Retrospective GIS-Based Multi-Criteria Decision Analysis: A Case Study of California Waste Transfer Station Siting Decisions

Geographic information science (GIS) and multicriteria decision analysis (MCDA) disciplines combine to provide valuable insights which guide decision-makers evaluating complex spatial criteria and alternatives, especially when there are conflicting stakeholder values and objectives. Although GIS and MCDA methods have been integrated to support forward-looking decision analyses, there is also advantage to applying these methods retrospectively in order to decipher the factors that composed previous spatially-complex decisions. The objective of this study is to demonstrate a methodology which applies “retrospective GIS-based MCDA” to characterize decision-maker value preferences in past siting decisions without a priori knowledge of the decision-making process. As a representative case study, retrospective GIS-based MCDA is performed on municipal solid waste transfer station site decisions in Los Angeles County, California. Potential attribute data were identified and compiled into a geographic information system. The attributes of actual facility sites and their surrounding vicinities were established as the presence case for a positive decision. This decision problem was structured considering two MCDA decision model types – value function using weighted linear combination and reference point. The attributes of historical site selections were decomposed and compared to unselected sites to identify attribute patterns. The value function MCDA model was parameterized using logistic regression to establish relative attribute weights which were applied to create a probability spatial distribution profile. The reference point MCDA rule model was parameterized contrasting attribute relative frequency Pareto between transfer station and general locations to create a satisfaction spatial distribution profile. These resulting models provide both relative rank and objective level of attributes represented in previous waste transfer station location decisions. The methodology is applicable to evaluation of spatial decisions in other domains, and can be extended to consider other MCDA decision models. Proceedings of the International Symposium on Sustainable Systems and Technologies (ISSN 2329-9169) is published annually by the Sustainable Conoscente Network. Jun-Ki Choi and Annick Anctil, co-editors 2015. [email protected]. Copyright © 2015 by John Cirucci, Douglas Miller, Justine Blanford, Licensed under CC-BY 3.0. Cite as: Retrospective GIS-Based Multi-Criteria Decision Analysis: A Case Study of California Waste Transfer Station Siting Decisions. Proc. ISSST, Cirucci, J.F., Miller, D.A., Blanford, J.I. http://dx.doi.org/10.6084/ m9.figshare.1512514 v3 (2015) Retrospective GIS-Based Multi-Criteria Decision Analysis: A Case Study of California Waste Transfer Station Siting Decisions Introduction. Every day, we are constantly making simple decisions, considering many criteria. Usually the process is implicit and the decision maker is an individual. In contrast, “multicriteria decision analysis” (“MCDA”) describes a collection of formal approaches that can be used to make complex, high impact decisions with multiple stakeholders. The general stages of an MCDA process and two representative decision rule models are depicted in Figure 1. As part of problem structuring, criteria and alternatives are identified. In MCDA, criteria are defined as system attributes or objectives which fulfill a desired outcome. Alternatives are the options from which a final decision is selected. There may be a few alternatives, or an effectively infinite number such as with continuous surface site selection. Problem structuring also requires identification of stakeholders. A significant advantage of many MCDA methods is explicit quantification of stakeholder values. During the model building stage, decision rules are applied to evaluate criteria so the relative worth of possible alternatives can be characterized. Value measurement models entail assigning partial values to all criteria then aggregating these through various combination options to derive a comparative value for each alternative. Reference point models involve establishing a threshold, reference level for each criterion by which alternatives are successively filtered. There are many variants on these and other decision rule types. These two decision rule types were examined in this study. The information that the decision rule model yields is synthesized to establish a course of action (Belton & Stewart, 2002). Figure 1. a) General MCDA Process b) Two Decision Rule Model Types Many decisions require spatial consideration – criterion or alternative characteristics may comprise location and proximity. A geographic information system (GIS) can be used in multicriteria decision analysis to assist decision-makers with spatial decisions, and, in fact, GISbased MCDA is an expanding field with increasing research and application interest. More than 800 peer-reviewed GIS MCDA articles were published between 1990 and 2010 with accelerating frequency covering a broad range of methods, decision problems and application domains (Malczewski & Rinner, 2015). Methodologies for GIS-based MCDA and MCDA, in general, deal entirely with a forward-looking view, resolving a current problem to achieve an improved future outcome. Alternatively, there could be significant information derived from past decisions that involved conflicting stakeholder objectives and complex evaluation of alternatives. Hypothesis. Thus, could evidence on historical, third-party decisions be reverse-engineered to elicit information about the stakeholders’ values and the decision-making process? In this study, geospatial statistical analyses were integrated with multiple criteria decision analysis methods to retrospectively examine prior location decisions which entailed multiple stakeholders with conflicting motivations and data uncertainty. An inverse problem approach was applied to evaluate possible criteria, develop value preference parameters and test different Problem Structuring