Multi-Criteria Decision Analysis (MCDA), developed in the environmental of Operation Research, aids analysts and decision-makers in situations in which there is a need for identification of priorities according to multiple criteria. This usually happens in situations where conflictive interests coexist (Gomes and Lins 2002). MCDA can incorporate both geographical data and stakeholders’ preferences into quantified values for assessment and further decisions (Malczewski 2004). The GIS analysis, if integrated with a procedure of data analysis and structuring, can be usefully developed when data are available but the decision context cannot indicate how these data have to be used to produce information and support decisions. The GIS support the solution of complex spatial problems, providing the decision-maker with a flexible environment in the process of the decision research and in the solution of the problem. The visualization of the context, structure of the problem and its alternative solutions is one for the most powerful components of a decision support system (Gomes and Lins 2002). Thus the integration GIS-MCDA has the objective of the supporting decision-makers, providing them with ways to evaluate several alternatives, based on multiple, conflictive criteria.
GIS is a set of tools for inputs, storage and tetrieval, manipulation and analysis, as well as outputs of spatial data (Malczewski, 1999). ArcGIS is acknowledged to be a powerful tool in solving the spatial problems. ArcGIS by ESRI GIS and mapping software was applied for spatial data analysis and mapping in this study. All the related data were collected from Terrasever and Terra lab at University of Regina. Land-use maps and administrative information were input into GIS digitally to establish a new geo-database, then overlapped with each other.
Meanwhile, policy analysis based on community plans and literature reviews were completed, serving as foundation for land use type categories. According to available data, land use for human activity were divided into five suitability levels. In this process, multi-criteria analysis method was used for classifying and weighing criteria. Quantitative analysis is necessary for multi-criteria analysis, including scoring, ranking and weighting.
Finally, an output map of the land use suitability with five classes was displayed and a comparison was conducted between the new land use pattern and the pre-existing land use status. The Halme et al. approach introduces the decision-maker’s preference in the efficiency analysis, by explicitly locating his most preferred solution vector on the efficient frontier.
The same authors highlight that when systematically exploring the neighborhoods of the Most Preferred Solution (MPS), one does not know explicitly the decision-maker’s value function, but its form becomes known when the end of the search for MPS is reached.
Weight product (WP) method has been introduced centuries ago and been advocated in the past few years. WP is a relative simple multiple attribute utility methods. Since WP is easily understood by decision makers and is easy to be conducted, this method have been widely applied in many fields. In this study, based on the WP method, three factors including population, employment and average income, hold significance in municipal land use planning. In the multi-criteria analysis process as showed in Figure 5, they were assign different weight to obtain a total score of every region based on the following formula (Gomes and Lins 2002):
Where Wj --- is weight of each criterion j = 1,2,….m,
And Ai---is the normalize the value of each grid cell, i = 1,….., n.
The general MCAD approach for this case may be seen in (1), where Xi, Yi,…, represent the value of the criterion X, Y…, for the alternation i; λ are the decision variables that represent the decision-maker’s preferences for the alternative i, i = 1,…,n. For this case study, λ vector representing the decision maker preferences.
Defining the criteria
The expansion of land has overwhelmingly been a response to fast-rising population decades ago, so population is considered the most essential drive force of land exploitation. The weight of each criterion has been shown in Table 1. Meanwhile, as the increasing of urban population, the urbanization of Regina fringe is an inevitable trend. Study of population can not only help analysis the existing land use pattern, but also assist land use trend forecasting. Based on the above-mentioned consideration, that population is specified the maximum weight among the criteria. The population distribution of Regina is shown in Figure 6.The employment is expanded to labor force, which is in turn expanded to population equivalents. From a land use point of view, although a city or a region is usually studied as a whole, it is also necessary to examine employment changes brought about by changes in economic. Therefore, employment is defined as a factor in the process of land use or land development. The employment situation of Regina is showed in Figure 7.
Furthermore, as like Hok Lin Leung mentioned that income investigate is essential in house marketing, income is equivalent important in land use planning. The income census of a certain area can reflect the consumption level and trend in a certain extent (Leung 2003). Thus, as one of the economic factor, income is also taken into account when it comes to the land use suitability assessment. The average income of a certain census tract in Regina is showed in Figure 8.