Geographical Information Systems - Essay Example

GIS Introduction During the last decade there has been a sharp increase in awareness of the adverse impacts of mankind's technological development on the environment. This has led to many new initiatives aimed at researching and understanding the mechanisms involved, and to new legislation aimed at minimizing or ameliorating the negative effects of development. These actions are generating growing amounts of information on the phenomena and problems concerned and this is leading, in some areas of activity, to information management problems (Eden, 1992). At the same time there has been a remarkable increase in the power and diffusion of information technology which can be applied to support and analyze the information generated, and one of the key elements in the new armory is the geographical information system (GIS). The problems concerned can be broadly described as 'environmental management problems'. Examples of environmental management problems range from those of global scale (climate change, ozone depletion), through international scale (atmospheric transportation of radiation and pollutants), to regional and local scales (river pollution, sitting of potentially hazardous facilities) (Wyatt, 1995). What they have in common is that they are all concerned with spatially distributed phenomena. This means that the information relating to the phenomena can be spatially referenced and an appropriately chosen GIS is now the natural means to support and analyze such information. In addition to information management and analysis there is also frequently a requirement for assistance in making decisions which are effectively choices between alternative possible future courses of action, and hence the need for decision support systems (DSS). The need for such decision support systems is now growing, particularly in urban and regional planning, where environmental impacts must now be taken into account and new regulations and directives respected. In the context of urban planning, the environmental management approach is particularly required with respect to atmospheric and acoustic pollution, protection of parks and green areas, and layout and sitting decisions which have a bearing on risks to the population and solar exposure. All these aspects can now be addressed to some extent by the use of GIS (Bowers, & Benford, 1991). Growth of Milton Keynes The Milton Keynes area has been subject to massive planned growth for nearly 35 years, and as we approach that anniversary it is worth reviewing the situation. The city -- it has always presumed to the title, although it was never officially conferred -- has become established effectively as the sub-regional centre at the interface of the South East, Eastern, and East Midlands regions of England. This growth has been on a scale unmatched anywhere in the EU, with over 5,000 companies resulting in the creation of over 85,000 jobs. Economic success is backed by a good quality of life for an inclusive range of residents, and a wide range of facilities and services, many of which are regional or even national attractions (Lock, 2002). The city currently has a population of more than 170,000 (213,000 in the borough). It has an unemployment rate of just 1.4 per cent (technically full). The number of full-time jobs has grown by some 17,000 in just the last three years. Around 900 houses are being built every year (Lock, 2002). The city will reach its planned growth of approximately 200,000 people by 2008 or thereabouts. To meet current structure plan requirements, Milton Keynes Council is already preparing a local plan that will see growth beyond the boundaries of the original new town. These sustainable urban extensions, together with the city's urban capacity will cause the city to reach somewhere around 250,000. Further ready expansion land still be drawn upon -- whatever spatial configuration is chosen next in the emerging local plan -- might see the city accommodate 280,000 comfortably (Lock, 2002). Aside from this inevitable growth -- the push of the city to fill its obvious envelope -- the pressures for super-growth are clear. They arise internally: the residents of Milton Keynes are now producing their second or third generation of households, from birth rates plus rates of household formation; the businesses of Milton Keynes are now demanding room to expand; and the culture of the city is demanding more facilities for entertainment, leisure, education, and the rest. They arise externally: with increasing restraint and constraint elsewhere in the South East, possible reception areas are few. They also arise internationally: Milton Keynes has proved to be exceptionally attractive to investors and companies from overseas (Lock, 2002). This community is used to seeing new development; most have grown up with it, and associate the city with growth and the new opportunities that it brings. Although there will be as fierce voices against change here as there are anywhere else, many residents accept that Milton Keynes cannot be stopped in its tracks. The Chamber of Commerce has found that this view is strongly held by the business community, too. So the Milton Keynes sub-region is very unusual in almost all respects. The study currently under way has the potential to open a new chapter in its history, by providing a spatial and organisational framework to mould its growth potential into sustainable forms (Lock, 2002). GIS as an Instrument for Decision Support GIS can be seen as providing three essential types of facility: database, graphical display and spatial analysis. The first two, database and linked graphical display, can already provide a powerful instrument for decision support in many contexts, particularly in facilities management. The possibility to interrogate appropriate spatially referenced information through a map-based graphical interface can frequently provide the decision-makers with the information needed to address and manage their problems. Spatial analysis provides a way of enriching the information available to the decision-makers by generating new parameters from the spatially referenced data. In environmental management such parameters are typically indicators of the environmental impacts of proposed developments, or the number of people or other living species exposed to risk, noise and pollution. Existing GIS now provide a range of spatial analysis facilities, such as Boolean and numerical operations on different thematic layers, area analysis and network analysis, but it is frequently necessary to link the spatial analysis to other external calculations or simulation models in order to arrive at the required parameters on which to base a decision. One of the interesting challenges posed by currently available GIS technology is to devise ways of using the available spatial analysis facilities to generate parameters that are of most relevance to the required decisions. Another challenge is to develop new types of spatial analysis which are appropriate for a given decision context (Saaty, 1994). An important aspect of GIS that is relevant to any planning situation is the capacity to integrate spatially referenced information coming from different sources. For example, in the context of urban planning the different sources could typically include cadastral maps, population census data, maps of technical networks such as water and electricity lines, aerial pho tographs and even satellite remote sensing data. Once the data from different sources are structured in an integrated system, with possibilities for analysis and combination, the whole becomes more useful than the sum of the separate parts. An overview of applications and progress in the use of GIS in urban and regional planning is given in Scholten and Stillwell (1990). The Need for Links with Multi-Criteria Decision Aid A feature that many environmental management problems have in common, in addition to being spatially distributed, is that they are multi-criteria problems. Environmental management problems are normally concerned with complex, interrelated phenomena, and the criteria on which decisions must be based are usually multiple. To take the example of reduction of river pollution, the indicators of pollution are frequently multiple (several biological and several chemical) and, even if the problem is restricted to the reduction of only one pollutant, the criteria involved in the reduction strategies would remain multiple (cost, employment, other social factors, other impacts of clean-up technology). Furthermore the multiple criteria are frequently conflicting. Cost is typically in conflict with other criteria which the decision-makers are trying to satisfy, but there can also be conflicts between criteria other than cost. For example, reducing impact on one class of land use, by altering a siting decision, would normally imply increasing the impact on some other class (Saaty, 1994). Now it has already been amply demonstrated that GIS can support the required spatially referenced data and provide further insights into the decision problem through spatial analysis, but there remains the problem of arriving at the final decision, in the face of multiple and conflicting criteria. This problem seems destined to become more severe as the quantity of information supported, and the number of criteria that can and should be considered, increases. This is therefore where linking multicriteria decision aid (MCDA) to GIS can be of great assistance. It is also a characteristic of many environmental management problems that the decisions involve not one individual or organization, but many. This leads to conflicts not only between criteria, but also between different interpretations of the criteria and different sets of preferences among the different actors. As will be shown in the next section, MCDA has already gone some way towards providing methods for handling these conflicts (Rosenhead, 1989). Multi-Criteria Decision Aid MCDA is a well established field within the general area of operations research, and in which a substantial body of theoretical and practical knowledge has been built up over several decades. The aim is to provide techniques and algorithms to aid the making of decisions in situations of multiple and conflicting criteria. Techniques have been developed for both discrete and continuous decision problems. In discrete situations a decision has to be made between a well determined set of alternatives, such as alternative sites or transportation routes (Eden, 1992). All discrete problems require the creation of the decision matrix (sometimes called the evaluation matrix), which is a twodimensional table listing the alternatives in one direction and the criteria under consideration in the other. In continuous cases there is a spectrum of alternatives, which can, in principle, be infinite in number, such as target values for physical indicators or costs. The generation of the decision matrix, complete with values for all criteria, can be seen as a significant step towards the solution of a decision problem. It is a well structured, and fair, description of the problem in the sense that all alternatives have been given similar consideration. It contains a condensed description of the available problem information in a form which apparently requires only a statement of the decision-makers' preferences, and a suitable algorithm for its solution. In continuous problems the extreme values of the criteria define the bounds of the decision space within which the solution should be found (Rosenhead, 1989). For the solution of discrete problems, outranking algorithms are normally used; while for continuous problems which can involve unlimited numbers of alternatives in the decision space, multi-objective programming techniques (Keeny and Raiffa, 1976) are usually used. The formulation of the required statement of preferences can present problems, especially in cases where there is more than one decision-maker; for example, the weights are probably different for the separate actors, and the actors frequently wish to modify their preferences when they see their impact on the solution. One approach used in discrete problems is to express the preferences in terms of binary relations stating the relative importance of pairs of criteria, and to allow the preferences to be restated, to generate new solutions, and thus provide more information on how they affect the whole problem. In continuous problems, interactive modeling of aspiration levels can be used. Experience with MCDA has shown that finding a solution (arriving at a decision) does not imply one run of the chosen algorithm to identify the best alternative, but rather a process of generating solutions together with information on why certain solutions are better, restatement of preferences and generation of new solutions, until the decision-makers have obtained sufficient insights into the problem under consideration, and into their own statements of preferences, to be confident that the final decision is the correct one. In this way MCDA provides structured, documented information on the problem, a focus for discussion, and a means for resolving conflicts by revealing how different preferences lead to different solutions. The information generated can also provide a basis for negotiation between the different actors. The spatial analysis functions of GIS can provide ready-made facilities for generating values of criteria to go into the decision matrix. Looked at the other way round, the decision matrix and MCDA provide a way of using the results of spatial analysis to arrive at decisions. This is illustrated in the next section by means of several examples of typical environmental management problems.Some further examples of the linking of GIS and MCDA can be found in Fedra (1986), Janssen (1992) and Carver (1991). Example Applications Relevant To Urban Planning for Milton Keynes Strategies For Treatment And Disposal Of Industrial Waste We will consider the case of decision support for strategies for industrial waste management. A prototype of such a system has been developed at the Joint Research Centre for the Lombardy Region under contract to the Italian Ministry of the Environment (Paruccini et al., 1993; Peckham, 1993). While this prototype in fact refers to the management of the wastes on a regional scale, most of the waste is produced in urban areas. It is also clear that similar methods could be applied for urban waste management and similar techniques applied to other aspects of urban planning. In this case the GIS should support information on the quantities of the different classes of toxic wastes being produced in different sub-regions, and on the locations, types and capacities of facilities for waste treatment and disposal. Spatially referenced information on population, land use, geology, rivers and water bodies, and the location of natural parks and sites of special interest for nature conservation, are required in order to assess the impacts of different strategies on these features (Simon, 1983). A description of the transportation network should also be included in order to estimate costs, risks and environmental impacts associated with the transportation of the wastes from the sites of production to the disposal sites. Once this information is set up in the GIS the spatial analysis facilities can be applied in the following ways: 1. In the assessment of the environmental impact of waste disposal facilities on the different classes of land use, for example by calculating the percentage areas of the different classes of land use in the sub-regions containing the facilities, or within a given radius of the facilities. 2. In the assessment of impact of waste disposal facilities on natural parks or biotopes, for example by finding the distance from each facility to the nearest biotope. Using this distance as a criterion, strategies can be devised giving preference to use of sites located further from biotopes. 3. In the assessment of risks to population, by calculating population densities in the areas surrounding facilities. 4. In the assessment of transportation risk, by calculating population densities near to transportation routes; if a path generator is included, alternative transportation routes and their corresponding risk criteria can be analysed automatically. 5. In the assessment of impacts on underground water by relating locations of facilities to maps of geology and depth of water table (Rosenhead, 1989) In 1-4 above the spatial analysis provided by GIS can generate parameters which, after normalization to become indices (on a scale 0-1), can be usefully inserted directly into the decision matrix. In 5, however, the spatial analysis may in some cases need to be linked to a ground-water simulation model (not a standard feature of GIS) for detailed analysis before useful parameters can be derived. This case shows that the integration of GIS and MCDA must in some cases provide for both direct transfer of the results of spatial analysis to the decision matrix, and for more complex information transfers via simulation models or other evaluation modules. Atmospheric Pollution The principal sources of urban atmospheric pollution are typically domestic heating plants, plants for generation of industrial and process heat, other industrial processes, and traffic. Heating plants can be represented in the GIS as point sources of emission, the spatially referenced information being for example plant size, theoretical efficiency, type of fuel, frequency of maintenance, date of last maintenance and measured efficiency (if available). If detailed survey data is not available, residential areas can be represented as polygonal sources characterized by area averages of sampled data (Friend, 1992). Emissions from traffic can be represented as line sources, where data for each stretch of road is obtained from measurements of the traffic flow and recordings of vehicle types. A GIS set up with such information can be used to evaluate different scenarios for emission reductions, such as improved heating plant maintenance, plant substitution, alterations to traffic patterns and changes of vehicle fuel. Spatial analysis can be used to identify areas with a high risk of heavy pollution by summing point, line and area sources (of the same pollutant) to create new maps of total emissions per unit area. In areas where terrain geometry has a strong influence on atmospheric concentrations, the spatially referenced information may need to be linked to an atmospheric simulation model for detailed evaluations. Again we see in this example that spatial analysis can generate criteria which can be used in the decision matrix (after appropriate scaling or normalization), but it may also be necessary to include the use of simulation models not normally found in GIS. An example of the use of GIS for air pollution monitoring and mapping is given in Galetto et al. (1993) and for pollution emissions inventory in Trozzi and Vaccaro (1993). Another traffic-related example, where the objective is the reduction of urban noise rather than air pollution, is given by Bilanzone et al. (1993). Conclusions Geographical information systems are being increasingly used as components of decision support systems for urban and environmental management. Since these kinds of management problems involve multiple criteria there is a strong case for including MCDA techniques within the DSS. Additional modules for generation of alternative scenarios and their simulation may also be required. There is no established formal methodology for design and implementation of such systems, but design and implementation choices can have a strong bearing on eventual conclusions and decisions made with such systems. An attempt has been made to derive some of the prerequisites for the design of useful DSS incorporating GIS, MCDA and other modules, and the advantages and disadvantages of three different implementation approaches have been described. The choice of approach should depend on the specific problem, and on the availability of existing systems, modules and data, but in the longer term the federated approach, making use of advances in object-oriented techniques for software development and information transfer, appears to offer a means for arriving at a consistent and reliable approach to design and implementation and for overcoming some of the barriers to use of these techniques in urban planning practice. References Bilanzone C, Chini P. and Salis A. (1993) A Case Study for a Noise Reduction Planning System for the City of Ancona. Proceedings of the 4th European Conference on Geographical Information Systems, Genoa, Italy, March 29-Apri l 1, 1993, pp. 96-105. Carver S. J. (1991) Integrating Multi-criteria Evaluation with Geographical Information Systems. International Journal of Geographical Information Systems, 5, pp. 321-339. Eastman J. R., Kyem P. A. K. and Toledano J. (1993) A Procedure for Multi-Objective Decision Making in GIS Under Conditions of Conflicting Objectives. Proceedings of the 4th European Conference on Geographical Information Systems, Genoa, Italy, March 29-April 1,1993, pp. 438-447. Fedra K. (1986) Advanced Decision-oriented Software for the Management of Hazardous Substances. Part II: A Prototype Demonstration System, CP-86-10, International Institute for Applied Systems Analysis, Laxenburg, Austria. Fedra K., Li Z., Wang Z. and Zhao C. (1987) Expert Systems for Integrated Development: A Case Study for the Shanxi Province, the People's Republic of China, SR-87-1, International Institute for Applied Systems Analysis, Laxenburg, Austria. Galetto R., Rinaldi S. and Velona F. (1993) Realization of a GIS Prototype for Air Pollution Monitoring and Mapping. Proceedings of the 4th European Conference on Geographical Information Systems, Genoa, Italy, March 29-Apri l 1, 1993, pp. 30-37. Haastrup P. (1994) Designing Risk and Environmental Management Support Systems. In Beroggi G. E. G. and Wallace W. A. (eds), Computer Supported Risk Management, Kluwer Academic, Dordrecht, pp. 49-59. Hershey R. and Whitehead D. (1991) The Federated GIS-A Macintosh Approach. Proceedings of the 2nd European Conference on Geographical Information Systems, Brussels, Belgium, April 2-5,1991, pp. 449-58. Janssen R. (1992) Multiobjective Decision Support for Environmental Management. Kluwer, Dordrecht. Keeny R. and Raiffa H. (1976) Decision with Multiple Objectives: Preferences and Value Tradeoff. John Wiley, New York. Microsoft (1991) Microsoft Windows User's Guide (Version 3.1) . Microsoft Corporation, Redmond, USA. Paruccini M., Mendes I. and Peckham R. J. (1993) A Computer-Based Decision Aid for Industrial Waste Management on Regional Scale, EUR Report No. EUR 15272 EN, Joint Research Centre, Ispra, Italy. Peckham R. J. (1993) Linking GIS and MCDA to Manage Lombardy's Industrial Waste. Geo Info Systems, 3 (3), pp. 46-50. Scholten H. J. and Stillwell J. C. H. (1990) Geographical Information Systems for Urban and Regional Planning. Kluwer, Dordrecht. Trozzi C. and Vaccaro R. (1993) Air Pollutants Emissions Inventory and Geographic Information System. Proceedings of the 4th European Conference on Geographical Information Systems, Genoa, Italy, March 29-Apri l 1, pp. 47-56. Lock, David, 2002. Growing Milton Keynes. (Off the Fence). Town and Country Planning. Harry Timmermans, 1997. Decision Support Systems in Urban Planning. Spon Press. Further Readings Bowers, J. M. and S. D. Benford (eds) (1991) Studies in Computer Supported Cooperative Work. Amsterdam, North Holland. Cherniak, C. (1986) Minimal Rationality. Cambridge, Mass, Bradford/MIT Press. Eden, C. (1992) Strategy Development as a Social Process. Journal of Management Science, 29:6, 799-811. Fabian, J. (1990) Creative Thinking and Problem Solving. Chelsea, Mich Lewis. Friend, J. (1992) New Directions in Software for Strategic Choice. European Journal of Operational Research, 61,154-164. Janis, I. L. and L. Mann (1977) Decision Making: A Psychological Analysis of Conflict, Choice and Commitment. New York, Macmillan. McClelland, J. L. and D. E. Rumelhardt (1988) Explorations in Parallel Distributed Processing. Cambridge, Mass, MIT Press. Nuttin, J. (1984) Motivation, Planning and Action. Hillsdale, New Jersey, Lawrence Erlbaum/Leuven University Press. Rosenhead, J. (ed. ) (1989) Rational Analysis for a Problematic World: Problem Structuring Methods for Complexity, Uncertainty and Conflict. Chichester, John Wiley. Russo, J. E. and J. H. Schoemaker (1989) Decision Traps: Ten Barriers to Brilliant Decision Making and How to Overcome Them. New York, Doubleday. Saaty, T. (1994) Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process. Pittsburgh, RWS Publications. Samuel, A. L. (1963) Some studies in machine learning using the game of checkers. In E. A. Feigenbaum and J. Feldman (eds), Computers and Thought. New York, McGraw-Hill. Simon, H. A. (1983) Reason in Human Affairs. Stanford, Stanford University Press. Wyatt, R. (1989) Intelligent Planning. London, Unwin Hyman. Wyatt, R. (1995) Evaluation of Strategies. Unpublished MSc Minor Thesis, Psychology Department, University of Melbourne, Melbourne. Read More