Current release: 1.0.0
A research paper describing the first version of fsr has been accepted to publication in ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2021 (ACM SIGSPATIAL 2021).
Many spatial objects (i.e., geographical objects) in reality may have inexact locations, vague boundaries, and/or blurred interiors, and hence cannot be adequately represented by well-known (crisp) spatial objects (e.g., those objects available in spatial libraries like GEOS and GDAL). Fuzzy set theory and fuzzy logic are powerful approaches to deal with spatial fuzziness.
Unfortunately, spatial fuzziness has so far not found its way into spatial data science projects due to a lack of complete implementations of software packages that can handle fuzzy spatial objects. Motivated by this lack of support, the fsr package has the following advantages:
- It is an implementation of the fuzzy spatial data types, operations, and predicates of the Spatial Plateau Algebra (SPA).
- It offers the construction of fuzzy spatial objects as spatial plateau objects from real point datasets.
- It has families of functions that permit users to conduct exploratory (spatial) data analysis by issuing geometric operations and topological predicates on fuzzy spatial objects.
- It provides methods for designing fuzzy spatial inference models to discover new findings from fuzzy spatial objects.
- It deploys algorithms to determine and only evaluate a subset of points that really contributes to finding the final but approximated answer to a user’s query window inference.
Install the development version from GitHub with
library(devtools)
install_github("accarniel/fsr")The project welcomes contributions from three different perspectives. You can contribute to fsr by making Pull Requests on this GitHub Repository. Alternatively, contact Anderson Carniel by sending an email to accarniel@ufscar.br. When a Pull Request or email is sent, Anderson Carniel will check it as soon as possible.