Implementable Representations of Level-2 Fuzzy Regions for Use in Databases and GIS

Verstraete, Jörg

doi:10.1007/978-3-642-31709-5_37

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Furthermore, they supply visualization methods in the form of a GRASS module and implement geometric set operations (except the difference between fuzzy lines). Verstraete (2012) proposes TA B L E 1 Comparison of fsr with related work. implementable representation methods for fuzzy regions, such as bitmap representations (Verstraete et al, 2006) and TINs.…”

Section: Rel Ated Workmentioning

confidence: 99%

“…Furthermore, they supply visualization methods in the form of a GRASS module and implement geometric set operations (except the difference between fuzzy lines). Verstraete (2012) proposes implementable representation methods for fuzzy regions, such as bitmap representations (Verstraete et al, 2006) and TINs. Carniel et al (2016b) implement fuzzy spatial data types as a PostgreSQL/PostGIS extension according to the Vague Spatial Algebra (VASA) (Pauly and Schneider, 2010).…”

Section: Related Workmentioning

confidence: 99%

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fsr: An R package for fuzzy spatial data handling

Carniel

Venâncio

Schneider

2023

Transactions in GIS

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Spatial libraries are core components in many geographic information systems, spatial database systems, and spatial data science projects. These libraries provide the implementation of spatial type systems that include spatial data types and a large diversity of geometric operations. Their focus relies on handling crisp spatial objects, which are characterized by an exact location and a precisely defined extent, shape, and boundary in space. However, there is an increasing interest in analyzing spatial phenomena characterized by fuzzy spatial objects, which have inexact locations, vague boundaries, and/or blurred interiors. Unfortunately, available spatial libraries do not provide support for fuzzy spatial objects. In this article, we describe the R package named fsr, which is based on the Spatial Plateau Algebra and is publicly available at https://cran.r-project.org/package=fsr. Our tool provides methods for building fuzzy spatial objects as spatial plateau objects and conducting exploratory spatial data analysis by using fuzzy spatial operations.

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Section: Rel Ated Workmentioning

confidence: 99%

“…Furthermore, they supply visualization methods in the form of a GRASS module and implement geometric set operations (except the difference between fuzzy lines). Verstraete (2012) proposes implementable representation methods for fuzzy regions, such as bitmap representations (Verstraete et al, 2006) and TINs. Carniel et al (2016b) implement fuzzy spatial data types as a PostgreSQL/PostGIS extension according to the Vague Spatial Algebra (VASA) (Pauly and Schneider, 2010).…”

Section: Related Workmentioning

confidence: 99%

fsr: An R package for fuzzy spatial data handling

Carniel

Venâncio

Schneider

2023

Transactions in GIS

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“…The authors provide visualization methods in the form of a GRASS module and implement geometric set operations (except the difference between fuzzy lines). Verstraete [17] proposes implementable representation methods for fuzzy regions, such as bitmap representations [18] and TINs. Carniel et al [1] introduce a PostgreSQL/PostGIS extension to deal with fuzzy points and fuzzy lines according to the model in [10].…”

Section: Related Workmentioning

confidence: 99%

Handling Fuzzy Spatial Data in R Using the fsr Package

Carniel

Galdino

Philippsen

et al. 2021

Proceedings of the 29th International Conference on Advances in Geographic Information Systems

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GIS and spatial data science (SDS) tools have been recently approaching each other by establishing bridge technologies between them. R as one of the most prominent programming languages used in SDS projects has been granted access to GIS infrastructure, while R scripts can be integrated and executed in GIS functions. Unfortunately, the treatment of spatial fuzziness has so far not been considered in SDS projects and bridge technologies due to a lack of software packages that can handle fuzzy spatial objects. This paper introduces an R package named fsr as an implementation of the fuzzy spatial data types, operations, and predicates of the Spatial Plateau Algebra that is based on the abstract Fuzzy Spatial Algebra. This R package solves the problem of constructing fuzzy spatial objects as spatial plateau objects from real datasets and describes how to conduct exploratory spatial data analysis by issuing geometric operations and topological predicates on fuzzy spatial objects. Further, fsr provides the possibility of designing fuzzy spatial inference models to discover new findings from fuzzy spatial objects. It optimizes the inference process by deploying the particle swarm optimization to obtain the point locations with the maximum or minimum inferred values that answer a specific user request.

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“…The level-2 fuzzy region model so far has not been implemented, as the theoretical foundations needed to be made first. The model is quite complex, and practical representation models have already been considered in [17]. Operations can be build on top of the operations for the fuzzy region model, as most can be brought back to multiple applications of corresponding operations on the candidate fuzzy regions.…”

Section: Applicationsmentioning

confidence: 99%

Distance calculation between Level-2 fuzzy regions

Verstraete¹

2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

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In many applications, spatial data often are prone to uncertainty and imprecision. To model this, fuzzy regions have been developed. Our initial model was a fuzzy set over a two dimensional domain, allowing for fuzzy regions and fuzzy points to be modelled. The model had some limitations: all points where treated independently, and it was not possible to group points together. Furthermore, it depended on meta-information to specify the interpretation. The model was extended to a level-2 fuzzy region to overcome these limitations; here the impact on the definition of the distance between regions will be considered.

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Implementable Representations of Level-2 Fuzzy Regions for Use in Databases and GIS

Cited by 6 publications

References 10 publications

fsr: An R package for fuzzy spatial data handling

fsr: An R package for fuzzy spatial data handling

Handling Fuzzy Spatial Data in R Using the fsr Package

Distance calculation between Level-2 fuzzy regions

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