Multi-criteria analysis in GIS environment for natural resource development - a case study on gold exploration

Nihar R. Sahoo, P.Jothimani and G. K. Tripathy
Tata Infotech Ltd, SEEPZ, Mumbai, 400 096


Abstract
GIS-based analysis of spatial data has been a new specialized process, capable of analyzing complex problem of evaluating and allocating natural resources for targeting potential areas for mineral exploration. This paper explains developing a data-driven decision-tree approach with multi-criteria evaluations in mineral potential mapping at the Hutti-Maski schist belt. An inference network based spatial data integration has been attempted which allows for incorporation of uncertainties into a predictive model. The procedure has produced a posterior probability map identifying favorable areas for gold exploration

GIS in Natural Resources Development
Land resource evaluation and allocation is one of the most fundamental activities of resource development (FAO, 1976). With the advent of GIS, there is ample of opportunities for a more explicitly reasoned land evaluation. Prediction of suitable areas for mineral exploration in a virgin area of specific type are problems that require use of various procedures and tools for development of decision rule and the predictive modeling of expected outcomes. GIS has come out as an emerging tool to address the need of decision makers and to cope with problems of uncertainties. A decision rule typically contains procedures for combining criteria into a single composite index and a statement of how alternatives are to be compared using this index. It is as simple as threshold applied to a single criterion. It is structured in the context of a specific objective. An objective is thus a perspective that serves to guide the structuring of decision rules. To meet a specific objective, it is frequently the case that several criteria will need to be integrated and evaluated, called multi-criteria evaluations. Weighted linear combinations and concordance-disconcordance analysis (Voogd, 1983 and Carver, 1991) are two most common procedures in GIS based multi-criteria evaluations. In the former, each factor is multiplied by a weight and then summed to arrive at a final suitability index, while in the later, each pair of alternatives is analyzed for the degree to which it outranks the other on the specified criteria. The former is straight forward in a raster GIS, while the later is computationally impractical when a large number of alternatives are present.
Information vital to the process of decision support analysis, is rarely perfect in earth sciences. This leads to uncertainties, which arises from the manner in which criteria are combined and evaluated to reach a decision. When uncertainty is present, the decision rule needs to incorporate modifications to the choice function or heuristic to accommodate the propagation of uncertainty through the rule and replace the hard decision procedures of certain data with soft-data of uncertainty. Bayesian probability theory (Bonham-Carter et al., 1988; 1990; 1995), Dempster-Shafer theory (Cambell et al., 1982) and fuzzy set theory (Duda et al., 1977) have been extensively in use in mineral targeting.

Theory of multi-criteria evaluation
Multi-criteria evaluation is primarily concerned with how to combine the information from several criteria to form a single index of evaluation. In case of Boolean criteria (constraints), the solution usually lies in the union (logical OR) or intersection (logical AND) of conditions. However, for continuous factors, a weighted linear combination (Voogd, 1983) is a usual technique. As the criteria are measured at different scales, they are standardised and transformed such that all factor maps are positively correlated with suitability. Establishing factor weights is the most complicated aspect, for which the most commonly used technique is the pair-wise comparison matrix.

Evaluation of the relationship between evidence (criteria) and belief is a forward chaining expert system. In this system the propagation of favourability measure through the inference network may include the Bayesian updating and fuzzy logic for computation of posterior values of favourability given evidence(s). In the real world, the evidences and hypotheses are uncertain. We cope with the problem by assigning probability values to evidences (Duda et al., 1977). There is unidirectional propagation of evidences through a hierachial network carries on towards an ultimate hypotheses.

In a rule based inference system, the rules are usually of the form:

If E1 and E2 and E3…………. and En, then H,
Where, Ei(i = 1,2……n) is the ith evidence and H is the associated hypotheses.

In a full fledged inference net, many pieces of evidences are linked to a single final hypotheses using the combination rules of conjunction, disjunction and independence (Bayes).

see on
http://www.gisdevelopment.net/application/geology/mineral/geom0002a.htm

GIS to help poverty mapping in Azerbaijan

19 December 2005
As London prepares to host the 2012 Olympics aerial photo mapping company BlueSky has launched a high resolution up to date birds eye view of the Thames Gateway. The aerial imagery created by BlueSky in partnership with ZMapping covers over 200 square kilometres extending for 40 miles along the River Thames from the London Docklands to Southend in Essex and Sheerness in Kent. It has a resolution of 12.5 cm, equivalent to a 1:500 map scale. This resolution means it is possible see details including street furniture, road markings, property boundaries and environmental features.

The aerial photography has been processed into a digital dataset that is map accurate. It is already being used to create a three dimensional visualisation of the Thames Gateway region. The imagery can be supplied in a variety of formats including hard copy prints, digital image files and as a ready to use dataset that is compatible with a wide range of GIS or CAD software packages. Matched to other types of data, it is valuable tool for a variety of applications including asset management, risk analysis, environmental monitoring and strategic planning.

Source : http://www.bakutoday.net

Rural Poverty Mapping in Mexico

This page presents the results obtained in the CIMMYT project “Geospatial Dimensions of Poverty and Food Security – A case study for Mexico” (June 2002 – June 2004), funded by the Government of Norway and implemented by the CGIAR Consortium for Spatial Information (CSI), FAO, and UNEP/GRID-Arendal. The page aims to provide an information resource for anyone with an interest in the spatial distribution of rural poverty in Mexico.

All key datasets are presented in the form of interactive GIS maps.

Project Summary

The project used small-area estimation techniques as described by Bigman et al (2000) applied to the National Survey of Household Incomes & Expenditures (ENIGH, 2000), the XII General Population and Housing National Census 2000. Data from the ENIGH of 2002 and the National Nutrition Survey of 1999 (ENN, 1999) were also incorporated for comparative purposes. A multiplicative heteroskedastic regression model was developed using the ENIGH 2000 data, this predicted per capita expenditure in Mexican pesos of 2000 per month as a proxy for welfare. To classify households among different levels of welfare, we employed the three poverty lines (food, capabilities & patrimony) developed by the Mexican Technical Committee for the Study of Poverty (Comite Tecnico para la Medicion de la Pobreza, 2002). The developed model incorporated variables relating to household size, education, housing characteristics, index of accessibility, fraction of indigenous language speakers, rural population, population density, climatic data and state location. These variables were chosen because of their potential relation to human welfare and that they could be directly linked to the national census data. Validation of the model, comparing it observed results and other studies, indicated that it performed well although had a tendency to overestimate the fraction of households under the food poverty line. Connecting the model results to GIS permitted the generation of rural poverty maps at the municipality and locality level. These provided opportunities to examine the spatial and temporal distribution, relate rural poverty to environmental / social / agricultural factors and provide a platform for targeting and priority setting.

For further details see the complete Final Project Report.

Key Project Results

• Non-uniform distribution, with predicted extreme (food) rural poverty concentrated in certain areas especially in southern Mexico and parts of the Sierra Madre Occidental (Map 1 & Map 2)
• Results obtained matched closely those of the Mexican government, developed through other methodologies, and used for targeting development activities (Map 3)
• A relationship between child malnutrition and predicted extreme rural poverty was observed (Map 4)
• The environment in which the core areas of rural poverty existed were characterized by high rainfall, steep slope (Fig. 1) and in many cases erosion-prone soils. All factors combined indicated that soil erosion may be strong factor in many of the extremely poor rural areas.
• Maize, and to some extent bean, cropping systems were highly coincident with the extremely poor areas and likely to be of high importance
• Some aspects of CIMMYT's maize-based research portfolio were considered positive, or potentially positive, in relation to the extreme rural poor. This included the improvement of materials adapted and used in the poor areas and appropriate technologies such as post-harvest storage techniques.
• The high density of extremely poor rural localities in specific areas indicated significant opportunities for targeting anti-poverty or development programs (Map 5)

© International Maize and Wheat Improvement Center (CIMMYT) 2004. All rights reserved. The designations employed in the presentation of materials in this publication do not imply the expression of any opinion whatsoever on the part of CIMMYT or its contributory organizations concerning the legal status of any country, territory, city, or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. CIMMYT encourages fair use of this material. Proper citation is requested. To the best of our knowledge all data may be freely used and reproduced for Non-Commercial purposes. All data are supplied with no guarantee, implicit or explicit, as to their accuracy. We have attempted to provide, to the best of our knowledge accurate data and information. However, neither the authors nor CIMMYT can be responsible for decisions taken on account of these data or information.

What are poverty maps?

Poverty maps are spatial representations of poverty assessments. The assessment information comes from a variety of sources and can be presented at various levels (global, national and local). Indicators of income poverty (such as GDP per capita or daily subsistence levels), or of well-being (such as life expectancy, child mortality, or literacy) are most frequently used in poverty maps, and are derived from national census data or household surveys. Sometimes various indicators are combined to give an index of poverty or human development (such as the Human Development Index, a composite of life expectancy, literacy and income).

As an example, in the World Bank Report: Using disaggregated poverty maps to plan sectoral investments(PDF) by combining survey and census data to create poverty maps to show where needs are the greatest, policymakers can focus scarce resources.

Why Use Poverty Maps?

Poverty maps also allow easy comparison of indicators of poverty or well being with data from other assessments, such as access to infrastructure or services, availability and condition of natural resources, and distribution of transport and communications facilities. Specifically;

• Poverty maps can quickly provide information on the spatial distribution of poverty that in turn proves the targeting of intervention or development projects.
• GIS based poverty analysis makes it easier to integrate poverty data from various sources
• Geo-referenced information can free analysis from the restrictions of fixed geographical boundaries. For instance, data can be converted from administrative to ecological boundaries which are often more meaningful in a natural resources management context.
• Mapped information on the levels and distribution of poverty make the results of analysis more easily understandable to a non-specialist audience.

This greatly assists in the targeting and implementation of development projects, and the communication of information to a wide range of stakeholders, as shown in Where Are the Poor?, a review of 14 case studies of applying poverty maps to development processes.