The document discusses the integration of geographic information systems (GIS) and decision making. It covers key components of GIS and decision making like fundamental and advanced data, analysis, and modeling. It also examines different approaches to integrating GIS and multicriteria decision making (MCDM), including loose coupling, tight integration, and interoperability. Recent developments have made GIS and decision making more open, distributed, and interoperable.

1. Agung Wahyudi Promoter: Prof Marc Van Meirvenne Co-promoter: ir. Liesbet Cockx Introduction GIS Decision Making Integration Recent Dev’t Conclusions Home

2. Introduction GIS Decision Making Integration Recent Dev’t Conclusions Geographic Information System Decision Making Fundamental Data Advanced Data Management Manipulation and Analysis Output How They Integrate? Loose integration Tight Integration Interoperable evaluation criteria, decision alt’s & constraints criterion weighing, decision rules, and sensitivity analysis Statistical Modeling Mathematical Modeling Introduction Home

3. What is GIS GIS Decision Making Integration Recent Dev’t Conclusions Fundamental Data Advanced Data Management Manipulation and Analysis Output Statistical Modeling Mathematical Modeling Conventional Statistics are based on random, independent variables that assume zero continuity and allow for no extension of each data value. Spatial Statistics is focuses on the spatial association between values observed at different locations (spatial dependency) and the systematic variation of phenomena by location (spatial heterogeneity or non-stationary) Optimization is a normative approach to identify the best solution for a given decision problem Simulation is a methodology for performing experiments using a model of the real world system. Theory Example Introduction Home

4. What is GIS Decision Making Integration Recent Dev’t Conclusions Theory Example Advanced function. Network analysis from ArcGIS GIS consensus weighting procedures and heuristics allow the evaluation and allocation of land for multi-objective planning Tools for multi-objective/multi-criteria decision support from IDRISI. Introduction Home

5. Decision Making GIS Decision Making Integration Recent Dev’t Conclusions Problem Definition Constraints Evaluation Criteria Alternatives Decision Matrix Decision-Maker’s Preferences Decision Rules Sensitivity Analysis Recommendation Intelligence Phase GIS Design Phase MCDM Choice Phase MCDM/GIS Evaluation Criteria involves; A comprehensive set of objectives that reflects all concerns relevant to the decision problem, and Measures for achieving those objectives Criterion Weighing Decision Rule Sensitivity Analysis Evaluation Criteria Decision Alt’s& Constraint The set of evaluation criteria can be developed through; examination of relevant literature, analytical studies, and survey of opinions “ To maximize soil fertility” Evaluation Criteria Introduction Home

6. Decision Making GIS Decision Making Integration Recent Dev’t Conclusions Problem Definition Constraints Evaluation Criteria Alternatives Decision Matrix Decision-Maker’s Preferences Decision Rules Sensitivity Analysis Recommendation Intelligence Phase GIS Design Phase MCDM Choice Phase MCDM/GIS Criterion Weighing Decision Rule Sensitivity Analysis Evaluation Criteria Decision Alt’s& Constraint Constraints Alternatives Decision Matrix The alternatives may represent different courses of action, different hypothesis, different land allocation, and so on. Decision variables can be grouped into deterministic, random, linguistic variables Introduction Home “ Decision is a choice between alternatives” Constraints are limitations imposed by nature or by human beings that do not permit certain action to be taken

7. Decision Making GIS Decision Making Integration Recent Dev’t Conclusions Problem Definition Constraints Evaluation Criteria Alternatives Decision Matrix Decision-Maker’s Preferences Decision Rules Sensitivity Analysis Recommendation Intelligence Phase GIS Design Phase MCDM Choice Phase MCDM/GIS A criterion is some basis for decision that can be measured and evaluated. Criterion Weighing Decision Rule Sensitivity Analysis Evaluation Criteria Decision Alt’s& Constraint Decision-Maker’s Preferences Pairwise comparison method was developed by Saaty in the context of Analytical Hierarchy Process (AHP). This method involves pairwise comparisons to create a ratio matrix. The decision maker’s preferences with respect to the evaluation criteria are incorporated into the decision model. Introduction Home

8. Decision Making GIS Integration Recent Dev’t Conclusions “ decision rules dictate which alternative is preferred to another”. Decision Making Criterion Weighing Decision Rule Sensitivity Analysis Evaluation Criteria Decision Alt’s& Constraint Decision Rules Decision Rule The procedure by which criteria are combined to arrive at a particular evaluation, and by which evaluations are compared and acted upon. Simple Additive Weighting (SAW) are the most often techniques used. This techniques are also called scoring methods since the decision maker directly assign certain weight to “relative importance” attributes. Introduction Home Problem Definition Constraints Evaluation Criteria Alternatives Decision Matrix Decision-Maker’s Preferences Decision Rules Sensitivity Analysis Recommendation Intelligence Phase GIS Design Phase MCDM Choice Phase MCDM/GIS

9. Decision Making GIS Integration Recent Dev’t Conclusions Problem Definition Constraints Evaluation Criteria Alternatives Decision Matrix Decision-Maker’s Preferences Decision Rules Sensitivity Analysis Recommendation Intelligence Phase GIS Design Phase MCDM Choice Phase MCDM/GIS Sensitivity analysis is a procedure for determining how the recommended course of action is affected by changes in the inputs of the analysis. Decision Making Criterion Weighing Decision Rule Sensitivity Analysis Evaluation Criteria Decision Alt’s& Constraint Sensitivity Analysis Monte Carlo simulation is a way of evaluating a large number of possible scenarios. Introduction Home “ if the weight change, will the final ranks vary?” Sensitivity of Weight by giving small changes in value of attributes

10. How They Integrate GIS Decision Making Integration Recent Dev’t Conclusions Loose coupling strategy combines the capabilities of separate models for GIS functions and MCDM by transferring files. to works in GIS-MCDM model we have to switch between GIS software, database/spreadsheet software, and MCDM software very often Loose Tight Interoperable Introduction Home User Loose Coupling; MC-SDSS MCDM User Interface GIS User Interface Shared Files

11. How They Integrate GIS Decision Making Recent Dev’t Conclusions Tightly or close integration strategy is based on a single data or model manager and a common user interface. With this strategy, there is no need to leave the GIS to run multicriteria decision analysis ArcGIS 9 Statistical Analysis Module IDRISI Decision Analysis Module Loose Tight Interoperable Integration Introduction Home User Tight Coupling; MC-SDSS MCDM Shared Files GIS User Interface

12. How They Integrate GIS Decision Making Recent Dev’t Conclusions Interoperable is the ability of two or more software components to directly cooperate/communicate despite of their differences in programming language, interface, and execution platform VBA (Visual Basic Application) code to deploy ADO (Microsoft Active Data Object) VBA (Visual Basic Application) code for GIS AS (Advisor System) Module VBA (Visual Basic Application) code for AHP Excel Application Eldrandaly et.al. (2003) Loose Tight Interoperable Integration Introduction Home Interoperable GIS database Code Spatial Analysis Software

13. Recent Development GIS Decision Making Integration Recent Dev’t Conclusions GIS systems have evolved from a ‘ close ’ expert oriented to an ‘ open ’ user-oriented technology An integration of MCDA and geo-computation can enhance the GIS-MCDA capabilities of handling larger and more diverse spatial data sets. GIS and decision making would likely to come in interoperable geo-processing services that can be chained to build specific spatial decision support services GIS and decision making will moves to distributed systems where everyone have an access to use it. Introduction Home

14. Conclusions GIS Decision Making Integration Recent Dev’t Conclusions We have reviewed the most important component of GIS and decision making GIS and decision making is different from common feature of GIS. GIS in the decision making framework still contains some limitation , the most important criticism in GIS is that GIS has limited ability to compare and asses different scenarios of alternatives In the framework of decision making, it is argued that GIS can only play significant role in intelligence phase, whereas in choice phase GIS has some limitation to play its role In the way to integrate Multicriteria Decision Making (MCDA) and GIS, there are three methods that can be proposed; loose coupling, tight coupling, and interoperable Recent development within the GIS and decision making framework are related with the development of interoperable software Further experiment to solve real world spatial problems is still challenging especially in the framework of soil science and decision making Introduction Home

15. Closing GIS Decision Making Integration Recent Dev’t Conclusions Thank You Merci Beaucoup Danke je Wel Vielen Dank Gracias Terima Kasih Kyay zuu Cám ón Shukron Arigato gozaimas Xie xie Dhanyabad Introduction Home

16. Closing Introduction Case Study GIS Decision Making Integration Recent Dev’t Conclusions Thank You Mercy Danke Wel Xie xie Terima Kasih Keumeun Shukron Home

17. Case Study Introduction Case Study GIS Decision Making Integration Recent Dev’t Conclusions Home The objective : to ensure the productivity of forest resources over time, taking into consideration the environmental, economic and social values of the forest. Multicriteria Evaluation Tools in Sustainable Forest Management Criteria : slopes, precipitation, temperature, soil type, distance from coast line and land use The application of the fuzzy functions for each of the factors allowed the creation of a series of raster maps that reflected their particular importance for the cultivation of each species.