A Study on the Location Analysis Using Spatial Analysis and Ordered Weighted Averaging Operator Weighting Functions

2008 ◽  
Vol 12 (6) ◽  
pp. 529-536 ◽  
Author(s):  
Se-Woong Oh ◽  
◽  
Gyei-Kark Park ◽  
Jong-Min Park ◽  
Sang-Hyun Suh ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Fuzzy Ahp ◽  
Selection Method ◽  
Total Order ◽  
Weighted Averaging ◽  
Decision Strategy ◽  
Owa Operators ◽  
Ordered Weighted Averaging ◽  
Definition Of

In this thesis, we proposed the method combining spatial analysis, selection method of weighting values, aggregating decision strategy. To select a sites proposed for ship anchorage, we analyzed spatial data. Fuzzy AHP was used as selection method of weighting values to incorporate the fuzzy set theory and the basic nature of subjectivity due to ambiguity to achieve a flexible decision approach suitable for uncertain and fuzzy environments. To obtain the score that corresponds to the best alternative or the ranking of the alternatives, we need to use a total order for the fuzzy numbers involved in the problem. In this paper, we consider a definition of such a total order: the degree of Orness (1, 3/4, 2/3, 1/2, 1/3, 1/4, 0) reflected with the ordered weighted averaging (OWA) operators. A numerical example was given to illustrate the approach.

scholarly journals A Study of OWA Operators Learned in Convolutional Neural Networks

2021 ◽  
Vol 11 (16) ◽  
pp. 7195
Author(s):  
Iris Dominguez-Catena ◽  
Daniel Paternain ◽  
Mikel Galar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Practical Method ◽  
Local Information ◽  
Weighted Averaging ◽  
Global Information ◽  
Owa Operator ◽  
Owa Operators ◽  
Ordered Weighted Averaging

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.

Spatial Autocorrelation and Association Measures

2011 ◽  
pp. 1502-1508
Author(s):  
J. Negreiros ◽  
M. Painho ◽  
I. Lopes ◽  
A.C. Costa
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Regional Analysis ◽  
Geographical Information ◽  
Association Measures ◽  
Share Data ◽  
Definition Of ◽  
Graphic Database

Several classical statements relating to the definition of GIS can be found in specialized literature such as the GIS International Journal, expressing the idea that spatial analysis can somehow be useful. GIS is successful not only because it integrates data, but it also enables us to share data in different departments or segments of our organizations. I like this notion of putting the world’s pieces back together again (ArcNews, 2000). “GIS is simultaneously the telescope, the microscope, the computer and the Xerox machine of regional analysis and the synthesis of spatial data” (Abler, 1988). “GIS is a system of hardware, software and liveware implemented with the aim of storing, processing, visualizing and analyzing data of a spatial nature. Other definitions are also possible” (Painho, 1999). “GIS is a tool for revealing what is otherwise invisible in geographical information” (Longley, Goodchild, Maguire, & Rhind, 2001). Certainly, GIS is not a graphic database.

Applications of Geographical Information System in E-Government

2011 ◽  
pp. 80-86
Author(s):  
Y. Yongling
Keyword(s):  
Information System ◽  
Spatial Analysis ◽  
Geographical Information System ◽  
Real World ◽  
Spatial Data ◽  
Geographical Information ◽  
The Real ◽  
Definition Of ◽  
At Will

Geographical information system (GIS) is one kind of information system that handles spatial data. It is difficult to give one definitive definition about GIS (Heywood, Cornelius, & Carver, 2002; Maguire, Goodchild, & Rhind, 2001). This variety of definitions can be explained by the fact that any definition of GIS will depend on who is giving it, and their background and viewpoint (Pinkles, 2002). The complete definition of GIS is selected here as: “a set of tools for collecting, storing, retrieving at will, transforming, and displaying spatial data from the real world for a particular set of purposes”(Burrough, 1986, p. 6). As an important part of e-government, is that it has functions of cartography, manages spatial data and spatial analysis.

GENERALIZED MOVING AVERAGES, DISTANCE MEASURES AND OWA OPERATORS

2013 ◽  
Vol 21 (04) ◽  
pp. 533-559 ◽  
Author(s):  
JOSÉ M. MERIGÓ ◽  
RONALD R. YAGER
Keyword(s):  
Moving Average ◽  
Aggregation Operators ◽  
Distance Measures ◽  
Weighted Averaging ◽  
Owa Operators ◽  
Ordered Weighted Averaging ◽  
Wide Range ◽  
Moving Averaging ◽  
Generalized Distance ◽  
Weighted Moving Average

The concept of moving average is studied. We analyze several extensions by using generalized aggregation operators, obtaining the generalized moving average. The main advantage is that it provides a general framework that includes a wide range of specific cases including the geometric and the quadratic moving average. This analysis is extended by using the generalized ordered weighted averaging (GOWA) and the induced GOWA (IGOWA) operator. Thus, we get the generalized ordered weighted moving average (GOWMA) and the induced GOWMA (IGOWMA) operator. Some of their main properties are studied. We further extend this approach by using distance measures suggesting the concept of distance moving average and generalized distance moving average. We also consider the case with the OWA and the IOWA operator, obtaining the generalized ordered weighted moving averaging distance (GOWMAD) and the induced GOWMAD (IGOWMAD) operator. The paper ends with an application in multi-period decision making.

scholarly journals Applying Hybrid Decision-Making Method Based on Fuzzy AHP-WOWA Operator for Emergency Alternative Evaluation of Unattended Train Operation Metro System

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Bobo Zhao ◽  
Tao Tang ◽  
Bin Ning
Keyword(s):  
Decision Making ◽  
Fuzzy Ahp ◽  
Multicriteria Decision Making ◽  
Emergency Situation ◽  
Weighted Averaging ◽  
Ordered Weighted Averaging ◽  
Train Operation ◽  
Emergency Decision ◽  
Response Task ◽  
Metro System

Optimal alternative selection to address the emergency situation is critical for dispatcher group in Unattended Train Operation (UTO) to guide emergency process. It is difficult to provide the precise decision value under one criterion and to evaluate the emergency alternatives among multiple dispatchers. This paper presents a hybrid emergency decision-making method integrating fuzzy analytic hierarchy process (FAHP) described by linguistic terms with enhanced weighted ordered weighted averaging (WOWA) operator. The enhanced WOWA operator aggregates the preference matrices of multidispatcher through the constructed emergency response task model of dispatcher group in OCC. This calculation approach takes into consideration the relations of emergency tasks to derive the importance weights of dispatchers and integrates them into the ordered weighted averaging (OWA) operator weights based on a fuzzy membership relation. A case study of applying the method in an emergency of a train fire is given to demonstrate the feasibility and usefulness of the methods associated with the group multicriteria decision-making (GMCDM) theory in emergency management of UTO metro system.

scholarly journals Exploring Multicriteria Decision Strategies in GIS with Linguistic Quantifiers – A Case Study of Residential Quality Evaluation

2021 ◽  
Author(s):  
Jacek Malczewski ◽  
Claus Rinner
Keyword(s):  
Evaluation Method ◽  
Weighted Averaging ◽  
Decision Strategy ◽  
Decision Strategies ◽  
Linguistic Quantifier ◽  
Residential Quality ◽  
Definition Of ◽  
Linguistic Quantifiers

Commonly used GIS combination operators such as Boolean conjunction/disjunction and weighted linear combination can be generalized to the ordered weighted averaging (OWA) family of operators. This multicriteria evaluation method allows decision-makers to define a decision strategy on a continuum between pessimistic and optimistic strategies. Recently, OWA has been introduced to GIS-based decision support systems. We propose to extend a previous implementation of OWA with linguistic quantifiers to simplify the definition of decision strategies and to facilitate an exploratory analysis of multiple criteria. The linguistic quantifier-guided OWA procedure is illustrated using a dataset for evaluating residential quality of neighborhoods in London, Ontario. <div><br></div><div>This is a post-peer-review, pre-copyedit version of an article published in Journal of Geographical Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10109-005-0159-2 <br></div>

scholarly journals Web-Enabled Spatial Decision Analysis Using Ordered Weighted Averaging (OWA)

2021 ◽  
Author(s):  
Claus Rinner ◽  
Jacek Malczewski
Keyword(s):  
Graphical Representation ◽  
Decision Support Tool ◽  
Fine Tuning ◽  
Weighted Averaging ◽  
Decision Strategy ◽  
Spatial Decision Support ◽  
Data Set ◽  
Support Tool ◽  
Ordered Weighted Averaging ◽  
Exploratory Approach

This paper presents a spatial decision support tool that implements the Ordered Weighted Averaging (OWA) method. OWA is a family of multicriteria evaluation operators characterised by two sets of weights: criterion importance weights and order weights. We propose a highly interactive way of choosing, modifying, and fine-tuning the decision strategy defined by the order weights. This exploratory approach to OWA is supported by a graphical representation of the operator's behaviour in terms of decision risk and tradeoff/dispersion between criteria. Our prototype implementation is based on the CommonGIS software, and thus, Web-enabled and working with vector data. We successfully demonstrate online, exploratory support of spatial decision strategies using a data set of skiing resorts in Wallis, Switzerland.<div><br></div><div>This is a post-peer-review, pre-copyedit version of an article published in Journal of Geographical Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s101090300095 <br></div>

scholarly journals Three Ways of Defining Owa Operator on the Set of All Normal Convex Fuzzy Sets

2017 ◽  
Vol 69 (1) ◽  
pp. 101-118
Author(s):  
Zdenko Takáč
Keyword(s):  
Fuzzy Sets ◽  
Linear Order ◽  
Recent Literature ◽  
Weighted Averaging ◽  
Owa Operator ◽  
Main Obstacle ◽  
Owa Operators ◽  
Ordered Weighted Averaging ◽  
Algebraic Properties

Abstract We deal with an extension of ordered weighted averaging (OWA, for short) operators to the set of all normal convex fuzzy sets in [0, 1]. The main obstacle to achieve this goal is the non-existence of a linear order for fuzzy sets. Three ways of dealing with the lack of a linear order on some set and defining OWA operators on the set appeared in the recent literature. We adapt the three approaches for the set of all normal convex fuzzy sets in [0, 1] and study their properties. It is shown that each of the three approaches leads to operator with desired algebraic properties, and two of them are also linear.

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