research-article

Handling Fuzzy Spatial Data in R Using the fsr Package

Authors:

Anderson Chaves Carniel,

Felippe Galdino,

Juliana Strieder Philippsen,

Markus SchneiderAuthors Info & Claims

SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information Systems

Pages 526 - 535

https://doi.org/10.1145/3474717.3484255

Published: 04 November 2021 Publication History

Abstract

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.

References

[1]

A. C. Carniel, R. R. Ciferri, and C. D. A. Ciferri. 2016. Handling Fuzzy Points and Fuzzy Lines using the FuzzyGeometry Abstract Data Type. Journal of Information and Data Management 7, 1 (2016), 35--51.

[2]

A. C. Carniel and M. Schneider. 2016. A Conceptual Model of Fuzzy Topological Relationships for Fuzzy Regions. In IEEE Int. Conf. on Fuzzy Systems. 2271--2278.

[3]

A. C. Carniel and M. Schneider. 2017. Fuzzy Inference on Fuzzy Spatial Objects (FIFUS) for Spatial Decision Support Systems. In IEEE Int. Conf. on Fuzzy Systems. 1--6.

[4]

A. C. Carniel and M. Schneider. 2018. Spatial Plateau Algebra: An Executable Type System for Fuzzy Spatial Data Types. In IEEE Int. Conf. on Fuzzy Systems. 1--8.

[5]

A. C. Carniel and M. Schneider. 2019. A Systematic Approach to Creating Fuzzy Region Objects from Real Spatial Data Sets. In IEEE Int. Conf. on Fuzzy Systems. 1--6.

[6]

C. Chen, T. R. Razak, and J. M. Garibaldi. 2020. FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language. In IEEE Int. Conf. on Fuzzy Systems. 1--8.

[7]

S. Davari and N. Ghadiri. 2015. Spatial Database Implementation of Fuzzy Region Connection Calculus for Analysing the Relationship of Diseases. In Iranian Conf. on Electrical Engineering. 734--739.

[8]

S. Davari and N. Ghadiri. 2019. Fuzzy Region Connection Calculus and Its Application in Fuzzy Spatial Skyline Queries. In Intelligent Computing. 659--677.

[9]

A. Dilo, P. Bos, P. Kraipeerapun, and R. A. de By. 2006. Storage and Manipulation of Vague Spatial Objects using Existing GIS Functionality. In Flexible Databases Supporting Imprecision and Uncertainty, G. Bordogna and G. Psaila (Eds.). Vol. 203. 293--321.

[10]

A. Dilo, R. A. de By, and A. Stein. 2007. A System of Types and Operators for Handling Vague Spatial Objects. Int. Journal of Geographical Information Science 21, 4 (2007), 397--426.

Digital Library

[11]

C.-C. Lee. 1990. Fuzzy Logic in Control Systems: Fuzzy Logic Controller, Part II. IEEE Trans. on Systems, Man, and Cybernetics 20, 2 (1990), 419--435.

[12]

E. Pebesma. 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10, 1 (2018), 439--446.

[13]

E. J. Pebesma and R. S. Bivand. 2005. Classes and methods for spatial data in R. R News 5, 2 (2005), 9--13.

[14]

R. Poli, J. Kennedy, and T. Blackwell. 2007. Particle swarm optimization. Swarm Intelligence 1 (2007), 33--57.

[15]

M. Schneider. 2014. Spatial Plateau Algebra for Implementing Fuzzy Spatial Objects in Databases and GIS: Spatial Plateau Data Types and Operations. Applied Soft Computing 16, 3 (2014), 148--170.

Digital Library

[16]

S. Schockaert, M. De Cock, C. Cornelis, and E. E. Kerre. 2008. Fuzzy region connection calculus: Representing vague topological information. Int. Journal of Approximate Reasoning 48, 1 (2008), 314--331.

Digital Library

[17]

J. Verstraete. 2012. Implementable Representations of Level-2 Fuzzy Regions for Use in Databases and GIS. In Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems. 361--370.

[18]

J. Verstraete, G. De Tré, and A. Hallez. 2006. Bitmap Based Structures for the Modeling of Fuzzy Entities. Control and Cybernetics 35, 1 (2006), 147--164.

[19]

H. Wickham and G. Grolemund. 2017. R for Data Science. O'Reilly Media.

[20]

L. A. Zadeh. 1965. Fuzzy Sets. Information and Control 8, 3 (1965), 338--353.

[21]

L. A. Zadeh. 1973. Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Trans. on Systems, Man, and Cybernetics SMC-3, 1 (1973), 28--44.

Cited By

Carniel Ade Venâncio PSchneider M(2023) fsr : An R package for fuzzy spatial data handling Transactions in GIS10.1111/tgis.1304427:3(900-927)Online publication date: 8-May-2023
https://doi.org/10.1111/tgis.13044
Chaves Carniel A(2023)Defining and designing spatial queries: the role of spatial relationshipsGeo-spatial Information Science10.1080/10095020.2022.2163924(1-25)Online publication date: 17-May-2023
https://doi.org/10.1080/10095020.2022.2163924
Jana SMahanta J(2023)A quantitative fuzzy-valued intersection matrix for obtaining fuzzy relationships between vague spatial objectsDecision Analytics Journal10.1016/j.dajour.2023.1003539(100353)Online publication date: Dec-2023
https://doi.org/10.1016/j.dajour.2023.100353
Show More Cited By

Index Terms

Handling Fuzzy Spatial Data in R Using the fsr Package
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Vagueness and fuzzy logic
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

Recommendations

Discovery of Spatial Association Rules from Fuzzy Spatial Data
Conceptual Modeling
Abstract
The discovery of spatial association rules is a core task in spatial data science projects and focuses on extracting useful and meaningful spatial patterns and relationships from spatial and geometric information. Many spatial phenomena have been ...
Spatial Plateau Algebra for implementing fuzzy spatial objects in databases and GIS: Spatial plateau data types and operations

Many geographical applications have to deal with spatial objects that reveal an intrinsically vague or fuzzy nature. A spatial object is fuzzy if locations exist that cannot be assigned completely to the object or to its complement. Spatial database ...
Design and implementation of finite resolution crisp and fuzzy spatial objects

Uncertainty management for geometric data is currently an important problem in spatial databases, image databases, and geographic information systems. Spatial entities do not always have homogeneous interiors and sharply defined boundaries but ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information Systems

November 2021

700 pages

ISBN:9781450386647

DOI:10.1145/3474717

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conference

SIGSPATIAL '21

Sponsor:

SIGSPATIAL

SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems

November 2 - 5, 2021

Beijing, China

Acceptance Rates

Overall Acceptance Rate 257 of 1,238 submissions, 21%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
126
Total Downloads

Downloads (Last 12 months)19
Downloads (Last 6 weeks)1

Reflects downloads up to 29 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Carniel Ade Venâncio PSchneider M(2023) fsr : An R package for fuzzy spatial data handling Transactions in GIS10.1111/tgis.1304427:3(900-927)Online publication date: 8-May-2023
https://doi.org/10.1111/tgis.13044
Chaves Carniel A(2023)Defining and designing spatial queries: the role of spatial relationshipsGeo-spatial Information Science10.1080/10095020.2022.2163924(1-25)Online publication date: 17-May-2023
https://doi.org/10.1080/10095020.2022.2163924
Jana SMahanta J(2023)A quantitative fuzzy-valued intersection matrix for obtaining fuzzy relationships between vague spatial objectsDecision Analytics Journal10.1016/j.dajour.2023.1003539(100353)Online publication date: Dec-2023
https://doi.org/10.1016/j.dajour.2023.100353
Ismail AHassaballa AAlmadini ADaffalla S(2022)Analyzing the Spatial Correspondence between Different Date Fruit Cultivars and Farms’ Cultivated Areas, Case Study: Al-Ahsa Oasis, Kingdom of Saudi ArabiaApplied Sciences10.3390/app1211572812:11(5728)Online publication date: 4-Jun-2022
https://doi.org/10.3390/app12115728

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents