GIS for Organizational Decisions Chapter 3 Slides from James Pick, Geo-Business: GIS in the Digital...

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• GIS and spatial technologies have a crucial role in supporting

people in businesses to make decisions.

• 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.

• 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)


Decision Support Systems (DSS)

• 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).

• DSSs have been available since the 1980s and continue to evolve into new versions that incorporate the web, intelligent agents, and mobile technologies.

– Note: many commercial versions including numerous on the web are not denoted as “DSS” but go under commercial product names


Structured, Semi-Structured, and Unstructured Decisions

• 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 semi-structured decisions, which can be partially calculated but not entirely. Some parts of the decision can be automated, so human intuition must comes into play.


DSS – its suitable decision areas

• 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.


Components of a Decision Support System (DSS)


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.

• 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.


SDSS - Structure


SDSS and Organizational Structure


Business Intelligence (BI)• 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.


Spatial Decision Support System (SDSS) and Spatial BI

• 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 and BI.


GIS, SDSS, and DSS


SDSS as software

• 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.

• In the latter case, a DSS vendor may offer a

product that interfaces with a GIS package.

• The BI product WebFocus is loosely-coupled with the GIS product ArcGIS.


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)


Example of a spatial BI product: WebFocus from Information Builders

• 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

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?


Conceptual Model of SDSS from Jaripathirun and Zahedi

(Source: Jarupathirun and Zahedi, 2005)


Visualization• 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.


Spatial Visualization

• Spatial visualization is not a standard 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.


Example of Experimental Study of Spatial Visualization

• 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.


Example of SDSS – Insurance Pricing for Typhoons

• 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.


SDSS as a solution to the problem of insurance pricing

• 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.


SDSS Architecture to Address the Typhoon Pricing Problem

• 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)


SDSS Architecture for Typhoons

(Li et al., 2005)Copyright (c) 2008 by John Wiley and Sons

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)Copyright (c) 2008 by John Wiley

and Sons

Insurance Risk Levels and SDSS Outputs for 14 Regions

in Guangdong Province,

China

(Li et al., 2005)


Summary• Spatial decision support has developed into an important

tool for businesses.

• 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.

• 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.


Summary (cont.)

• This SDSS/Spatial BI area is in an early maturity growth stage, but is positioned to move into the next stage of rapid growth.

• 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.


GIS for Organizational Decisions Chapter 3 Slides from James Pick, Geo-Business: GIS in the Digital...

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