5 the application of a simple spatial multi criteria...

5 The Application of a Simple Spatial Multi-Criteria Analysis Shell to Natural Resource Management Decision Making

Robert G Lesslie1, Michael J Hill2, Patricia Hill3, Hamish P Cresswell4 and Steve Dawson1

1 Australian Government Bureau of Rural Sciences, ACT, Australia 2 Department of Earth System Science and Policy, University of North Da-kota, USA

3 CSIRO Sustainable Ecosystems, ACT, Australia 4 CSIRO Land and Water, ACT, Australia

Abstract: Natural resource management decision making generally requires the analysis of a variety of environmental, social and economic information, incorporating value judgement and policy and management goals. Justifiable decisions depend on the logical and transparent combination and analysis of information. This chapter describes the application of spatial multi-criteria analysis to natural resource assessment and priority setting at regional and national scales using a newly developed spatial multi-criteria analysis tool — the Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S). MCAS-S is designed for use in participatory processes and workshop situa-tions where a clear understanding of different approaches to spatial data management and information arrangement is necessary. The MCAS-S work environment provides for multiple map display, combination and manipula-tion, live update of changes, and development of spider/radar plots important in ecosystem service assessments. These and other capabilities promote clear visualisation of the relationships among the decision, the science, other con-straints and the spatial data. The regional scale example illustrates the analy-sis of biodiversity and salinity mitigation trade-offs in revegetation in a par-ticipatory process. The national scale application illustrates reporting to policy clients on the tensions between resources use and conservation in Aus-tralian rangelands — essentially an expert analysis.

74 RG Lesslie, MJ Hill, P Hill, HP Cresswell and S Dawson

5.1 Introduction

Government policy makers, local authorities and land managers with re-sponsibility for natural resource management decision making are often required to analyse large amounts of environmental, social and economic information. The transparent and logical treatment of this information in-cluding the incorporation of community opinion, public policy and man-agement goals can be achieved using a spatial multi-criteria analysis (MCA) approach (Kiker et al. 2005; Hill et al. 2005a; Malczewski 2006).

Spatial MCA capability is available in a range of commercial geo-graphic information systems (GIS) and this may be customised for particu-lar purposes in spatially explicit decision support tools. However, simple, flexible spatial multi-criteria shells with easy adaptability to any problem are not readily available, particularly without a programming requirement. For participatory processes and workshop situations, a spatial multi-criteria analysis shell requires a flexible interface and transparency among spa-tially explicit assessments, the input data, and the classification and com-bination rules used to generate them.

This chapter describes the application of spatially explicit multi-criteria analysis to natural resource management decision making using a flexible, easy-to-use, spatial multi-criteria analysis shell — the Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S). The utility of the software, which provides visual cognitive links between the MCA partici-patory process and spatially explicit data, is illustrated using two case stud-ies. The first is a regional level land use planning process that maps prior-ity locations for revegetation in the West Hume region of south-eastern New South Wales (NSW). The second is a national level assessment of factors affecting the sustainability of extensive livestock grazing in the Australian rangelands.

5.2 Multi-criteria Analysis

MCA is a technique that allows for the measurement and aggregation of the performance of alternatives or options, involving a variety of both qualitative and quantitative dimensions. As a means of considering the links among biophysical, economic and social data with human impera-tives, it is therefore particularly useful for approaching complex interac-tions and effects in the context of land use and land management.

There are many variants of the general MCA method that can be ap-plied in a wide variety of contexts. Many approaches are based on the pair-

The Application of a Simple Spatial Multi-Criteria Analysis Shell 75

wise comparison method of the Analytical Hierarchy Process (Saaty 2000; Ramanathan 2001). Well-developed MCA methods usually share a number of characteristics. Generally, they are flexible, enable the capture of quan-titative and qualitative data and issues, are relatively simple for clients and stakeholders to use, permit the development of many alternative scenarios, allow the exploration of trade-offs, and enable stakeholders to factor re-sults into decision making.

The MCA process is a tool to assist decision makers in reaching out-comes — it does not do the decision making, or produce a solution. Atten-tion must be given to how information quality and uncertainty is factored in and integrated with stakeholder viewpoints and biases, political and structural realities, and achievability versus optimality. It is important that each stage of the MCA process is carried out rigorously, in parallel with stakeholder engagement. Matching the spatial and temporal scale of the input information and analysis to the issues and processes under considera-tion is also critical.

5.2.1 Spatial Applications

There is a long history of the use of MCA in operations research, but these applications are essentially non spatial although there are recent crossover developments with traditional methods such as DEFINITE (DEcisions on a FINITE set of alternatives; Janssen and Van Herwijin 2006). The devel-opment of spatial applications of MCA has accelerated as GIS software has improved, and as computer operating systems have become more suited to implementation of GUI (Graphical User Interface) approaches. There are three major groupings:

• GIS-based applications utilising either established MCA modules (e.g. IDRISI) or involving the use of GIS modelling and object-based programming linkages (e.g. ArcInfo and ArcGIS)

• hybrid approaches that combine GIS, MCA, models and other ca-pability via programming

• stand-alone software specifically designed for an application.

Generic MCA capability within existing GIS products is tied to specific software, although in some cases standalone executables can be spawned. Table 5.1 provides a selected bibliography of the development of spatial MCA applications. Increased publication of these approaches in recent years indicates a heightened interest in MCA in combining methods and in applications that aid decision making in the coupled human–natural sys-tem.


Table 5.1. Multi-criteria analysis applications and software development: a se-lected list of GIS-based and standalone software-based applications

Software/analysis Application Reference 1. GIS-based applications IDRISI (®Clark University) GIS-based MCA

Earthquake hazards; crop suitability; soil erosion in Ethiopia

Ceballos-Silva and Lopez-Blanco (2003); Dragan et al. (2003)

ASSESS (A System for SElecting Suit-able Sites) written in ArcInfo AML (®ESRI)

Radioactive waste repo-sitory; soil conditions; catchment condition

Veitch and Bowyer (1996); Bui, (1999); Walker et al. (2002)

ArcView (®ESRI) GIS-based MCA Planning tool; urban land use

Pettit and Pullar (1999); Dai et al. (2001)

Model-GIS (ArcInfo) coupling with MCA

Non point source farm pollution

Morari et al. (2004)

ILWIS GIS Nature conservation value of agricultural land

Geneletti (2007)

MapInfo (®) GIS-based DSS Urban transport policies Arampatzis et al. (2004)

Other GIS-based MCA Watershed manage-ment; biodiversity conservation

Horst and Gimona (2005)

2. Hybrid applications PROMETHEE integrated with ArcGIS Land parcels ranked for

housing suitability Brans et al. (1984); Marinoni (2005)

SIMLAND – cellular automata, MCA and GIS written in C and using ArcIn-fo GIS

Land use change Wu (1998).

HERO (Heuristic multi-objective optimisation) combined with GIS, AHP and Bayesian analysis

Forest planning; habitat suitability

Kangas et al. (2000); Store and Kangas (2001); Store and Jo-kimaki (2003)

3. Stand-alone software LMAS – Land Management Advice System

Spatial expert system Cuddy et al. (1990)

MULINO-DSS (MULti-sectoral, INtegrated and Operational DSS) combines simulation models, mapping and MCA

Water resources Giupponi et al. (2004)

GIWIN (Geographic Information Workshop for WINdows) LRA (Land Resource Allocation)

Land resource allocation for rice paddy fields

Ren (1997)

MEACROSS – Multi-criteria Analysis of Alternative Cropping Systems

Cropping systems Mazzetto and Bonera (2003)

IWM – decision support system for Management of Industrial Wastes

Industrial waste Manniezzo et al. (1998)

GSA (Global Sensitivity Analysis) in SimLab (Software for Uncertainty and Sensitivity Analysis)

Hazardous waste disposal

Gomez-Delgado and Tarantola (2006)


5.2.2 The Decision-making Process

Policy and program development and decision making about natural re-sources are often driven by questions that are deceptively simple. Ques-tions such as: ‘Where in the landscape is agricultural production approach-ing the limits of sustainable resource use?’, or ‘Where can we most effectively invest in revegetation?’ raise complex issues of equity, eco-nomic performance, and biophysical impact. Revegetation, for example, may have benefits for biodiversity, water quality and amenity, and costs associated with reduced water supply and agricultural production. Usually there is no ‘right’ answer — complex trade-offs may be involved, with cost and benefit considerations resting on value judgment or opinion. The generalised spatial multi-criteria analysis and the priority-setting process are illustrated in Fig. 5.1, and its stages are described below.

Others…Productivity

SensitivitySalinity

Biodiversity

CompositemeasuresMaps for eachdecision criterion

Opinions and viewsTime series metrics

Spatial measuresModel outputs

Measurements

Primary dataand information

Problem definitione.g. issues, where &how much to invest?

Synthesis- options- scenarios- trade-offs

Decisione.g. funding priorities& policy interventions

Decision criteriaCriteria influencing thedecision (e.g. strategicplan)

Evaluation anddecision making

Analysis- coincidences

- tensions

Transformationand combinationindicators & weights

Fig. 5.1. The spatial multi-criteria analysis and the priority-setting process

Problem Definition

The more specific the problem, the easier it will be to detail decision crite-ria. For example, an investment decision might be restricted to a particular set of outcomes such as water quality or improved farming practices, or to a particular set of mechanisms such as market-based instruments.


Decision Criteria

The criteria that are to be taken into account in making investment deci-sions. These criteria will depend on the nature and scope of the problem. They will usually be derived from a number of sources including legisla-tion, agreements, policy initiatives and programs.

Primary Data and Information

Primary data and information is the store of available primary information upon which decision making ultimately depends. In a natural resource de-cision-making context this includes land, soil, water, biodiversity, social and economic information. It takes the form of measurements, model out-puts, time series information, and expert knowledge.

Transformation and Combination

This is a participatory process that involves using available primary data and information (above) to obtain an assessment against each decision cri-terion. Outputs are usually represented as maps of composite measures (below), or indexes. For example, the creation of a map of ‘species conser-vation value’ may involve the combination of species distribution data, with measures of diversity, endemism, rarity and endangerment along with landscape information. The process is usually iterative.

Composite Measures

Composite measures, or indices, which provide an assessment against each decision criterion. These could include measures such as species conserva-tion value, biodiversity threat, agricultural potential, agricultural value, water availability and salinity risk. More than one composite measure may be needed to represent a decision criterion.

Analysis

Analysis involves the exploration of coincidences and tensions among composite measures.

Synthesis

Synthesis is the outcome of the analysis process. Consensus positions may not be reached. The synthesis process usually involves the development of options and scenarios that represent particular stakeholder perspectives,


and alternative pathways forward. These options and scenarios are the choices for decision makers.

Decision Making

Decision making involves the resolution of inconsistencies, contradictions and competing claims.

5.3 The MCAS-S Approach

As MCA is useful for understanding coupled human–environment sys-tems, it is a process that should be routinely available to natural resource policy makers and practitioners. With this in mind, the design goal for MCAS-S was to create a portable and easy-to-use exploratory multi-criteria analysis shell with generic functionality, independence from data and pro-ject type, and freedom from GIS-based barriers to flexibility and interac-tive use (Hill et al. 2005b). MCAS-S functionality promotes participatory processes that require the understanding of relationships between decision-making requirements and the available data. Of particular value in this re-gard are software features that enable interactive cognitive and ‘live up-date’ mapping of alternative views.

5.3.1 Design Principles

In designing MCAS-S, the decision was made to focus functionality on as-sisting the decision-making process by promoting improved visualisation and transparency of relations among spatial data, value judgments, deci-sions and results. The tool thus requires the use of readily available GIS for spatial data management and analysis, and does not seek to provide these functions.

The first key design principle of MCAS-S is coupling the human cogni-tive process with the display and interrogation of spatial data (Bisdorff 1999) so that the origins of complex summary indices and queries are transparent. In MCAS-S, direction arrows connect data elements showing their contributions to combined layers. The second key principle is the automatic update of available functions according to the type of user-selected data layers. These two features make the interface highly intuitive and easy to use.

The third key principle is the maintenance of a live link between the primary data and all subsequent indicator, composite and analysis layers.


This allows the user to make adjustments to the treatment of primary data and immediately see the consequences as derived layers.

A final major construct behind MCAS-S is enabling simple visual com-parison of multiple themes in pairs (using a two-way comparison matrix) and in groups (using multi-way radar or spider plots).

5.3.2 Key Functions

MCAS-S requires the user to prepare raw spatial data for a project in a raster format of consistent geographical extent, pixel resolution and projec-tion. Primary data can then be selected from the menu and dragged into a display workspace whereupon the spatial data layer can be classified using a variety of simple classification methods and tools for tailoring input data. With the creation of individual class or rule layers, the user can apply them in weighted combinations to construct composite layers contributing to themes of interest. Key functions for combining and comparing spatial layers are illustrated below in two natural resources management applica-tions, and represented schematically in Fig. 5.2.

A composite layer interface will appear into the workspace when se-lected from the menu button. The weighted contribution of any selected layer to any composite can be set by using this interface. The composite map dynamically updates as the user changes the weightings on input lay-ers. The user can see in the workspace a hierarchical, cognitive ‘map’ showing the development of individual layers culminating in a final sum-mary layer representing a theme. Relationships among themes, specific views and particular indicators may be examined using several methods.

Two-way comparison enables the user to create a two-way comparison map, explore the association among input classes, and define a colour ramp and value scale to highlight association of high or low values, or fea-ture a particular geographical region.

Multi-way comparison is used when the spatial association of two or more data layers is required. The multi-way analysis uses the radar plot as the basis for visualisation. The user can create radar plots with spokes rep-resenting layers or themes. The multi-way function provides for the crea-tion of a binary layer that spatially delimits the area either inside or outside the limits of the radar plot, that is, the area that meets or does not meet cer-tain user-defined criteria levels. The multi-way comparison may also be displayed as a grey-scale surface showing ‘distance’ from selected criteria values.

A range of ancillary facilities includes a viewer that presents — for a selected grid cell — dynamic feedback of values for all related layers, in-


cluding two-way and multi-way comparison. Masking and overlay func-tions, a reporting utility, image and process logging, and data export are also available.

Fig. 5.2. Flow chart for MCAS-S showing stages and functionality


5.4 Applications

The remainder of this chapter demonstrates the use of MCAS-S functional-ity in two natural resources management applications:

• land use planning process that involved mapping priorities for revegeta-tion in the West Hume region of southern NSW

• national assessment of factors affecting the sustainability of extensive livestock grazing in the Australian rangelands.

5.4.1 Prioritising Revegetation Investment in West Hume NSW

A practical catchment investment planning process needs to focus on in-dicative zones for investment in landscape change, consistent with maxi-mising multiple environmental outcomes. In a regional context an effective prioritisation process provides for the best possible use of existing datasets and the technical expertise of participants, integrating knowledge and bal-ancing landscape options in a transparent and objective way, and at a level that allows prioritisation of on-ground works and incremental improve-ment over time, bringing together new information to help decision mak-ing.

In 2006, CSIRO Land and Water and the Murray Catchment Manage-ment Authority (CMA) collaborated in identifying priority areas for revegetation — to decide ‘what needs to be planted where’ — in the West Hume area of southern NSW (Hill et al. 2006a). The West Hume area is approximately 86,000 ha of predominantly mixed cropping agricultural land, in the southern part of NSW within a region managed by the Murray CMA (Fig. 5.3). This area was previously identified by the CMA as being within a salinity and biodiversity management priority area (Murray Catchment Management Board 2001). The Murray CMA co-invests with land owners in local-scale environmental activities such as revegetation with native plant species and establishment of perennial pasture. Targeting such investment in locations where environmental outcomes are likely to be maximised, and having a transparent and defensible prioritisation mechanism, is a primary CMA objective.


Fig. 5.3. Location of the West Hume area of southern New South Wales

Priority mapping was developed using MCAS-S from a series of rules or guidelines and spatial analysis procedures for the definition and creation of suitability ranked input data layers for biodiversity and salinity. Spatial data used for the creation of these input data layers included:

• historical rainfall and temperature • elevation (and derivatives, e.g. slope) • soil landscapes and profile classes • soil water holding capacity • current (or recent) land use • pre-settlement vegetation • threatened species (point) data • groundwater pressure and quality data • groundwater flow systems mapping • drainage networks, stream flow and water quality • location of important assets or infrastructure such as towns and roads.

The relative importance of rules or guidelines was assessed using a pair-wise comparison procedure by a CSIRO and Murray CMA focus group. The pair-wise comparison used the method of the Analytical Hier-


archy Process (AHP) (Saaty 2000; Ramanathan 2001). In this method, each guideline rule was compared with every other guideline rule and ranked as more or less important by each member of the focus group. The results were combined in a cross-tabulation and converted to a series of relative importance weightings and rankings for the salinity and biodiver-sity guidelines (Table 5.2). The focus group also assessed the relative im-portance of the biodiversity and salinity themes.

Table 5.2. Weighting and ranking for biodiversity and salinity guidelines

Guidelines Weighting (rank: 1=most important)

Biodiversity guidelines (rules) 1 Revegetate for geographical dispersal 0.0974 (6) 2 Revegetate biophysically heterogeneous areas 0.0541 (9) 3 Revegetate rare broad vegetation types 0.1038 (5) 4 Revegetate areas with rare species 0.0935 (7) 5 Revegetate in areas with dense patch distribution 0.1502 (1) 6 Revegetate close to large patches 0.1302 (4) 7 Revegetate close to streams 0.1492 (2) 8 Revegetate enclosed areas 0.07312 (8) 9 Revegetate to form corridors 0.1305 (3) 10 Revegetate land with low production potential 0.0179 (10) Total 1.000 Salinity guidelines (rules) 11 Revegetate areas: responsive groundwater flow systems 0.1678 (1) 12 Protect high value water resources 0.0939 (6) 13 Protect high value biodiversity assets 0.1039 (5) 14 Protect high value built assets 0.0730 (7) 15 Revegetate soils with high salt stores 0.1262 (3) 16 Revegetate high recharge potential areas 0.1551 (2) 17 Revegetate in areas with high rainfall 0.0683 (8) 18 Revegetate away from saline discharge zones 0.0641 (9) 19 Revegetate low value agricultural land 0.1180 (4) 20 Revegetate areas with high forest production potential 0.0299 (10) Total 1.000

Each of the 20 spatial suitability surfaces for each of the guidelines was imported into an MCAS-S project. Three new composite layers were added to the project: Salinity, Biodiversity and Combined. The 10 biodi-versity guidelines were used to create the Biodiversity composite, using the weightings developed in the AHP pair-wise comparison. The same process was repeated for the Salinity composite using the 10 salinity guidelines and their associated weightings. The Salinity and Biodiversity composites were then combined to create a Combined composite, with the biodiversity layer


receiving a 0.4 weighting and the salinity layer receiving 0.6 weighting (Fig. 5.4).

The spatial multiple-criteria analysis with MCAS-S used within a par-ticipatory process defined locations appropriate for revegetation based on the number of desirable criteria (guidelines) that were met, and the relative importance of these criteria (weighting). The analysis provided a straight-forward and logical means for allocating investment in revegetation ac-cording to relative suitability in a catchment context, using simple first-principle criteria with available spatial data. Further decisions on sites for revegetation work are made in the context of local farm plans and farmer land management objectives.

Fig. 5.4. A MCAS-S workspace linking spatial rule layers to prioritise revegetation in West Hume

5.4.2 Assessing the Sustainability of Extensive Livestock Grazing in the Australian Rangelands

The Australian rangelands occupy approximately three-quarters of the Australian continent. Land use in the rangelands is dominated by extensive sheep and cattle grazing on native pastures, with rainfall generally being too low or too variable for dryland cropping or grazing on improved pas-


tures. Understanding the implications of alternative land management op-tions is important in the rangelands because of the large area of Australia they occupy and the reliance of rangeland communities and industries on sustainable management of natural resources. Spatial multi-criteria analy-sis approaches have been used to support policy analysis on aspects of sus-tainability in the Australia rangelands (Stafford-Smith et al. 2000; Hill et al. 2006b).

From a pastoral perspective, livestock grazing in the rangelands can be characterised as sustainable where economic resilience and stability can be achieved in conjunction with the regional maintenance of native species and other ecosystem services. Informed public policy development re-quires an understanding of where in the landscape these ecological and economic influences are operating. Sustainability may be at risk in loca-tions where the resource base has limited resilience to livestock grazing and pastoral land use is viable in terms of potential productivity (Fig. 5.5) (Stafford-Smith et al. 2000).

Fig. 5.5. Scope for public intervention in rangeland management (adapted from Stafford-Smith et al. 2000)

Lesslie et al. (2006b) illustrate how appropriate spatial data layers and expert opinion can be combined using the MCAS-S tool to allow spatial exploration of these relationships. First, relevant input data were combined using the MCAS-S composite function to produce composite indexes repre-senting the attributes on each axis shown in Fig. 5.5. Primary data input layers were selected and their role in contributing to composite measures examined in a workshop process utilising expert advice. The potential pas-


toral productivity index was, for instance, developed by the weighted combination of input layers using the logic that potential productivity will generally be greater where:

• landscapes have higher productive potential (forage potential) • rainfall is relatively consistent within and between seasons • there is better access to markets, supplies and labour.

The result of composite development within MCAS-S for potential pastoral productivity is shown in Fig. 5.6.

Fig. 5.6. A representation of potential productivity for livestock grazing in the Australian rangelands

Second, the MCAS-S two-way function was used to enable exploration of the spatial relationship among derived composite layers of potential pas-toral productivity and the sensitivity of the resource base to livestock graz-ing. The two-way layer on the right hand side of the MCAS-S desktop in Fig. 5.7 highlights locations where conditions in the upper right quadrant of the matrix shown in Fig. 5.5 apply on the basis of derived index values (i.e. where there is a coincidence of high potential productivity and high resource sensitivity index values). Locations where other relationships ap-ply (e.g. high potential productivity and low resource sensitivity; low po-


tential productivity and high resource sensitivity; low potential productiv-ity and low resource sensitivity) can be interactively explored in MCAS-S by simply clicking on the appropriate cell in the matrix at bottom left. In this way the two-way function facilitates the spatially explicit landscape condition assessment and policy analysis.

Fig. 5.7. A two-way analysis of potential productivity and resource sensitivity

The spatial relations among more than two layers can be interactively explored using the MCAS-S multi-way comparison facility. Figure 5.8 shows the creation of a binary layer (at bottom right) that identifies loca-tions that meet specified conditions for each of three composite layers de-veloped for the Australian rangelands study: potential productivity, sensi-tivity of the resource base to livestock grazing, and total grazing pressure. The spatial association and visualisation process involves the use of the ra-dar plot, with spokes that represent the scale of each respective input layer. Class limits on these spokes are interactively manipulated to delimit loca-tions that satisfy user defined criteria levels. The multi-way binary layer shown in Fig. 5.8 identifies locations where there is a coincidence of rela-tively low potential productivity, relatively high sensitivity of the resource base to livestock grazing, and relatively high total grazing pressure. The multi-way layer may also be displayed as a grey-scale surface showing ‘distance’ from selected criteria values.


Fig. 5.8. A multi-way analysis of potential productivity, resource sensitivity and total grazing pressure

5.5 Future Trends

As a versatile spatial multi-criteria analysis shell, MCAS-S has many prac-tical applications, including the assessment of ecosystem services and the visualisation of complex analyses of land use management and land man-agement practices. The MCAS-S composite and multi-way analysis func-tions enable the development and comparison of multiple spatial data themes (e.g. carbon, water, aesthetics, production and ecosystem function) as described by Foley et al. (2005). Similarly, MCAS-S multi-criteria visu-alisation procedures can be applied with land use and land management practices information (Lesslie et al. 2006a) to assist evaluation of complex land management change and trade-off options.

Additional functionality will be added to MCAS-S. In the West Hume study a pair-wise comparison of guidelines was completed in order to rank and weight them for analysis. This was conducted outside the MCAS-S software with weightings applied in the creation of the biodiversity and sa-linity composite layers. Further development of the MCAS-S software will


add a pair-wise comparison module to MCAS-S so that the rankings, rank-ing matrix table, and weightings can all be created within the software and associated with each layer. A map calculator function for composite devel-opment is also under development.

Description of time and space-based complexity will enhance the func-tionality of MCAS-S. Time series describe many important and spatially explicit processes in the landscape. Full information extraction from time series may require application of a suite of approaches including classical time series decomposition (Roderick et al. 1999), calculation of curve met-rics (Reed et al. 1996), piece-wise logistic functions to capture curve tra-jectories (Zhang et al. 2003), time-based classifications (Moody and John-son 2001) and Fourier and wavelet analysis (Li and Kafatos 2000). Recent research has highlighted the importance of global sensitivity analysis (Gomez-Delgado and Tarantola 2006) and multi-criteria weight sensitivity analysis (Feick and Hall 2004) to define the response space and impact of input layers on MCA outcomes.

The implementation of a Bayesian Belief Network within MCAS-S is also being explored. There are recent examples of the application of Bayesian network approaches and multi-objective model land system and environmental assessments (Dorner et al. 2007; Pollino et al. 2007). Con-sideration could be given to inclusion of soft systems approaches that en-able users to create partial weight tables to capture soft attributes such as belief, commonality and plausibility (Beynon 2005).

5.6 Conclusion

Natural resource management decision making by communities, rural in-dustries and governments is usually undertaken in a context of uncertainty where, ideally, the decision-making process engenders trust and engage-ment among stakeholders. Informed debate about competing demands on natural resources and the desirability of alternative futures can benefit from the use of versatile spatially explicit decision support tools such as MCAS-S.

The particular advantages of the MCAS-S tool are its flexibility, sim-plicity, the capability to visualise spatial data and the linkages between spatial data, and its suitability for use in group and participatory processes. The West Hume land use planning application combines simple first prin-ciple criteria with readily available spatial data to prioritise locations ap-propriate for revegetation based on the number of desirable criteria that were met, and the relative importance of these criteria. The process dem-


onstrates how a regional investment strategy can be translated into mapped priorities by focussing on identifying where multiple environmental and production outcomes can be achieved at minimal cost. The utility of the tool in a broader context of policy analysis and development is demon-strated by the Australian rangelands application. The wider adoption of this type of planning process in Australia could substantially improve the effectiveness of current investment in natural resource management.

5.7 Future Research Directions

There is a growing expectation that spatial science and information can be successfully applied to decision making on complex issues affecting cou-pled human–environment systems. This expectation is promoted by the in-creasing sophistication of spatial multi-criteria analysis tools and more readily accessible economic, biophysical and social spatial data. While tools such as MCAS-S can help integrate factual information with value judgements and policy goals in a transparent and flexible way, there is still considerable scope for improvement in spatial data handling technologies and analytical methods. A particular need is for technologies that are capa-ble of delivering information at many levels of scale and complexity with a cognitive context, through visualisation, that aligns more naturally with human thinking and decision making.

Key research challenges include:

• the analysis of time series data, particularly the discrimination of pat-terns in trend, periodicity and magnitude

• the linkage of time series and signal processing methods to spatial analysis

• resolving scale, particularly in relation to economic and social data and issues arising from changes in scale

• managing uncertainty, especially in the linkage of scientific and social domains.

Improvement in visualisation is important so that scenario outputs from spatially explicit modelling and decision processes can be made more meaningful to non-experts.


Acknowledgements

The MCAS-S system was developed for the Natural Resource Management Division of the Australian Government Department of Agriculture, Fisher-ies and Forestry with funding from the Natural Heritage Trust. We ac-knowledge funding support for the West Hume study from the Murray Catchment Management Authority and the CSIRO Water for a Healthy Country Flagship program. Jean Chesson is thanked for her advice on the multi-criteria analysis and priority-setting process. The chapter also bene-fited from the comments of two anonymous referees.

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