GIS for Organizational Decisions
Chapter 3 Slides from
James Pick, Geo-Business: GIS in the Digital
Organization, John Wiley and Sons, 2008.
Copyright © 2008 John Wiley and Sons.
DO NOT CIRCULATE WITHOUT
PERMISSION OF JAMES PICK
Copyright (c) 2008 by John Wiley
and Sons
GIS for Decision Making
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GIS and spatial technologies have a crucial role in supporting
people in businesses to make decisions.
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The IS field has well-known systems for decision-support
including decision support systems (DSS) and business
intelligence (BI) (Marakas, 2002; Power, 200 ; Gray, 2006).
For GIS, there are the relatively new concepts of spatial
decision support systems (SDSS) and spatial business
intelligence.
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Example -- spatial models and data that help bank managers
overseeing a rapidly expanding branch network to make
better decisions on locating some branches, while closing,
merging, or relocating others. (Large Personal Corporate
Bank –LPCB case in Geo-Business)
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Decision Support Systems (DSS)
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A DSS is a software package that has a database,
model base, knowledge engine, and a user interface that
are programmed to support management decisions
(Marakas, 2002, Gray, 2006).
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DSSs have been available since the 1980s and continue
to evolve into new versions that incorporate the web,
intelligent agents, and mobile technologies.
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Note: many commercial versions including numerous on the web
are not denoted as “DSS” but go under commercial product
names
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Structured, Semi-Structured, and
Unstructured Decisions
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A structured decision implies that the steps to reach it are
logical and can be automatically calculated. For instance, the
accounting calculations to produce a company balance sheet
are logical and automatic. A person does not have to make
the decisions, and the steps can be delegated to a computer.
An unstructured decision occurs in situations where the
objectives of decisions conflict, the alternatives for a decision
are not clear, and/or the impacts of a decision are not known
(Power, 2002; Marakas, 2002). An unstructured decision
cannot be exactly calculated because its mechanisms are not
known or it is too complex to model. For example, the
decision on the strategy of a political campaign cannot be
automated into fixed steps.
Between structured and unstructured decisions are semistructured decisions, which can be partially calculated but not
entirely. Some parts of the decision can be automated, so
human intuition must comes into play.
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DSS – its suitable decision areas
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A DSS is best suited for semi-structured or
unstructured decisions (Gorry and Scott Morton,
1989; Marakas 2002).
MIS (management information system)
emphasizes regular reporting.
Examples of DSS.
– A DSS that helps oil company managers clean up an
oil spill.
– DSS to support the daily problems and challenges
facing the manager of a large rent-a-car facility.
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Components of a Decision Support
System (DSS)
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Components of a DSS
• Data management
– Stores, retrieves data; provides security
• The model management determines which model to invoke, gathers the
relevant data, accesses the knowledge engine if appropriate, and manages
the interactive user interface.
• The knowledge-based models applies logical reasoning based on rules,
tasks sometimes called expert systems.
– They store and utilize logical rules, constraints, and heuristic principles
(Marakas, 2002).
– The rules are extracted from human experts and stored in the
knowledge base.
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The last major component, the user interface (sometimes called graphical
user interface or GUI) provides the interactive interface for the manager or
other user to communicate back and forth with the DSS. It is crucial
because the typical DSS user, a manager or executive, has limited time to
communicate and is usually not a technical expert.
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SDSS - Structure
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SDSS and Organizational Structure
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Business Intelligence (BI)
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Business Intelligence (BI) is closely related to DSS, but
is broader and draws from more diverse sources.
A BI is a combination of data gathering, data storage,
and knowledge management with analysis and modeling
tools that provides complex enterprise-wide corporate
and competitive information to decision makers (Gray,
2006).
The BI gathers more far-reaching and diverse
information and tends to be focused on more complex
situations than a DSS. Like DSS, BI has components for
data management, model management, knowledge
manager, and a user interface.
Its knowledge management function is broader and more
powerful than for a DSS. In the book, we regard DSS
and BI as close relatives that share many features and
constitute the ends along a continuum of decision
systems.
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Spatial Decision Support System
(SDSS) and Spatial BI
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A spatial decision support system (SDSS) is a DSS
that provides locational decision support where
there is a geographic or spatial component in the
decision making (Keenan, 2005; Jarupathirun and
Zahedi, 2005).
Likewise a spatial business intelligence system
(spatial BI) is a BI that provides locational decision
support where there is a spatial element in the
decision-making. The generic SDSS is seen in
Figure 3.3 falls in between GIS and DSS (Huerta,
Navarrete, and Ryan, 2005). The same added
components of spatial analysis and spatial boundary
layers apply to Spatial BI, which falls between GIS
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and BI.
and Sons
GIS, SDSS, and DSS
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and Sons
SDSS as software
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A SDSS combines GIS and DSS features. It
may be formalized into a single system or
product, or reside in a combination of two or
more loosely-coupled products.
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In the latter case, a DSS vendor may offer a
product that interfaces with a GIS package.
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The BI product WebFocus is loosely-coupled
with the GIS product ArcGIS.
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WebFocus with GIS Adapter allowing
web connection (ArcIMS)
Figure 3.10. BI Example in Insurance with WebFocus from Information Builders. This example uses
WebFocus and ArcGIS from ESRI to locate and display the spatial distirbution of high value insurance
policies in a part of Florday that is most exposed to risk. The user can toggle between the
spreadsheet report and map, to refine the analysis.
(Source: Information Builders, 2006)
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Example of a spatial BI product:
WebFocus from Information Builders
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Information Builders is a leading vendor of BI software.
Although mostly it’s non-spatial BI, it does include spatial
add-ons.
– Web Focus and ArcGIS from ESRI are “engines” that can
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work together through the add-ons. This type of
arrangement is referred to as “loosely coupled,” i.e. the
strengths of each software package can complement the
other.
Advantage. The spatial user has access to modeling,
tables, graphs, and maps
Advantage. The business user who is used to WebFocus
does not have to adjust his/her user interface.
Class Question. What do you consider the disadvantages
of this approach?
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Conceptual Model of SDSS from
Jaripathirun and Zahedi
(Source: Jarupathirun and Zahedi, 2005)
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Visualization
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Visualization refers to the visual display of
spatially arranged elements. The user can
better understand spatial relationships and
assimilate new concepts through visualization
(Gonzales, 2004; Gershon and Page, 2001).
People think and communicate visually (DiBiase,
1990).
This understanding helps in decision making.
Visualization is made more meaningful through
display of the outcomes of spatial analysis, such
as buffers, graphics, and overlays, and 3-D.
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Spatial Visualization
• Spatial visualization is not a standard
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aspect of transaction processing systems,
management information systems, or
decision support systems (see Table 3.4,
modified from Jarupathirun, 2005).
The visualization helps the user gain richer
understanding and allows him/her to make
complex comparisons.
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Example of Experimental Study of
Spatial Visualization
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An elaborate study (Mennecke et al., 2000) showed that
experts are more accurate than novices in utilizing GIS to
perform geographic tasks.
Experiments on 240 student and professional-planner
subjects measured cognitive fit, map interpretation and
reading.
The students and professionals showed more efficiency
with SDSS compared to maps, when they worked on
complex problems,
Further, the professionals were more accurate yet less
efficient than students.
In all, the investigation confirmed that there were decision
gains from SDSS compared to paper maps, particularly for
more complex projects.
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Example of SDSS – Insurance
Pricing for Typhoons
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An example of SDSS is on designed to make decisions
regarding the insurance risks of typhoons in China (Li et
al., 2005).
This SDSS is a prototype developed by academic
researchers in close consultation with industry. It
illustrates advanced SDSS applied to a difficult business
problem.
Typhoons are a severe threat in certain areas of the
world, including Guangdong Province in China, a huge
area of 70,312 square miles that borders the Pacific
Ocean for 2,105 miles.
This SDSS focuses on typhoon applications for
Guangdong. The province’s prior typhoon losses were
huge, involving billions of U.S. dollars and loss of
thousands of lives.
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SDSS as a solution to the problem
of insurance pricing
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The goal of insuring against severe hazards is to pool
together a portfolio of premiums that would be
unacceptable individually, but can be insured as a group.
The traditional pricing approach is “claims-based
methodology,” which prices by deterministic calculations,
based on normal typhoon activity, population profiles,
construction patterns, and past insurance coverage and
losses (Li et al., 2005).
One solution is to utilize GIS combined with math,
statistical, pricing, and other modeling tools. The spatial
analysis leads to recognition of the true environmental
threat.
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SDSS Architecture to Address the
Typhoon Pricing Problem
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Consists of an in-house user interface and
central control module that connect to a set of
modules for statistics, math, spatial statistics,
expert systems, and insurance pricing.
The SDSS also has a database, as well as a
GIS-COM library that supports programming
features of the system.
The SDSS has the capability of producing
standard business graphics and written reports.
(Li et al., 2005)
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SDSS Architecture for Typhoons
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(Li et al., 2005)
Model Base for the Typhoon SDSS
1. Hazard occurrence model. This expert systems model predicts the
spatio-temporal patterns of typhoons based on weather theories and
historical data. It creates simulated typhoons, which are stored in
the hazard database.
2. Comprehensive risk analysis model. It analyzes comprehensive risk
for entire portfolios of exposed assets based on deterministic or
stochastic simulations. Also, the model can identify geographic hot
spots.
3. Zonal correlation model. It calculates spatial correlations between
zones that are vulnerable to the typhoon. This model is useful in
determining insurance rates across multiple zones.
4. Loss analysis model. The insurance rate for a zone can be
computed through a series of steps. The model computes
comprehensive loss, consisting of the direct losses from the typhoon
hit and secondary losses from off-shoots of the typhoon itself, such
as peripheral storms, floods, and wind damage.
5. Rate making and pricing. Based on the specific exposure of an
insured in a certain zone, the model estimates insurance rate and
consequently the premium amounts.
(Li et al., 2005)
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Insurance
Risk Levels
and SDSS
Outputs for
14 Regions
in
Guangdong
Province,
China
(Li et al., 2005)
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Summary
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Spatial decision support has developed into an
important tool for businesses.
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Companies face decisions on a daily basis.
Traditional tools of DSS and business intelligence
can be enhanced with spatial components, or
spatial analysis in standard GIS software can be
built up to offer robust decision modeling.
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The formal SDSS and spatial BI tools are scarce
in businesses, but likely to expand as they
become more affordable and user-friendly and as
spatial teams in firms learn more and get to know
the applications and benefits better.
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Summary (cont.)
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This SDSS/Spatial BI area is in an early maturity
growth stage, but is positioned to move into the
next stage of rapid growth.
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Well-thought-out and focused spatial decision
support can yield competitive benefits. In some
cases such as Large Insurance Company (LIC), it
may take unpreparedness for a devastating
business event to startle the firm into advancing its
spatial decision support.
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and Sons