A Fuzzy Set Approach to Using Linguistic Hedges in Geographical Information Systems (original) (raw)

Fuzzy reliability analysis in the implementation of geographic information systems

2002

We use fuzzy logic for proposing a classification model which divides a geographic map in isoreliable zones. This classification is based on a software tool called FUZZY-SRA (Spatial Reliability Analysis) integrated in a Geographical Information System (GIS) of the PROCIDA island, located near Naples (Italy). The GIS is realized with technology E.S.R.I. and the logical operations, for processing the linguistic approximations and the classification of the zones, are defined in the context of an algebraic structure, already known in literature.

Enhancing a database management system for GIS with fuzzy set methodologies

1999

The methods used in commercial GIS packages for both the representation and analysis of geographic data are inadequate, because they do not handle uncertainty. This leads to information loss and inaccuracy in analysis with adverse consequences in the spatial decision-making process. The incorporation of fuzzy set methodologies into a DBMS repository for the application domain of GIS should be beneficial and will improve its level of intelligence. Focusing on this direction the paper addresses both a representation and a reasoning issue. Specifically, it extends a general spatial data model to deal with the uncertainty of geographic entities, and shows how the standard data interpretation operations available in GIS packages may be extended to support the fuzzy spatial reasoning. Representative geographic operations, such as the fuzzy overlay, fuzzy distance and fuzzy select, are examined, while a real world situation involving spatial decision making is presented.

A Comparative Evaluation of Existing Gis Uncertainty Reasoning Theories

Information plays a key role in decision-making process. In order to have a proper decision-making, correct, precise and update information is necessary. Geospatial Information Systems (GISs) as the science and technology of optimum management of spatially referenced data is widely used as a spatial decision support system. One of the key functionalities of GIS is to integrate geospatial data with different levels of uncertainty collected from various sources. The value of GIS output products obtained from uncertain data and/or improper analysis is vague. It is therefore essential to implement reliable means for uncertainty management. There is a number of uncertainty handling approaches namely probability theory, information theory, fuzzy set theory, theory of evidence, non-monotonic logic, interval method, etc. knowledge of the potential and pitfalls of different uncertainty measures is essential for geospatial information (GI) community. This paper has critically reviewed the abo...

Querying Uncertain Data in Geospatial Object-relational Databases Using SQL and Fuzzy Sets

Spatial data is inherently uncertain. The main sources of uncertainty include uncertainty in the collection, processing and representation of data (e.g., missing or ambiguous data, measurement uncertainties, raster-to-vector conversion, indeterminate borders of geographical objects, ambiguities in object identification, uncertainty in the interpretation and digitization of data, image classifications, interpolation of values and determination of attributes, etc.). In many situations it is necessary to make important decisions based on an uncertain data analysis. The functions and operations which are performed with the data in information systems are mostly implemented by making use of crisp rules and criteria based on Boolean logic, which often leads to a loss of information resulting ABSTRACT This paper deals with uncertainty modeling in spatial object-relational databases by the use of Structured Query Language (SQL). The fundamental principles of uncertainty modeling by fuzzy sets are applied in the area of geographic information systems (GIS) and spatial databases. A spatial database system includes types of spatial data and implements the spatial extension of SQL. The implementation of the principles of fuzzy logic to spatial databases brings an opportunity for the efficient processing of uncertain data, which is important, especially when using various data sources (e.g., multi-criteria decision making (MCDM) on the basis of heterogeneous spatial data resources). The modeling and data processing of uncertainties are presented in relation to the applicable International Organization for Standardization (ISO) standards (standards of the series 19100 Geographic information) and the relevant specifications of the Open Geospatial Consortium (OGC). The fuzzy spatial query approach is applied and tested on a case study with a fundamental database for GIS in Slovakia.

Fuzzy Logic and Spatial Analysis in Gis Environment

2015

In the context of the fuzzy logic we use a system of max-min fuzzy relation equations to solve a problem of spatial analysis in a Geographical Information Systems (GIS). The geographical area under study is divided in subzones to which we apply our process to determine the outputs after that an expert sets the whole SFRE with the values of the coefficients impacting the input data. We find the best solutions by associating the results to each subzone and thematic maps are extracted from the GIS. Keywords: system of max-min fuzzy relation equations, GIS, triangular fuzzy number

Combining Fuzzy Sets and Databases in Multiple Criteria Spatial Decision Making

Flexible Query Answering Systems, 2001

Spatial decision making is a fundamental function of contemporary Geographic Information Systems (GIS). One of the most fertile GIS development areas is integrating multiple criteria decision models (MCDM) into GIS querying mechanisms. The classic approach for this integration has been to use Boolean techniques of MCDM with crisp representations of spatial objects (features) to produce static maps as query answers. By implementing: 1) fuzzy set membership as a method for representing the performance of decision alternatives on evaluation criteria, 2) fuzzy methods for both criteria weighting and capturing geographic preferences, and 3) fuzzy object oriented spatial databases for feature storage, it is possible to visually represent query results more precisely. This will allow decision makers to be more informed, and thus, more correct. We conclude the paper with future research directions and implementation prototype strategies.

Uncertainty Management for Spatial Datain Databases: Fuzzy Spatial Data Types

Advances in Spatial Databases, 1999

In many geographical applications there is a need to model spatial phenomena not simply by sharply bounded objects but rather through vague concepts due to indeterminate boundaries. Spatial database systems and geographical information systems are currently not able to deal with this kind of data. In order to support these applications, for an important kind of vagueness called fuzziness, we propose an abstract, conceptual model of so-called fuzzy spatial data types (i.e., a fuzzy spatial algebra) introducing fuzzy points, fuzzy lines, and fuzzy regions. This paper ? focuses on de ning their structure and semantics. The formal framework is based on fuzzy set theory and fuzzy topology.