Improving spatial decision support systems · improving spatial decision support systems articles 05-3 or by hardware capacities increase makes it relatively easy the update of territory

IMPROVING SPATIAL DECISION SUPPORTSYSTEMSMETHODOLOGICAL DEVELOPMENTS FOR NATURAL RESOURCES ANDLAND MANAGEMENTHedia Chakroun, Researcher, Institut National de Recherche en Génie Rural, Eaux et Forêts (Tunisia)Correspondance to Hedia Chakroun: [email protected] Goze Bertin Benie, Centre d’Application et de Recherche en Télédétection (Canada)Correspondance to Goze Bertin Benie: [email protected]

The use of geographic information systems (GIS) in the past two decades helped in formulating and solvingspatial decision-making problems. In spite of their huge capacities in the acquisition and the storage ofspatial data, GIS have some limits when it is a matter of solving semi-structured problems that representmost real-world spatial decision cases. The improvement of GIS analytic capacities can provide supportrequired in multiple decision-making phases. We use advanced spatial analysis techniques applied toraster data representing a set of constraints that may be encountered in a land management project. Digitalmaps and a digital elevation model (DEM) have been used to produce the constraint spatial database forthe case study. Each spatial feature had been subject to an evaluation process and a utility value wasgiven to represent its tolerance to the management project according to the constraints identified previously.Results obtained from this methodology have been compared to conventional cases of suitability mappingfrom the original set of constraint maps. Results show that suitability maps for the management projectderived from this study represent multiple scenarios leading to the improvement of the design and choicephases of decision-making process.

1. INTRODUCTIONNowadays, land managers and decision makers are daily faced with problems related to agrowing development under a constant diminution of resources; they are frequently encounteringplanning issues characterized by the complexity of interactions between environmental and socio-economic systems. This complexity is essentially related to the diversity of decision alternativesand their variability in the space, the diversity of criteria nature (some may be qualitative whileothers may be quantitative), and the fact that decisions are often surrounded by uncertainty. Forthese reasons, the availability of powerful tools should help to afford a flexible problem-solvingenvironment in which problems can be explored, understood and solved under the multipleconflicting objectives. In the past two decades, the use of spatial information technologies, espe-cially remote sensing (RS) and geographic information systems (GIS) had widely assist managersin their work. However, more technical expertise is required to improve the decision-makingprocess. Indeed, in spite their huge capacities in the acquisition and storage of spatial data, GIShave some limits when it is a matter of solving most real-world spatial decision problems. Hence,improvement of spatial information integration in decision-making support is becoming a necessityunder the multiple challenges facing decision makers who are supposed to present efficientsolutions under many forms of pressure like demographic and urban growth and scarceness ofearth resources. This is the principal aim of the present work where we explore the effects ofinterfacing spatial analysis tools within the decision- making environment. We begin by a review

ARTICLES

APPLIED GIS, VOLUME 1, NUMBER 1, 2005 MONASH UNIVERSITY EPRESS 05-1

of decision-making process in a spatial context. Next, we present a methodological frameworkdesigned to improve the decision making process in a land planning project.

2. DSS AND SDSS: A REVIEW

2.1. DEFINITIONS

Decision Support systems (DSS) have been developed in the early sixties for business applications(corporate strategic planning, scheduling of operations, etc.). Simon (1960) suggests that anydecision-making process can be structured into three major phases: intelligence, design and choice.According to Malczewski (1997), the “Intelligence phase” involves searching or scanning theenvironment for conditions calling for decisions. The “design phase” involves inventing, devel-oping, and analyzing a set of possible decision alternatives for the problem identified in the intel-ligence phase, and the “Choice phase” involves selecting a particular decision alternat-ive from those avail able; each alternative is eval uated and ana lyzed in re lation to oth ers interms of particular decision rules. There are interactions between the phases of a decision makingprocess as it will be explained later (section 4.1).

A Spatial Decision Support System (SDSS) has the main characteristics of a DSS. In addition,it should be adapted to the specificity of spatial data. Densham et al. (1994) defined a SDSS asa geoprocessing system designed to support the decision research process for complex spatialproblems. SDSS are also defined as conceptual framework that assists decision makers in solvingcomplex spatial problems. Hence, a SDSS has to provide input for spatial data and to allowstorage of complex structures common in spatial data. This kind of system should also includeanalytical techniques that are unique to spatial analysis and produce outputs in the form of maps,reports, charts and other spatial forms.

2.2. PRINCIPAL COMPONENTS OF SDSS

Many authors have given definitions to what a SDSS should contain. These definitions may begathered into three components represented by Figure 1.

The Data Base Management System (DBMS) integrating functions to manage spatial andattribute data. In a spatial context, the DBMS is inherent to the Geographic Information System(GIS).

The Model Base Management System (MBMS) containing functions to manage models. Theefficiency of this component is related to the analytic capabilities of the SDSS. The linkage betweenthe DBMS (in the GIS) and the MBMS may be a weak coupling based on the exchange of databetween DBMS and MBMS; or a strong coupling where models are embedded within GIS orvice versa (Batty 1995).

The Dialog Generation and Management System (DGMS) that manages the interface betweenthe user and the rest of the system.

One of the most important characteristics of a SDSS is to support users while solving semi-structured or ill-structured spatial decision problems (Figure 1). According to Simon (1960),decision problems fall on a continuum ranging from completely structured to unstructured de-cisions: the first ones occur when the decision-making problem can be structured either by thedecision maker or on the basis of relevant theory, whereas the second ones must be solved bythe decision maker without any assistance from a computer (they are non programmable).

IMPROVING SPATIAL DECISION SUPPORT SYSTEMS ARTICLES05-2

Most real-world spatial decision problems, are somewhere between these two extreme cases:they can be solved partially by the use of computer programs and the final solution is generallygiven by the intervention of the user or from a negotiation of a group of actors; this is the areawhere the SDSS concept has major applications.

Figure 1. Principal components of SDSSAdapted from Malczewski (1997)

3. CHALLENGES IN SOLVING SPATIAL DECISION PROBLEMSImpact studies for infrastructure projects such as dams or mountain lakes, the determination ofsuitable areas for waste deposits or the selection of the best network for water alimentation orpower lines are examples of projects where environmental, technical and economical constraintscould be advantageously integrated into a GIS by means of appropriate integration models. Themultiple actors implicated in such projects use their expertise and knowledge to affect priorities,defined also as scores, to spatial entities that are subject to an alteration by the project or thatrepresent a potential area to improve the project. For instance, when it is a matter of choosingthe best site for a culture, the terrain slope is an important factor to consider in the study; thusthe agriculture engineer uses previous knowledge to determine a kind of a mathematical function-relating slope to a suitability factor. This process corresponds to the elaboration of evaluationmodels whose formulation is necessary in the SDSS.

The use of spatial information technologies affords large possibilities in formulating evaluationmodels inside the SDSS. Many factors are improving the capacities of using GIS as principalcomponents in SDSS. Improvement of data capacities storage either by compression techniques

IMPROVING SPATIAL DECISION SUPPORT SYSTEMS ARTICLES 05-3

or by hardware capacities increase makes it relatively easy the update of territory information.Else, the availability of high-resolution satellite images makes them a reference tool in updatingland use and landscape evolution. These factors combined with accessibility to huge amounts ofdata via the World Wide Web make spatial information available for the integration into landplanning and natural resources management. However, this is not sufficient to explore all thepossible solutions for a given problem under the diversity of constraints and the multiplicity ofinterest groups. For example, in the case of land management projects implicating numerousconstraints that may be represented by spatial layers, digital maps overlay, widely used for itssimple conceptual bases, may lead to very restricted suitable areas because of the binary rigidity(a spatial site is either accepted or rejected). Limits of commercial GIS are then easily highlightedwhen it is a matter of complex decision problems related to land management characterized bynumerous actors and points of view. Then, more research and development efforts should bemade to explore new tools to evaluate theoretical alternative scenarios for territory managementprojects and to improve the interaction among the members of the project (decision makers,community members, associations interested in the effects of the project, etc.).

In the next paragraph, we present an approach having as objective to provide tools for theuser to help him in the exploration of the space solution of his planning project.

4. METHODOLOGICAL APPROACHMost of land planning projects and natural resources management use land suitability assessmentwhose objectives are to maximize economic efficiency and to minimize environmental impacts.The general process of land suitability assessment may be divided into the following four steps:

1. identifying and mapping land use, environmental and technical impacts on separate maps;2. constructing several combinations of maps based on priorities determined by the evaluation

process;3. deriving suitability;4. making choice by decision maker(s).

4.1. FRAMEWORK DEVELOPMENT

The SDSS elaborated in the present study is given by Figure 2.The first step consists in gathering multi-thematic data representing principal constraints

identified by the project actors. These data may derive from existing spatial database (analogicaland numeric) or may be produced for the study. For example, remote sensing data, field surveyingand GPS technologies constitute multi-source data acquisition.

Studies, statistics and expertise reports describing the management problem representknowledge and information that are generally combined with spatial data in order to elaboratespatialized evaluation model that will help the analyst in the formulation and the resolution ofthe problem. All the expertise and the previous similar impact projects are gathered in knowledgebase. In the case study section, we will present a description of the evaluation model used forthe development of the project knowledge base.


Figure 2. General framework for SDSS in a planning management problem

The knowledge base elaboration consists in translating expertise and information concerningthe planning project into priorities or scores. The score is a numerical value resulting from anevaluation process (Voogd 1983); it may be a qualitative or a quantitative entity. In some cases,priorities are expressed in the form of mathematical functions called “utility functions”. Theelaboration of consistent database and flexible interface is an important step in the intelligencephase of the decision process because it allows the analyst to refine the definition of his problem’sprincipal components. Also, the capacity of the system to incorporate new data and updated in-formation is very important for the implementation of open SDSS.

In the design phase, the user needs to explore the solution space (the options available) byapplying multiple models inside the system to generate a series of feasible alternatives to hisproblem. At this stage, it is important to extract the potential solutions or alternative for solvingthe problem. Thus, we make use of spatial analytic integration tools, which were developed inprevious research in order to improve the design phase of the decision process.

4.2. CONTEXT-ORIENTED INTEGRATION OF MULTI-THEMATIC DATA

Our methodology has been basically inspired from the research trend calling to replace conven-tional perception of territory represented by multi-thematic layers overlaying available in allcommercial GIS. Thus, we made the following hypothesis: considering spatial context in theformulation of suitability model leads to the elaboration of more potential scenarios that affordmore feasible alternatives. We suggest a new concept of multi-thematic maps integration insidethe SDSS based on the development of a context-oriented analysis. In the following sections, wefirst give a general formalism of suitability spatialization process, next we illustrate the method-


ology by a simple computation example and finally we explain the adaptation of the frameworkto a Voronoi diagram structure.

4.2.1. FORMALISM OF SUITABILITY SPATIALIZATION

Let CI denote a constraint spatial feature map set, n is the number of distinct spatial elements inCI. Let UI denote the corresponding utility map: each input feature from CI set is associated witha utility value derived from a knowledge base produced by experts. For example, if CI set is aland-use map produced from a classified satellite image, then the number of classes is equal ton; each class is given a utility value that reflects its sensitivity to the planning project. Interactionamong these sets is symbolized by:CI -------(UFI)-----------> UI -----------------(SFI)-------> SIci----------(UFI)-------> ui = UF( ci) -----------(SFI)---------> si = SFI (ui)CI : Constraint map number Ici : a class feature belonging to the constraint map CIUFI : Utility function applied on constraint map CIUI : Utility map number Iui : utility value corresponding to ciSFI : Suitability function applied on UISI : Suitability Map number Isi : suitability value for class ci.

Utility function (UFI) is formulated according to the expertise stored in a knowledge base.Suitability function (SFI) translates utilities into suitability values producing a suitability map(SI) where the study area is classified from the least to the most suitable for the planning action.

Figure 3. Suitability modelisation using spatial analytic capacities


If the constraint map is in a raster format where X is a cell, X belongs to a unique constraintclass ci and has a unique utility value ui, thus it is possible to compute its suitability.

In Figure 3 we show two ways of integrating multi-thematic constraints maps:

1. the first model is a conventional one and it is based on an algebraic suitability computationmodel;

2. the second model is a regionalized model where context has been integrated in the suitabilitycomputation process.

Formulation of suitability function is expressed as the simple standardization of utility valuerelatively to all other utility values. For example, see Equation 1.

Equation 1.

If cell X is within a context where there is only m features classes (m < n), then we formulatemodel 2 suitability computation by Equation 2.

Equation 2.

In spatial management and planning problems, constraints are represented by several maps.If we have N constraint maps, the total suitability of an X cell is calculated as the average of allX suitability values. See Equation 3.

Equation 3.

A computation example is given by Figure 4.In this example, we consider a landuse map composed of four classes (forest, agriculture,

urban, recreation) as shown in Figure 4(a). For each class a utility value had been affected (Figure4(b)). The study area was divided into two parts called context A and context B (Figure 4(c)).We choose two cells XA and XB belonging to forest class, XA is within context A and XB iswithin context B. Suitability computation by models 1 and 2 highlights a different suitabilityvalue found by the context oriented model when compared to conventional model. This spatial-ization consists in the adaptation of utility functions to the context inside which they are beingapplied.


Figure 4. Computation example of suitability values by model 1 and model 2

4.2.2. SUITABILITY SPATIALIZATION IN VORONOI DIAGRAMS

The development of a context-oriented integration tool had been the object of research work inthe field of advanced spatial analysis methodologies (Chakroun et al. 1997; Chakroun et al. 2000and Chakroun et al. 2003). The summary of this work is as follows: we have considered thelocal neighbourhood defined inside a Voronoi structure (Voronoi, 1908) which is a neighbourhoodframework well adapted to raster and vector maps. See Figure 5.

Figure 5. Voronoi diagram

For more details about this choice justification, refer to Chakroun et al. (2003). The “context”illustrated in Figure 3 is then defined by the neighbouring structure of this diagram by overlayingit on the study area in order to subdivide it into contiguous regions called context. The Voronoidiagram structure offers many ways to consider the context:

• the context is the space within each polygon;• the context is the first order Voronoi diagram of a given polygon;• the context is the second order Voronoi diagram of a given polygon.


For each context, utility functions may be adapted and, consequently, derived suitabilitymaps are different from each other. The main implication of such spatialization is the definitionof locally utility functions adapted for each situation; as a consequence, for the same spatialfeature, its suitability is not only dependant on the absolute utility value but also on the contextwhere the feature belongs.

The study case presented in the next section explains how it is possible to explore the effectof the context-oriented modelisation of suitability and its implications on spatial decision process,especially the improvement of solution space exploration that is essential to accomplish in thechoice phase of a planning project.

5. CASE STUDY

The developed approach has been tested in the research of appropriate zones for power line im-plementation in a study area in southern Quebec (Canada). This is a typical spatial planningproblem where constraints represent spatial features sensitivity from environmental, technicaland economical points of view. The public institution responsible for the implementation ofpower lines made an impact study where the multiple effects of the project had been examined(Hydro-Quebec 1990). We use this study to apply the SDSS described above (Figure 3).

5.1. SPATIAL DATABASE DESIGN

Spatial features identified as the most sensitive to the project in the impact study have beengathered by categories; next a raster map was produced for each constraint.

Table 1 summarizes the maps, their categories and the utility values associated with eachspatial feature (explanation of utility determination is given in section 5.2). Landuse, agriculture,particular areas and urban maps were produced from the digitalization of existing paper maps.Relief and landscape constraint maps were derived from an existing digital elevation model(DEM). The slope of a terrain generates a technical constraint for the implementation of pylons.The DEM of the study site has been used to calculate the slope by applying derivative operatorsin the x and y directions and computing the magnitude of the resultant vector. The DEM wasalso used to elaborate a visibility map because it is important to determine the landscape alterationby the project. Computation of visibility was as follows: the highest points in the study area wereidentified, then we compute for each cell the number of points from which the cell may be seen.

Spatial resolution of constraint project raster maps was unified to 100x100 meters. We buildthe spatial database for the project in the raster format inside a GIS. Figure 6 shows the six rastermaps elaborated for the study.


Table 1. Principal components of constraint spatial database


Figure 6 (a–b). Raster maps used in the study


Figure 6 (c–d). Raster maps used in the study


Figure 6 (e–f). Raster maps used in the study


5.2. KNOWLEDGE BASE

The knowledge base allows the user to feed the SDSS with all the expertise concerning the project.As mentioned above, the existing “Hydro-Quebec” impact study was the model applied in thisresearch. The main purpose of this model was to characterize each spatial feature encounteredin the study region by a value reflecting its resistance to the power line project. Two steps helpedin the formulation of this resistance:

• the evaluation of project impact on the spatial feature on the basis of three impact levels(high, medium, low);

• the intrinsic value of each spatial feature based on the evaluation of its scarceness, its import-ance and the legislation. This results in five value levels.

An evaluation matrix relating impact levels and intrinsic values was elaborated by the experts;thus for each spatial feature, a resistance value has been affected on an ordinal scale.

We use this model in the present research by expressing resistance values into utility functionvalues shown in Figure 7. Since data are multi-thematic and represent a diversity of spatial featurescategories, we note that scales representing these data vary from nominal scale (landuse, agricul-ture, particular areas and urban maps) to ordinal scale (visibility) and cardinal scale (slope).

Figure 7. Utility functions applied elaborated from the evaluation process


Utility functions are expressed in terms of cost: a spatial feature that does not represent astrong resistance to the project obtains the least utlity values. Thus, our objective is to highlightspatial areas with the weakest utilities in all constraint maps because they represent the mostsuitable areas for the project.

5.3. SUITABILITY COMPUTATION

Models described in section 4.2 and represented in Figure 3 have been used to produce suitabilitymaps from the integration of constraint maps set. Model 1 (cell-based algebraic model) was appliedon utility maps by the way of normalization (Equation 1) and average suitability computation(Equation 3). Application of model 1 is done by means of layers algebraic combinations availablein all commercial GIS raster format. The application of model 2 designed as the cell-context-oriented integration in Voronoi diagram structure, takes advantage of a stand-alone “C” programintegrating multi-thematic raster maps by considering the contextual information in the calculationof suitability.

We briefly describe the basic stages of model 2 program (detailed algorithms of model 2 aredescribed in Chakroun et al. 2000 and Chakroun et al. 2003). The context is defined inside eachpolygon: first, categorical spatial features are identified, next suitability functions are computed.The algorithm was designed to check the homogeneity level inside the polygons. On the light ofthis test, polygons with high heterogeneity are divided and homogeneous polygons are mergedtogether.

We applied the developed algorithm to our case study: the plan of the six constraint mapswas originally partitioned into 1500 Voronoi polygons whose generators were chosen randomly.We made an algorithm configuration so that the user can choose to compute a suitability mapfor each partition.

Figure 8. Voronoi diagram segmentation of map resulting from the summation of the six utility maps


Figure 8 illustrates subdivision of the study area into polygons.Both model 1 and model 2 compute suitability values on a cell base. Next, inside each polygon,

an average suitability value is computed.

5.4. ANALYSIS

Generic suitability maps produced from model 1 and model 2 are elaborated for a 200 polygonspartition; their statistical distribution are illustrated by histograms of Figure 9.

Figure 9. Representation of model 1 and model 2 suitability variation

The main difference observed between the two charts is the dynamic of suitability valuesvariation in model 2 that is very important compared to model 1. The example given at the endof section 4.2.1. shows that for the same spatial feature, the context-oriented model led to twodifferent suitability values. This explains the wide range of possible suitability values frommodel 2 compared to model 1.

For comparison purpose between the two models, we have made a reclassification processof suitability values calculated by model 1 into four classes: very high, high, moderate and low.Thus, we may represent model 2 suitability values within these four classes. This representationis illustrated by Figure 10. This crossed histogram reflects the fact that for a single coarse suitab-


ility class resulting from model 1, the context-cell-based model allows its refinement into muchmore suitability classes.

Figure 10. Crossed histogram of model 2 suitability within model 1 suitability

Context-oriented suitability computation is more than a combination of all utility valuesfrom constraint maps, it contains also a supplement information which is a spatial informationreflecting the interaction of the cell with its neighborhood. Indeed, the knowledge base representedby the utility functions expression is being “spatialized” by the adaptation of suitability compu-tation to local context. In terms of scenario diversity, this result may be very interesting: the ri-gidity of simplistic algebraic layer combination is compensated by the integration of a contextualinformation. Concretely, this means that computation of spatial area suitability is being relatedto interactions between spatial features located in this area. Thus, we may obtain as many scen-arios of suitability as the partition space schemes since each partition represents a backgroundfor a new potential alternative that would not be identified if the contextual dimension were ig-nored.

5.5. IMPROVEMENT OF CHOICE PHASE

5.5.1. PLANNING MODEL: POWER LINE PATHS

The SDSS applied in the present study (Figure 3) takes advantage of the development of thespatial analytic integration tool described above. We showed in the previous section how thecontext-oriented suitability model leads to a wide variety of scenarios that improves the designphase of spatial decision process. As a consequence, the choice phase will also be improved becauseeach suitability scenario generates at least an alternative for the planning project. Indeed, thepotential number of alternatives may be higher than one since for each partition scheme, it ispossible to consider different levels of Voronoi neighbourhood (Figure 5). Thus, the analystshave to make a choice from a largest solution space.


In our case study, the objective is to determine the least cost power lines path from technical,environmental and economical points of views. For this purpose, we applied the suitability de-veloped approach in order to calculate impedance maps that are integrated in the research of theleast cost paths. The raster version of the shortest path algorithm (Dijkstra 1959) has been usedso that for each suitability map scenario, it is possible to determine a path and to make an eval-uation according to the constraints fixed in the beginning. The least cost-path model representshere the planning model in Figure 3. For each suitability scenario, it is possible to compute apower line path. Thus, a decision table known in multicriteria analysis systems may be integratedinto the SDSS to make the choice of the final solution more systematic and to help the analystin the feed back process (Figure 2). Indeed, feed back process is essential in a SDSS if we takeinto account the fact that ill-structured problems are generally subject partially or totally to ne-gotiation between the multiple actors.

5.5.2. INTEGRATION OF MULTICRITERIA EVALUATION PROCESS INTO SDSS

The potential solutions for power line paths are derived from the application of Dijkstra shortestpath algorithm applied on each suitability map. Thus, for the same partition of the study areainto Voronoi polygons, we may calculate two impedance maps, one for each model (model 1and model 2). Next, we make the evaluation of obtained paths seeking the determination of leastcost ones. To achieve this objective, we use a multicriteria decision making technique (MCDM).The elaboration of the use of this technique in SDSS is beyond the scope of this work. Manystudies have already dealt with this issue, especially the adaptation of MCDM to GIS context;see, for example Carver (1991), Pereira et al. (1993), Jankowski (1995), and Jankowski et al.(2001).

As it is well known, an MCDM process has three components:

• the definition and formulation of criteria;• the generation of possible choices or alternatives;• the evaluation of each alternative in the light of multiple criteria.

The last component needs the choice of an evaluation technique from a wide variety of existingtechniques (refer to Vincke, 1986).

The evaluation of paths obtained by different spatial partition should be done according tothe spatial criteria represented by original maps used as input and reflecting the essential spatialconstraints to the implementation of power lines. The other criterion that should be consideredis the monetary cost of the path, which is directly influenced by its length. Paths calculated fromeach partition of the plan maps are characterized by a score cost (S) resulting from their super-position to each one of the six raster maps ((S) equals the product of the spatial feature scorevalue by the area of the spatial feature crossed by the path) and the path length (L) that may beis expressed in length distance. The principal stages of the evaluation process are illustrated inthe flow chart of Figure 11.


Figure 11. Flowchart choice phase in a SDSS applied to power line implementation

5.5.2.1. DESCRIPTION OF AN MCDM TECHNIQUE: IDEAL POINT TECHNIQUE

The IP technique known as compromise programming is based on the comparison of alternativesto an ideal alternative by some measure of distance (Goicoechea et al. 1982). The ideal point isa hypothetic alternative with the least scores observed in all criteria. All available alternativesare ranked according to a multidimensional distance to the ideal point. Zeleny (1982) gave ageneral expression of such a distance: see Equation 4.


Equation 4.

E(I,J): effectiveness values corresponding to the standardized score cost or length of alternativeI for criterion J;E*(I,J): effectiveness value for the «ideal» alternativeWJ: weight of criterion JNC: number of criteria

E*(I,J) is the «ideal» alternative whose scores are the minimum ones observed from all thealternatives on every criterion. The p factor is an integer number, which determines the degreeof compensation between criteria. Pereira et al. (1993) give the following explanation for theeffect of p factor: for example, when p = 1, there is a total compensation between criteria, (adecrease of one unit on a criterion is totally compensated by an increase of one unit in anothercriterion). The more p increases, the less compensation is observed between factors. For a largevalue of p there is no compensation at all between criteria.

5.5.2.2 RESULTS AND ANALYSIS

We derive shortest paths from weight maps corresponding to 18 schemes of study area partitioninto Voronoi diagrams (190 to 20 polygons with a 10 polygons step). For each partition, twoweights maps are calculated from models 1 and 2. The effectiveness value (E(I,J) in Equation 4)for each alternative (path) is being calculated. Details of these computations are given in annex.

Since there is a compromise between the path score cost and its total length, we suggest tostudy three cases of criteria weight affectation (generally obtained by a consensus between themultiple actors of the project):

• Case 1: the cost in terms of score is weighted in the same way than the length of the path (inthis case, each spatial criteria has a weight equal to 0.5/6 and the length has also a weightequal to 0.5; total sum of weights equals to 1);

• Case 2: the spatial criteria have much more importance than the length (each spatial criterionhas a weight equals to 0.9/6 and the length has a weight equal to 0.1);

• Case 3: the length has much more importance than the raster maps (each raster map has aweight equals to 0.1/6 and the length has a weight equal to 0.9).

Standardized effectiveness values (table 3 in annex) have been used in the calculation of theparameters of the IP technique. The ideal point or alternative is determined from table 3 in annexas the least value observed in each criterion [E*(I,J) = (0; 0.463; 0.439; 0.515; 0.462; 0.436; 0)].We compute the distance dp for three critical values of factor p (p = 1, p = 2 and p = 10). Resultsare reported in table 4 in annex. Next, for each partition scheme, we calculate the differencebetween the distances dp for alternatives obtained with model 2 compared to alternatives obtainedwith model 1.


Figure 12. Distance difference to ideal point between alternatives obtained with model 2 and model 1

Figure 12 summarizes the results represented as cumulative area of the dp difference. Whenthe path length criterion is as important as the spatial criteria or more important, alternativesobtained by model 1 are costless than those obtained with model 2. This last model gives costlessalternatives when spatial constraints are highly weighted than the length. In this latter case, the


compensation effect introduced by the p factor is more perceptible: when there is total compens-ation between criteria (p = 1), most of alternatives resulting from model 2 are closer to the idealpoint than those resulting from model 1. As the compensation factor increases, the distance dif-ference between the two sets of alternatives decreases.

In summary, this multicriteria analysis shows two major results:The improvement of choice phase of decision process was verified since we have obtained

alternatives that are substantially different from those obtained without integration of contextualinformation.

Context-oriented suitability mapping led to more advantageous solutions (costless paths)when we give more importance to spatial criteria traducing environmental and technical constraintthan economic criteria (path length).

6. DISCUSSION AND CONCLUSIONThe principal scope of the present work was the development of a methodological approach toimprove decision-making process in land planning and natural resources management. We integ-rate tools elaborated in the field of advanced spatial analysis in a spatial decision support system.We showed how it is possible to interface these tools within the different stages of SDSS process.The developed methodology was applied to the problem of suitability assessment because manyplanning and resource management problems use this information support in negotiation betweenthe multiple actors and as a support for representing feasible solutions. The practical case studywas based on an impact study where multiple environmental, technical and economical constraintsare present. Combined with multi-thematic spatial data, these constraints were tarnsformed intospatial evaluation database used as input for suitability computations models. These models arethe principal component in the improvement of SDSS design and choice phases. Two major resultshave been obtained by the present research:

The elaboration of context-oriented suitability model characterized by the adaptation ofconventional algebraic models to the local context. This led to the improvement of the SDSSdesign phase by adapting utility functions to the local context where they are applied. Thus,numerous potential solutions have been added to the solution space; these solutions representthe bases of elaborating new potential scenarios for the planning or management studied project.

The context-oriented suitability applied to least cost power line research highlights moreadvantageous solutions than conventional suitability model. This had been proved by a multicri-teria evaluation process.

Although the huge possibilities offered by the developed approach, it is clear that the adapt-ation of utility functions to local context is highly affected by the definition of the context itself.For example, if we consider the first-order Voronoi diagram, the solution space may be totallydifferent than the case where second-order neighborhood is considered. Besides, the partitionschema of study area may be infinite, and so does the solution space. It is then clear that the de-veloped approach is not an optimization process that allows the convergence to «the» partitionof space which is «the» best one for spatialization of suitability model. Nevertheless, the strengthof the developed tool lies in the huge possibilities of generating potential scenarios translatingrelations between spatial features.


APPENDIXEach alternative effectiveness computation E(I,J) has been determined on this basis:

For each path, S and L are computed. Results are showed in Appendix Table 1.

Appendix Table 1 Effectiveness matrix of 36 alternatives resulting from the application of suitability models 1 and 2 on 18partition scheme of the study area

In order to standardize the effectiveness values, we calculate two limit cases for each scoremap

The case where the weight layer is the score map;The case where the weight layer is a constant-value grid with equal-size score map.Case 1 affords a path with a score cost Smin and length Lmax and case 2 gives a path with

Smax and Lmin.Results are summarized in Appendix Table 2.


Appendix Table 2 Min-Max values of score cost and length

Appendix Model 1 Min-Max values of score cost and length

Appendix Model 2


Appendix Table 3 Standardized effectiveness matrix of 36 alternatives resulting from the application of suitability models1 and 2 on 18 partition scheme of the study area

Appendix Table 3 summarizes standardized effectiveness matrix.


Appendix Table 4 Results of dp calculation from the ideal point for the alternatives obtained with model 2 and model 1

Appendix Table 4 summarizes the results of dp calculation from the ideal point for the altern-atives obtained with model 2 and model 1.

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Cite this article as: Chakroun, Hedia; Benie, Goze. ‘Methodological development for improving spatialdecision support systems in natural resources and land management’. Applied GIS, 1, DOI:10.2104/ag050005


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Improving spatial decision support systems · improving spatial decision support systems articles 05-3 or by hardware capacities increase makes it relatively easy the update of territory