MCAS-S - data.daff.gov.audata.daff.gov.au/brs/mcass/docs/userguide.pdf · MCAS-S is designed particularly for workshop situations, where it helps participants visually link mapped

For Spatial Decision Support

MCAS-SMulti-Criteria Analysis Shell

Version 2.0

User Guide

A product of the Australian Collaborative Land Use Mapping Program

April 2008

Disclaimer

The Australian Government, acting through the Bureau of Rural Sciences, has exercised due care and skill in the preparation and compilation of the information and data in this product. Notwithstanding, the Bureau of Rural Sciences, its employees and advisers disclaim all liability, including liability for negligence, for any loss, damage, injury, expense or cost incurred by any person as a result of accessing, using or relying upon any of the information or data set out in this publication to the maximum extent permitted by law.

Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) is provided ‘as is’ without guarantee or warranty of any kind, either expressed or implied, including without limitation, any warranty of merchantability or fitness for a specific purpose. The Bureau of Rural Sciences is under no obligation to update MCAS-S, or correct any inaccuracy which may become apparent at a later time.

The Bureau of Rural Sciences does not represent or warrant that calculations in MCAS-S are accurate, correct, useful or meaningful, and does not accept any responsibility for the use of MCAS-S in either the form as supplied or as modified by others.

The Bureau of Rural Sciences may at any time, at its discretion, amend, vary or modify these terms and conditions. Modifications to these terms and conditions will be effective immediately.

Postal address: Bureau of Rural Sciences GPO Box 858 Canberra, ACT 2601 Internet: http://www.brs.gov.au

Copies available from: BRS Publication Sales GPO Box 858 Canberra ACT 2601 Internet: http://www.brs.gov.au

© Commonwealth of Australia 2008

This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process without prior written permission from the Commonwealth. Requests and inquiries concerning reproduction and rights should be addressed to the Commonwealth Copyright Administration, Attorney General’s Department, Robert Garran Offices, National Circuit, Barton ACT 2600 or posted at http://www.ag.gov.au/cca.

MCAS-S: Multi-Criteria Analysis Shell for Spatial Decision Support | Version 2.0 �

Introduction 5

What is MCAS-S? 5

Who can use MCAS-S? 5

What’s new in version 2.0? 6

Multi-criteria analysis 6

Approaches and applications 6

Assessment 7

Feedback 7

Getting started 8

System requirements 8

Installation 8

Data 8

Sample datasets for MCAS-S 8

Preprocessing 9

Key functions 11

Opening a project 11

Primary input data 12

Data classification 15

Classifying continuous data 15

Classifying categorical data 15

Composite development 18

Manual (default option) 19

Function 19

Analytical Hierarchy Process (AHP) 21

Two-way comparison 22

Multi-way comparison 23

Contents

� MCAS-S: Multi-Criteria Analysis Shell for Spatial Decision Support | Version 2.0

Vector overlay 26

Masking analysis 27

Mask view and data 28

Ancillary features 29

Copy log as text 29

Copy layer as image 29

Delete 29

Export 29

Reporting 30

Save image 31

Print 31

Viewer window 31

Changing class colours and names 32

Adding colour ramps 33

References 34

Appendix 1: Primary input data 35

Appendix 2: Overlay data 37

Appendix �: Mask data 38


Introduction

What is MCAS-S?

Decision-makers often need to access and analyse large amounts of environmental, social and economic information. The transparent and logical treatment of this information and the use of value judgement incorporating public opinion and policy and management goals can be achieved using multi-criteria analysis (MCA).

The Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) is a software tool developed by the Bureau of Rural Sciences that brings the MCA process into the decision-makers realm. It is an easy-to-use, flexible tool that promotes:

• insightful desktop combination and study of different types of mapped information

• understanding of the relationships between the decision-making process and the available spatial data

• interactive ‘live-update’ and mapping of alternative project scenarios. GIS programming is not required, removing the usual technical obstacles to non-GIS users.

Who can use MCAS-S?

Managers, policy-makers and land management researchers at the national, state and local level involved in land resource evaluation and decision-making will find MCAS-S a helpful tool, particularly those working with spatial data with limited GIS support.

MCAS-S can assist in participatory processes and workshop situations where transparency between different approaches to the classification of spatial data and potential combination rules is needed. Stakeholders can see the potential impacts that their decisions may make.

MCAS-S allows users to:

• select spatial data, place them into the display workspace window, change their information and create composite datasets using the primary data

• see multiple datasets simultaneously displayed as map windows in the display workspace, modify values within the datasets interactively, and see both the relationships between the datasets and the flow-on effects of modifying spatial data values

• carry out two-way and multi-way comparisons to form a cognitive flow diagram of maps, display their relationship to each other on the screen and manipulate them in the display workspace.

� MCAS-S: Multi-Criteria Analysis Shell for Spatial Decision Support | Version 2.0

An advantage of the system is that it automatically selects analysis functions and panels according to map type. The display and analysis functions change automatically when the user accesses different types of spatial display maps, such as raw input data, composite indicators, and two-way and multi-way comparisons of datasets. These features make the MCAS-S interface very intuitive to use.

What’s new in version 2.0?

In response to user demand, a number of additional functions and refinements are introduced in MCAS-S Version 2. These improvements enable:

• recognition of time series in spatial data inputs, enabling expressions derived from time series data to be created in the MCAS-S display workspace

• development of composite spatial datasets using a pair-wise comparison Analytical Hierarchy Process approach

• creation of composite spatial datasets using algebraic expressions in a ‘map calculator’

• improved reporting, with additional statistical information relating to spatial datasets

• improved masking, including multiple intersection and union combinations

• expanded use of colour and flexible methods for generating colour ramps.

Multi-criteria analysis

Approaches and applications

MCA assessment of complex issues in coupled human–environment systems has found wide application across business, government and communities around the world.

There are many variants of the general MCA approach, and the process can be applied in a wide variety of contexts. Well-developed MCA approaches generally share a number of characteristics; for example, they:

• are highly flexible and relatively simple to use

• enable the capture of quantitative and qualitative data and issues

• permit the development of many alternative scenarios

• allow the exploration of trade-offs

• enable the stakeholder to factor results into decision-making processes.

MCAS-S is designed particularly for workshop situations, where it helps participants visually link mapped information to the problem solving process.

As a decision-support tool, MCAS-S has wide functionality—a project can be constructed at any scale and resolution. Since users have the capacity and freedom to carry out inappropriate and invalid data associations, any assessments using MCAS-S should take advantage of expert opinion and stakeholder advice, and results should be clearly articulated in the context of data dependencies, assumptions, actions and user perspectives.


Assessment

There are six steps in the MCA assessment process:

1. Define the problem and decision criteria.

2. Identify variables that influence outcomes and decision criteria.

3. Assemble data inputs.

4. Design methods for synthesis.

5. Develop viewpoint profiles with clients/interest groups.

6. Workshop the results, and develop a consensus view or sets of options.

The key element is step 1, because the information gained at this step determines the subsequent steps in the process.

MCA does not make decisions and does not produce a solution. The outputs from an MCA assessment reflect the expert knowledge of the stakeholder in their assessment of the values contained within a dataset and an understanding of the relationship between the datasets. For example, for an ecosystem services assessment, the relationship between water quality, biodiversity and adjacent land use may be issues that determine the long-term sustainability of existing agricultural practices.

Attention must be given to how information quality and uncertainty is factored into and amalgamated with stakeholder viewpoints, biases, political and structural realities, and what is achievable versus what is optimal. It is important that each stage of the MCA process is carried out rigorously, in conjunction with stakeholder workshops and decision-making.

Feedback

Comments and suggestions that increase the usefulness of this decision support tool are appreciated and will assist with future developments. A web-based MCAS-S tool is currently under development.

Manager Land Use Mapping Bureau of Rural Sciences GPO Box 858 Canberra ACT 2601 Email: [email protected]

MCAS-S updates and downloads are available from the MCAS-S website at: www.brs.gov.au/mcass.

8 MCAS-S: Multi-Criteria Analysis Shell for Spatial Decision Support | Version 2.0

Getting started

The MCAS-S application runs on most standard computers, and is ready for immediate use on an appropriate desktop computer once the MCAS-S file structure is copied to a convenient location in a directory structure, as described below.

System requirements

MCAS-S requires the following:

• Windows (NT, 2000, XP or later)

• 512 MB of RAM (minimum recommended)

• 1 GHz or faster CPU

• 500 MB of disk space

• Microsoft .NET Framework Version 1.1 installed on your computer.

Installation

The MCAS-S CD contains a number of files, as shown in Figure 1.

Figure 1 MCAS-S files

Double clicking on the MCAS folder will bring up a series of instructions that guide the user through the installation of the MCAS-S software. The software should be installed on the C: drive, except for the Data folder and User Guide, which can remain on the MCAS-S CD. Installing the program will add the MCAS-S icon to the desktop. Double clicking on the MCAS application icon will start MCAS-S (the screen shown in Figure 2 will appear).

Data

Sample datasets for MCAS-S

Included with MCAS-S Version 2.0 are a number of sample spatial datasets for Australia, suitable for use at the national scale. Appendixes 1–3 list sample datasets distributed with the MCAS-S application software. Users can either copy the sample datasets from the Data


folder or create and install their own datasets. The sample datasets listed in Appendix 1 can be copied to the \Data\Primary folder; Appendix 2 datasets to the \Data\Overlay folder; and Appendix 3 datasets to the \Data\Mask folder. Gridded sample datasets have been generated as geographic raster images at approximately five-kilometre resolution.

Preprocessing

MCAS-S data inputs must conform to a common spatial referencing system (i.e. a common projection). Also, gridded input data must be of consistent grid resolution and spatial extent. For example, gridded sample data included with the MCAS-S CD is in the Geocentric Datum of Australia (commonly referred to as GDA94) and geographic coordinates (decimal degrees) formats, with a five-kilometre grid resolution.

MCAS-S spatial data inputs can be of three types:

• Primary data—refers to raster data1 for analysis. These datasets can be imported in BIL, ArcInfo float, GeoTIFF and IDRISI raster formats, and made ready for use within MCAS-S by saving the files in the project directory \Data\Primary. MCAS-S can also recognise ArcInfo grids.

MCAS-S Version 2.0 also recognises time series data. These data should be stored as a stack of raster files in the appropriate format in a separate folder created within the \Data\Primary folder. The names of the datasets in the time series stack can be flexible but for MCAS-S to recognise the data as a time series, each component dataset needs to contain a string of either six digits or eight digits: six digits is assumed to be YYYYMM; eight digits is assumed to be YYYYMMDD. For example ‘rain_198001’ would be a time slice of rainfall for January 1980; ‘rain_20051231’ would be a time slice of rainfall for 31 December 2005.

1 GIS systems use two types of data—raster and vector. For raster data, representation of objects is based on the elements of a matrix, given as grid points or pixels. For vector data, representation is based on distinct points described by their co-ordinates and relations.

Figure 2 MCAS-S interface


• Overlay data—refers to vector data for contextual overlays (optional).These datasets can be imported as ESRI shapefiles into the project directory \Data\Overlay.

• Mask data—refers to raster data defining the geographic limits for analysis and reporting (optional). All cells of interest have a value and, by using particular masks, only the information within the area of interest will be considered in the analysis and displayed. These datasets can be imported in BIL, ArcInfo float, GeoTIFF and IDRISI raster formats by saving them in the project directory \Data\Mask. Text (.txt) files with the same name as the mask files linking labels with grid values can be included in the Mask folder. The labels within the text file will be displayed in the Mask drop-down menu on the MCAS interface (Figure 3).

Figure � Display of the text file contents for the NRM regions mask data

In Figure 3, text files can be located with the mask data in the Mask data folder. The left window of the figure shows NRM region labels and grid values from the NRM regions text file. The right window shows the corresponding list of NRM regions from the Mask drop-down menu on the MCAS-S display workspace.

The use of a proprietary GIS will be required if spatial data is being assembled from disparate sources. MCAS-S exports gridded output datasets as GeoTIFF files.

The preprocessing phase of setting up MCAS-S is completed when spatial data inputs are located within the project file structure and ready for use in MCAS-S drop-down menus under the headings Primary Input Data, Overlay and Mask. The next step is to undertake MCAS-S analysis.

MCAS-S: Multi-Criteria Analysis Shell for Spatial Decision Support | Version 2.0 11

Key functions

Opening a project

Once preprocessing is complete, a new project can be created by going to the File drop down menu and selecting New. This will display a template (Figure 4) that can be used with existing data from a current project. Alternatively, by selecting a new project structure, users can place their own files into the Primary data, Overlay data and Mask data project files. Another alternative is to open a saved project by going to the File menu, clicking on Open and browsing to the folder in which it is saved.

Figure � Display of the new project template

There are several ways to create a new project by clicking on New under the File menu. The first option—Store alongside open project, reusing project data—is the same as the Save as option that allows the user to modify a project and save it as a new version. This option is available when working in a project.

The second option—Create alongside existing project, reusing project data—allows a new blank project to be created using an existing dataset. This involves simply naming the new project and then browsing to the required dataset.

The third option—Create new project folder structure—allows a new input and a new dataset to be created in a specified directory. This option requires construction of a dataset as described in the Data section, above, or the creation of a new dataset as a subset of an existing larger dataset (using copy and paste from an existing dataset to a new one). Once a project is open, the primary data grids, overlays and masks saved in the Data folder will be available for data analysis.


Primary input data

The Primary folder contains a range of datasets for display and undertaking analysis.

Primary data layers are selected from a drop-down menu under the heading of Primary Input Data by clicking and dragging them in turn into the display workspace with the mouse (Figure 2). Data layers can be sorted into separate folders under the Primary folder.

The two types of data usually included in the Primary folder are categorical data and continuous data (Figure 5).

Figure � The Layer Data Format window generated by dragging a categorical primary data layer into the MCAS workspace

Categorical data (also referred to as frequency or qualitative data) is grouped or categorised according to some common property, such as soil type or vegetation type. The data have labels that describe a category or group of interest. Although primary data grids only contain numerical values, labels describing categorical data can be displayed by including a text file with the same name as the grid file in the same data layer folder (see the Classifying categorical data section of the MCAS interface shown in Figure 2).

Continuous data has a potentially infinite number of possible values along a continuum. This includes data that have no breaks or spaces and can be continuous in the geometry or range of values. In practice, the range of values for a particular item of data has a minimum and a maximum value, such as surface elevation and rainfall. Continuous data includes items such as densities, rates and percentages, which are classified according to project requirements.

Continuous data layers selected from Primary Input Data will appear in the display workspace and will initially be classified into five classes (Figure 6). The user can select 2–10 classes. Figure 6 shows an example of the MCAS-S display workspace, with the drop-down menu for Primary Input Data, and the histogram and classification option for input data in the interface panel (discussed later).

MCAS-S: Multi-Criteria Analysis Shell for Spatial Decision Support | Version 2.0 1�

Figure � A Primary Input Data layer called elevation dragged from the Primary Input Data menu has automatically been allocated into five classes, and the user has classified the data according to Equal area

Expressions derived from continuous time series data prepared in accordance with the specifications outlined in the section ‘Preprocessing’ can also be imported into the MCAS-S display workspace. The folder containing the time series in \Data\Primary is selected from the drop-down menu under the heading of Primary Input Data and dragged into the display workspace. An Import interface appears showing the list of gridded datasets available within the time series. One or more of the datasets listed in the Import interface may be selected by clicking on a listed grid, and/or holding down the shift key to select a group of grids, or the control key to select further individual grids for the group. Clicking on a selected function button on the Import interface will derive a new layer expressing that function for the selected grids. The derived layer will appear in the MCAS-S display workspace (see Figure 7).

1� MCAS-S: Multi-Criteria Analysis Shell for Spatial Decision Support | Version 2.0

A single grid is selected for inclusion as a layer in the display workspace by clicking on the listed grid and the Single function. Functions that can be applied to selected grids in the Import interface are as follows:

• Minimum—returns the minimum value for each cell from the selected grids

• Maximum—returns the maximum value for each cell from the selected grids

• Range—returns the difference between the maximum and minimum values for each cell (absolute variation) from the selected grids

• Average—returns the mean for each cell from the selected grids

• Standard Deviation (Std Dev)—returns the standard deviation for each cell based on the selected grids

• Coefficient of Variation (Coef. Var.)—returns the coefficient of variation for each cell based on the selected grids.

Figure � Time series data dragged from the Primary Input Data menu can be imported as individual datasets (Single option) or as a function of the selected datasets (Minimum, Maximum, Range, Average (mean), Standard Deviation or Coefficient of Variation). This example shows the ‘Average’ summer rainfall for 1�80–1�8�.


Data classification

Data can be classified as continuous or categorical. Once a primary data layer has been classified the user is able to save settings for that layer, even if it is subsequently deleted from the project.

Classifying continuous data

Each primary data layer can be classified into up to 10 classes using an Equal interval, Equal area or Custom (user-defined) classification (Figure 8). Classifying the data according to Equal interval groups the data into regular classes regardless of their distribution, whereas Equal area allocates the same number of data points to each class. The Custom option allows the user to set the specifications for data classification; for example, to specify threshold values. Default class names (e.g. ‘class 1’ or ‘class 2’) can be changed by typing into the text boxes.

If selecting the Custom classification, the values can be set by sliding each vertical boundary on the histogram to the desired value (which changes both the map colours and the corresponding value in the classification box), or by entering the values in the classification boxes. The values can be truncated from the bottom of the range (Truncate values that are out of bounds in the drop-down menu) by checking the box ‘Truncate values <’, and assigning a value below which values will be included in the lowest class. Values can also be truncated from the top of the range by checking the box ‘Truncate values >’, and assigning a value above which values will be included in the highest class. This facility allows the range of classified values to be managed, particularly outlier values or highly skewed distributions.

Figure 8 The elevation data layer has been classified into five classes using the Equal area option and a lower range limit of 200 m; all values (heights) less than 200 m have been allocated to class1 (<21�.228 m, displayed in blue)


In Figure 8 the user has chosen to truncate values <200. The five-class Equal area classification has been applied to the range 200–2021.18. Values below 200 m have been allocated to class 1, which therefore includes all values <214.228 m.

The minimum and maximum values of the range of complete or truncated values for the selected data layer are displayed above the histogram. This is useful when working with the data; for example, in deciding where to make breaks in classes when displaying the data using the Custom option.

Values can also be removed from both the top and bottom of the range by selecting Discard values that are out of bounds from the same drop-down menu, and again assigning a minimum and/or maximum value. Areas with values outside the selection will appear as grey in the corresponding map window.

Class drop-down menu allows data to be classified into 2–10 classes.

Histogram shows the distribution of values and the associated class colour. Here, the data has been classed using Equal area. Values for the range of data are shown above the histogram.

Class information—colour and minimum value. The colour scheme can be reversed by checking the Allocate classes in reverse order box above. Colours can also be manually changed by clicking on the colour box, and classes can be named by clicking in the class text box.

Toggle allows the user to switch

between classifying the data layer are

continuous or categorical data.

Here, this option is greyed out because

the data are continuous.

This drop-down menu allows the user to

truncate or discard values from the top

or bottom of the range of values of the

selected data layer.

In this example, all data below 200 m

have been truncated and will be allocated

to ‘class 1’. If the discard option is

selected, data outside the range appears in

grey on the map.

Figure � Detail of the continuous Primary Input Data interface panel for the elevation example shown in Figure �

Colour ramp provides options to display as default colours, user

created colour ramps or in black and white


The final option in the continuous Primary Input Data interface panel is an Allocate classes in reverse order check box, which simply reverses the order of the class colours. Figure 8 shows details of the continuous Primary Input Data interface panel that appears on the left of the MCAS-S display workspace.

Time series data should be classified in the same way as continuous data.

Classifying categorical data

Categorical data can be classified using the categorical Primary Input Data interface panel to suit project requirements. The categorical Primary Input Data interface panel displays both the classes (up to 10) as well as the categories of which the Primary Input Data layer consists (Figure 10). Default class names (e.g. ‘category 0’, ‘category 1’ or ‘category 2’) can be changed by typing into the text boxes.

Figure 10 Detail of the categorical Primary Input Data interface panel with a primary data layer of dominant vegetation types classified into broad species groups


Clicking on the button top-right of the panel changes the categorical classification to continuous, and automatically allocates your classes. As the program will default to five classes, the user needs to reselect the number of classes required.

The user specifies how classes are grouped together (e.g. ‘high through to low’, ‘good through to poor’ and ‘vegetation structure’) and manually types these groups into the Classes area of the interface panel. Classes can be manually allocated by first clicking on the appropriate colour in the Categories or Classes area of the interface panel, then clicking the corresponding categories in the Primary Input Data layer.

Composite development

Once individual data layers have been created, they can be combined to construct composite indicators. When a new map window is dragged from the menu button Composite into the display workspace, the interface panel for creating a composite appears automatically. The interface panel lists data layers currently on the display workspace and available for the construction of the composite (Figure 11).

There are several ways to combine the data layers using the interface panel:

• the Manual option allows the simple weighted combination of data layers

• the Function option enables the creation of a composite map from layers using an algebraic expression

• the AHP option enables the weighted combination of data layers using a pair-wise comparison Analytical Hierarchy Process.

In each case the combination of the input layers is shown as an expression in the interface panel. As a default, composite results are displayed as normalised on a scale of 0–1. To display as raw figures, uncheck the ‘Normalise composite result’ button on the interface panel.

Manual (default option)

When a composite map window is created the Manual option is the default layer combination method. Each layer has a slider bar and entry box, where the weighting of the contribution of individual data layers to any composite can be set. The composite map dynamically updates as the weightings on the input layers change. MCAS-S applies a simple additive weighting procedure, where cell values for each selected input data layer are multiplied by a nominated weighting factor and then summed. Values in the resultant composite layer are rescaled or normalised to the range of 0–1 (where 0 = minimum value and 1 = maximum value). Normalised composite data layers may be classified into 2–10 classes, as per the standard classification procedure.

As shown in Figure 11, a number of individual and composite data layers can be included in the display workspace and grouped by theme. This creates a cognitive or mental map of the relationships between each component in a project. A pathway showing the relationship between each component can be followed all the way through to a final summary composite.


Figure 11 An MCAS-S interface showing the development of a composite indicator based on the unweighted Manual combination of three Primary Input Data layers, with weightings for each indicator shown at the top of the left panel

Function

The Function option allows input datasets to be combined using an algebraic expression. To enter a function, select the Function radial button then press the Edit button. An expression combining input layers (including numerals as operands) can be typed in the Function Editor window. Input layers should be entered in the Function Editor using their desktop name in braces {} if raw data values are required and box brackets [ ] for classified data values. For example, the simple expression {layer1} * {layer2) will produce a composite map of values which are the product of the raw values of layer1 and layer2.

The following functions are supported by MCAS-S:

+ - * / < <= = <> > >= exp() log() pow() and or iif()

Syntax examples for these functions are shown in Table 1. Unless specified, when using a conditional statement, the number 1 will apply where the statement is true and 0 where the statement is false (see Figure 12).


Figure 12 An MCAS-S interface showing the development of a composite indicator based on the Function combination of four Primary Input Data layers. The expression used to create the composite map is shown in the interface panel

Table 1 Syntax examples for developing a composite map using the Function Editor*

Syntax Description

{layer1}*2 Returns the raw values of ‘layer1’ multiplied by 2.

[layer1]*2 Returns the classified values of ‘layer1’ multiplied by 2

{layer1}*3 + [layer2] Returns the raw values of ‘layer1’ multiplied by 3 and then added to the classified values of ‘layer2’

[layer1]/[layer2] Returns the classified values of ‘layer1’ divided by the classified values of ‘layer2’.

pow({layer1}, 2) Returns ‘layer1’ raw values to the power of 2.

exp([layer1]) Returns the exponential of ‘layer1’ classified values.

log([layer1]) Returns the base 10 logarithm of ‘layer1’ classified values

{layer1} > 50 and {layer1} < 200 (see Figure 12)

Returns a value of 1 where raw values of ‘layer1’ are between 50 and 200. Otherwise returns a value of 0.

iif({layer1} > 200 and {layer2} > 20, {layer3}, 0)

Returns the raw value of ‘layer3’ where ‘layer2’ is greater than 200 AND ‘layer3’ is greater than 20. Otherwise returns a value of 0.

* Note: results will be normalised as a default

MCAS-S: Multi-Criteria Analysis Shell for Spatial Decision Support | Version 2.0 21

Analytical Hierarchy Process (AHP)

The Analytical Hierarchy Process (AHP) option provides a more structured alternative to the simple additive weighing procedure used for Manual composite development. Input layers are assessed against each other on a pair-wise basis with judgements made as to comparative importance. Selecting the AHP option opens a window in the interface panel which enables selection of relevant layers from those in the display workspace. Selecting the Edit button on the weighting panel opens an AHP Editor window that includes an interactive AHP matrix enabling the user to rank input layers as less or more important compared to other input layers. Pair-wise weightings can be edited by clicking on the light grey number boxes in the AHP Editor window and then selecting a ranking option from the drop-down menu. Once a weighting option has been selected the relevant number boxes will turn white. Dark grey boxes cannot be edited. Figure 13 shows an example of AHP combination using three input data layers.

Figure 1� A MCAS-S interface showing the development of a composite indicator based on the AHP combination of three Primary Input Data layers


Two-way comparison

The spatial relationship between data layers (including composites) may be examined using several methods in MCAS-S. A two-way comparison allows you to explore the spatial association between two data layers, and define a colour ramp and value scale to highlight the association of high and low values of the contributing layers. Clicking on the Two-way menu button and dragging a new map window into the display workspace brings up the two-way interface panel at the left (Figure 14).

Two data layers are selected from those displayed in the display workspace by using the Variables selection on the interface panel. The two-way comparison is visualised in a dynamic two-dimensional colour matrix linked to the map display in the display workspace.

The number of classes the data layers have been classified into will be represented on the x and y axis by the matrix (up to 10 x 10 classes). The two-way comparison can be classified in up to 10 classes, and class colours changed to specific project requirements. Right clicking the mouse moves the focus of the colour ramp to any point within the matrix (Figure 14).

Figure 1� Dynamic two-dimensional colour grid showing the relationship between annual rainfall and elevation, with the colour grid set to identify locations where there is a coincidence of high rainfall and high elevation classes


Alternatively, the two-way comparison map can be customised by assigning a specified colour to selected cells in the matrix. Cell selections are made by pointing to desired cells in the matrix shown in the two-way interface panel and left clicking on the mouse (Figure 15).

Figure 1� A two-way display workspace showing the relationship between annual rainfall and elevation, customised to highlight locations where there is a coincidence of the highest four classes of rainfall and elevation

Multi-way comparison

Multi-way comparison is used when the spatial association of two or more data layers is required. When a map window from the Multi-way menu button is dragged into the display workspace, a Source Layers panel appears within the interface panel listing the data layers displayed in the display workspace. Data layers can be selected for multi-way comparison by checking those listed within this panel. The multi-way analysis uses the radar plot as the basis for visualisation (Figure 16). Each vector on the radar plot represents a single selected data layer, scaled according to class values.

The user can identify sets of class values for each input data layer by adjusting the slider scales on the Multi-way map shown in the interface panel, and can also set maximum and minimum boundary values on each vector in the radar plot. In this way, the user can specify a set of conditions that they wish to satisfy. Locations where this set of conditions applies are shown in the multi-way comparison map window. When the slider scales in the interface panel change, the multi-way map updates to show the region satisfying


these criteria. For example, the multi-way map in Figure 16 shows areas of high elevation, high annual rainfall and high maximum temperature.

The Multi-way Mask function displays results in a binary format—distinguishing those locations that satisfy criteria values from those that do not (Figure 16).

The Multi-way Continuous function displays continuous surface as a grey-scale—indicating the degree to which locations satisfy (or are distant from) selected criteria values (Figure 17).

The Multi-way Composite function combines and scales all data layers (hence the absence of the selected criteria values on the radar plot), in a manner similar to the standard composite analysis (see Composite development section) (Figure 18).

Figure 1� Multi-way display, showing the interface panel that lists the data layers visible in the display workspace, with check boxes for inclusion in a multi-way analysis. Using the Multi-way Mask function, the black areas on the map represent regions that satisfy class values specified by the white area of the multi-way map (radar plot) in the interface panel, and the grey areas represent regions that do not satisfy these conditions


Figure 1� Multi-way continuous function displaying as a grey-scale surface, which lightens with increasing ‘distance’ from the selected criteria values

Figure 18 Map for a multi-way analysis, using the multi-way composite function


Vector overlay

The Overlay drop-down menu allows the user to select and display line data such as roads, boundaries, rivers and the coast over data layers. When overlays are selected, the default colour is black. Line colours can be changed by clicking on the colour box next to the overlay layer in the drop-down menu, which brings up a colour palette from which the user can select a colour. This is a useful function when displaying multiple overlays (Figure 19).

Figure 1� Sample elevation data layer with the vector overlays for state boundaries (in black) and major roads (in red); colours can be selected from a palette to differentiate each overlay


Figure 20 Annual rainfall data layer with a Victoria Mask View Only applied

Masking analysis

Masks can be used to select specific areas for analysis. Using masks, it is possible to display only the data for a selected region (such as a catchment or bioregion) within the map windows in the display workspace (Figure 20). Masks are introduced by checking a selection from those available in the Mask drop-down menu (see also System requirements for installing mask data layers). An aggregated mask can be created by checking one of the masks and holding down the shift key to select further masks. A mask created from the intersection of two or more masks may also be created by clicking on the intersect symbol at the base of the Mask drop-down menu.

Once initiated, masking applies to all functions and processes carried out in an MCAS-S project.


Mask view and data

Figure 21 illustrates the application of the Mask View and Data function. Only annual rainfall values specific to Victoria are displayed, and this is reflected by the changes in the class allocation in the interface panel and in the display in the map window.

The values in the classification previously ranged from 201.931 to 926.7766. However, the application of the Mask View and Data function changes the value range from 321.5148 to 1108.933.

Figure 21 Annual rainfall data layer, with a Victoria Mask View and Data applied


Ancillary features

Right clicking on any active map window makes several options available (Figure 22).

Figure 22 Functions obtained by right clicking on the active map window

The functions that some of these options relate to are outlined below.

Copy log as text

The Copy Log As Text function copies a log of processing steps associated with a data layer in the active map window to the clipboard; these steps can then be pasted directly into a document.

Copy layer as image

The Copy Layer As Image function copies a selected map window as an image to the clipboard that can be pasted directly into a document.

Delete

The Delete function removes the active map window from the display workspace.

Export

The Export function saves the data layer in the active map window. Any data layer can be exported, including two-ways and multi-ways. Exported data can be saved by right clicking on the active map window with the mouse, and selecting Export. An Export Classified Data window will appear; this allows the user to include a description of the classified data layer, give it a file name, and save it in a group folder (Figure 23). Saving the classified data to groups may be useful for ordering large numbers of derived data layers, and can be

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structured according to project needs. MCAS-S saves these data layers as a GeoTIFF. The classified layer can be retrieved from its saved location in a listing under the Classified Data menu item either directly, if the layer has not been allocated to a group, or under the group folder if the layer has been allocated.

Figure 2� Export classified data window

Reporting

The Reporting function will generate statistics for regions defined by user-nominated mask data layers and export these statistics to an Excel spreadsheet. Save by right clicking on the active map window with the mouse, and selecting Reporting. A Reporting window will appear that allows the user to select the mask data layer that contains the reporting areas; select from the reporting options Normalized Counts or Cell Counts. The Normalized Counts option is useful for calculating proportional amounts of each class, whereas the Cell Counts option is useful for estimating areas of each class in each reporting region (Figure 24). Each option will report the maximum, minimum, range, mean and standard deviation for each reporting area.

Note: For masked data layers, the reporting function should only be used when the Mask View and Data option is selected.

Figure 2� Reporting template obtained by right clicking on the active map window and selecting Reporting

MCAS-S: Multi-Criteria Analysis Shell for Spatial Decision Support | Version 2.0 �1

Save image

The Save Image function provides the option of saving the selected map window as a Portable Network Graphics (png) image file.

Print

The Print function brings up the printer options window so you can print the display workspace directly.

Viewer window

The Viewer window in the display workspace provides details from the data layer in the active map when the mouse is poised over any map window in the display workspace. It provides the values from the data layer, and additional information from the analyses (Figures 25, 26, 27 and 28). The Viewer window provides a range of information depending on the type of map window that is open at the time. The viewer can be closed. To reopen, go to the Edit drop-down menu and click Show viewer.

Figure 2� Primary Input Data viewer, which provides information about cells on an active map when the pointer is placed over the Primary Input Data map window. In this example of a data layer from the Primary Input Data (elevation) the viewer indicates the value for a specific grid cell

Figure 2� Composite viewer, which provides information about points on an active map when the pointer is placed over a Composite map window. In this example, a composite map (elevation and annual rainfall) has been created from two data layers. The viewer indicates the ‘normalised’ value of the two cells, weighting of each layer and their values for a specific grid cell


Figure 2� Two-way viewer, in which the viewer shows the layer values of the selected cell in a Two-way map window. The viewer indicates the value of a selected cell in relation to the rest of the Two-way matrix. The values for a specific grid cell are displayed at the bottom of the viewer

Figure 28 Multi-way viewer, showing value selected from the cell on a multi-way comparison in the Viewer window composite map (elevation, maximum temperature and annual rainfall) created from three data layers. The viewer indicates the value for a specific grid cell

Changing class colours and names

A default blue-red colour ramp is applied automatically to data layers in MCAS-S. An additional set of colour ramps can be selected from MCAS-S interface panels, including a black and white option.

In the data layer information panel shown separately here, class colours can also be changed from the default colours by either left or right clicking on the class colour box. A colour window appears and basic or custom defined colours can be selected (Figure 29).

MCAS-S: Multi-Criteria Analysis Shell for Spatial Decision Support | Version 2.0 ��

Figure 2� Colour window opened by right clicking on a class colour box and, in this case, selecting the Define Custom Colour option

Class names can also be added in the interface panel. For example, classifying values within layers could be classed as required and then named, for example, as ‘Low’, ‘Medium’ or ‘High’ instead of the default values ‘class 1’, ‘class 2’, ‘class 3’, etc (Figure 27). This can also be done when classifying categorical data; for example, grouping vegetation into structure classes such as ‘Eucalyptus’, ‘Callitris’ and ‘Casuarina’ (Figure 9).

Figure �0 Each class can be named by typing directly in the class text box

Adding colour ramps

Personalised colour ramps can created and saved in two locations. To create a colour ramp that can be accessed by all MCAS-S projects, simply add an entry to the ramps.txt file located in the MCAS-S installation folder (where the MCAS.exe file is located). To save a colour file specific to a particular project, save a file called ramps.txt within the Data folder.

Colour ramp entries should be saved using the following convention (separated by commas with no spaces):

‘colour ramp name’,’start colour code’,’middle colour code’,end colour code’

e.g.: YlGnBl,FFFFD9,41B6C4,061D58

which is a colour ramp starting at yellow going through green to blue

Colour codes should be saved as six digit webhex codes.

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References

Hill MJ, Lesslie R, Donohue R, Houlder P, Holloway J, Smith J, and Ritman K, (2006). Multi-criteria assessment of tensions in resource use at continental scale: a proof of concept with Australian rangelands, Environmental Management 37(5):712–731.

Hill MJ, Lesslie R, Barry A, and Barry S (2005). A simple, portable, spatial multi-criteria analysis shell – MCAS-S. In: MODSIM 2005 International Congress on Modelling and Simulation, Zerger A and Argent RM (eds), Modelling and Simulation Society of Australia and New Zealand, December 2005.

Lesslie R, Hill MJ, Hill P, Cresswell HP, and Dawson S, (2008). The application of a simple spatial multi-criteria analysis shell (MCAS-S) to natural resource management decision making. In Pettit C, Cartwright W, Bishop I, Lowell K, Pullar D, and Duncan, D, (Eds) Landscape Analysis and Visualisation. Lecturer Notes in Geoinformation and Cartography Series, Springer, Berlin (In press).


Appendix 1 Primary input data

Data set Description Source

Biophysical

Soil landscapes Soil Landscapes—major soil units from Digital Atlas of Australian Soils (Northcote Classification). This layer only gives one soil type for each landscape.

Bureau of Rural Sciences

Surficial geology Surficial geology of Australia—major structural units from Digital Atlas of Australian Soils. This layer only gives one structural unit for each landscape.

Geoscience Australia, Bureau of Rural Sciences

Elevation Digital Elevation Model of Australia. Elevation (m) derived from 9 second digital elevation model (DEM).

Geoscience Australia

NPP Mean Mean Annual Net Primary Production (t/ha/annum). These are modelled data using a variety of inputs including meteorological services, the Soils Atlas and satellite imagery. An indicator of the growth or carbon assimilation rate of the system.

CSIRO Land and Water

Climate

Annual evaporation Evapotranspiration values across Australia and the mean data are based on the standard 30-year period 1961–1990. Mean Annual Potential Evaporation (mm).

Bureau of Meteorology

Annual rainfall Distribution and quantity of mean annual rainfall (mm). Generated using Anuclim version 5.


Rain Reliability Annual

Rainfall Reliability (Interannual). Probability of receiving at least 75% of annual mean rainfall for April–March.

Derived from Rainfall Reliability Wizard, Bureau of Rural Sciences (www.brs.gov.au/rainfall)

Rain Reliability Winter-Spring

Rainfall Reliability (Winter–Spring Interseasonal). Probability of receiving at least 75% of annual mean rainfall for June–November.

Derived from Rainfall Reliability Wizard, Bureau of Rural Sciences

Rain Winter–Spring Winter–Spring Average Rainfall—mean rainfall for June–November (mm).

Derived from Rainfall Reliability Wizard, Bureau of Rural Sciences

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Maximum temperature

Mean annual maximum daily surface air temperature (°C) for the period 1980–1999. Surfaces are derived from Bureau of Meteorology (BoM) data, interpolated to daily time step and a spatial grid of 0.05 degrees by the Queensland Department of Natural Resources (QDNR). Mean Annual Maximum Daily Temperature (°C).


Minimum temperature

Mean Annual Minimum Daily Temperature (°C).


Rain Autumn Mean Mean Seasonal Rainfall—Autumn (mm).* Derived from Rainfall Reliability Wizard, Bureau of Rural Sciences

Rain Winter Mean Mean Seasonal Rainfall—Winter (mm).* Derived from Rainfall Reliability Wizard, Bureau of Rural Sciences

Rain Spring Mean Mean Seasonal Rainfall—Spring (mm).* Derived from Rainfall Reliability Wizard, Bureau of Rural Sciences

Rain Summer Mean Mean Seasonal Rainfall—Summer (mm).* Derived from Rainfall Reliability Wizard, Bureau of Rural Sciences

Land use

Land use— Secondary

Land Use of Australia Version 2. Land use grouped according to the ALUM classification (secondary classes). A land use map of Australia showing agricultural and non-agricultural land uses for 1996/1997.

National Land and Water Resources Audit/Bureau of Rural Sciences

Vegetation

Forest type Forest type from National Forest Inventory. Results of a survey of farm forestry plantations by regions across Australia.


NVIS National Vegetation Information System 2000. The NVIS Information Hierarchy is based on 6 levels of structure and floristic components. NVIS level 3, a broad floristic formation, describes the dominant growth form, cover, height and dominant landcover genus.

Department of the Environment and Water Resources, Environmental Resources Information Network (ERIN)

Time series

Evaporation Mean monthly evaporation values across Australia for the year 1980 (mm).

Australian Water Availability Project (CSIRO, Bureau of Meteorology and the Bureau of Rural Sciences) www.daff.gov.au/brs/climate-impact/awap)

* A defined ‘season’, showing the approximate mean (average) rainfall in millimetres for that season. The mean is always based on all years of record.


Appendix 2 Overlay data


Catchments Australian river basins 1997. Shows a series of physical boundaries of Australia’s basins as defined by the Australian Water Resources Management Committee.

Geoscience Australia

Coast Coastline of Australia. Geoscience Australia GEODATA 250k Series 2

Highways National highway system. Geoscience Australia GEODATA 250k Series 2

NRM Regions Natural Resource Management (NRM) Region Boundaries (formerly known as Natural Heritage Trust II Region Boundaries). Version dated 12 December 2006.


Roads Major roads. Geoscience Australia GEODATA 250k Series 2

State boundaries

Australian states and territories boundaries. Geoscience Australia GEODATA 250k Series 2

Water Drainage. Geoscience Australia GEODATA 250k Series 2


Appendix 3 Mask Data


IBRA Regions Interim Biogeographic Regionalisation for Australia (IBRA) boundaries. Version 5.1.


NRM Regions Natural Resource Management (NRM) Region Boundaries (formerly known as Natural Heritage Trust II Region Boundaries). Version dated 12 December 2006.


State boundaries

Australian states and territories boundaries.

Geoscience Australia GEODATA 250k Series 2

Version 2.0

2008

Documents

MCAS-S - data.daff.gov.audata.daff.gov.au/brs/mcass/docs/userguide.pdf · MCAS-S is designed particularly for workshop situations, where it helps participants visually link mapped