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Ecological Indicators xxx (2013) xxx–xxx

Contents lists available at ScienceDirect

Ecological Indicators

j o ur na l ho me page: www.elsev ier .com/ locate /eco l ind

sing multi-criteria analysis for the identification of spatial land-useonflicts in the Bucharest Metropolitan Area

ristian Ioan Ioja, Mihai Razvan Nita, Gabriel Ovidiu Vânau, Diana Andreea Onose ∗,thanasios Alexandru Gavrilidis

entre for Environmental Research and Impact Studies, University of Bucharest, N. Balcescu Boulevard, No. 1, Sector 1, 010041 Bucharest, Romania

r t i c l e i n f o

rticle history:eceived 22 April 2013eceived in revised form 25 July 2013ccepted 10 September 2013

eywords:ulti-criteria analysis

and-use conflictspatial indicators

a b s t r a c t

The appearance of land-use conflicts represents one of the expressions of the increasing human pressureon the environment, especially in complex metropolitan areas. We used a multi-criteria analysis appliedin the Bucharest Metropolitan Area to create a tool for integrating land-use conflicts into the strategiesfor territory planning at the metropolitan level. We selected ten main criteria for the analysis, dividedthem into two categories, i.e., spatial indicators and urban development indicators, and standardizedtheir values. Using the method of pair-wise comparison with an expert-opinion system, we determinedthe relative importance of each criterion in the form of a criteria weight. The spatial indicators revealhigh probabilities for land-use conflicts in the proximity of Bucharest (range: 7.19–52.48), whereas urban

rban development indicatorsucharest Metropolitan Area

development indicators show a scattered spatial distribution of land-use conflicts (range: 10.00–30.01).The variability of the total score for the spatial indicators is greater than that of the urban developmentindicators (standard error of 0.64 > 0.30, respectively). The total scores reveal local administrative unitscharacterized by a critical or high incidence of spatial-land use conflicts as well as their location in theBucharest Metropolitan Area. Further research should concentrate on improving the expert-opinion input

reve
for assigning weights and
. Introduction

Because indicators represent a form of conscious connection tohe environment (Golusin and Ivanovic, 2009) and the resiliencef nature (Passeri et al., 2013), society uses indicators to simplifyeality or for comparison with a normal or reference state (EEA,010). Indicators measure the characteristic values of perceivedealities (Kurtz et al., 2001) to produce information and describehenomena relative to other parameters (Antrop and Van Eetvelde,000; Caeiro et al., 2012; de Leeuw, 2002; Hasse and Lathrop, 2003).

In the context of the increasing human pressure on the envi-onment, especially in urban and metropolitan areas (Patroescut al., 2009), indicators represent instruments that can be used toeduce information gaps (Uuemaa et al., 2013), analyze environ-ental impacts (Ioja et al., 2007), compare scenarios (Petrov et al.,

Please cite this article in press as: Ioja, C.I., et al., Using multi-criteriaBucharest Metropolitan Area. Ecol. Indicat. (2013), http://dx.doi.org/1

011), communicate results to the public (Li et al., 2009) and aid inhe decision-making process (Jaeger et al., 2010).

∗ Corresponding author. Tel.: +40 213103872.E-mail addresses: [email protected] (C.I. Ioja),

[email protected] (M.R. Nita), gabi [email protected]. Vânau), [email protected], [email protected]. Onose), athanasios [email protected] (A.A. Gavrilidis).

470-160X/$ – see front matter © 2013 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.ecolind.2013.09.029

aling the potential for replication in other areas or on different subjects.© 2013 Elsevier Ltd. All rights reserved.

Indicators of land-use changes in metropolitan areas have var-ied over time, from informal methods to formal methods (i.e.,cost-benefit analysis or multi-criteria analysis) (Kamruzzaman andBaker, 2013). The chosen indicators should allow intuitive interpre-tation based on mathematical simplicity and modest data (Jaegeret al., 2010) and should be quantifiable, sensitive to changes in landcover, temporally and spatially explicit and scalable (Larondelle andHaase, 2013); they should be able to reflect not only the analyzedphenomena but also the particular needs and goals representedby the diversity in a chosen study area (Shen et al., 2011). Interna-tional institutions and organizations have developed their own setsof indicators for monitoring of environmental aspects, e.g., the coreset indicators of the European Environment Agency (EEA, 2011)or the sustainability indicators of the Organization for EconomicCo-operation and Development.

Urbanization promotes rapid social and economic development,but at the same time, it leads to many environmental problems(Li et al., 2009) that are becoming increasingly contentious. There-fore, the need exists for instruments aimed at better understandingand management of these issues (Asah et al., 2012). Frequently,these environmental problems lead to the emergence of environ-

analysis for the identification of spatial land-use conflicts in the0.1016/j.ecolind.2013.09.029

mental conflicts (Kamruzzaman and Baker, 2013) between variousresource owners and users.

One of the most important environmental conflict sources island-use changes (Sleeter et al., 2012) due to their effect on the

dx.doi.org/10.1016/j.ecolind.2013.09.029


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atural energy and material cycles of ecosystems (Tong et al., 2012),he local climate conditions, biodiversity, and water resources (Liut al., 2011). Conflicts over land uses represent a characteristic ofrban development, especially in the context of metropolitan areasPacione, 2013), which are confronted with a high degree of pres-ure from various developers (Patroescu et al., 2009).

As a result, conflicts emerge either between various land usesresidential vs. industrial or agricultural) or between different eco-omic or social groups (residents, farmers, developers, etc.) (Darlynd Torre, 2013). Land-use conflicts involve multiple parties whohoose terms that favor their respective positions (Shmueli, 2008)nd frequently exceed the community scale (Sze and Sovacool,013) to involve a range of societal actors, governmental bodies,on-governmental organizations, and private interests (Saarikoskit al., 2013).

The spatial component of land-use conflicts has shown a highanifestation in the last two decades in the countries of Eastern

urope, where the shift from a centralized planning system to anncontrolled urban development (Patroescu et al., 2009) has facili-ated the emergence of these types of conflicts in various forms andlaces.

Land-use conflicts are increased by the fact that decisions onrojects and developments for different land uses are still madeased on incomplete information (Uuemaa et al., 2013); ideally,hese situations would require accurate and high-quality decision-

aking to resolve these complex issues (Kamruzzaman and Baker,013), therefore illustrating the need for adequate indicators toeasure the relevant driving forces and impact factors (Mubareka

nd Ehrlich, 2010).Authorities and planners should find a compromise to accom-

odate all of the land uses needed for a specific region (Helbront al., 2011) by controlling urban sprawl, preventing environmentalegradation and addressing the difficulties posed by transport andhe provision of public services (Darly and Torre, 2013; Pacione,013; Koschke et al., 2012). At the same time, these authorities andlanners must continue to provide the minimum amount of build-ble land in response to demands for housing and services in ruralreas (Darly and Torre, 2013; Patroescu et al., 2011) and maintainhe balance between current and future needs (Sze and Sovacool,013; Koschke et al., 2012). The large volume of spatial data that

ends geographical expression to the economic, social, cultural andcological aspects of society (Jeong et al., 2013) requires analysisethods that will integrate these aspects according to their impact

n the final outcome (Saaty, 1990).Multi-criteria analysis (MCA) is one method that can be used

n spatial planning to aid decision-makers in exploring and solvingultiple and complex problems (Jeong et al., 2013). The MCA rep-

esents decision-making analysis based on decision-science theoryhat is able to quantitatively evaluate alternatives by taking intoccount different perspectives and priorities to produce a commonutput (Barfod et al., 2011; Convertino et al., 2013). Choosing theCA algorithm that best fits the research problem is challenging,

owever, because the subjective scaling of the clear quantitativeumbers needed for pair-wise comparison of elements in the sameierarchy may lead to losses in accuracy (Kowalski et al., 2009).

Various studies (Jeong et al., 2013; Koschke et al., 2012;onvertino et al., 2013) have used MCA to assess the spatial distri-ution of environmental problems, whereas others have combinedhe MCA with cost-benefit analysis (Barfod et al., 2011; Gühnemannt al., 2012). All MCA analyses have a common pattern: define thelternatives to be ranked, identify the criteria that will influencehe outcome, assign “weights” to the criteria and normalize them,


nd determine the final values (Convertino et al., 2013). Uncer-ainties exist with respect to the impact levels and weights, butn most cases, neither the time nor resources exist to follow the fullonstruction of the model (Gühnemann et al., 2012).

PRESStors xxx (2013) xxx–xxx

The assessment criteria are chosen to a greater or lesser extentby how well they relate to the functional and livable dimensionsof land uses (Sze and Sovacool, 2013; Gühnemann et al., 2012).The MCA is employed in land-use conflicts analysis to classify therisk levels of significant impacts on the affected area (Helbron et al.,2011) in the establishment of public policy contexts (Kowalski et al.,2009) or as applied to conflict resolution (Kamruzzaman and Baker,2013).

Spatial conflicts have a high incidence in the BucharestMetropolitan Area due to this region’s administrative hetero-geneity (98 local administrative units from five counties with areduced degree of law enforcement by local administrations) andits dynamic characteristics (increasing proportion of constructedsurfaces, appearance of environmental degradation sources, modi-fication of functional areas, reduction of green areas, fragmentationof properties) specific to the Eastern European countries (Zolin,2007; Ioja and Tudor, 2012).

The current study develops a methodology for identifying areasprone to spatial land-use conflicts by focusing on a post-communistarea in which phenomena such as the abandonment of agricul-tural activities, increasing attractiveness for residential areas andchanges in the profile of industrial activities have a large incidence.

The aim of our study is to provide a tool for integrating land-useconflicts into strategies of territory planning at the metropolitanlevel.

The main objectives of our paper are the following: (a) toderive a set of indicators with spatial distributions that are use-ful for analyzing the spatial land-use conflicts in the BucharestMetropolitan Area and (b) to integrate these indicators into a multi-criteria assessment that will allow (c) the spatial identification ofareas characterized by a high incidence of land-use conflicts at themetropolitan level.

2. Methodology

2.1. Study area

The Bucharest Metropolitan Area (Fig. 1) is situated in the south-eastern region of Romania and contains 98 local administrativeunits (cities and communes corresponding to EU NUTS 5) includedin five counties (Nita, 2012) with a total surface of 5080 km2. Weremoved Bucharest, the capital city of Romania from our studyarea, because its characteristics and range of issues are of a com-pletely different type (urban phenomena) than the remainder ofthe metropolitan area (rural side mixed with urban functions). Theland use is mainly agricultural (76.8%), with the built-up environ-ment representing only 4.65% of the metropolitan area’s total land(Patroescu et al., 2011).

The dominant relief is characterized by low elevation plainsand river floodplains (Patroescu et al., 2009). The main rivers aretributaries of the Danube (which represents the southern limit ofthe metropolitan area and the border to Bulgaria). Forests oncecovered most of the study area, which is fragmented by lakesand rivers (4.9% is covered by aquatic surfaces), but centuries ofhuman activities (mainly agricultural) have reduced the foreststo a small proportion (10.5% covered by forests) (Patroescu et al.,2011). The study area has a total population of 571,315 inhabi-tants, but numerous inhabitants of Bucharest have second homesin this area that are inhabited for a smaller or larger period of time(Patroescu et al., 2009). The average population density is 111.5inhabitants/km2 (Rey et al., 2007) with notable territorial variations


(range: 21–1010).The changes in land properties after 1989 (shift from public

to private owned lands) has increased the pressure of land-usechanges according to individual purpose (Golusin and Ivanovic,


ARTICLE IN PRESSG ModelECOIND-1686; No. of Pages 10

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009), regardless of the public interests, impacts on productivitynd the efficiency of the surrounding areas (Patroescu et al., 2009).hese new areas have lost viability and suffered processes of aban-onment or losses of productivity (Ioja et al., 2011). Many landses have appeared in areas that were not able support them, andxternal functions (by definition) have entered the built-up areasf human settlements (Nita, 2012).

We obtained statistical data for several indicators (accessibilityf the inhabitants to public infrastructure) from the National Insti-ute of Statistics and City Halls of the Local Administrative Unitsf the metropolitan area and extracted demographic data from the992 and 2011 national census. Spatial data for the dynamics ofesidential areas were obtained from the analysis of topograph-cal maps (at a scale of 1:25,000) published in 1977 and aerialmages from 2008 using the ArcGIS 9.3 software from ESRI. Webtained data on the surface areas of agricultural land, built-upreas and semi-natural surfaces that provide ecological servicesrom a combination of sources, including aerial images and theocality’s statistical charts.

.2. Selection of indicators

We based our selection of indicators on their relevance to thebjectives of the study and the availability of data. Based on thetatistical criteria, we selected ten indicators (Table 1) in two cat-gories: spatial indicators and urban development indicators. Ourndicators are intended to assess the potential for the appearancef land-use conflict and not to assess the current distribution ofand-use conflicts.

Spatial indicators (X1–X4) express the direct relationshipetween the presence and absence of certain land uses (agri-


ultural, forestry, aquatic, built-up) at the metropolitan level.gricultural surfaces account for the highest percent (76.8%) among

he land uses in the Bucharest Metropolitan Area and represent aain direction for the development of built-up areas. Residential

ropolitan Area.

surfaces represent the function with the highest vulnerability toland-use conflicts and have significant importance for quality oflife. Semi-natural surfaces, in addition to providing much-neededecological services (air cleaning, climate regulation, water manage-ment), also represent buffer areas between potential conflictingland-uses.

Urban development indicators (Y1–Y6) are an indirect expres-sion of the pressure on or needs of the population from each localadministrative unit for certain elements of public infrastructure.Population tendencies and density are indicators of the magnitudeof the conflicts and the potentially affected inhabitants, whereasaging expresses the dimension of a sensible age group that is amongthe first affected by the negative externalities of land-use conflicts.Aging also expresses the resilience of the inhabitants of certainlocalities to changes. By their presence, the indicators of publicinfrastructure (water, gas, sewage) display a high potential forurban development because areas that benefit from these infras-tructures are preferred by developers for their planned projects.

Scores of indicators with different measurement scales (percent,inhabitants/surface, rate of growth) cannot be compared directlyand must be standardized to a dimensionless value (Sudhakaranet al., 2013). We standardized the data using Mathematical Pro-gramming (Munier, 2004) via transformation to percent in aprocess that considers the worst value equal to 1 (the minimumdata value for indicators X1, X3, Y4, Y5, Y6 and the maximum datavalue for indicators X2, X4, Y1, Y2, Y3) and the best value equal to100. The standardized values were determined based on formulas(1) and (2):

f (Z) = 1 − 100max − min

∗ Z + 100 ∗ max − 1 ∗ minmax − min

(1)


if maximum represents the worst value, and

f (Z) = 100 − 1max − min

∗ Z + 1 ∗ max − 100 ∗ minmax − min

(2)



4 C.I. Ioja et al. / Ecological Indicators xxx (2013) xxx–xxx

Table 1Selected indicators used in the analysis.

Categories of indicators Indicator code Indicator name Worst situationexpressed by

Explanation

Spatial indicators

X1 Agricultural land Minimum value Percent of agricultural areas from the totalsurface in 2008

X2 Built-up areas Maximum value Percent of built-up areas from the total surfacein 2008

X3 Semi-natural surfacesproviding ecologicalservices

Minimum value Percent of forests and water bodies from thetotal surface in 2008

X4 Residential areatendency

Maximum value Rate of growth for the surface of residentialareas between 1977 and 2008

Indicators of urbandevelopment

Y1 Population tendency Maximum value Rate of growth for the number of inhabitantsbetween 1992 and 2011

Y2 Population density Maximum value Density of population for each locality in 2011Y3 Aging Maximum value Percent of elderly population (age above 60) in

2011Y4 Sewage Minimum value Percent of inhabitants with access to public

sewage systems in 2008Y5 Water Minimum value Percent of inhabitants with access to the public

imoa

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Fu

Y6 Gas

f minimum represents the worst value. In these equations,ax = the maximum value of a data set, min = the minimum value

f a data set, and Z = the value of indicators Xi and Yi for each localdministrative unit.

.3. Multi-criteria analysis

The identification of areas with spatial land-use conflicts was


onducted based on a multi-criteria analysis (MCA) (Beinat andijkamp, 1998; Munier, 2004). The general outline of the method-logy can be described by the following stages (Fig. 2), similar tohose of other studies (Jeong et al., 2013):

ig. 2. Flow chart of the methodology for mapping the spatial distribution of land-se conflicts.

water network in 2008Minimum value Percent of inhabitants with access to the public

gas distribution network in 2008

- Constructing a GIS database containing all of the spatial informa-tion on the Bucharest Metropolitan Area and connecting it to thedata available for each local administrative unit.

- Selecting the representative indicators and transforming theminto elements of the multi-criteria analysis.

- Using a method of pair-wise comparisons to determine the rela-tive importance of each criteria in the form of a criteria weight.

- Aggregating the data with the criteria weights and obtaining thescores of each area.

- Spatially representing the distribution of the scores obtained foreach local administrative unit.

For the analysis, we considered 10 main criteria as defined aboveand 98 cases corresponding to the local administrative units fromthe Bucharest Metropolitan Area. We selected the criteria for con-sistency, transparency and ease of understanding as required in aMCA analysis (Gühnemann et al., 2012).

Using the Analytical Hierarchy Process (Saaty, 1990), we estab-lished the weights of each of the 10 criteria (gi) by considering thesum of weights equal to 1 for the purpose of aggregating them into asingle overall value and producing a ranking. The weights reflect theimportance of each criteria and were assigned based on a pair-wisecomparisons (Table 2), although the weight value defined for eachone can still be considered arbitrary (Kong et al., 2012; Gühnemannet al., 2012). We established the weights of each criterion based onan expert opinion applied by the authors of the present study.

By multiplying the weights of each criterion with the standard-ized values, we obtained ten partial scores. We obtained the finalscore for each local administrative unit by aggregating the partialscores with the aid of formula (3). The overall score is representedby the weighted average of its scores on all of the criteria.

S =10∑

i=1

(gi ∗ f (Zi)) (3)

As a result, the partial and total scores can vary between 1 and100. In the final stage, we represented the score for each criteria


and the total scores on maps of the Bucharest Metropolitan Areasusing a division of classes (Adler et al., 2010; Dixon et al., 2009)to express differences between the magnitudes of spatial land-useconflicts.




Table 2Relative importance of pair-wise comparisons and numerical rates – after Jeong et al. (2013).

More important intensity Definition Less important intensity

1 Equal importance or preference 12 More or less equal to moderate importance or preference 1/23 More or less moderate importance or preference 1/34 More or less moderate to strong importance or preference 1/45 More or less strong importance or preference 1/56 More or less strong to very strong importance or preference 1/67 More or less very strong importance or preference 1/78 More or less very to extremely stro9 More or less extreme importance o

Table 3Statistical parameters of the raw data and intermediate standardized values.

Indicator Raw data Intermediatestandardized values

Average Max Min St. dev. Average St. dev.

X1 76.42 93.60 34.71 13.23 29.88 22.24X2 6.10 42.39 1.28 6.31 12.62 15.19X3 0.11 0.43 0.00 0.10 75.05 22.14X4 55.72 433.50 −50.45 50.65 22.72 10.36Y1 −10.11 151.47 −64.09 24.53 25.79 11.27Y2 150.59 1009.94 21.38 155.33 13.94 15.56Y3 24.73 41.65 13.22 6.79 41.08 23.65Y6 16.83 74.50 0.08 17.04 77.72 22.67

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Y7 7.03 105.00 0.00 13.61 93.37 12.83Y8 13.81 129.90 0.00 24.78 89.47 18.89

. Results

The differences between the values of the raw data for each indi-ator were significant (Table 3), with certain of them showing apread of values (Y2 – range of 21.38–1009.94, ±155.33) while oth-rs presented a more concentrated pattern (X3 – range of 0–0.43,0.10). Using the standardization method, we normalized the val-es to a common level (all standard deviations of the intermediatealues ranged in the 10.36–23.65 interval).

We obtained the weights for each criterion from the pair-ise comparisons conducted by each author of the present study

Table 4). It is notable that although the number of spatial indi-ators is smaller than that of urban development indicators, theirotal weight is larger (0.53). Indicators with the highest weightsend to represent residential surfaces (X4 – 0.19), the tendency ofopulation (Y1 – 0.16) and the total surface of built up areas (X2 –.15). The lowest weights were assigned to indicators representinghe infrastructure development (Y4–Y6). The sensitivity analysisn the expert weighting process has been assessed by constructingcatter plots of the output variables against a randomly selectednput variable. The scatter plots revealed reasonable results across


he analysis, and no significant differences were recorded.We aggregated the weights of each indicator with the inter-

ediated standardized values, thus obtaining the scores for each

able 4eights of the selected indicators.

Indicator Weight Indicator Weight

X1 0.13 Y1 0.16X2 0.15 Y2 0.14X3 0.06 Y3 0.05X4 0.19 Y4 0.04Total weight ofspatial indicators

0.53 Y5 0.04Y6 0.04Total weight of urbandevelopmentindicators

0.47

ng importance or preference 1/8r preference 1/9

criterion and for each local administrative unit from the BucharestMetropolitan Area.

The spatial indicators (Fig. 3) show a relatively similar distribu-tion in the metropolitan area, with certain areas of concentrationsaccording to the characteristics of each indicator. Indicator X1 –agricultural land displays heterogeneous values in the metropoli-tan area, with the lowest values found in the eastern part. IndicatorX2 – built-up areas shows the highest values concentrated inlocalities situated in close proximity to Bucharest, and interme-diate values in certain regional poles of development. IndicatorX3 – semi-natural surfaces providing ecological services shows areversed homogenous distribution, with the majority of values dis-tributed to the worst-case scenarios due to the lack of both forestsand water bodies and with low values corresponding to locali-ties from the north and western regions characterized by largeforest areas and water bodies. Indicator X4 – residential areas ten-dency presents a dominance of two medium classes of values, bothcharacterizing increases in the surface of residential areas in therespective localities.

The total aggregation of spatial indicators (Fig. 4) shows locali-ties with a high probability of land-use conflicts incidence situatedparticularly in proximity to Bucharest and in local administrativeunits that have a legal urban status. The scores range between 7.19(lowest values in the eastern portion of the metropolitan area) and52.48 (Voluntari, which neighbors Bucharest in the northeast), withfour other local administrative units recording scores between 25and 50 (all small cities situated in proximity to Bucharest).

The urban development indicators (Fig. 5) vary correspondingly:Indicator Y1 – population tendency and Indicator Y2 – popula-

tion density show a small number of localities with high values forthe worst situation (characterizing localities that suffered impor-tant developments of residential areas in the past 20 years) whilethe others recorded medium values. Indicator Y3 – aging showsa concentration of high values in the eastern portion of themetropolitan area, and smaller values are recorded in the cen-ter. All of the infrastructure accessibility indicators, i.e., IndicatorY4 – sewage, Indicator Y5 – water and Indicator Y6 – gas, showa reversed homogenous distribution, with the majority of valuesdistributed to the worst situation case, and only a small number oflocal administrative units present the best scenario.

The total aggregation of urban development indicators (Fig. 6) inthe Bucharest Metropolitan Area reveals a scattered distribution ofa small number of local administrative units characterized by theexistence of spatial land-use conflicts. The scores range between10.00 (Joita) and 30.01 (Chitila), with the small values located in theperiphery of the metropolitan area, and high values characterizinglocalities that represent poles of development.

The variability of the total score for the spatial indicators isgreater than that of the urban development indicators (standard


error 0.64 > 0.30). The total aggregation of indicators characterizingland-use conflicts in the Bucharest Metropolitan Area are presentedin Fig. 7 with scores ranging between 17.53 (Gurbanesti in theeastern portion of the metropolitan area) and 76.01 (Voluntari in


Please cite this article in press as: Ioja, C.I., et al., Using multi-criteria analysis for the identification of spatial land-use conflicts in theBucharest Metropolitan Area. Ecol. Indicat. (2013), http://dx.doi.org/10.1016/j.ecolind.2013.09.029



Fig. 3. Scores of the spatial indicators in the Bucharest Metropolitan Area.

Fig. 4. Aggregated scores of spatial indicators in the Bucharest Metropolitan Area.




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Fig. 5. Scores of the urban development

he north-eastern area of Bucharest), and small values character-zing areas with a high rural degree. One local administrative unitxhibits a value greater 75 (Voluntari) and three present a valuebove 50 (Bragadiru, Chiajna and Chitila); all are small cities locatedn proximity to Bucharest.


. Discussion

The distribution of scores for the spatial indicators inhe Bucharest Metropolitan Area can be explained using the

tors in the Bucharest Metropolitan Area.

characteristics of the environment and socio-economic aspects.Agricultural lands (X1) show heterogeneous scores that directlyrelated to the total surface of the local administrative areas andtheir position in the metropolitan area, with peripheral and largelocalities showing an availability of agricultural lands for furtherdevelopment (Golusin and Ivanovic, 2009) and thus mitigating the


spatial conflict role of other land uses. Built-up areas (X2) and thetendency toward residential areas (X4) have a direct connectionto the position of the local administrative unit in the metropolitanarea. High values characterize areas in close proximity to Bucharest




pmen

adni

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Fig. 6. Multi-criteria assessment of urban develo

nd the northern area, which is a pole of growth for residentialevelopment (Nita, 2012) due to the large proportion of semi-atural surfaces providing ecological services (as expressed by

ndicator X3).Population tendency (Y1) shows an evolution similar to that of

he residential areas (X4), whereas population density (Y2) is rel-


vant for local administrative units with small surfaces (generallyrban) that are situated in proximity to Bucharest. Aging (Y3) and

ndicators of the accessibility of population to public infrastructurei.e., sewage systems (Y4), drinking water (Y5) and natural gas (Y6))

Fig. 7. Multi-criteria assessment of total indicators of spatial

t indicators in the Bucharest Metropolitan Area.

express the rural degree of the metropolitan area, especially in itseastern and peripheral areas. The lack of such basic infrastructureis a situation that characterizes a large proportion of rural areas inRomania (Rey et al., 2007).

The total aggregation of spatial indicators allows the iden-tification of spatial land-use conflicts in two categories of


local administrative units: urban areas situated in proximity toBucharest (Otopeni, Buftea, Chiajna, Voluntari) and those charac-terized by elements of attractiveness for developers (Snagov). Thetotal aggregation of urban development indicators follows the same

land-use conflicts in the Bucharest Metropolitan Area.


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attern as the spatial indicators but at a smaller scale. The differenceetween the standard error of the spatial indicators (0.64) and therban development indicators (0.30) could indicate the uncertaintyf the comparison and might require a refinement of the indicatorsncluded in the analysis. The total scores reveal local administra-ive units characterized by a critical (Voluntari) or high (Bragadiru,hitila, Chiajna) incidence of spatial-land use conflicts.

The identification of spatial land-use conflicts allows managersnd planners to promote sustainable use of lands at both the localnd the regional levels (Li et al., 2009). When analyzing the locationf new large-scale projects (public or private,) the decision-makersust be aware of where unbalanced situations already exist due to

onflicting land-uses and where there are reserves of space with-ut such problems. In addition, because conflicting land uses ofteniminish the quality of life for inhabitants, any reduction in theiristribution can help correct the uneven distribution of environ-ental conditions between poor and affluent residents (Romero

t al., 2012) and between the resident population and real-estateevelopers due to differences in purchasing power between the twoategories. However, this process does not represent an absoluteolution because traditional planning should shift to an appropri-te management of land that can integrate both public participationnd private interest (Cerreta et al., 2012).

The use of MCA in analyzing spatial land-use conflicts in theucharest Metropolitan Area has shown that even if every land useonflict has its own particularities (Sze and Sovacool, 2013), ourriteria can provide instruments (Jeong et al., 2013) to land plan-ers and administration for decision-making processes aimed atoordinated urban and rural development between metropolitanrban and rural areas (Shen et al., 2012). The simplicity and speedf calculation of the MCA indicates that the method is effective inesponse to rapid land-use changes (Uuemaa et al., 2013).

The method identifies spatial land-use conflicts at theetropolitan level and the specific LAU involved or at the statis-

ical level for which the administration can provide the necessaryata. Additional in-depth analysis could be performed, providedhat each LAU can offer more spatially detailed data.

It remains to be proved whether this method is convenient forhe comparison of different regions and phenomena because it gen-rally involves area-proportionate additive measures (Jaeger et al.,010). Further attention should be focused on the structure of theeight sets (Gühnemann et al., 2012) because they could have aajor impact on the final outcome of the study. Additionally, theethod does not allow a clear differentiation of the nature of con-

icts (Darly and Torre, 2013); in most cases, a variation of land-useonflicts would be expected for such a large area.

. Conclusions

The presented sets of indicators of spatial land-use conflicts areseful instruments for improving planning policies and strategies,

ncreasing the social awareness of the population and providingirect economic projections for developers. Their integration into

multi-criteria analysis allowed us to map the areas character-zed by a high incidence of land-use conflicts, which can be furthersed for sustainable development of the metropolitan area. These of multi-criteria analysis in identifying the areas of land-useonflicts can be of real help to policy makers and authorities in theecision-making process, especially in Eastern European countries,ecause this methodology can represent a starting point for thesessessments.


The main limitations of the methods lie in the availability ofata required for the construction of each criterion. In addition,o principles or guidelines are currently available to select theost representative criteria for the characterization of land-use

PRESStors xxx (2013) xxx–xxx 9

conflicts; therefore, the authors chose them according to their ownexpertise and the availability of data. Improvements should be pur-sued via organizing expert-opinion sessions with specialists fromdifferent fields of study. Additionally, further research is necessaryto analyze whether such a multi-criteria assessment can be appliedbased on other criteria or on other study areas.

Acknowledgements

This work was supported by a grant funded by the Min-istry of Education and Research through the Young ResearchTeams project PN-II-RU-TE-2011-3-0285. Modeling of environ-mental impact induced by typologies of incompatible land uses inhuman settlements.

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