Logic Scoring Of Preference Method Research Articles

Integrating multiple perspectives in marine spatial planning using the GIS-based Logic Scoring of Preference method

Marine spatial planning processes require integrating diverse opinions, knowledge, and goals of multiple stakeholders as well as large volumes of environmental data. Multicriteria evaluation (MCE) methods have been used to combine spatial data in a way that represents stakeholder preferences; however, conventional methods are limited in their ability to represent and integrate multiple stakeholder perspectives. The main goal of this research study is to implement a generalized multicriteria evaluation method, Logic Scoring of Preference (LSP), integrated with geographic information systems (GIS) to represent different perspectives on the suitability for conservation areas for endangered killer whales (Orcinus orca). Both two and three dimensional (3D) criteria have been used for killer whale acoustic refugia suitability analysis in the study region of the Salish Sea between British Columbia, Canada and Washington, USA. Resulting suitability values were then combined in an overall integrated suitability analysis (OISA) to represent different decision-making outcomes such as consensus and trade-offs, logically defined by LSP aggregators, between stakeholders’ perspectives. The results demonstrate the ability of the Logic Scoring of Preference method to derive overall integrated suitability maps using a variety of logical aggregators. This method can be applied to facilitate marine spatial planning by enabling stakeholders and decision makers to explore, analyse and visualize possible decision scenarios and their trade-offs.

Explainable Decision-Making for Water Quality Protection

All professional decisions prepared for a specific stakeholder can and must be explained. The primary role of explanation is to defend and reinforce the proposed decision, supporting stakeholder confidence in the validity of the decision. In this paper we present the methodology for explaining results of the evaluation of alternatives for water quality protection for a real-life project, the Upper Neuse Clean Water Initiative in North Carolina. The evaluation and comparison of alternatives is based on the Logic Scoring of Preference (LSP) method. We identify three explainability problems: (1) the explanation of LSP criterion properties, (2) the explanation of evaluation results for each alternative, and (3) the explanation of the comparison and ranking of alternatives. To solve these problems, we introduce a set of explainability indicators that characterize properties that are necessary for verbal explanations that humans can understand. In addition, we use this project to show the methodology for automatic generation of explainability reports. We recommend the use of explainability reports as standard supplements for evaluation reports containing the results of evaluation projects based on the LSP method.

Spatial Decision-Making for Dense Built Environments: The Logic Scoring of Preference Method for 3D Suitability Analysis

As many urban areas undergo increasing densification, there is a growing need for methods that can extend spatial analysis and decision-making for three-dimensional (3D) environments. Traditional multicriteria evaluation (MCE) methods implemented within geographic information systems (GIS) can assist in spatial decision-making but are rarely suited for 3D environments. These methods typically use a simplified decision logic that limits the number of evaluation criteria and variability of output suitability scores. In this study, the logic scoring of preference (LSP) as a generalized MCE method is used for 3D suitability analysis to better represent human reasoning through flexible soft computing stepwise decision logic operators. This research: (1) implements the LSP–MCE method to compare the suitability of high-rise residential units in 3D, and (2) performs criteria weight sensitivity and cost–suitability analyses using datasets for the City of Vancouver, Canada. LSP aggregation structures are developed for unique priorities and requirements of three demographic profiles. The results demonstrate the method’s flexibility in representing unique preference sets comprising 2D and 3D criteria, and that cost has a significant effect on residential unit attractiveness in a dense built environment. The proposed 3D LSP–MCE method could be adapted to benefit other stakeholders, such as property tax assessors, urban planners, and developers.

Improving integrated environmental zoning from the perspective of logic scoring of preference and comparative advantage: A case study of Liangjiang New Area, China

Rapid industrialization and urbanization have caused serious conflict between humans and nature due to the spatial variability of natural environmental conditions. Integrated environmental zoning (IEZ) is an efficient tool to regulate sustainable development in developing countries such as China. Previous studies explore application of IEZ based on assessment of resource and environmental carrying capacity (RECC) and evaluation of urban development land-use suitability (UDLS). However, existing IEZ oversimplify the complexity of UDLS evaluation, and have low spatial resolution. In addition, it is difficult to express overlay results of RECC and UDLS from a multi-dimensional perspective. Therefore, this study sets forth an improved IEZ by a logic scoring of preference (LSP) and a comparative advantage evaluation. The LSP method and normalized revealed comparative advantage (NRCA) index is applied to assess UDLS and the comparative advantage of RECC (CARECC). The results of IEZ are integrated by overlaying the CARECC zones with numerically processed UDLS maps. A small-scale, rapid development area, Liangjiang New Area (LJNA), serves as the study area. The highly suitable, suitable, moderately suitable, marginally suitable, and not suitable land of LJNA covers 276.60, 368.40, 183.45, 143.22, and 228.33 km2, respectively. Furthermore, the spatial distribution of RECC in LJNA clearly differ. Based on the results of UDLS and RECC, ten types of integrated environmental zones in the form of “category + intensity” are delineated. The improved IEZ is consistent with the LJNA Master Plan, reflects the logic of human reasoning more accurately, increases the spatial resolution, achieves zoning from multi-dimensional perspectives, restricts the category and determines the sequence of urban development, which can serve as a reference for optimization of land space in LJNA and is also significant to other areas and places.

GIS-based Logic Scoring of Preference method for urban densification suitability analysis

Urban Densification Suitability Analysis (UDSA) approach has been proposed using the GIS-based Logic Scoring of Preference (LSP) decision method. Our goal is to provide the methodology for the evaluation of suitability of locations for high density urban development to facilitate the spatial decision-making process and help justifiable urban planning. The main objectives of this research study are (1) to implement the GIS-based LSP method for high density urban growth suitability analysis, and (2) to perform an overall integrated suitability analysis for two key stakeholders, urban developer and urban planner. The LSP method is based on soft computing and used in the decision-making process to integrate large number of attribute criteria in a way that is consistent with human logic reasoning. The GIS-based LSP method is implemented using geospatial data for Metro Vancouver Region, Canada. Main criteria used to characterize the urban densification include recreation, transportation, existing development, economy, terrain, restrictions, and demography. The obtained results provide the distributions of suitability values and indicate that the opinions of two stakeholders differ. They select different locations as the most suitable for urban densifications and rather agree on the most unsuitable locations. The overall integrated suitability analysis has been performed with scenarios of possible decision-making outcomes between the two stakeholders. We introduce the concept of suitability agreement maps along with urban densification decision criteria. The analysis indicates that the GIS-based LSP method is a useful tool for measuring the similarities and differences in stakeholders' views and facilitating decision-making process for urban densification.

Suitability Analysis of Acoustic Refugia for Endangered Killer Whales (Orcinus orca) Using the GIS-based Logic Scoring of Preference Method.

An emerging priority in marine noise pollution research is identifying marine "acoustic refugia" where noise levels are relatively low and good-quality habitat is available to acoustically sensitive species. The endangered Southern Resident population of killer whales (Orcinus orca) that inhabits the transboundary Salish Sea in Canada and the USA are affected by noise pollution. Geographic Information Systems (GIS) and spatial multicriteria evaluation (MCE) methods have been used to operationalize suitability analysis in ecology and conservation for site selection problems. However, commonly used methods lack the ability to represent complex logical relationships between input criteria. Therefore, the objective of this study is to apply a more advanced MCE method, known as Logic Scoring of Preference (LSP), to identify acoustic refugia for killer whales in the Salish Sea. This GIS-based LSP-MCE approach considers multiple input criteria by combining input data representing killer whale habitat requirements with noise pollution and other factors to identify suitable acoustic refugia. The results indicate the locations of suitable acoustic refugia and how they are affected by noise pollution from marine vessels in three scenarios developed to represent different levels of vessel traffic. Identifying acoustic refugia can contribute to efforts to reduce the effect of marine noise pollution on killer whale populations by highlighting high-priority areas in which to implement policies such as traffic-limiting measures or marine protected areas. Moreover, the proposed LSP-MCE procedure combines criteria in a stepwise manner that can support environmental management decision-making processes and can be applied to other marine suitability analysis contexts.

GIS.LSP: A soft computing logic method and tool for geospatial suitability analysis

AbstractThis research study extends the Logic Scoring of Preference (LSP) as a general multicriteria evaluation (MCE) method by presenting and evaluating a new GIS.LSP method and software tool implemented within the geographic information systems (GIS) environment. For the evaluation and validation of the method and software tool, we describe a case study of urban densification suitability analysis using geospatial data for the Metro Vancouver Region, Canada. The criteria, LSP structures, and aggregators groups were developed from the perspective of urban developers who are key stakeholders in the densification process. We compare two group of aggregators and perform sensitivity and cost–suitability analysis of the LSP method by variation of input suitability scores, input attributes, and aggregators. The results indicate the GIS.LSP method is effective in providing a flexible and sensitive workflow to create realistic and justifiable outcomes from complex criteria that are bounded by stakeholders' goals and requirements.

A UML profile for quality-based web service selection using logic scoring of preference method

In the modern information infrastructure, Web services have a very important role. Web services are one of the main tools for information creation, delivery and processing in society and economic life; for the formation and further evolution of the information space. Among the existing and constantly growing number of Web services, in a natural objective way, there are many Web services with the same or similar functional properties. This makes it possible to select among them such Web service that best satisfies the consumer’s requirements and is most suitable to consumer’s preferences on quality characteristics, for instance, cost, reliability, response time, etc. On the other hand, the consumer is not always possible to decide which Web service is the best from his point of view, because Web service has multiple quality characteristics. In this regard, many researchers formulate the problem of quality-based Web service selection as a multi-criteria decision-making problem and integrate the facilities for the decision-making into decision support systems. One of the methods of multi-criteria decision-making is the Logic Scoring of Preference (LSP) method. Given the current trends in the transition from document-based to model-based systems engineering, it is expedient to provide the design templates (meta-models or profiles) for the decision-making support systems developers. In this paper, a UML profile to design the quality-based Web service selection systems using the LSP method was proposed. Standard UML extension mechanisms (stereotypes, tag value definitions, and constraints) to capture the basic quality properties of the Web service and the rules of applying the LSP method operators for this profile were used. The efficiency of the proposed profile by the examples of modelling the LSP aggregators to aggregate the Web service quality characteristics was demonstrated. The correctness of the models created using the proposed profile in the MagicDraw modelling environment was verified.

Evaluating the Quality of Social Web Applications Using the LSP Method

Quality is an essential determinant of the success of every type of software and social Web applications are not an exception. It is therefore of great importance that the examination of the degree to which social Web applications meet predefined requirements related to particular facets of quality is performed effectively and frequently. With an objective to facilitate evaluation procedure and enable comparison of social Web applications at all levels of the quality model, we initiated a research into development of a methodology that will aggregate quality requirements into a single score. The work presented in this paper draws on the employment of the logic scoring of preference (LSP) method and outlines only some parts of the aforementioned methodology. After identifying quality attributes that constitute the requirement tree, elementary criteria for both objective and subjective performance variables were introduced. As a follow up, field experts were included in the study in order to determine weights of performance variables within particular performance subsystem. Finally, the appropriate logic aggregation operators were selected based on the relevance of performance variables

Urban geosimulations with the Logic Scoring of Preference method for agent-based decision-making

The Logic Scoring of Preference (LSP) method is a generalized multicriteria evaluation (MCE) decision making approach with origins in soft computing. The method can model a wide range of aggregators to suit various evaluation objectives that are close to human reasoning. The LSP method can aggregate an unlimited number of inputs without loss of significance. The main objective of this study is to develop and implement an integrated approach using the LSP method to represent the decision-making process of actors influencing urban residential development represented within an agent-based model (ABM). Geospatial data for the Clayton-Cloverdale neighborhood of the City of Surrey, Canada, was used to incorporate the LSP agent-based model to simulate land-use change at the cadastral level. The geosimulations incorporated the decision-making process and interactions of agents as residents, developers, and city planners known as the main stakeholders with separate and sometimes conflicting priorities. The simulation results indicate a higher number of residents tend to choose mid-rise to high-rise buildings over single residential dwellings for a longer period of the time. This can be attributed to the lack affordability and developable land for housing in the future. The LSP method captured different agent decision-making reasoning that is closer to actual human logic which has resulted in the modeling outcomes of urban residential land-use to be in accordance to long term city plans.

A GIS-based Logic Scoring of Preference method for evaluation of land capability and suitability for agriculture

The growth of urban areas and industrial intensification has contributed to a reduction in valuable agricultural lands and to various environmental impacts including climate change. This reduction in agricultural land severely impacts food production and food security. In order to effectively address this issue, spatial analytical and optimization methods based on evaluating multiple spatial criteria are needed to evaluate the capability and suitability of available lands for current and future food production. The objective of this study is to implement the GIS-based Logic Scoring of Preference (LSP) method as an improved method of multicriteria decision making for evaluating areas suitable for agriculture. Evaluation criteria included soil, topographic, climatic, economic, land use and accessibility attributes. The LSP method allows for the stepwise aggregation of a large number of inputs to represent the full range of human decision logic without losing weighted significance of the inputs. Geospatial datasets from Boulder County, Colorado, USA provides the context to demonstrate the LSP method for agricultural land capability and land suitability assessment. The LSP method uses a large number of inputs and suitability functions integrated into the development of LSP aggregation structures to represent the full range of human decision-making logic and to evaluate decision-making objectives. The method incorporates a larger number of criterion inputs, represents the full range of human decision-making, and produces realistic agricultural land capability and suitability maps thereby making it an effective tool for integrated regional land-use planning.

Comparison of GIS‐Based Logic Scoring of Preference and Multicriteria Evaluation Methods: Urban Land Use Suitability

Multi‐criteria evaluation (MCE) methods are useful tools to evaluate the land suitability for various uses and assist in the effective management of available land. Many common GIS‐based MCE methods, such as analytical hierarchy process (AHP), ordered weighted averaging (OWA), and a combination of AHP and OWA methods (AHP–OWA) are not able to fully represent all the logic that constitute a wide range of human decision‐making reasoning. Consequently, improved GIS‐based MCE methods such as Logic Scoring of Preference (LSP) method are needed. The main objectives of this study are to: (1) implement the GIS‐based LSP method for land suitability evaluation and (2) compare qualitatively and quantitatively the suitability maps generated by LSP and three GIS‐based MCE methods. This study was implemented with data sets from Boulder County, Colorado, USA for the case study of the urban land suitability evaluation. The qualitative properties of MCE methods and the Receiver Operating Characteristic (ROC) statistics were used as comparison metrics. The results indicate that soft computing methods and particularly LSP performed the best among GIS‐based MCE methods for the urban land use application.

Soft Computing Methodology for Software Evaluation

Abstract Evaluation, comparison, and selection of software products are frequent decision problems. Soft computing offers efficient methodology for solving such problems. Our evaluation models are based on the Logic Scoring of Preference (LSP) method for system evaluation. We present an overview of the LSP method, and techniques for building evaluation criteria compatible with the ISO 9126 standard for software product evaluation. To exemplify our methodology we present the design and use of criteria for evaluation and comparison of search engines, Java IDEs, and windowed environments.

Asset Management of Vehicles: A Practical Application of the Logic Scoring of Preference Method of Optimizing Programmatic Investment Decisions in a Performance based System

This paper is an application of the Logic Scoring of Preference (LSP) methodology, a second-order decision model, to asset management of a public agency vehicle fleet. Current vehicle allocation management (VAM) modeling can readily identify the best and worst vehicles of the fleet, but does not adequately address the remaining vehicles in the fleet. Five weighted preferences are applied to assessment of mission role, age, fuel type, miles per gallon and cumulative mileage. These weighted preferences are then further optimized, providing a measure of how well the model is close to an ideal solution (benefits). A life- cycle cost is then generated for each vehicle. These two inputs create a benefit-cost ratio for each vehicle which will allow for a unique side-by-side comparison of all vehicles in the fleet, a much needed improvement over current VAM modeling.

LSP method and its use for evaluation of Java IDEs

In this paper we propose a quantitative model for evaluation and selection of integrated development environments (IDEs) for Java enterprise applications. Our goal is to determine the extent to which major IDEs satisfy typical software developer requirements. Our evaluation model is based on the Logic Scoring of Preference (LSP) method for system evaluation. We present an overview of the LSP method, the structure of IDE evaluation criterion, and a sample evaluation and comparison of three competitive systems: IBM WebSphere Studio Application Developer, Borland JBuilder, and SUN ONE Studio. In this paper we also introduce rectangular diagrams, an efficient new notation of LSP criteria.

Computer selection and criteria for computer performance evaluation

Computer evaluation, comparison, and selection is essentially a decision process. The decision making is based on a number of worth indicators, including various computer performance indicators. The performance indicators are obtained through the computer performance measurement procedure. Consequently, in this environment the measurement procedure should be completely conditioned by the decision process. This paper investigates various aspects of the computer performance measurement and evaluation procedure within the context of the computer evaluation, comparison, and selection process based on the Logic Scoring of Preference method. A set of elementary criteria for performance evaluation is proposed and the corresponding set of performance indicators is defined. The necessary performance measurements are based on a standardized set of synthetic benchmark programs and include three separate measurements: monoprogramming performance measurement, multiprogramming performance measurement, and multiprogramming efficiency measurement. Using the proposed elementary criteria, the measured performance indicators can be transformed into elementary preferences and then aggregated with other nonperformance elementary preferences obtained through the evaluation process. The applicability of presented elementary criteria is illustrated by numerical examples.

Criteria for computer performance analysis

Computer evaluation, comparison, and selection is essentially a decision process. The decision making is based on a number of worth indicators, including various computer performance indicators. The performance indicators are obtained through the computer performance measurement procedure. Consequently, this procedure should be completely conditioned by the decision process. This paper investigates various aspects of computer performance measurement and evaluation procedure within the context of computer evaluation, comparison and selection process based on the Logic Scoring of Preference method. The set of elementary criteria for performance evaluation is proposed and the corresponding set of performance indicators is defined. The necessary performance measurements are based on the standardized set of synthetic benchmark programs and include three separate measurements: monoprogramming performance measurement, multiprogramming performance measurement, and multiprogramming efficiency measurement. Using the proposed elementary criteria, the measured performance indicators can be transformed into elementary preferences and aggregated with other non-performance elementary preferences obtained through the evaluation process. The applicability of presented elementary criteria is illustrated by numerical examples.

Criteria for computer performance analysis

Computer evaluation, comparison, and selection is essentially a decision process. The decision making is based on a number of worth indicators, including various computer performance indicators. The performance indicators are obtained through the computer performance measurement procedure. Consequently, this procedure should be completely conditioned by the decision process. This paper investigates various aspects of computer performance measurement and evaluation procedure within the context of computer evaluation, comparison and selection process based on the Logic Scoring of Preference method. The set of elementary criteria for performance evaluation is proposed and the corresponding set of performance indicators is defined. The necessary performance measurements are based on the standardized set of synthetic benchmark programs and include three separate measurements: monoprogramming performance measurement, multiprogramming performance measurement, and multiprogramming efficiency measurement. Using the proposed elementary criteria, the measured performance indicators can be transformed into elementary preferences and aggregated with other non-performance elementary preferences obtained through the evaluation process. The applicability of presented elementary criteria is illustrated by numerical examples.