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Driving forces include population growth, health and wealth, technology, politics, economics etc. by which human society will set up targets and goals on
how to improve the quality of life Bregt, 1997.
2.5 Multi Criteria Decision Analysis MCDA
Multi Criteria Decision Analysis MCDA or could be defined as MCDM Multi Criteria Decision Making techniques have largely been a spatial, but they
are different in GIS context. Spatial MCDA which is applied in GIS requires both data on criterion values and the geographical locations of alternatives
Malczewski, 1999. The main concept combination between MCDA and GIS is to support the
decision maker in achieving greater effectiveness and efficiency. Some techniques used to support MCDA in decision making by using decision rules, to choose the
best or the most preferred alternatives Malczewski, 1999. .
The main method in weighted linear combination WLC assigns relative weight to each attribute. Decision maker directly assigns weights to
each attributes. The highest overall score is chosen for the alternative. The weighted linear combination
formula is as follows:
W
t
= Σ
i
W
i
.X
i
……Wn.X
n
1
Where; W
t =
Total Weight W
i
= Weight value in each parameter i to n X
n
= Score value in each parameter i to n
2.6 Spatial Multi Criteria Analysis
Spatial Multi Criteria Decision Making MCDM is a process where geographical data is combined and transformed into a decision. In case the spatial
MCDM, geographic data were used as input to the decision making. Spatial MCDM is more complex and difficult in contrast to conventional MCDM, as
large numbers of factors need to be identified and considered, with high correlated relationships among the factors Malzewski, 1999.
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GIS have capability to store and analyze spatial data effectively, and it could be spatial modeling to support decision analysis Center for GIS, 2005.
GIS has long experience in decision making and map design, and it can integrate with MCDA system to support decision makers Zhao and Garner, 2009.
The goal of spatial MCDM is to achieve solutions for spatial decision problems that take the input from multiple criteria. These criteria, also called
attribute have to be identified very carefully to ensure that the final goal could be achieved Prakash, 2003 in Dewi, 2008.
Spatial data analysis is in many ways the most important part of Geography Information System GIS, because it includes all of the
transformations, manipulations, and methods that can be applied to geographic data to add value to them, to support decision, and to reveal patterns and
anomalies that are not immediately obvious. It is desirable that the geographical data management and analysis component contain a robust set of tools that are
available in full fledged GIS system Malczewski, 1999. Method of analysis used MCDA approach for priority development area of
rubber plantation criteria which is integrated with GIS. Assigning weighted value for criteria use Weighted Linear Combination WLC as described in equation 1
and Pair-wise Comparison Method PCM.
2.7 Analytical Hierarchy Process AHP
