Getting serious about integrating decision support
mechanisms into geographic information systems
Jonathan Frez1, Nelson Baloian2, Gustavo Zurita3
1
School of Informatics and Telecommunication, Universidad Diego Portales
Vergara, Santiago, Chile
jonathan.frez@gmail.com
2
Department of Computer Science, Universidad de Chile
Blanco Encalada 2120, Santiago, Chile
nbaloian@dcc.uchile.cl
3
Department 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.
as a key actor, generating alternatives, re-defining and remodelling 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.
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,
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 GISMCDM:





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 Information
Uncertain 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 DempsterShafer 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 I
LOCATION/# DEER VALUES
location
#deer
1
12
2
20
3
15
4
23
5
26
TABLE 2
LOCATION/ #DEER RANGE VALUES
location
#deer
1
[10,20]
2
[15-25]
3
[10-25]
4
[17-29]
5
[20-32]
TABLE 3
LOCATION/# 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 4
LOCATION/ #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 (SMaps) 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) Description
In 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)”
<CONJUNCTION> ::= <AND> | <OR>
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}”
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
B. Scenario Generation Language (SGL) Grammar
An 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:
<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>
<S> ::= <QMAP> <BEHAVIOR> <INTERACTION>
|<QMAP> <BEHAVIOR> | <QMAP> <INTERACTION>
| <QMAP>
<BEHAVIOR2>::=<HIPO> |
<HIPOQ><AT>|<Q><AT> | <BEHAVIOR2>
<QMAP> ::= <MAPS> | <MAPS> <WHERE>
<HIPO> ::= “{“ <CLLIST> “}”<NUM>
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:
<HIPOQ> ::= “{“ <CLLIST> “}”<Q>
<MAPS> ::= <MAPTYPE> <FUNCTION> |
<MAPTYPE> <CLASS>
<CLLIST> ::= <CL> | <CL> “,” <CLLIST>
<Q> ::= “?”
<AT> ::= at <AT2>
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>
<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”
Value Attributes
o Mean value
o 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 Attributes
o Weight
o Certainty
o Plausibility
<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:
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.
v2
prob = P{x > 𝑣1 , 𝑥 < 𝑣2} = ∫ f(x)dx
v1
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:
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.
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
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.
[9] P. A. Burrough and A. U. Frank, Geographic objects
[10]
[11]
[12]
Fig 6. Schema showing the four core parts of the developed
application and the interaction between them.
[13]
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