Annals of GISVol. 17, No. 1, March 2011, 43–62

MCDA-GIS integrated approach for optimized landfill site selection for growing urban regions:an application of neighborhood-proximity analysis

Yashon O. Oumaa,b∗, Emmanuel C. Kipkorirc and Ryutaro Tateishia

aCenter for Environmental Remote Sensing (CEReS), Chiba University, Chiba, Japan; bCivil and Structural Engineering,Moi University, Eldoret 30100, Kenya; cEnvironmental Planning & Management, Moi University, Eldoret, Kenya

(Received 4 October 2010; final version received 27 December 2010)

The exponential rise in urban population and the resulting urban waste generation in developing countries over the past fewdecades, and the resulting accelerated urbanization phenomenon has brought to the fore the necessity to engineer environ-mentally sustainable and efficient urban waste disposal and management systems. Intelligent and integrated landfill siting is adifficult, complex, tedious, and protracted process requiring evaluation of many different criteria. Optimized siting decisionshave gained considerable importance in ensuring minimum damage to the various environmental sub-components as well asreducing the stigma associated with the residents living in its vicinity. This article addresses the siting of a new landfill usinga multi-criteria decision analysis integrated with overlay analysis within a geographical information system. The integratedmulti-criteria decision analysis–geographical information system employs a two-stage analysis, synergistically, to form aspatial decision support system for landfill siting in fast-growing urban centers. Several correlated factors are consideredin the siting process including transportation systems, water resources, land use, sensitive sites, and air quality. Weightingswere assigned to each criterion depending upon their relative significance and ratings in accordance with the relative mag-nitude of impact. The results, analyzed using neighborhood-proximity analysis, show the effectiveness of the system in thesite-selection process for Eldoret Municipality (Kenya), in the short- and long-term solid waste disposal siting options.

Keywords: municipal solid waste management; landfill siting; spatial analysis; multi-criteria decision analysis;neighborhood-proximity analysis; spatial decision support system

1. Introduction and background

The rate of population growth in urban regions is increasingrapidly with increased urban–rural migrations, especiallyin developing countries. As such, towns and cities areexpanding without regards to the future development planrules and scenarios, more so with regards to the gener-ated waste (Brockerhoff 2000). The increased consumptionof different natural and man-made composite resources inthese urban areas results in huge quantities of refuse andother waste materials that must be properly managed, ifsustainable growth is to be realized. Traditionally, therehave been some ways of disposing urban-produced waste,of which one of the most coherent ways is dumping in suit-able landfills (e.g., decommissioned quarries) outside thetowns or cities (Kohbanani et al. 2009).

Although waste disposal in most towns and cities isdone in the simple form of landfill deposing, less atten-tion has been paid to the use of expert and engineeringknowledge to find the most optimal waste disposal site inmunicipal solid waste management (MSWM). One of the

∗Corresponding author. Email: yashon@graduate.chiba-u.jp

most important aspects in well engineered waste disposalsiting is the determination of a long-term optimal wastedepot location (Awomeso et al. 2010).

Landfill site selection can generally be divided intotwo main steps: the identification of potential sites throughpreliminary screening, and the evaluation of their suit-ability based on environmental impact assessment, eco-nomic feasibility, engineering design, and cost comparison(Karadimas and Loumos 2008). As a consequence, landfillsiting can be classified as a difficult, complex, tedious, andprotracted process (Allanach 1992).

Many siting factors and criteria should be carefullyorganized and analyzed by experts. An initially chosencandidate site may be later abandoned because oppositionarises due to previously neglected but important factors.Such a delay increases costs and postpones the final deci-sion of a landfill site. The ‘not in my backyard’ and ‘notin anyone’s backyard’ phenomena is becoming popularnowadays creating a tremendous pressure on the decision-makers and experts involved in the selection of a landfill

ISSN 1947-5683 print/ISSN 1947-5691 online© 2011 Taylor & FrancisDOI: 10.1080/19475683.2011.558021http://www.informaworld.com

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site, as inappropriately sited waste facility may adverselyaffect the surrounding environment and other economicand socio-cultural aspects (Chang et al. 2008).

The criteria used for preliminary site screening are pri-marily to examine the proximity of potential sites withrespect to geographic objects that may be affected by thelandfill siting (e.g., rivers, ground-water wells) or that mayaffect landfill operations (e.g., areas with steep slopes).Methodologies used are normally based on a compositesuitability analysis using thematic map overlays (O’Learyet al. 1986), and their extension to include statistical anal-ysis (Anderson and Greenberg 1982). With advancementsof geographical information systems (GIS), landfill sitingprocess is increasingly based on more sophisticated spa-tial analysis and modeling. Jensen and Christensen (1986)demonstrated the use of a raster-based GIS with its associ-ated Boolean logic map algebra to identify potential wastesites based on suitability of topography and proximity withrespect to key geographic features. The utilization of GISfor a preliminary screening is normally carried out by clas-sifying an individual map, based on selected criteria, intoexactly defined classes or by creating buffer zones aroundgeographic features to be protected. All map layers are thenintersected so that the resulting composite map containstwo distinct areas (Kao et al. 1997). It is worth noting thatconcepts of GIS spatial analyses were initiated by McHarg(1969) in his Richmond Parkway studies.

Multifaceted decision-making approaches using multi-criteria decision-making and the relevant methods weredeveloped and applied with more or less success dependingon the specific problem. In the past, analytic hierarchyprocess (AHP) (Saaty 1994) was one of the useful method-ologies, which plays an important role in alternativesselection (Fanti et al. 1998, Labib et al. 1998, Chan et al.2000). AHP is an analytical tool that enables explicitranking of tangible and intangible criteria against eachother for the purpose of selecting priorities. The processinvolves structuring a problem from a primary objective tosecondary levels of criteria and alternatives. Once the hier-archy has been established, a pairwise comparison matrixof each element within each level is constructed. AHPallows group decision-making, where group members canuse their expertise, experience, and knowledge to breakdown a problem into a hierarchy and solve it by the AHPsteps. Participants can weigh each element against eachother within each level, each level is related to the levelsabove and below it, and the entire scheme is tied togethermathematically. For evaluating the numerous criteria, AHPhas become one of the most widely used methods forthe practical solution of multi-criteria decision-makingproblems (Cheng 1997, Akash et al. 1999, Chan et al.2000). The main difficulty arises in the estimation of therequired input data that express qualitative expert obser-vations and preferences. The AHP is mainly used in nearlycrisp decision applications and does not take into account

the uncertainty associated with the mapping of people’sjudgment to an evaluation scale (Chen 1996, Hauser andTadikamalla 1996, Cheng 1997). To overcome the short-comings of the crisp AHP, this study proposes a weightedmulti-criteria decision analysis (MCDA) and GIS indetermining the most optimal alternative for landfill siting.

This association of MCDA and GIS not only per-mits us to manage the spatial reference information butalso to apply analysis methods permitting to have themost pertinent and most profitable information at spatial-temporal scales. The MCDA-GIS hybrid approach usesMCDA to take into account not only the traditional quan-titative criteria, but also the qualitative and imprecisecriteria, from experts, for site localization. The objectiveof this study is to develop a landfill siting methodol-ogy that integrates the MCDA, which is the AHP andweighted linear combination method, within a GIS envi-ronment. This proposed approach, detailed in Section 3,is applied to Eldoret Municipality (EM) in Kenya, withthe aim of evaluating the potential areas for landfill sit-ing within the municipality, and proposes the most suitablearea for siting a new landfill by integrating relevant sitingdeterminants.

Eldoret Town, with more than 500,000 residents, isan industrial and agricultural town located in the RiftValley, western part of Kenya. Despite its rapid growthin population and economic activities, the town has neverbeen equipped with an organized ravage system, and con-sequently garbage and other refuse materials are mostlydiscarded outside of the town, without applying any spe-cific managed strategy. Such an unsuitable procedure hasinflicted substantial damage on the environment (Photo 1).

1.1. Landfill siting criteria and the potentialapplication of GIS

A good waste disposal area has few characteristics. Thisarea should be away from the regions in which there is ahistory of flooding. Otherwise, the wastes can be a seri-ous source of water pollution thus ground-water table datashould be used to avoid ground-water pollution. Anotherconstraint for a proper waste disposal area is to have aspecific distance from the faults in the region. The geo-logical maps can be used for the purpose of identifyingfaults and locations where the structure of crust is weak.Land use maps, road maps, and other environmental fac-tors should also be considered in locating a safe andenvironment-friendly waste disposal area (Akbari et al.2008).

The GIS is widely used in landfill site selection toachieve the combination of the identified criteria to gen-erate the suitability maps (Leao et al. 2004). Presently, itis the most reliable tool as it has the capability to store,retrieve, and analyze a large amount of data as well as

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Photo 1. (a) Pollution of river Sosiani through leaching and solid waste from the current disposal site. (b) Evident health risks toinhabitants living close to the current disposal site and using the waters of river Sosiani.

outputs visualization (Kao and Lin 1996, Basagaoglu et al.1997, Kao and Lin 1999, Kontos et al. 2003, Sener et al.2006).

The rationale of the current study is that for spatialplanning and decision-making, particularly in the findingof a precise dumping site, GIS should be linked to otheradvanced mathematical modeling capabilities such as theMCDA, to identify the single most preferred option from aset of potential options.

The methodology proposed in this article presents anintegrated approach to construct a spatial decision supportsystem for landfill siting via a two-stage analysis. To deter-mine the suitable sites, the first-stage analyses use GISdigital map overlay techniques. In the second stage, MCDAis used to rank the proposed candidate sites for final selec-tion (refer to Section 3.2 for the theoretical justification ofthe proposed approach).

The rest of the article is organized as follows: Section 2concentrates on the study area and data, whereas Section3 presents the proposed methodological framework andconceptualization. Sections 4 and 5 detail the data prepara-tion and results, respectively. Finally, the study discussionsand conclusions are, respectively, presented in Sections 6and 7.

2. Study area and data

This section consists of a description of the study areaand checking the nature of each data available and ways

of analyzing all data based on the criteria which meet therequirements for landfill siting.

2.1. Geographical extent and background informationabout the study

The study area (Figure 1) is EM, which is the headquartersof Uasin Gishu County. The municipality covers an area ofapproximately 78 km2, lies at an average altitude of 2085 mabove sea level, and is estimated to be the fastest growingtown in Kenya.

2.2. Data capture: sources and significance

There were generally four sources from which the data usedin this study were obtained:

(1) Spatial information and the satellite image of thetown. The satellite image used was a 15 m pan-sharpened Landsat ETM+ (Figure 1). Most of thevector data layers used in this study were pre-pared through digitization of existing base maps,followed by geo-rectification using map-to-mapregistration. The vector maps were overlaid on thesatellite images to verify the precision of the basemap.

(2) The spatial data that give the information about thelocation of different articles, for example, the cur-rent waste disposal site, schools, hospitals, airport

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Figure 1. Color composite image of the study area (bands 2, 3, and 4 of ETM+ sensor of Landsat data) dated 30 July 2001. The extentof the town is outlined and the current location of the disposal site is also shown in the western part of the town.

locations were determined using the GPS device.Such spatial information were correlated and incor-porated onto the vector map layer.

(3) The third is the attribute information – quantita-tive and qualitative information about the spatialelements.

(4) Other information elements were collected throughinterview and questionnaires administered to therelevant Eldoret Municipal Council (EMC)management experts including the town mayor;town clerk; ward representatives (councilors);the municipal council section heads of the fol-lowing departments: Planning and Development,Engineering, Public Health, Legal Affairs,Finance, Transportation, Environment and NaturalResources Management, Water and SewerageServices, Education Services, and Community andSocial Services.

2.3. Data analyzed

As part of the data analyzed, the average sources and quan-tities of solid waste produced in EM in tons per month, in2008, are presented in Table 1.

Table 1. Average quantities of solid waste produced within theEldoret Municipality in 2008.

Source Quantity (tons per month)

Domestic 9000Industrial 160Commercial 2910Hospital 30

From Table 1, about 400 tons/day of solid waste is pro-duced and hardly a half of this amount is safely handledon a daily basis. The sources of wastes are also variedand therefore a suitable disposal site needs to be devel-oped to have environmental safety criteria that presentconsciousness about the environment and public health.

GIS data sets of land use, rivers, wetlands, roads,demography, airports, digital elevation models (DEMs),and slope were collected for the Eldoret Municipal from:Ministry of Lands, Ministry of Transport, Department ofPhysical Planning, Ministry of Water, and the Departmentof Statistics (Table 2).

The criteria for data selection were based on con-straints and factors for an ideal landfill siting (Table 3), withthe broad criteria including the terrain parameters, naturalresources, and human infrastructure.

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Table 2. GIS map layers used in the study.

Data Scale Data source

Rivers 1:50,000 Ministry of waterLand use 1:50,000 Department of physical planningRoads 1:25,000 Ministry of transportUrban areas (land use) 1:100,000 Satellite image and EMC planning departmentDigital elevation model (DEM) 1:25,000 Digitized topographical maps and GPSSlope 1:25,000 Digital elevation model (DEM)EMC census data – Kenya national Bureau of statistics (KNBS)

Table 3. Characteristics of the data layers analyzed in the study.

Criteria Items Constrains

Terrain parameters Size Minimum lot sizeElevation To minimize dumping costsFloods Buffer zones to avoid catastrophesSlopes Erosion, drainage, and stabilityFaults Avoid land environmental/cultural

sensitivityNatural resources Water bodies Vacant lots to minimize acquisition cost and

contamination of surface waterVegetation cover –Erosion rate Avoid very sloppy areas

Human infrastructure Roads Distance to existing roadTransportation networks Distance to waste disposal facilityPopulation centers Distance to residential propertiesTown center Distance to central business district (CBD)Airport Location of airport

3. Methods

3.1. Conceptual framework: a methodological overview

Landfill siting is a complicated process requiring a detailedassessment over a vast area to identify suitable location forconstructing a landfill subject to many different criteria.GIS offers the spatial analysis capabilities to quickly elimi-nate parcels of land unsuitable for landfill site (Chang et al.2008). This study employed GIS to perform a screeningprocess that led to identification of a potential suitable can-didate sites based on site-specific criteria. The suitabilitycriteria are defined with the focus to minimize any poten-tial health risks from direct or indirect contamination dueto the proximity of a landfill site with respect to key geo-graphic features. Thus, the first-stage analysis using GIS isessential for the initial identification of a potentially suit-able landfill sites prior to undertaking further analyses orfield investigations. Although the initial screening is basedon criteria related to environmental and ecological factorsinvolved in the site-selection process, there are certain cri-teria such as impact on historical markers, public comfort,and economic factors for which data are not always readilyavailable, which cannot be included in the first stage.

A second-stage analysis based on a handful of suitablesites from the initial GIS screening was performed with theobjective of including the opinions of experts in the region

through MCDA. MCDA is considered useful in addressingthe issue of lack of availability of data for certain impor-tant criteria as well as to incorporate human judgmentinto the selection process that can prove useful in solvingpolitical debates. This approach captures the fuzzy natureof decision-making. The second stage of analysis usingMCDA was applied to rank the proposed candidate sitesand summarize the final selection. Such method followedin the process of identifying the most suitable landfill site.

Qualitative parameters can be used directly as linguis-tic variables or can be transformed into cardinal ones andthen used as quantitative variables (Munda et al. 1994).The ranking of the options after aggregating and taking allinformation about the impacts of the options and the pref-erences of the decision-makers plus the concerned groupsinto account presents the result. It depends on the availabledata, the structure of the information, chosen aggregationmethod, and preferences.

In general, a MCDA consists of the following steps(Munda et al. 1994, Strassert 1995): (1) definition andstructuring of the problem; (2) generation of options; (3)definition of a set of evaluation criteria; (4) choice betweendiscrete and continuous methods; (5) preparation of thedecision (supply of data); (6) identification of the prefer-ence system of the decision-maker and eventually affected

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groups of the society; and (7) choice of an aggregation pro-cedure carrying out of the procedure; usage of feedbackloops with the people affected by the decision.

Following the above framework, the present investi-gation applied an integrated GIS-MCDA-based approach(Figure 2), by aggregating all relevant layers to locatethe proper waste disposal-dumping site and to reducethe search area over vast land coverage to leave onlythose areas that are suitable for siting a prospect land-fill. The aggregation of the data layers is based on theneighborhood-proximity information around the determi-nant spatial elements.

3.2. Brief theoretical exposition on MCDA andintegration with GIS

MCDA for structuring decision problem and evaluat-ing alternatives provides a rich collection of methods(Malczewski 2006). In most management and decision-making problems the management team has already awell-defined goal that must be achieved. To reach that aim,it is necessary to choose from a number of options. Theseoptions, in the field of the MCDA, are referred to as alter-natives. However, the alternatives may not be obvious, as inthe case of optimal landfill siting.

The decision-makers consider the existing alterna-tives that have different attributes and characteristicsand the final task is to choose the best among them.Choosing among the alternatives is done by consideringthe impact of these alternatives on the quality of the finalresult alongside with shortcomings of every alternative.Therefore, effects of alternatives on different issues such asenvironmental issues, financial matters or cost–benefit con-siderations, social considerations, technical problems, etc.,

give rise to consideration of several criteria which playimportant roles in finalizing the project.

However, the criteria demonstrate the characteristicsand important issues on which the final goal is evaluated.If a decision-maker wants to rank the importance of thealternatives, this only is according to the criteria. But itshould be noticed that according to every different criteria,the ranking between the alternatives would be different.

For applying MCDA, a tool is needed which takes theranking and comparison data to process them and calcu-lates the weights of different alternatives or criteria. MCDAis based on a number of pivotal evaluation criteria, definedaccording to conditions of the case study that should beconsidered. Carver and Openshaw (1992) stated four mainbenefits for using integrated MCDA and GIS:

(1) the ability to perform the complex analysis of manydifferent criteria;

(2) the model uses factor weighting to integrate theanalysis for value judgments;

(3) it creates a systematic framework of analysis;(4) it has all of the advantages of the GIS database.

To combine MCDA and GIS, each of the criteria shouldbe represented as a map in the GIS database (Malczewski1999, Baban and Wan-Yusof 2003). It should be noticedthat two types of criteria maps, factor maps and constraintmaps, are not the same. A factor map represents the qual-ity of achieving an objective through a spatial distribution(Malczewski 1999). However, a constraint map representsrestrictions or limitations on decision-making problem thatdo not allow certain actions to be taken (Malczewski 1999).

The first requirement of nearly all discrete methods isa performance table containing the evaluations or criteria

Constraint

mapsLand use

Roads

Rivers andwetlands

Railroads

Towns

Factor maps

Distance to surface water

Distance to roads

Map overlay

Final constraint

map

Final factor map

Final suitability

map

Map overlay

Map overlay

based on MCDA

Neighborhood-proximity

considerations

Figure 2. Model applied for locating suitable land for solid waste landfill siting. The initial map overlay is based on the neighborhood-proximity analysis.

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scores of a set of alternatives on the basis of a set ofcriteria. The alternatives are decision objects from whichthe decision-maker should choose one or several onesto implement. Let A = {a1, a2, . . . , an} denote a set of nalternatives. The evaluation criteria are factors on whichalternatives are evaluated and compared. Formally, a cri-terion is a function g, defined on A, taking its values inan ordered set, and representing the decision-maker’s orexpert’s preferences according to some points of view. Theevaluation of an alternative a ∈ A according to criterion gis written as g(a), with G = {g1, g2, . . . , gm} being a set ofm evaluation criteria and F = {1, 2, . . . , m} being the set ofcriteria indices.

The next step consists in the aggregation of the differentcriteria scores using a specific decision rule (or aggregationprocedure) and taking into account the decision-maker’spreferences, generally represented in terms of weights thatare assigned to different criteria. The term ‘aggregation’is used both in GIS and multi-criteria analysis (MCA)literatures. In GIS community, this term implies spatialaggregation (e.g., aggregating spatial units). It is a com-bination function that ‘aggregate’ different map layers.In MCA community, this term implies functional aggre-gation of partial evaluations of alternatives (using, fore.g., weighted sum technique) into a unique and globalevaluation. The aggregation of criteria scores permits thedecision-maker to make comparison between the differentalternatives on the basis of these scores. Decision rules aresomehow the identities of the multi-criteria methods.

How to combine the several criteria maps to form asingle index of evaluation is the primary issue in the multi-criteria decision-making problem (Eastman 2003). As thesolution lies in the intersection, logical AND, of the con-ditions; the constraint maps simply are multiplied together;that is, c = ∏

cj where cj = constraint map j.Mathematically, a criterion map cj is the set{(

s, gj (s))

: s ∈ Sj

}, where Sj is a set of spatial mapping

units and gj is a mono-valued criterion function defined asfollows (E is a measurement scale):

gj : Sj → E

s → gj(s)

It is important to mention that multi-criteria analysis hasbeen used, since its emergence, to spatial decision prob-lems without the use of GIS. Nevertheless, multi-criteriaanalysis alone is not able to take into account explicitlythe spatial dimension of spatial decision problems. Kooand O’Connell (2006), for instance, remarks that withconventional spatial multi-criteria analysis (i.e., withoutthe use of GIS), the performances of decision alterna-tives are either intrinsically a spatial or aggregated into aunique value. In other words, these approaches supposethat the study area is spatially homogeneous, which is

unrealistic. Furthermore, most multi-criteria analysis soft-wares are of little utility in spatial decision-making as theylack functionalities required to: (1) collect, store, man-age, and analyze spatial data, and (2) represent the spatialdimension of decision problems. These two problems arewell handled by GIS. Thus, GIS and multi-criteria analysisare two complementary tools, each of which has advan-tages and some limitations in spatial decision-making; theirintegration permits us to avoid these limitations.

The uncertainty and the fuzziness generally associatedwith any decision situation require a sensitivity analysisenabling the decision-maker(s) to test the consistency ofa given decision or its variation in response to any mod-ification in the input data and/or in the decision-makerpreferences.

The final recommendation in multi-criteria analysismay take different forms, according to the manner in whicha problem is stated. Roy (1996) identifies four types ofresults corresponding to four ways for stating a problem:(1) choice: selecting a restricted set of alternatives, (2) sort-ing: assigning alternatives to a set of pre-defined categories,(3) ranking: classifying alternatives from best to worst witheventually equal positions, and (4) description: describingthe alternatives and their follow-up results. The currentlyavailable multi-criteria methods permit us to deal with thechoice, sorting, and ranking cases only.

For continuous factor maps, a weighted linear combi-nation (Eastman 2003) is used. In this method, the factormaps are multiplied by a certain weight which is assignedto each of them and then the summation of the resultedmaps produce an overall suitability map. The result map isderived by multiplying the overall factor map and overallconstraint map.

MCDA incorporates the expert knowledge of decision-makers in getting solutions that are spatially structured andfuzzy in nature, by inputting the preferences and intuitivejudgments in the decision-making process.

3.3. Site-selection criteria: review and case studyconsiderations

According to Chang et al. (2008), a landfill site must besituated at a fair distance away from biophysical elementssuch as water, wetlands, critical habitats, and water wellsto reduce the risk of contamination from landfill. Differentstudies used different buffer distances from streams andrivers based on the size of the watershed, such as bufferof 0.8 km (Siddiqui et al. 1996), 180 m (Zeiss and Lefsrud1995), and 2–3 km (Kao and Lin 1999). Considering thesize of municipality, the growth rate and consultations withthe related departments, and expert-based variable bufferdistances were determined as in Table 4.

Table 4 gives a guideline for EMC, based on the expertopinions, in short- and long-term planning considerations.From related studies, it has been suggested that proximity

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Table 4. Site-selection criteria and the proposed variable bufferzones.

Criteria Suitability buffer (m)

Highway (roads) 300Lakes/ponds/reservoirs 300River/streams 300Drinking water source/surface water

intake300

Storm water management pond 100Hotel/restaurant/any food-processing

facility300

Schools, church, hospital, public parks 300Wetland/swampy areas 100Unstable areas 100Occupied dwelling house 1500Hospital/health facility 1500Airport (from perimeter fence) 3000Height above seasonal high water table 1500Maximum height above mean natural

elevation of area750

Existing water wells 500Any other structures/buildings (e.g.,

telecom masts)100

of a landfill to a ground-water well is an importantenvironmental criterion in the landfill site selection so thatwells may be protected from the runoff and leaching ofthe landfill. In this study, a buffer distance of 300 m fromthe wells is suggested to prevent contamination from land-fill due to leaching of pollutants. Due to the abundance ofwater within the EM, there are hardly any wells, as most ofthe inhabitants are served by piped water systems.

Slope is also an important factor when siting a landfillbecause higher slopes would increase runoff of pollutantsfrom the landfill, and thereby increasing the contaminationzone area (Kao and Lin 1999). Kao and Lin (1999) sug-gested that a slope less than 12% would be suitable forthe prevention of contaminant runoff. In this study, regionswith slopes greater than 12% were defined as unsuitablefor a landfill site. The slope information was obtained fromDEM data sets of 30 m resolution obtained from digitizingtopographical map and verified using GPS surveys.

Additionally, landfills should be situated at a significantdistance away from urban residential areas due to pub-lic concerns, such as esthetics, odor (Tagaris et al. 2003),noise, influence on property value (Zeiss and Lefsrud1995), and general health concerns (Nagar and Mizra2002). Urban buffers may range from 150 m (Kao and Lin1999) to 5 km (Zeiss and Lefsrud 1995). This range how-ever is subjective to the specific case and the local authorityregulatory guidelines.

Economic considerations include finding the most cost-effective route for transporting wastes and locating themost suitable land for the candidate sites based on landvalue (Siddiqui et al. 1996). Developments on or too closeto existing road and rail networks would hinder public

transportation and may have an impact on tourism in theregion (Zeiss and Lefsrud 1995). Baban and Flannagan(1998) used a 50 m buffer for roads, whereas Chang et al.(2008) used a 300 m buffer.

The above-considered factors were re-grouped intothree main components: (1) environmental and ecologi-cal impact); (2) transportation issues, and (3) economicalimpact. Any other factor was considered under OtherTechnical (OT) factors. OT factors included the eco-nomic criteria and public perception. The factors ecologicalimpact, transportation issues, and economical impact fallwithin the broad criteria set in Table 3, namely: (1) terrainparameters, (2) natural resources, and (3) general social andtechnical infrastructure.

These factors were weighted by the experts follow-ing AHP according to Saaty (1994, 2006). The weight-ing requires a MCDA, which is derived from a widespectrum of facility management and based on expertjudgment. MCDA can be represented, under normalized

weighting conditions as:n∑

i=1ωi = 1. That is, all the deter-

minants should sum to unity within the decision system.The MCDA-GIS was integrated according to Chang et al.(2008) (Figure 3).

4. Thematic maps preparation and GIS analysis

The different constraint maps developed in this studyincluded (1) environmental, (2) water body (river andwells), (3) slope, (4) urban, (5) transportation systemsconstraint, and (6) facilities map. The constraint maps,developed in GIS, were subject to advanced assessmentin the second-stage analysis. The characteristics of theconstraint map layers are discussed in the next sections.

4.1. Contour map generation

The contour map layer was derived in ArcView by dig-itizing from the existing topographical map. The resultof this analog to digital data conversion is presented inFigure 4. From the digital contour map, the DEM, slope,and Triangular Irregular Networks (TIN) terrain character-istic can be defined. GPS surveys were used to verify onthe accuracy of the digitized maps and to ensure accurateterrain TIN representations for flow analysis.

4.2. Transportation system: road and railway networkmap

This comprised feature dataset displaying linear road net-work within the municipality. All the roads in the munici-pality were digitized from the topographic map as polylinesand a shape file for roads was created. Typically, denserroad networks are within the town center (Figure 5).

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Annals of GIS 51

Urban-GIS data collection

Conversion to raster maps of

uniform grid sizes

Raster map calculator in ArcGIS to eliminate

unsuitable land from each raster based on

the selection criteria

Overlay of rasters to produce

suitable sites based on the

defined criteria

Identification of the relative

candidate sites

STAGE I: GIS data analysis

Obtaining expert judgments

on the suitability of n candidate

sites

Assessment of weight for the

criteria by the experts

Model formulation and integration:

f (GIS-MCDA)

STAGE II: MCDA

EIω1 ω2 ω3 ω4

TI EC OT

GIS-MCDA for identification of the

most suitable site

(neighborhood-proximity analysis)

Figure 3. Flowchart of the methodology of the components of the integrated MCDA-GIS model for Eldoret Municipality (modified fromChang et al. 2008).

In Figure 5, the transportation system within the studyarea consists of a main highway, single main rail system,main roads, secondary roads, and tracks. The concentrationof the roads network depicts the concentration of humansettlements outside the town center.

4.3. Land-use/land-cover information

The land-use and land-cover information within the studyarea comprised the urban and rural areas. Typically, thewaste disposal areas should not be in the vicinity of the

populated urban land used and rural settlement areas. Forthis purpose, a minimum buffer zone of 300 m aroundthese areas was applied (Table 4). An indicator of land-use/land-cover information was defined from the satelliteimagery (Figure 1) and the concentration or density of roadnetworks.

4.4. Other infrastructure information

A layer comprising schools, hospitals, religious institu-tions, airstrip, and current disposal site was generated from

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Figure 4. Digitized contour map of the study area.

the individual point feature maps using GPS. The results ofthe composite overlay are presented in Figure 6. The resultsin Figure 6 show that schools, hospitals, and religiousinstitutions are located within the vicinity of each other.

4.5. Elevation, slope, and water bodies map

A TIN for the area of study was created from the contoursdata, consisting of elevation attributes, and verified usingGPS observations. This was converted to the DEM and thewater body (river Sosiani) superimposed (Figure 7). Fromthe DEM, the slope map was derived (Figure 8). The slopemap is important in defining the direction of flows from apotential landfill site.

5. Ranking and rating for each criterion and thegoverning algorithm

For each of the above layers, specific restrictions takethe form of buffering at specified distances (S) surround-ing (and, usually, including) the above areas. The initial

‘buffered’ layers (zones), containing the areas that are to beexcluded from landfill siting, are then defined. The overlays(unions) between successive buffered layers are then per-formed, by each category individually and by all categoriesas a total, as follows:

Li −→BufferingwithdistancesSi

Bi −→Overlaying

withBi+1

Ui −→Overlaying

withUj+1

Uti

where Li are the initial layers, Bi the buffered layers, Ui thesuccessive unions between the buffered layers of each cate-gory, and Uti the total unions between the successive unionsof each subcategory. This stage considers the neighborhoodand proximity of the landfill siting to the existing urbanland use and the envisaged urban zoning and expansion.

The above spatial analysis setup produces all the candi-date landfill sites, as the remaining sites of the ‘exclusionprocess’. For each remained candidate site, all the charac-teristics concerning its relationship to the exclusion criteria

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Figure 5. Road and railway network within the Eldoret Municipality. The concentration of the road networks is an indicator of the urbanpopulation concentration and general land-use activities.

may be given: distances, visibilities, volumes, etc., fromlayers Li.

After determining the unacceptable areas, the remain-ing areas are classified into classes of high and low priorityfor being used as waste disposal areas. This is done throughtwo steps of weighting process. In the first step, each layeris internally weighted based on the minimum and maxi-mum distances. In the second step, each layer is externallyweighted based on how critical and important the data layeris to the waste disposal problem.

5.1. Internal weighting

In this part, each data layer is studied individually. Thelocations of each data layer can take a weight betweenzero and nine, based on their direct distance to the fea-tures, implementation as well as on expert judgment. Asan example, considering the transportation networks, thelocations that are close to the roads have a higher weightthan the ones far away from the road network. Similarly,

from the geological aspect, the locations are weightedbased on whether the soils have a low penetration fac-tor or are hard to dig or are close to clay area (whichcan be used to cover the surface wastes) or not. For theriver, water wells, and fault layers, for example, the loca-tions which are far from them have higher weight andvice versa. For urban and rural areas, the locations areweighted based on their distance to these centers. The dis-tance should not be so far that the transportation becomesa problem and not so close that it provides an unpleas-ant appearance to the sight seeing, parks, and recreationalfacilities, which are mostly in the suburbs of cities. Foragricultural and industrial centers, the highest weight isgiven to locations with a distance of 2–5 km. In respectof slope, the highest weight is given to the slopes between0% and 2% and the lowest weight is given to the slopesover 20%. This is due to the fact that waste disposaltrucks have difficulty moving on roads with slopes over20%, and slopes greater than 20% results in rapid flows ofwater.

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Figure 6. Schools, hospitals, and religious institutions and airfield represented as point data, from GPS field observations.

The internal weighting was carried out using the bufferzones generation through neighborhood-proximity analy-sis. The results of the different buffer zones are as follows(Figure 9).

Because of the different scales on which the criteriawere measured, it was necessary that factors be standard-ized before combination. In this research, a linear scalingmethod is applied using the minimum and maximum valuesas scaling points for standardization (Equation (1)).

Xi =(

Ri − Rmin

Rmax − Rmin

)× standardized_range (1)

where Xi is the standardized value for ith criterion, Ri theraw score for the ith criterion, Rmin the minimum score forthe ith criterion, and Rmax the maximum score for the ithcriterion. An option is provided to standardize factors toa 0–255 byte scale, which can be rescaled between twoclasses: unsuitable (value 0) or most suitable (value 10).

Reclassification of datasets is required before thedatasets can be combined, for data uniformity. The buffer

zone datasets were reclassified to assign values of prefer-ence according to the criteria in question. The datasets werefirst set to a common scale. This is a scale that shows howsuitable a particular location is. Therefore, each dataset wasset to a common scale within the range of 1–10, assigninghigher values to attributes within each dataset that are moresuitable for locating a landfill site.

As the sanitary landfill should be located far away fromhuman settlements and other land use such as schools, hos-pitals, and churches, the input datasets for the buffer layerswere reclassified. The most suitable locations, which are ofcourse the furthest from human inhabitance being given 10and the least suitable were the closest places which wereassigned a value of 1. The output is a reclassified distancemap (Figure 10).

5.2. External weighting

The primary issue in multi-criteria evaluation is how tocombine the information from several criteria to form a

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Triangular irregular network

Legend

Elevation

River Sosiani

2131.111–2153

2109.222–2131.111

2087.333–2109.222

2065.444–2087.333

2043.556–2065.444

2021.667–2043.556

1999.778–2021.667

1977.889–1999.778

1956–1977.889

Boundary

N

S

E3 1.5 0 3 km

W

Figure 7. Elevation and surface water map of the study area.

single index of evaluation. A criterion is some basis for adecision that can be measured and evaluated.

In this study, two kinds of criteria were considered: fac-tors and constraints. A factor is a criterion that enhances ordetracts from the suitability of a specific alternative for theactivity under consideration. A constraint serves to limitthe alternatives under consideration and classifies the areasinto two classes: unsuitable (value 0) or suitable (value 1).

In Section 5.1, the locations are weighted within eachdata layer internally. However, it is obvious that the datalayers do not have equal weight for the problem in hand.Therefore, each data layer is weighted based on the techni-cal, implementational, safety, environmental, economical,and other factors. Table 5 shows the external weightingschema used in this study, which is based on the jointideas of the focus expert groups as outlined in Section 2.2.These focus expert groups, comprising of the managers andtechnocrats, under the Local Government Act, are chargedwith the formulation and implementation of the broadMunicipal Council’s policies and economic developmentagenda.

The external weighting is implemented by: (1) perform-ing pairwise comparisons, (2) assessing the consistencyof the pairwise judgments, and (3) finally computing therelative weights, for each indicator (i.e., data layer compo-nent). The weights are derived by computing the principaleigenvectors of the pairwise comparison matrix.

Preference ranking organization method, based onbuilding ‘fuzzy’ dominance relations through the use ofpreference functions was adopted to determine the weigh-tage of the expert decisions. The difference between thelevels of criterion achievement of two alternatives is com-pared. If the values do not differ for a particular criterion,no preference is indicated. This MCDA is considered totake care of the drawbacks of the AHP, which is mainlyused in crisp decision applications. The MCDA takes intoaccount the uncertainty associated with the mapping anddepicting of people’s judgment to an evaluation scale thatis weighted and linearly combined.

With a weighted linear combination, factors are com-bined by applying a weight to each followed by a summa-tion of the results to yield a suitability map (Equation (2)):

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Slope map

Legend

Slope map

<VALUE>

River

0%–0.93%

0.94%–1.86%

1.87%–2.99%

3%–4.43%

4.44%–6.29%

6.3%–8.56%

8.57%–11.45%

11.46%–15.26%

15.27%–20.21%

20.22%–26.29%

bound2

N

S

Projected local coordinates

0 0.45 0.9 1.3 2.7 3.6km

EW

Figure 8. Slope map derived from the DEM map for topographical analysis.

S =∑

wixi (2)

where S is the suitability factor, wi the weight of factor i,and xi the criterion score of factor i.

In the presented method that Boolean constraints alsoapply, the procedure can be modified by multiplying thesuitability calculated from the factors by the product of theconstraints (Equation (3)).

S =∑

wixi ×∏

Cj (3)

where Cj is the criterion score of constraint j and � theproduct.

The result of this multi-criteria evaluation will be a finalaggregated suitability image. In the aggregated suitabilityimage, zones whose area is smaller than 20 hectares areeliminated from the allocation process. The next step thendetermines the land suitability of a zone by calculating theaverage of the suitability of the cells belonging to a zoneusing the zonal land suitability index (Sz) (Equation (4)).

Sz =∑

(Li)z

nz(4)

where: Sz is the zonal land suitability, (Li)z the local suit-ability of the cells i belonging to the zone z, and nz thenumber of cells of zones z.

The results from the land suitability (Sz) are the poten-tial sites for landfill sites with varying land suitabilityindices. The zone indices are then ranked for evaluation.The evaluation criteria used in the present study are thoseindicated in Table 5.

The results in Table 5 show that external weightingsin landfill siting are highly influenced by proximities totransportation networks, surface water bodies, and urbansettlements, each with a weight of 0.25. These in turninfluence the slope factor which is ranked fourth with aweight of 0.10. The slope weight ensures siting and furthersite management over long temporal and varied climaticconditions. Hospitals, schools, and religious institutions,which are mostly located within the same vicinity and

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near primary and secondary schools, exhibit the same leastweight of 0.05 each. This follows from the locations andweightings of the considered determinants.

The external weighting results are presented in Figure11. The results in Figure 11 show that the current solidwaste disposal site (Figure 1), which was chosen randomly

(a)

Distance to schools

Legend

Schools

Distance to school

0–883

884–1770

1780–2650

2660–3530

3540–4420

4430–5300

5310–6180

6190–7060

7070–7950

7960–8830

2 1 2 km0

(b)

Legend

Airfield

Boundary

0–17501760–2870

2880–3890

3900–49604970–6030

6040–7100

7110–8170

8180–9290

9300–10500

10600–12400

3 km

Projected local coordinates

01.53

Distance to airfield

(c)

Legend

RiverRoads

Boundary

0–637

638–12701280–1910

1920–25502560–3190

3200–3820

3830–4460

4470–5100

5110–5730

5740–6370

Distance to roads

(d)

Legend

Hospitals

Boundary

Distance to hospital

<VALUE>

0–670

680–1300

1400–2000

2100–27002800–3400

3500–4000

4100–4700

4800–5400

5500–6000

6100–6700

2.5 km01.252.5

Distance to hospitals

Figure 9. Buffer zones generated from internal weighting: (a) schools, (b) airfield, (c) roads, (d) hospitals, (e) religious institutions, and(f) river through neighborhood-proximity analysis.

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(f)

Legend

River

Boundary

<VALUE>

0–770

780–1500

1600–2300

2400–3100

3200–3800

3900–4600

4700–5400

5500–6200

6300–6900

7000–7700

Distance to river

(e)

LegendDistance to religious institutions

0–920930–18001900–2800

Distance to religious institution

2900–37003800–46004700–55005600–64006500–74007500–83008400–9200

2.5 km01.252.5

Figure 9. (Continued)

Table 5. External weighting schema for the considered datalayers. The weights are derived by computing the principaleigenvectors of the pairwise comparison matrix.

Data layer Weight

Infrastructure/transportation network 0.25Surface waterbodies 0.25Urban–rural settlements 0.25Slope 0.10Hospitals 0.05Schools 0.05Religious institutions 0.05

Total external weights 1.00

based only on the fact that the site was a decommissionedquarry mine, lies in a highly unsuitable zone.

The result in Figure 11b shows that the most suitablesite is located in the northern part of the Central BusinessDistrict. A physical evaluation of this site reveals its suit-ability in size and slope, being away from the surfacewater systems, and not far off when considering transporta-tion or haulage costs and yet far enough from the humansettlement and activities.

6. Discussions

In most developing towns, less attention has been paidtoward the utility of expert-based multi-decision analysisfor optimal waste disposal siting. This study reveals that

implementing broad-based expert knowledge is essential inthe management of waste disposal in short- and long-termconsiderations.

In this study, the following key determinants were con-sidered in the site-selection process: (1) available land – interms of size and land filling life span; (2) haul distance– to cater for operating costs; (3) topographical aspects –to guide in natural flows; (4) surface water hydrology –in considering drainage and surface water pollution; (5)local environmental conditions – odor, vector (health) con-siderations; and (6) general urban land use. The resultsin Figure 11 proved that although all the above six fac-tors are important, their relative contributions, expressedas weights, greatly influence the location of the solid wastedisposal site.

Preference ranking organization method, based onbuilding fuzzy-dominance relations through the use ofpreference functions was adopted to determine the weightof the expert decision. The difference between the levelsof criterion achievement of two alternatives was compared,and it was found that if the values do not differ for a particu-lar criterion, no preference is indicated. From the results ofthis comparative analysis, negative and positive probabilitymatrices were developed.

From the results in Figure 11a, equal weighting resultedin locations that were not viable mostly due to haulagedistance (being too far) and topographical disadvantages,that is, near surface water and being steep. The sitealternative in Figure 11b presents the most optimal location

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Figure 10. Reclassified buffer distances corresponding to (a) slope, (b) schools, (c) airfield, (d) railway line, (e) hospitals, (f) religiousinstitutions, and (g) roads.

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Figure 11. Classified maps derived showing two scenarios of solid waste suitability site locations for Eldoret Municipality: (a) usinguniform weights and (b) using variable weights as in Table 5.

for a growing urban scenario. The most suitable solidwaste disposal site is indicated as the white circle withinthe highly suitable area in the northeastern part of themunicipality.

The current approach can be advantageous in solv-ing controversial municipal planning debates in the future.Further, the advantage of the proposed approach is placedupon the capability to incorporate the knowledge ofthe domain evaluation process in the uncertain decision-making process when there is a lack of crisp informationrelated to certain criteria.

Nevertheless, the drawback of this method is that theselection of the best candidate site is dependent on thejudgments of the experts and can be sensitive to changesin the decision weights associated with criteria. In cer-tain situations, two experts may have highly contradictingjudgments about suitability of a candidate site. Hence, itis required to assess the extent of difference or similaritybetween the experts in association with decision weights.Where the experts are forced to give ranks to the pre-defined candidate sites, the selections are only made amongthese. But some might argue that it may not be the verybest and ideal case. If the screening process is looseneda little bit, then may be a candidate site can be selected,and may be on some criterion, it will score higher. Torespond to this challenge, a field check was done in theearly stage and in the middle of this study to ensure that themost suitable site would be the approved one at the fieldeventually.

For future considerations in terms of expansion ofthe proposed landfill site, decision-maker set preferences

0

Pref

eren

ce v

alue

10

Integrated distance (km)

Too close: Obstacle to expansion

Optimal mid-range

Too far: High transport costs

Figure 12. Future expansions of decision-maker set preferences:a theoretical perspective.

can be used, as conceptualized in Figure 12, where thedistance from generation points are integrated with expan-sion parameters to determine the preference values—suchthat too close integrated distances are considered obsta-cles to expansions whereas too far distances constitute hightransportation costs, and the best options are those in themid-ranges.

7. Conclusions

Landfill-based disposal option is the most widely usedin many countries. An open approach, considering engi-neering, social, economic, and political factors, should beadopted in the facility siting. An important element of thelandfill siting process is a technique for evaluating the basicsuitability of all available land for sanitary landfill as an aidin selection of a limited number of sites for more detailed

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evaluation. The technique should establish criteria to illus-trate the relative suitability of land for sanitary land filling.In addition, the technique should be practical, taking intoaccount the broad-based expertise opinions.

This study presented a MCDA-GIS integrated approachto overcome the drawbacks of the crisp AHP. From theresults of the MCDA-GIS integrated approach presentedin Figure 11b, it is evident that the current disposal site(Figure 1) falls in the critically unreliable zone. The opti-mal location should therefore be 4.8 km northeast of thecurrent site. This site lies on a fairly flat open area anddistant from the surface water and human settlement.Further, there is already an access road to this proposedsite. The proposed siting is in view of the fact that Eldoretis a growing municipality with increasing industrial andcommercial activities.

The findings of the research are significant as theapproach used can be generalized and applied to thesocioeconomic and natural characteristics of the region.Consequently, a spatial decision support system maystrengthen the generation and evaluation of alternativesby providing an insight of the problem among the variedobjectives and granting support to the process of decision-making under uncertainty. In response to the vague orfuzzy conditions in decision-making toward site selection,domain experts, in the second stage, can be used.

Future developments of the proposed model mayinclude sensitivity analysis to detect the effect of bufferdistance changes on the total remaining area and the inclu-sion of dynamic spatial layers. It is however advisable thatbefore resorting to the landfill option, towns and citiesshould consider waste minimization through recycling andreuse, and waste transformation alternatives.

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