In recent years, life quality of the urban areas is a growing interest of civil engineering. Environmental quality is essential to displaythe position of sustainable development and asserts the corresponding countermeasures to the protection of environment. Urbanenvironmental quality involves multidisciplinary parameters and difficulties to be analyzed. *e problem is not only complex butalso involves many uncertainties, and decision-making on these issues is a challenging problem which contains many parametersand alternatives inherently. Multicriteria decision analysis (MCDA) is a very prepotent technique to solve that sort of problems,and it guides the users confidence by synthesizing that information. Environmental concerns frequently contain spatial in-formation. Spatial multicriteria decision analysis (SMCDA) that includes Geographic Information System (GIS) is efficient totackle that type of problems. *is study has employed some geographic and urbanization parameters to assess the environmentalurbanization quality used by those methods. *e study area has been described in five categories: very favorable, favorable,moderate, unfavorable, and very unfavorable. *e results are momentous to see the current situation, and they could helpto mitigate the related concerns. *e study proves that the SMCDA descriptions match the environmental quality perceptionin the city.
1. Introduction
*ere has been a growing interest on life quality of the urbanareas due to many reasons in recent years. *e matter holdsmany components like buildings, physical environment,health, security, and community. *e environmental issuesof urbanization are the major portion of the physical en-vironment difficulties. *e quality of urban environmentinvolves multidisciplinary parameters, and they are inter-connected parameters containing the distribution of greenzones, the urban heat island, air quality, and building densityand geometry [1]. *ese parameters, vital for densely pop-ulated cities, can be monitored easily [2]. A large numberof studies enlighten the problems, their sources, and miti-gation measures [3–6]. *is complex problem also containsmany uncertainties and subjective judgements. *erefore,
decision-making on these problems is tough and enclosesmany parameters and alternatives inherently.
Multicriteria decision analysis (MCDA) comes forwardto handle that type of problem-solving; it is because theobjective and subjective parameters can be defined andevaluated. Multicriteria decision-making takes accountsof several choices or behavior patterns when there exista number of alternatives which disaccord to a major extent[7]. A set of systematic procedures to analyze multifacetedproblems is employed in decision analysis. *e problem isallocated into small logical parts each of which is analyzed,and the parts are integrated in a rational manner to forma meaningful solution [8]. MCDA is a very effective methodto handle such problems, and it leads the decision makersconfidence by combining that information.*is practice maybe intuitive or analytical [9]. Hence, experience as well as data
HindawiAdvances in Civil EngineeringVolume 2018, Article ID 6915938, 7 pageshttps://doi.org/10.1155/2018/6915938
is vital because the parameters have comparative significance.*e criteria may be both qualitative and quantitative [10].
MCDA supports users in analyzing potential engage-ments or options based on multiple unmeasurable factors/criteria, using decision rules [8]. *ere are two approachesto classify the MCDA methods, namely, multiobjectivedecision-making (MODM) and multiattribute decision-making (MADM). *e main difference between them isthe number of evaluated options. *e first one is moresuitable to tackle the multiobjective planning, while the latteris designed for selecting discrete alternatives [11]. In thissense, the MCDA methods are classified into three groups:value measurement models, goal, aspiration, or referencelevel models, and outranking models [7]. *e values of al-ternatives reflect a preference order in the first group. *esecond group leads methods for “situations in which usersmay consider it very difficult to express trade-offs or im-portance weights but may be able to describe outcomescenarios, expressed in terms of satisfying aspirations or goalsfor each criterion.” *e outranking models focus on “pair-wise evaluation of alternatives, identifying incomparabilitiesas well as assessing preferences and indifferences” [11].
*e analytic hierarchy process (AHP) as a type ofMCDAwas developed by Saaty [12]. *is method consists of threedistinct phases: the principle of “building hierarchies,” theprinciple of “setting up priorities,” and the principle of“logical consistency” [13]. *e first principle is based onfindings and detects relations between them. People cannotrecognize and analyze the effective factors of a wholestructure without dividing it into small parts. It is beingperformed by a logical process, which aims at buildingproper hierarchies. “A hierarchy is a specific system type,which is grounded on the assumption that the user assignedentities, can be grouped into separate sets, with the assets ofone group impressing the assets of only one other group, andbeing impressed by the assets of only one group” [14]. *esimplest model of hierarchy contains three levels: the firstone is the aim of the decision-making problem and the othertwo comprise criteria and options. Pairwise comparisons inthe AHP are engaged for setting up weights among com-ponents of the same hierarchical level. All the components ofthe level are paralleled in pairs with respect to the logicalcomponents in the next higher level, obtaining a pairwisecomparisons’ matrix. In order to represent the relativeimportance of one component over another, a pairwisecomparison scale is announced. Its values range from 1 to 9,and they are assigned to judgement in comparing compo-nent pairs in each level [15]. It compares criteria pairwise ona fuzzy-linguistic ratio scale and calculates the overall rel-ative weights based on cumulative computations of allpairwise ratios [9]. AHP also offers a contributive, hierar-chical accumulation of criteria [16]. *e method was en-gaged in numerous studies like engineering, nature, andsocial sciences [17–19]. It is also frequently used in envi-ronmental studies [20, 21].
*e function of Geographical Information System (GIS)in early times was only storage, displaying geographic data.Presently, GIS has become a significant technology by theinclusion of end users in navigation devices, GPS- (global
positioning system-) enabled smartphones, and so on.Moreover, its engagement in spatial analysis by collectingand evaluating geographical and nongeographical datawidens the employment of the method. GIS can promotemaking decisions by integrating some geographical skillslike GPS and remote sensing [22]. *is capability of GISmade it a powerful tool in many branches of science andtechnology, namely, natural sciences, archeology, engi-neering, medical, and social sciences [23–25].
*e combination of MCDA with GIS is an effective tool,and they take advantage of each other [26]. *e automation,management, and evaluation of spatial data for end usershave made GIS an essential player for examining MCDAissues. Nevertheless, increasing GIS applications are called asstructures for assisting MCDA problems, and many of themlack the spatial analysis needed by decision makers. MCDAapplications offer many skills and measures to help decisionmakers’ preferences. Combination of these methods througha program enabling a user to communicate with a computerdelivers the outline of the SMCDA support system. It helpsto develop the efficiency of the decision-making by includingend users’ decisions. Normally, MCDA uses mean or overalldata that are uniform in the whole area, which is unlikely inmany studies [27]. Nevertheless, the geographical locationsof alternatives and criterion values are needed in SMCDA[16, 28]. *e GIS and MCDA synthetization began at early90s. Carver [29] has described the basics and restrictions ofmerging them. For the comprehensive literature, some lit-erature is available for interested readers [8, 15, 30]. Envi-ronmental quality is beneficial to determine the presentposition of sustainable development and asserts the corre-sponding countermeasures to the protection of environment.
*is study deals with physical environmental quality ofthe Denizli (Turkey) municipal area as a part of urbanenvironmental quality. *e topography that designatesparticular natural hazard risks and some urbanization pa-rameters is employed by using SMCDA and GIS. *e resultsare momentous to assess the current status and to mitigatethe related issues.
2. Site Characteristics
Denizli municipal area is one of the highly developedprovinces and located at SW Turkey (inset of Figure 1). *earea is in a graben area which is bounded by high mountainsboth in the north and in the south. *e downtown is close tothe northern part of the graben, and dominant slope di-rection is from north to south as illustrated in Figure 1.
Environmental assessment zoning maps of the mu-nicipal areas are based on the particular natural hazard risksand some urbanization parameters, namely, altitude, roaddensity, green field density, and traffic noise. *e altitude inthe area controls both sediment size and flooding risk, and itis engaged as a natural hazard parameter. *e sediment sizedistribution is controlled by slopes [31]. *e larger sedi-ments have higher strength than the smaller ones as seen inFigure 2 [32].*ey cause less damage during an earthquake.Lower altitudes are also under the flooding risks inherently.*e altitude values are obtained from the digital elevation
2 Advances in Civil Engineering
model (DEM) of Denizli. �e elevation and grain size of thesediments are getting smaller from south to north. Green�eld density and road density are employed to re�ect theurbanization parameters. Because of the data location density,a square grid plan with 500 meters in size is established andevery grid is considered for the calculation. �e green �eldsare marked as blocks in the development plans. To �nd outthe green �eld density, the area of green �eld blocks isproportioned to the area of building blocks in each grid.Similarly, the total road area in a grid cell is proportioned tothe block area.�e tra�c noise is one of the major parametersfor life quality [33], and it is the fourth parameter in this study.�e tra�c noise data aremainly based on the Cetin study [34].
It has not covered the whole study area, and the incompletedata were supplemented later.
�is study has engaged the weighted grading as aMCDA. �e criteria were compared with respect to theirweights after identifying them. In order to implement it,a pairwise comparison matrix composed the relativeweights. �en, the generated data were normalized, and theeigenvector associated with the maximum eigenvalue of theratio matrix. MapInfo® was engaged to analyze and imaginethe spatial data.
3. Analyses by MCDA
�e employed parameters are, namely, altitude, road density,green �eld density, and tra�c noise. �ese data are dividedinto ten groups and graded by using these groupings. �ealtitude, road density, green �eld density, and tra�c noisevalues and grades are given in Table 1. �e altitude variesfrom 250 meters to more than 475 meters (Figure 3). Higher
0.1 1 1.0 10020
30
40
50
60
Median particle diameter, D50 (mm)
Ang
le o
f she
arin
g re
sista
nce, ϕ
(deg
ree)
ϕ = 2.64 ln D50 + 38.13
Figure 2: Correlation between the median particle diameter andthe angle of shearing resistance [32].
altitudes have some advantages in terms of natural risks like�ooding. Moreover, soil types at higher locations arecoarser than that at the lower ones. It means coarser soilspresent better seismic characteristics than the �ne soils.After all, higher altitudes are more preferable, re�ect lessnatural hazard risk, and have higher grades. �e roaddensity is intensive at southern and northern parts of thearea (Figure 4). Higher road density represents easy accessand more parking spaces in the Turkish case, and it isdesirable. �erefore, higher values have high grade points.Similarly, high green �eld density expresses better envi-ronmental conditions. �e green �eld density of the area isillustrated in Figure 5. �e vast majority of the municipalarea has very low green �elds, less than 10%. Limited areasat western and southern parts of the area have more than20% green area density. Noise displays poor environmentalsettings. �e lowest noise level measured in the area is60 dB, while the highest one goes up to 90 dB (Figure 6).�ecentral, southern, and northern fragments of the municipalarea are noisier than the other parts. Especially, western andeastern districts are quieter, and they have noise level lessthan 70 dB.
�e intensity scale submitted by Saaty [14] was employedin the study, and the measures were de�ned based on theprofessional knowledge of the authors. Table 2 lists theformed ratio matrix and the intensity importance values.Normalizing the weights was done by dividing the weightsby the sum of the column (Table 3). Average of the cell in therows gave the priority vector. �e ratio matrix and priorityvector were multiplied to �nd out D vector that was dividedby the priority vector to get the eigenvector.
�e validation of the consistency of the results is done bythe “consistency ratio (CR).” �e maximum value of the CRis 0.10. When it is higher than that value, the results areassumed to be consistent and should be revised. Random-ness Index (RI) and the maximum eigenvalue (Λmax) werecalculated to obtain the CR.
Randomness Index (RI) is the number of criterion-dependent values (n), and Saaty [12] has proposed it as0.9. �e ratio of Consistency Index (CI) to RandomnessIndex (RI) is called “consistency ratio (CR),” and it is equalto 0.04. Multiplying the eigenvector of each criterion to thedesignated points of this criterion in the alternatives givesthe weighted value. �e ultimate value at the localities wascalculated by adding these values at each point, and the �nalthematic map of environmental assessment is accomplishedby these data.
4. Discussion and Conclusions
Several disciplines that have di¤erent approaches andmethods deal with the environmental quality phenomenon.�ese branches vary from engineering to health, sociology,economy, and so on, and they have their own approachesand valuations. Furthermore, assessment of some criteriamight be subjective when the perception of the people isconcerned. Ironically, the models and/or evaluations shouldre�ect the general perception.
Some problems became visible throughout the study.�e �rst problem to tackle was the choice of the MCDAmethod. It is well known that MCDA procedures causedissimilar results. �is study has engaged AHP as it o¤ersa structured, yet �exible method to decision-making.Additionally, AHP permits to control the inconsistencyof the value judgements. Moreover, the observed data arebeing employed in a spatial manner. �e second and themore critical one was minimizing the risks and cost of
681000N
SW
4187
000
4185
000
4183
000
4181
000
4179
000
683000 685000 687000
0 2,000
km
Road density (%)
20–2525–3030–3535–4040–4545–5050–5555–6060–65>65
E
Figure 4: Road density map.
681000N
SEW
0 2,000
km
Green fielddensity (%)
0–44–88–1212–1616–2020–2424–2828–3232–36>36
4187
000
4185
000
4183
000
4181
000
4179
000
683000 685000 687000
Figure 5: Green �eld density map.
4 Advances in Civil Engineering
environmental urbanization while maximizing the publicacceptance or not. �e results should be compatible withthe general perception in the city.
�e preferences for urbanization quality may be alteredby virtue of social, economic, and technological level of thecommunities. However, globalization eliminates the physicaldissimilarities between nations, and multinational companiesdominate not only the economic systems but also the lifestyleand cities.�is process leads to global hierarchy of cities, which
results in increased living standards. Consequently, there area large number of di¤erent criteria to evaluate the quality of theurban areas, and the attributes change in time. �e studiesshow that the considered parameters are not the same.�ey areinfrequently measured by the same units even if they areidentical. In many cases, the names of social and politicalattributes may be misrepresentative [35].
�e study has considered some certain natural hazardrisks and life quality parameters. �e altitude in the studycontrols the sediment size which is related to strength of thesoil as foundation. Moreover, higher soil strength mitigatesthe earthquake damages. Altitude, naturally, governs the�ooding risk of the municipal area. In this case, this criterioncovers basic construction requirements. Tra�c noise, roaddensity, and green �eld density are the most common andproblematic parameters in many urban areas like Denizli.Air quality has been added in many cases, but it is nota major problem in this example.
�e classi�cation techniques frequently use �ve clustersfrom “very pro.” to “very con.” Obviously, this descriptiongives more detail than the three clusters (good, moderate,and bad). In this study, the weighted values have been dividedinto �ve equal parts, and the following �ve zone descriptionshave been employed from higher to lower values:
(i) Very favorable(ii) Favorable(iii) Moderate(iv) Unfavorable(v) Very unfavorable
�e distribution of the zones is illustrated in Figure 7.Generally speaking, most of the zones directed in NW-SEdirection. “Very favorable” and “favorable” zones which have
Figure 7: �ematic map of environmental urbanization.
Advances in Civil Engineering 5
the highest calculated points are located at the south,southwest, and northern parts of the municipal area. *eseregions have higher altitude and partly lower road density.*e green field density and noise level are at the averagevalues. On the contrary, “very unfavorable” and “unfavor-able” zones are mainly located around the central part of thestudy area. *ese two parts, except the northwestern parts,have low altitude, average road density and noise, and toolow green field density values. *ese zones involve highflooding and soil amplification risks (high seismic risk),which are unacceptable for an environmental urbanization.*e “very unfavorable” and “unfavorable” zones are mainlyused for trading and light industrialized activities. *e“moderate” areas lie between these two groups and coverwidely in northwestern parts of the study area.
A comparison with multicriteria model results and theperception in the city might be valuable. Ozer et al. [36] hasdesignated the same area on the basis of socioeconomic pa-rameters. *e definitions of the AHPmodel have meaningfullymatched with those descriptions. *e described zones reflectthe general perception in the city, which is the main goal of thestudy. *e central part of the municipal area is depicted as“miserable,” while southern and southwestern parts of the studyare known as the best part of the living area parallel to the study.
*e study proves that the SMCDA can be employed toassess the urban environmental quality perception in a citywhen the criteria are set well. *e method is beneficial to seethe present situation, and such a study may trigger thecountermeasures to mitigate the related concerns in anadministrative manner.
Conflicts of Interest
*e authors declare that they have no conflicts of interest.
Acknowledgments
*e authors thank the reviewers specifically K. M. Cubukcufor the contributions and Feridun Cetin, Irem Ganiz, BurakIsikhan, Eren Bayramoglu, Semih Sari, and Emre Erkol forthe traffic noise data.
References
[1] J. Nichol and M. S. Wong, “Modeling urban environmentalquality in a tropical city,” Landscape and Urban Planning,vol. 73, no. 1, pp. 49–58, 2005.
[2] V. G. M. Annamdas, S. Bhalla, and C. K. Soh, “Applications ofstructural health monitoring technology in Asia,” StructuralHealth Monitoring, vol. 16, no. 3, pp. 324–346, 2017.
[3] M. Cerreta and P. De Toro, “Urbanization suitability maps:a dynamic spatial decision support system for sustainable landuse,” Earth System Dynamics, vol. 3, no. 2, pp. 157–171, 2012.
[4] J. S. Yang, E. S. Chung, S. U. Kim, and T.W. Kim, “Prioritizationof water management under climate change and urbanizationusing multi-criteria decision making methods,” Hydrology andEarth System Sciences, vol. 16, no. 3, pp. 801–814, 2012.
[5] E. Borgogno-Mondino, G. Fabietti, and F. Ajmone-Marsan,“Soil quality and landscape metrics as driving factors ina multi-criteria GIS procedure for peri-urban land use
[6] F. F. Dong, Y. Liu, H. Su, Z. Y. Liang, R. Zou, and H. C. Guo,“Uncertainty-based multi-objective decision making withhierarchical reliability analysis under water resources andenvironmental constraints,” Water Resources Management,vol. 30, no. 2, pp. 805–822, 2016.
[7] S. Belton and T. S. Stewart, Multiple Criteria DecisionAnalysis: An Integrated Approach, Kluwer Academic Pub-lishers, Norwell, MA, USA, 2002.
[8] J. Malczewski, GIS and Multicriteria Decision Analysis, Wiley& Sons, Toronto, ON, Canada, 1999.
[9] T. L. Saaty, Fundamentals of Decision Making and Priority2eory, RWS Publications, Pittsburgh, PA, USA, 2000.
[10] C. Kahraman, Fuzzy Multi-Criteria Decision Making: 2eoryand Applications with Recent Developments, Springer, NewYork, NY, USA, 2008.
[11] G. A. Mendoza and H. Martins, “Multi-criteria decisionanalysis in natural resource management: a critical review ofmethods and new modelling paradigms,” Forest Ecology andManagement, vol. 230, no. 1–3, pp. 1–22, 2006.
[12] T. L. Saaty, “A scaling method for priorities in hierarchicalstructures,” Journal of Mathematical Psychology, vol. 15, no. 3,pp. 234–281, 1977.
[13] T. L. Saaty and J. M. Alexander, Conflict Resolution-theAnalytic Hierarchy Process, Praeger, New York, NY, USA,1989.
[14] T. L. Saaty, Multicriteria Decision Making, the Analytic Hi-erarchy Process: Planning, Priority Setting, Resource Alloca-tion, RWS Publications, New York, NY, USA, 1990.
[15] A. Montis, P. Toro, B. D. Franke, I. Omann, and S. Stagl,“Assessing the quality of different MCDA methods,” in Al-ternatives for Environmental Evaluation, M. Getzner,C. L. Spash, and S. Stagl, Eds., Taylor & Francis, New York,NY, USA, 2005.
[16] R. Greene, R. Devillers, J. E. Luther, and B. Eddy, “GIS-basedmultiple criteria decision analysis (MCDA),” GeographyCompass, vol. 5, no. 6, pp. 412–432, 2011.
[17] T. L. Saaty, “Absolute and relative measurement with theAHP; the most livable cities in the United States,” Socio-Economic Planning Sciences, vol. 20, no. 6, pp. 327–331, 1986.
[18] G. Dinardo, D. Levy, and B. Golden, “Using decision-analysisto manage Maryland river herring fishery—an application ofAHP,” Journal of Environmental Management, vol. 29, no. 2,pp. 193–213, 1989.
[19] G. Munda, ““Measuring sustainability”: a multi-criterionframework,” Environment, Development and Sustainability,vol. 7, no. 1, pp. 117–134, 2005.
[20] P. Klungboonkrong and M. A. P. Taylor, “A microcomputer-based-system for multi criteria environmental impacts eval-uation of urban road networks,” Computers, Environment andUrban Systems, vol. 22, no. 5, pp. 425–446, 1998.
[21] J. Solnes, “Environmental quality indexing of large industrialdevelopment alternatives using AHP,” Environmental ImpactAssessment Review, vol. 23, no. 3, pp. 283–303, 2003.
[22] S. K. Ghanem, “*e relationship between population and theenvironment and its impact on sustainable development inEgypt using a multi-equation model,” Environment, Devel-opment and Sustainability, vol. 20, no. 1, pp. 305–342, 2016.
[23] D. Cowen, “GIS versus CAD versus DBMS: what are thedifferences?,” Photogrammetric Engineering and RemoteSensing, vol. 54, no. 11, pp. 1551–1555, 1988.
[24] M. F. Vine, D. Degnan, and C. Hanchette, “Geographic in-formation systems: their use in environmental epidemiologic
6 Advances in Civil Engineering
research,” Environmental Health Perspectives, vol. 105, no. 6,pp. 598–605, 1997.
[25] F. C. Dai, C. F. Lee, and X. H. Zhang, “GIS-based geo-environmental evaluation for urban land-use planning:a case study,” Engineering Geology, vol. 61, no. 4, pp. 257–271,2001.
[26] S. Sener, E. Sener, B. Nas, and R. Karaguzel, “Combining AHPwith GIS for landfill site selection: a case study in the LakeBeysehir catchment area (Konya, Turkey),” Waste Manage-ment, vol. 30, no. 11, pp. 2037–2046, 2010.
[27] R. J. Tkach and S. P. Simonovic, “A new approach to multi-criteria decision making in water resources,” Journal ofGeographic Information and Decision Analysis, vol. 1, no. 1,pp. 25–43, 1997.
[28] F. Joerin, M. *eriault, and A. Musy, “Using GIS and out-ranking multicriteria analysis for land-use suitability assess-ment,” International Journal of Geographical InformationScience, vol. 15, no. 2, pp. 153–174, 2001.
[29] S. J. Carver, “Integrating multi-criteria evaluation with geo-graphical information systems,” International Journal ofGeographical Information systems, vol. 5, no. 3, pp. 321–339,1991.
[30] I. Linkov and E. Moberg, Multi-Criteria Decision Analysis:Environmental Applications and Case Studies, CRC Press,New York, NY, USA, 2012.
[31] W. E. Galloway and D. K. Hobday, Terrigenous Clastic De-positional Systems: Applications to Fossil Fuel and Ground-water Resources, Springer Verlag, Berlin, Germany, 2ndedition, 1996.
[32] J. J. Wang, H. P. Zhang, S. C. Tang, and Y. Liang, “Effects ofparticle size distribution on shear strength of accumulationsoil,” Journal of Geotechnical and Geoenvironmental Engi-neering, vol. 139, no. 11, pp. 1994–1997, 2013.
[33] D. Botteldooren, L. Dekoninck, and D. Gillis, “*e influenceof traffic noise on appreciation of the living quality ofa neighborhood,” International Journal of EnvironmentalResearch and Public Health, vol. 8, no. 3, pp. 777–798, 2011.
[34] F. Cetin, “Map of traffic noise in Denizli,” M.S. thesis,Celal Bayar University, Manisa, Turkey, 2010, in Turkish.
[35] B. Ulengin, F. Ulengin, and U. Guvenc, “A multidimensionalapproach to urban quality of life: the case of Istanbul,”European Journal of Operational Research, vol. 130, no. 2,pp. 361–374, 2001.
[36] I. Ozer, H. Kargi, and E. Akyol, Crime Map and Analysis ofDenizli City Center, Project No: 105G090, (GIS Group),TUBITAK, EGM, Ankara, Turkey, 2009, in Turkish.
