvalidationpoint
However,evenwithinGoogleEarth,someareasstill wasidentifiedfortheyearsinquestionasfollows:(1)a
hadthickcloudcoverwhichmadeitdifficulttoidentify visual interpretation of the true colour composite for
the specific land cover there with a high degree of the 1986 image (2) for 2001 image: land use map of
certainty.Forthese areas,ground-truthingwascarried 2000 on a scale of 1: 50000 combined with visual
outinordertoascertainthelandcoverclassestowhich interpretation of VHR Google Earth image and (3)
theybelonged.CombiningtheGoogleEarthverification visual interpretation of VHR Google Earth image of
and ground-truthing techniques, we found that the 2014. The accuracy assessment yielded overall clas-
significantpartofthe2001Landsatimagecoveredby sificationaccuraciesof92.0 %,88.20and92.0 %,and
clouds, were the non-built-up areas (i.e. natural vege- a corresponding overall kappa statistics of 0.80, 0.76
tation)withonlysmallportion(\5 %)coveringsome and 0.80 for the 1986, 2001 and 2014 images
built-upareas.Theportionsofthe2001imagecovered respectively. Compared with the accuracy results of
by clouds and which were identified to be natural urbangrowthstudies,ouraccuracyassessmentresults
vegetation were selected as areas of interest to be was considered very good to allow for accurate
recodedintotheactuallandcoverclassesasnon-built- analysisoftherateofurbanexpansionintheGKSR.
up. Similarly, the portions identified as built-up were
recodedintothelandcoverclassesasbuilt-upland. Quantifyingsettlementgrowthandexpansion:
spatialmetrics
Landsatsatelliteimageclassificationandaccuracy In using the classified images to detect and quantify
assessment urban expansion in the GKSR, we adopted three
complementaryspatialmetrics/indexes.Thefirstmetric
Using the supervised maximum likelihood classifica- applied is the Average Annual Urban Expansion Rate
tionalgorithminERDASIMAGINE,weclassifiedthe (AUER).AUERisahistoricalmetricthatcomputesthe
Landsat images into three discrete land cover classes mean annual rate of expansion of built-up land of a
identified as ‘Built-up’, ‘Non-built-up’ and Water. spatialunitbetweentwoperiods—thebaseyearandthe
Due to the additional and differentiated bands of endingyear. AsdepictedinEq. 1,AUER isamodifi-
Landsat OLI/TIRS data, the combinations used to cationofthe compoundgrowthrate formula used, for
create the natural colour composites differ from the example,inestimatingmeanannualpopulationgrowth
previousseries.Forexample,bands7,5and3areused rate.Theresultoftheindex,isthereforea representa-
to create the natural colour composite of Landsat 7 tional figure which estimates the rate at which the
ETM?images.Inthisstudy,weusedbands7,6and4 quantumofbuilt-uplandofaspatialunitischanging.
of the Landsat OLI/TIRS image since this combina-
tion provides a natural-like rendition while also AUER ¼"(cid:2)ULAti2(cid:3)t2(cid:3)1t1(cid:3)1#(cid:4)100 ð1Þ
penetrating atmospheric particles, smoke and haze. i ULAt1
i
Following conventional classification typologies, the
built-up cover class comprised the physical aspect of whereAUERiisAnnualUrbanExpansionRate;ULAti2
theurbanfabricincludingroads,allbuildingsusedfor andULAti1 aretheareaofbuilt-uplandattimet2andt1
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respectively.TheAUERisnotaffectedbythesizeof the urban differentiation index of the overall study
the spatial unit and does not have upper and lower area—the sub-region in the context of this study—is
limits. always equal to 1, and serves as the yardstick for
In addition, we computed Urban Expansion Inten- identifying the development hotspots in the sub-
sityIndex(UEII).UEII,asshowninEq. 2,computes region. Generally, there could be three possible
the average annual proportion of newly increased categories of UEDI: (1) when the constituent spatial
built-uplandofaspatialunit,standardizedbythetotal unit (i.e. district) has a differentiation index[1 in
areaofthatspatialunit(HUetal.2007;Lietal.2010). which case, the district is categorized as ‘‘fast’’
growing area in relation to the sub-region; (2) where
ULAt2 (cid:3)ULAt1
UEII ¼ i i (cid:4)100 ð2Þ thedifferentiationindexofthedistrictis\1inwhich
i TLA (cid:4)Dt
i casethedistrictiscategorizedas‘‘slow’’growingarea
where UEII is Urban Expansion Intensity Index of in relation to the sub-region and (3) when the
i
uniti;ULAt2 andULAt1 aretheareaofbuilt-uplandat differentiating index of the district is equal to 1 in
i i
timet andt respectively;TLA isthetotallandarea which case the district is categorized as ‘‘moderate’’
2 1 i
withinthestudyareaiandDtisthestudytimeperiod growingareainrelationtothesub-region.
(i.e.t (cid:3)t ). The UEII describes the degree of differ- The urban expansion quantification using the
2 1
entiation of urban expansion in different directions classified images and the metrics discussed above,
anddenotesthegrowthofthebuilt-upareasofaspatial was carried out in ArcGIS 10.1 software. The results
unit as a percentage of the total area of land in the of the analysis are presented at two spatial scales. At
study period (HU et al. 2007). The division standard themacroscale,thevariousurbanexpansionindexes
for interpreting UEII values is as follows: values are interpreted for the GKSR as a whole. This is
[1.92is‘‘high-speed’’;valuesbetween1.05and1.92 followedbyamicro-levelanalysiswhereweinterpret
is ‘‘fast’’; values between 0.59 and 1.05 is ‘‘medium- the same indexes for the individual administrative
speed’’;valuesbetween0.28and0.59is‘‘low-speed’’; districtsinthesub-region.
and values between 0 and 0.28 is considered ‘‘slow’’
(seee.g.Renetal.2013).
The third metric used is the Urban Expansion
Results
DifferentiationIndex(UEDI).UEDIreferstotheratio
of the urban expansion rate of a spatial unit to the
UrbanexpansiondynamicsinGKSR:macrolevel
urban expansion rate of the study area. Unlike the
analysis
UEII, UEDI quantifies the urban land expansion
disparity between different spatial units, thereby
Over the last approximately three decades, urban
making those units comparable. This metric is useful
expansion in the GKSR has been massive and very
inevaluatingregionalurbanlandexpansiondifferen-
rapid.Increasingannuallyatanaveragerateof5.6 %,
tiation and identifying urban expansion hotspots (Lu
the total built-up land of the sub-region more than
etal.2014).TheUEDIisshowninEq. 3: quadrupled from an estimated 88 km2 in 1986 to
(cid:4) (cid:4) 400 km2in2014.Thus,whereasin1986only3.1 %of
(cid:4)ULAt2 (cid:3)ULAt1(cid:4)(cid:4)ULAt1
(cid:4) i i (cid:4) the total land area of the GKSR was built-up, this
UEDIi ¼(cid:4)(cid:4)ULAt2 (cid:3)ULAt1(cid:4)(cid:4)(cid:4)ULAt1 ð3Þ increasedto14 %in2014(Fig. 2).
i i i
Breaking the analysis down into the first 15-year
where UEDI is the urban expansion differentiation
i period (i.e.1986to2001) andthelast13-yearperiod
index of unit i; ULAt2 and ULAt1 indicate the area of
i i (i.e. 2001–2014), we found that the pace of urban
built-uplandofunitiattimet andt respectively;and
2 1 expansion in the sub-region has been occurring at an
ULAt2 andULAt1 indicatethetotalareaofurbanland increasing rate within the latter period. This is
inthestudyareaattimet2 andt1 respectively.Unlike evidenced by the fact that the Annual Urban Expan-
UEII,UEDI,doesnotfollowastandardcategorization sion Rate increased from 4.8 % between 1986 and
range. It compares urban expansion of a constituent 2001 to 6.5 % between 2001 and 2014. Indeed, the
spatialunittotheoverallstudyarea.Mathematically, extent of urban expansion over the two broad time-
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Fig.2 Changeinbuilt-uplandandpercentageshareofbuilt-uplandtototallandareainGKSR
periods is confirmed by the broad land cover classi- Moreover, the Urban Expansion Intensity Index
ficationmapsdepictedinFig. 3.Inabsoluteterms,the (UEII),whichstandardisestheAnnualUrbanExpan-
total built-up land in the sub-region increased by sion Rate by the total land area of the sub-region,
approximately 313 km2 over the 28-year period. Out reveals a similar spatio-temporal pattern of built-up
of this, approximately 72 % (224 km2) occurred land cover change. With a UEII value of 0.207, the
during the last 13 years compared with 28 % intensityofurbanexpansionwasslowduringthefirst
(89 km2)duringthefirst15 years.Thus,thequantum 15 years. However, the sub-regions’ UEII increased
ofurbanexpansionthatoccurredoverthelast13-year almost three times to 0.605 during the last 13 years,
period was about two and half times that of the first indicating a moderate intensity of urban expansion
15 years. over the period. The available census information
Fig.3 Broadlandcoverclassification,1986,2001and2014
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indicatesthatoverthepasttwoandahalfdecades,the between 2001 and 2014 was relatively slower com-
sub-region’spopulationhasalsobeengrowingrapidly pared to the remaining seven districts. One possible
atanannualrateof5.1 %.Therapidincreaseinbuilt- explanationfortheobservedtrendofurbanexpansion
up land has therefore been occurring in tandem with isthat,markingthehistoricaloriginsofurbangrowth,
rapidpopulationgrowthinthesub-region. the KMA initially attracted a significant share of all
development in the sub-region as evidenced by the
UrbanexpansiondynamicsinGKSR:microlevel relatively higher expansion rate between 1986 and
analysis 2001.Overtime,asmostofthelandbecomebuilt-up,
andsomeofthenewdevelopmentoccurinpreviously
Districts’annualrateandamountofbuilt-upland built-up areas through redevelopment and infilling,
change lateral expansion would slow down, resulting in the
observed falling trend in the rate of built-up land
As shown in Table 2, over the 28-year period of increaseduringthelast13 years.
analysis,eachofthedistrictsincreasedintheamount The Asokore Mampong municipality, which pre-
of built-up land within it. The analysis show that the viously formed part of the administrative area of the
KMA with AUER of 5.03 %, recorded one of the KMA, shows a rather interesting trend of urban
highest rates of urban expansion between 1986 and expansion. Between 1986 and 2001, the district
2001. The AUER of 3.89 % in KMA was however, increased the size of its built-up land at a rate of
oneofthelowestbetween2001and2014.Inaddition, 2.87 %, the lowest among all the districts. This is
over the entire 28-year, KMA expanded its built-up explainedbythefactthatalthoughitformedpartofthe
land at a rate 4.50 % per annum, the lowest rate KMA duringthisperiod, itperipherallocationmeant
recorded among all the districts except Asokore- thatiturbanizedatarelativelyslowerpacecompared
Mampong. Despite the diminishing rate of urban tothecoreareasofthemetropolis.Thelargershareof
expansion relative to the other districts, the KMA in itslandwouldthereforehavebeenundevelopedasone
absoluteterms,wasthemostdominantintermsofthe would expect. Between 2001 and 2014 however, the
distribution of built-up land in the sub-region. It rate of expansion in Asokore Mampong increased to
increased its built-up land from 51.8 km2 (24.4 % of 4.39 %. This implies that while urban expansion
itstotallandarea)in1986to177.5 km2(83.7 %ofits intensity in the KMA has stagnated over the last
totallandarea)in2014.Thismeansthat,althoughthe 13 years,AsokoreMampongasoneoftheperipheral
quantumofbuilt-uplandincreaseinthemetropolitan districtsoftheKMA,hasbeenattractingasignificant
coreofthesub-regionwasthebiggestoverthe28-year shareofnewphysicaldevelopmentinthesub-region.
period, the rate and intensity of increase, particularly Indeed, the size of the built-up land of Asokore
Table2 Amountandrateofbuilt-uplandchangeinGKSR
District/subregion Totalarea(km2) Built-upland(km2) AnnualUrbanExpansionRate(%)
1986 2001 2014 1986–2001 2001–2014 1986–2014
AtwimaNwabiagya 596.979 6.746 9.768 41.822 2.499 11.837 6.733
Ejisu-Juaben 723.216 6.611 12.423 38.192 4.295 9.023 6.464
KwabreEast 134.822 4.289 10.154 32.752 5.913 9.426 7.530
AtwimaKwanmowa 290.721 3.862 9.213 30.305 5.968 9.591 7.635
Afigya-Kwabre 517.277 5.411 10.172 41.701 4.298 11.464 7.566
K.M.A 212.093 51.756 108.007 177.501 5.027 3.895 4.500
Bosomtwe 352.575 3.392 7.728 22.436 5.642 8.544 6.980
AsokoreMampong 22.249 5.903 9.033 15.807 2.877 4.398 3.580
GreaterKumasiSub-Region 2849.933 87.970 176.499 400.516 4.752 6.506 5.563
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Mampong increased from 5.903 km2 in 1986— depicted in Figs. 4 and 5 are interpreted together as
26.5 %ofitstotal land area—to15.807 km2 in2014 follows.
representing71.04 %ofitstotallandarea.Similarto AsshowninFig. 4,fouremergentpatternsbecome
KMA, Asokore Mampong over the entire 28-year apparentwithrespecttourbanexpansiontrendsinthe
periodofanalysiswithAUERof3.58 %,experienced districts.ThefirstdescribestrendsinKMA,themost
arelativelyslowerpaceofurbanexpansioncompared urbanizedareainthesub-regionintermsofpopulation
to the remaining six districts although. Thus, being sizeandbuilt-uparea.KMA’sshareofthetotalbuilt-
originally part of the metropolitan core of the sub- uplandinthesub-regionincreasedfrom59 %in1986
region,AsokoreMampongsharesimilarurbanexpan- to61 %in2001(seeFig. 4).Thisrepresentedcloseto
sioncharacteristicswithKMA. two-thirds(64 %)ofthetotalbuilt-uplandchangethat
Furthermore, the analysis shows that all the occurredinthesub-regionoverthefirst15-yearperiod
remaining six peripheral districts recorded Average (see Fig. 5). Between 2001 and 2014, although
Annual Urban Expansion Rates which were higher KMA’s built-up land increased in absolute terms by
than GKSR average of 5.56 %. Breaking the results about70 km2,themetropolis’sshareoftotalbuilt-up
downtothetwobroadyearsofanalysis,showsamore land change to the sub-region fell to below half
nuanced trend of urban expansion among these (44 %)—seeFig. 4.Overthis13-yearperiod,KMA’s
districts. Over the first decade and half, three out of contribution to the total built-up land change in the
the six peripheral districts namely; Atwima Kwan- GKSR, diminished substantially to less than a third
woma,KwabreEastandBosomtweexpandedrapidly (31 %) for reasons outlined in the previous sec-
than the sub-region at rates of 5.96, 5.9 and 5.64 % tion. This notwithstanding, the metropolis being the
respectively. During the same period, Antwima focal point of historical urban development, main-
Nwabiagya recorded the lowest rate of expansion at tained its status as the dominant area of urban
2.49 %. In the last 13 years however, the dynamics expansion within the sub-region. In fact, the KMA’s
changed considerably. Notably, Atwima Nwabi- shareoftotalbuilt-uplandof177.5 km2in2014was
agya—whichrecordedthelowestexpansionrateover more than four times that of Atwima Nwabiagya’s
the first period—emerged as the as the fastest urban- 41.8 km2which representedthesecondhighestshare
izingdistrictinthesub-regionwithannualexpansion oftotalbuilt-uplandintheGKSR.
rateof11.84 %whichwasaccompaniedbyasix-fold The second pattern shows expansion trends in
increaseinitsbuilt-uplandfrom6.746to41.822 km2. districts that increased their share of built-up land
Similarly, Afigya-Kwabre—after expanding at a rate throughouttheyears.Threedistrictsnamely;Kwabre
below that of the sub region over the initial 15-year East, Atwima Kwanwoma and Bosomtwe fell within
interval—cropped up as the second fastest growing this group. Kwabre East increased its built-up land
districtduringtheperiodbetween2001and2014with sharefrom4.9 %in1986to5.8 %in2001andfurther
annualexpansionrateof11.46 %. to8.2 %in2014(seeFig. 4).Also,thecontributionof
The accelerated rate of urban expansion, particu- KwabreEasttotheincreaseintotalbuilt-uplandinthe
larly over the last 13 years observed among the sub-region increased from 6.04 % during the first
peripheral districts as compared to the generally 15-year period, to 9.1 % in the last 13-years (see
slowed pace of expansion in the metropolitan core Fig. 5).Similarly,AtwimaKwanwomaappreciatedin
further supports the finding that urban expansion in built-up land share from 4.4 to 5.2 % between 1986
recent years has spilled over from the latter into the and 2001, and subsequently to 7.6 % in 2014 (see
former. Fig. 4);thedistrict’scorrespondingcontributiontothe
built-up land increase in the entire sub-region how-
Districts’shareandcontributiontobuilt-upland ever,decreasedmarginallyfrom9.4 %between1986
change and 2001 to 8.5 % between 2001 and 2o15 (see
Fig. 5). Also, Bosomtwe district recorded an incre-
Inthissectionwecalculateeachdistrict’sshareofthe ment in built-up land share from 3.8 % in 1986, the
total built-up land in GKSR (see Fig. 4) and their leastduringtheyear,to4.4 %in2001andfurtherup
contribution to the 313 km2 increase in built-up land 7.6 %therebymovingastepuptheladderin2014(see
over the 28-year period (see Fig. 5). The results, Fig. 4).Bosomtwe’scontributiontothebuilt-upland
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