Getting serious about integrating decision support mechanisms into geographic information systems

Jonathan Frez1, Nelson Baloian2, Gustavo Zurita3

1School of Informatics and Telecommunication, Universidad Diego PortalesVergara, Santiago, Chile

jonathan.frez@gmail.com2Department of Computer Science, Universidad de Chile

Blanco Encalada 2120, Santiago, Chilenbaloian@dcc.uchile.cl

3Department of Computer Science, Universidad de Chile Blanco Encalada 2120, Santiago, Chile

gzurita@fen.uchile.cl

Abstract— Although Geographic Information Systems have been extensively used by decision makers when dealing with spatial related issues most of them do not provide the functionalities for supporting the classic decision making process. This process consists in identifying the problem, modelling the situation, and then a cycle in which the decision maker(s) generates various scenarios according to different hypotheses and evaluates them until a satisfactory solution to the problem is identified. In this work we present a system which allows implementing a decision making cycle, abstracting the modelling, minimizing the GIS knowledge needed and allowing building several scenarios and comparing them easily. A key element of this system is the scenarios generating language, a SQL-like language which allows to easily specifying the characteristics a suitability map shoud have and showing the results to the user in a versatile way.

Keywords— Decision support systems, Geographic Information Systems, Dempster-Schäfer Theory, Suitability map, Scenario Generation Language

I. INTRODUCTION

Decision Support Systems have been defined as interactive computer-based systems that help decision makers in the use of data and models in the solution of unstructured problems. A simplified model for the decision making process (DM) includes the following stages: 1) Identifying the problem, 2) identifying and modelling the objective(s) of the decision, 3) collecting, generating and or combining data to generate alternative scenarios 4) evaluate alternatives according to objectives established, 5) chose an alternative and make a sensitive analysis. If decision maker(s) estimate(s) there is enough information the process ends with a final decision, otherwise the flow goes back to the identifying objectives (2) or to the generating alternatives stages [2] (see Fig 1). Like for Artificial Intelligence, the boundaries for defining what falls under Decision Support Systems (DSS) seem to be diffuse. However, most of the authors who have tried to give a definition agree that one of the most important characteristics is that the man remains in the decision making process cycle, as a key actor, generating alternatives, re-defining and re-modelling objectives

since this is a task involving creativity, which cannot be mechanized. Computers in their turn can help humans in gathering data, generating various decision alternatives and evaluating their outcome according to the decisions goals, visualize these results adnc communicate this results to others.

Geographic Information Systems (GIS) are often used to support decision making processes for which intensive use of geo-referenced information is needed in order to generate and evaluate the outcome of the various alternative scenarios. These kinds of decision making processes involve among other activities, generating a large set of alternatives, each one with multiple evaluation criteria.

Fig. 1 Decision Making Process [1]

From the available literature about GISs being used to support DM, we realize that there is an important number of modelling tools available, which can generate a certain scenario for a geographical area applying certain evaluation functions and showing the output, like vegetation winter survival [2] wind farms locations [3] or forest production estimation [4]. However, the great majority of the existing GISs are not explicitly designed to implement a DM cycle, which means that the process of generating

various alternative scenarios according to different criteria and compare them is n most cases a difficult and time consuming task In order to implement a DM process we need to abstract the modelling part from the DM cycle and allow the decision maker to generate multiple scenarios and compare the solutions in a simple and systematic way.

For example, one of the most common use of a GIS as a DSS is finding a suitable area given some requirement, for example in [5], explains how to find specific locations for constructing artificial water recharge aquifers using floods. The decision process in a suitability decision seems to follows a pattern: The decision maker is an expert on the decision criteria (in this case aquifers recharge), but he is not necessarily and expert in GIS; Historical information is needed, which can easily be represented using GISs; A suitability criteria/formula is designed by the decision maker, and this criteria is used for formulating a query to the GIS and a suitability map is returned showing the suitability level of each point of the map to satisfy a requirement.

The process using GIS as a DSS has two main problems: First, a single hypothesis of what area can be suitable is represented by the criteria/formula, and a single map is returned, so the decision maker takes the decision based on a single scenario generated by a single hypothesis, Secondly, is hard to evaluate the impact of an hypothesis change, because in order to generate a another alternative, a new “project” must be created, configured, computed, and finally evaluated. In order to create multiple alternatives the decision maker must create multiple maps, and compare them. However, most GIS are designed to create maps, not to compare maps, or taking decision based in multiples alternatives maps generated under various criteria.

In this work we are going to present a design and prototype that allows implementing a DM cycle, abstracting the modelling, minimizing the GIS knowledge needed and allowing building several scenarios and comparing them easily.

II. RELATED WORK

In general DSS are designed to support less well-structured problems; they use various models and analysis techniques and there are intended to be used by non-computer experts. A DSS must be very interactive, flexible and adaptable in order to support different solutions approaches.

A DSS for spatial problems must support the cyclic processes of decision making, generating multiples alternatives, providing tools to compare its and allowing a sensitive analysis. Furthermore, DSSs oriented to spatial problems must be able to model the environment and evaluate the impact of changes under various hypotheses. Also spatial information is inherently fuzzy and uncertain [6], this means that fuzzy analysis techniques are needed.

In [8] Ashley Morris says that most MCDM criteria combinations rules are mostly boolean, and according to [7] this is true, however weighted techniques are also used. For example a weighted approach that has been used is Analytical Hierarchy Process (AHP), this process allows to combine weights in a hierarchy structure. This method can be used for two objectives: combining different layers information (with the same attribute, but different values) and for priority assignment to spatial objects.

In order to clarify the applications of the integration between GIS and MCDM, we list some real applications examples:

Geographic objects with indeterminate boundaries [9]. Combining fuzzy sets and databases in multiple criteria

spatial decision making [10]. Application of fuzzy measures in multi-criteria evaluation

in GIS [11]. A multiple criteria decision support system for testing

integrated environmental models [12]. Environmental assessment fuzzy decision analysis of

integrated environmental vulnerability assessment of the mid-Atlantic region [13].

Integrating high resolution remote sensing, GIS and fuzzy theory for identifying susceptibility areas of forest insect infestations [14].

GIS-based multicriteria evaluation and fuzzy sets to identify priority sites for marine protection [7].

In [10] proposed to design serious Spatial Decision Support Systems (SDSS) providing an analysis that allows continuous or fuzzy functions to assign fuzzy values. It also proposed that maps must be dynamically generated in order to instantly reflect the impact of a range of different parameter values and options on the decision making process.

The following sections will revise the state of the art of two the two main areas related to the work presented in this paper: Geographical Information Systems used for Multi Criteria Decision Model (GIS-MCDM) and how uncertain information has been used in Decision Making processes.

A. Geographical Information Systems used for Multi Criteria Decision Models

     In order to support fuzziness and uncertainty, one of the most fertile GIS development areas is integrating multicriteria decision models into GIS. This has been an active area of research in spatial decision analysis [7]. MCDM assist decision makers in evaluating multiple alternatives using multiple decision criteria.

Finally, the explicitly or implicitly spatial alternatives mean that each alternative must be an answer two questions: what to do? and where to do it?. These questions can be explicit or implicit. The explicitly and implicitly spatial criteria mean that the MCDM criteria are based on spatial characteristics, as size, length or location. These characteristics can be explicit or implicit.

According to [7] there are five main components of GIS-MCDM:

A goal, or set of goals that the decision maker attempts to achieve along the evaluation criteria.

The decision maker is involved with his preferences. A set of decision alternatives (or variables). A set of uncontrollable variables (Nature). A set of outcomes associated to each alternative.

GIS-MCDM systems can be also categorized according to three main characteristics:

Raster or vector based Explicit or implicit spatial criteria

Explicit or implicit spatial alternatives

Raster and vector based means that the multicriteria combination of rules is performed using raster or vector data. This kind of GIS-MCDM is commonly implemented as an extension of standard GIS software.

B. Decision Making with Uncertain InformationUncertain in decision making has been commonly treated as an

element of risk assessment. For example, in genetics it is used to represent a possibility of developing a genetic disease [15]. However it is not clear how to represent multiples possibilities based on uncertain hypothesis. This problem cannot be solved using probabilities, because the possibility of developing two different genetic diseases depends of uncertain information and cannot be calculated just by multiplying the weights. In spatial problems we have similar conditions. For example, the probability of the presence of animals of a certain secie in a specific area can be related to the presence of food, water, and other environmental conditions, and this kind of information is often uncertain.

The literature regarding geographic information with epistemic properties shows a trend to use belief functions, in particular, the Dempster-Shafer Theory [16]. The Dempster-Shafer theory was developed in 1967 by Dempster and extended by Shafer, and proposes to use sets of hypotheses regarding a variable (e.g. the temperature values in X are always between t1 and t2) associated with a weight of being correct. This theory is a complete new way of supporting decision making, and it is based on formulate a set of hypotheses and develop a scenario based on that. This scenario can be used to make a decision or it can be compared to another scenario with different hypothesis.

In order to explain Dempster-Shafer theory, we will use an example: Table 1 shows deer quantity values associated to a certain location. In addition we have a query Q= [10,25] looking for locations with more than 10 and less than 25 deer. In this case, 4/5 of the locations meet this condition. Now Table 2 contains a "range" of deer registered for each location. In this case, only 3/5 of the locations meet the condition. The theory defines two types of acceptances to queries:

Plausibility: is the possibility that the random variable takes values within the range to query.

Certainty: is the possibility that the whole range of the distribution of variable (D) is within the range to query.

Using the Dempster-Shafer evaluation, we can calculate that the Certainty level is 40% and possibility level is 100% (see Table 3). These values are considered as lower and upper bounds of possibility, i.e. between 40% and 100% of the locations have a similar amount of deers to the range queried. When a single element is associated with multiple scenarios, weights are assigned to each. Table 4 shows an example. In this case, since Q= [10.25], the accuracy is 70% (0.2 +0.15 +0.35) and the possibility is 100%. In both cases, the weight assignment is conducted by an expert. The result if processing the weight will result in a new scenario.

In this example the weights were assigned based on existing information, however according to the theory, there is no restriction to include experts’ assignments. These characteristics, makes Dempster-Shafer an excellent alternative to process spatial information mixing existing information and the decision maker knowledge.

TABLE ILOCATION/# DEER VALUES

location #deer

1 12

2 20

3 15

4 23

5 26

TABLE 2LOCATION/ #DEER RANGE VALUES

location #deer

1 [10,20]

2 [15-25]

3 [10-25]

4 [17-29]

5 [20-32]

TABLE 3LOCATION/# DEER D-S

location #deers

1 [10,20] Certainty

2 [15-25] Certainty

3 [10-25] Certainty

4 [17-29] Plausibility

5 [20-32] Plausibility

TABLE 4LOCATION/ #DEER D-S WEIGHTS (SCENARIO)

#deer weight

[10,20] 20%

[15-25] 15%

[10-25] 35%

[17-29] 20%

[20-32] 10%

III. DESIGNING SPATIAL DECISION SUPPORT SYSTEM (SDSS)Our proposed SDSS can be described as having four main

components: Data Representation (DR), Scenario Generation

Engine (SGE), Scenario Generation Language (SGL) and Scenario Visualization (SV).

Each component follows the fundamental guidelines described in the early works in GIS, DSS and MCDM. However they are implemented using well known modern techniques. For example the DR implements “spatial information are inherently fuzzy and uncertain”  using spatial fuzziness for representing discrete geometries, probability distribution functions for non-spatial data and belief functions for uncertain information

In order present the full design we are going to describe each component in a top-down order, starting by SV and ending with DR.

A. Scenario Visualization (SV)The most common objective in SDSS is to find suitable areas

given a specific requirement. In order to support this particular decision making process the suitability maps (S-Maps) visualization concept was developed in [17].

S-Maps are defined as a spatial distribution of all degree of suitability for a specific use. All degrees are represented in 0-1 scales.  The S-map is divided into cells, each cell correspond to an (X,Y) coordinate. The Z components are the suitable degree (0-1) (see Fig. 2).

Fig. 2. An exemple of a Suitability Map from the a rural area in the south of Chile showing bilief of finding a certain range number of deer in each cell.

The dynamic generation of S-Maps provides a tool for decision makers that allow them to analyze the impact of changes in the hypotheses in ‘real time’.

In the original definition of S-Maps the rendering is made by defining an attributes tree in the query.  For each attribute, a values interval must be defined in the query, and a fuzzy function must be associated. The query is calculated using LSP (Logic Scoring of Preference).

In our implementation we defined three kind of S-Maps: a Belief-Weight Map (BW-Map), a Certainty Map (C-Map), and a Plausibility Map (P-Map).

The BW-Map is equivalent to the S-map, however its meaning is a degree of belief that the requirement is suitable in a cell. The C-Map and P-map meaning are related to a degree of certainty (and plausibility) of the information source. The source can be a set of databases or experts knowledge, and the C-Map (and P-Map) allows representing the trustability of the suitability scenario for a specific area.

B. Scenario Generation Language (SGL) DescriptionIn order to support the decision maker to generate various

alternative suitability maps in order to explore the impact of a change in the hypotheses under different scenarios (for example, to perform a sensibility analysis), we designed a simple specification language called Scenario Generation Language (SGL). SGL is inspired by SQL (Simple Query Language), however it is important to understand that this language is not designed to query data, but to generate a scenario based on expert knowledge, empirical data, and existing environmental models. SGL is divided in three main statements: VMAPS, BEHAVIOR and INTERACTIONS. In VMAPS statement, the decision maker can define what kind of visualization want to generate, and apply some basic filters like “belief > 20%”  or “shops.capacity > 20” and also restring the analysis to a specific area. The following is a VMAPS statement example

“BW-map @persons where belief > 20 and @shops.capacity > 20 and inside “POLIGON ((33.22,23.23) (33.22,23.23) (33.22,23.23) (33.22,23.23))”

In the BEHAVIOR statement, the expert can express his knowledge using the Dempster-Shafer framework, for example, if the expert is looking for persons, one hypothesis may be “persons are in shops with a 20% of belief” or “persons are in schools or workplaces with a 40% of belief”. We also defined query hypothesis: “persons are in shops just like in place X,Y”.  In the behavior statement the expert can define multiple hypotheses, which are combined using Dempter-Shafer combination rules. Furthermore, this complex scenarios are designed by the expert without requirement any kind of GIS expertise. The following is a BEHAVIOUR statement example:

“Behavior {@cinema}20 {@school, @workplace}30 {@shops}? at point(33.22,22.33)”

Finally, the interaction statement is designed to represent real world interactions between the elements in the data source. For example, a specific type of vegetation cannot grows on the water and arid soils. This kind of interaction complements the behavior statement by adding environmental rules. This rules can be expressed has values in a [-100,100] interval. For example, if we are generating a scenarios for “@grapes, we add in the interaction statement -100 value for coast areas and lakes. This value can increase or decrease the belief level in the area. The following is an INTERACTION statement example:

“Interaction @valley{50} @coast{-100}”

A full Scenario definition will look like the following:

“BW-map @grapes where belief > 60 and @countryside.extension > 30 Behavior {@countryside}20 {@valley,@workplace}30 {@soil}?  

at point(33.22,22.33) Interaction @valley{50} @coast{-100}”

B. Scenario Generation Language (SGL) GrammarAn initial version of the SGL grammar considers the following

characteristics: Four combinations of statements starting with the <QMAP> non terminal. a QMAP is defined as the maptype to be generated using class filters, spatial filters, Dempster-Shafer filters and objective functions. Thus the top sentence production rule is:

<S> ::= <QMAP> <BEHAVIOR> <INTERACTION> |<QMAP> <BEHAVIOR> | <QMAP> <INTERACTION> | <QMAP>

<QMAP> ::= <MAPS> | <MAPS> <WHERE>

A Map can be generated from class suitability (for example @grapes), or an objective function. Thus the generation rule for a map is as follows:

<MAPS> ::= <MAPTYPE> <FUNCTION> | <MAPTYPE> <CLASS>

The <WHERE> symbol of the <QMAPS> production rule corresponds to a filter which can be applied to a Dempster Shafer belief metric, a spatial condition, or a class attribute. The <WHERE> production rule is as follows:

<WHERE>::=  <WHERE1>

<WHERE1>::=<BELIEFCONDITION> | <ATTRCONDITION> | <GEOCONDITION> | <CONJUNCTION> <WHERE1>

<CONJUNCTION> ::= <AND> | <OR>

A belief condition can be applied to belief, certainty and plausibility.

<BELIEFCONDITION>::=<DSPROP> <COMP> <VAL>

<DSPROP> ::= “belief” | “certainty” | “plausibility”The <ATTRCONDITION> symbol describes class attribute filters which can be applied only to the ontology class definitions in the DR (Data Representation). Currently a class cannot be defined in the SGL. The production rule for defining attributes to the condition is:

<ATTRCONDITION>::= <CLASS> “.” <ATTRIBUTE> <COMP> <VAL>

In order to minimize the needed GIS knowledge, we included only two spatial operators in the SGL: inside and outside

<GEOCONDITION>::=<GEOOPERATOR> <GEOMETRY>

<GEOMETRY> ::= // WKT geometry format

<GEOOPERATOR> ::= “inside” | “outside”

A behavior can be described has a list of Dempster-Shafer Hypothesis. However, the weights can be assigned by the expert or calculated from el well known location.

<BEHAVIOR>::= “behavior” <BEHAVIOR2>

<BEHAVIOR2>::=<HIPO> | <HIPOQ><AT>|<Q><AT> | <BEHAVIOR2>

<HIPO> ::= “{“ <CLLIST> “}”<NUM>

<HIPOQ> ::= “{“ <CLLIST> “}”<Q>

<Q> ::= “?”

<CLLIST> ::= <CL> | <CL> “,” <CLLIST>

<AT> ::= at <AT2>

<AT2> ::= <POINT>

<POINT> ::= “point(“ <NUM> “,”<NUM> “)”

The interactions are defined as a list of factors to be applied to the QMAP final values. These factors are applied only if there is a presence of the class in the evaluated cell.

<INTERACTION> ::= interaction <INTERACTION2>

<INTERACTION2> ::= <INTER> | <INTERACTION2> <INTER>

<INTER> ::= <CL> ”{“ <NUM> ”}”

A class is identify by the “@” prefix. However the rest of the definitions are similar to SQL.

<CLASS> ::= <CL> | <CLVALUES>

<CL> ::= “@”<STRING>

<ATTRIBUTE>::= <STRING><MAPTYPE> ::= “suitability” | “heatmap” | “radar”

<CLVALUES> ::= <CL>”[“<NUM>“,”<NUM>”]”

<FUNCTION> ::=<STRING>“(“<STRING>“)”

<COMP> ::= <GT> | <LT> | <EQ> | <DIFFERENT>

<GT> ::= “>”

<LT> ::= “<”

<EQ> ::= “=”

<DIFFERENT> ::= “!”

<VAL> ::= <NUM> | <STRING>

C. Scenario Generation Engine (SGE)Instead traditional GIS software, the scenario generation requires

a considerable amount of computing resources. The main reason for this is the large amount of information required to calculate the belief of each cell.  In the recent GIS development there are initiatives to integrate GIS-Databases with Hadoop technologies; the result of this is the ability to work with geo-referenced using Big-Data techniques as MapReduce. However this approach is an improvement for the GIS-DSS integrations. In our case, we are generating data, so we need a different approach.

The SGE it’s a spatial oriented distributed engine, and it basically consist on converting cells or group of cells into processing tasks, and each task will be assigned to an specific machine which contains the area information. (See Fig 3)

D. Data Representation (DR)In our data representation model we define a hierarchy ontology

class structure. Each source of data must be matched to an class definition. For example a map of growing grapes will be associated to a @grape class. All classes must have the following attributes:

Value Attributeso Mean valueo Probability Distribution Function (PDF) of the

mean value.o PDF attribute values. Standard Deviation in the

normal function case. Spatial Attributes

o Discrete Geometry representation.o Spatial fuzziness function and its attributes.

Dempster Schaffer Attributeso Weighto Certaintyo Plausibility

Fig 3. Cells distributed processing architecture

As described in the SGL, a Scenario contains an interval values specification in the CLVALUES non-terminal which is used in the QMAP definition. This interval represent the probability of x >v1 and x<v2 where x is the mean value of the PDF.

prob=P {x>v 1 , x<v2 }=∫v 1

v 2

f (x)dx

Using this fuzzy representation of the data the cell value will be represented by:

QV * prob * SF

QV is the QMAP values after evaluating the behaviour hypothesis, applying the filters and pondering the interactions. SF is the spatial fuzzy distribution of the discrete geometries defined in the class. Typically a SF can be defined with a 1 value inside the geometry and a normal distribution outside.

The graphical Data Representation model can be seen has a transformation of discrete geometries with metric values to a fuzzy value with fuzzy spatial distribution, in Fig 4 we plot a graphical example of the transformation:

Fig 4. On the top: Traditional GIS representation. At the bottom: Fuzzy representation of values in spatial distributions.

E. Relations between Components The four described components are part of a developed

prototype application and interact with each other in order to implement the necessary functionalities to support the decision maker in the cycle of generating scenarios and analyzing them until enough information has been accumulated for making a decision. The application´s interface can be seen in Fig. 5.

The process starts by entering an SGL instruction. The string containing the instruction is wrapped in a JSON format in order to be passed to the SGE which checks that all the mentioned classes are available and proceeds to divide the graphical area over which the map should be computed and shown. After that, the geographical information of each cell (e.g. polygons and associated information) is retrieved from the DR module. Also all classes associated to each one of the data types existing in the cell and mentioned in the SGL sentence are requested. Finally the SGE generates a matrix of belief values for the type of map requested in the SGL sentence. This matrix is converted into JSON format and passed to SV module in order to be renderized and displayed. The four components of the system and their interactions are shown in Fig. 6.

Fig 5. Top: Traditional GIS representation. Bottom: Fuzzy representation of values in spatial distributions.

IV. CONCLUSIONS

The main contribution of this work is to propose a way in which traditional GIS systems can be integrates in a decision support system in order to support decision makers dealing with issues where geo-referenced data is at the core of the problem.

The lack of an effective support for the decision maker while performing the cycle of generating scenarios based on certain hypotheses, analysing the results and then, based on these results generating new scenarios based on other hypotheses has been the main reason why GIS systems implemented so far cannot be considered effective Decision support systems [4]. The tool presented in this work is a step forward towards having that GIS fulfilling such requirements. We believe that an important contribution of this work is the design and implementation of a Scenario Generation Language which allows the decision maker to generate behaviour and/or suitability maps over geographical areas according to different hypotheses in a versatile and agile way.

The development of this first prototype has opened future research lines: there is still necessary to develop a method to effectively compare the various maps generated in order to allow the decision maker to better analyse the impact of a variation in the hypotheses. Another research subject in order to improve the support to decision makers who have no knowledge of query languages like SQL and may found difficult to learn and master a language like SGL would be to develop an interface which could allow the decision maker to input SGL sentences in a “Query by example” way, which means not having to write the whole sentence but just filling the parameters of pre-formulated sentences in an easy an intuitive way.

Fig 6. Schema showing the four core parts of the developed application and the interaction between them.

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