LSAPy Submission

[baptistehamon]

Submitting Author: @baptistehamon
All current maintainers: @baptistehamon
Package Name: LSAPy
One-Line Description of Package: Package to help and ease Land Suitability Analysis (LSA) workflow.
Repository Link: https://github.com/baptistehamon/lsapy/
Version submitted: v0.2.0
EiC: TBD
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

LSAPy is a highly customizable Python library designed to streamline and enhance Land Suitability Analysis (LSA) workflows. The package implements a fuzzy-logic approach and provides three core objects - SuitabilityFunction, SuitabilityCriteria, and LandSuitabilityAnalysis that work together to deliver a flexible and user-defined LSA framework.

Scope

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  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

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  • Who is the target audience and what are scientific applications of this package?

LSA have been widely used by the scientific community to assess the suitability of agricultural products. In this context, LSAPy contributes to the reproducibility of such studies. Although initially intended for research purposes, LSAPy can be used by land managers/planners or for educational purposes, while being applicable to any type of land use (e.g., urban planning), thus extending its use beyond agricultural applications.

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

To my knowledge, the only Python package similar to LSAPy is PyLUSAT (Python Land-Use Suitability Analysis Toolkit) but the two packages work in completely different ways. PyLUSAT provides a vector-based GIS routines and determines suitability evaluating the spatial relationship between objects, while LSAPy assesses suitability by aggregating gridded criteria indicators. Morevover, PyLUSAT workflow can easely be integrating into LSAPy providing the relevant spatial relationship between cells and objects as suitablility criteria. Finally, agricultural application of LSA often rely on gridded temporal climate data that PyLUSAT don't support, limiting its potential use for such studies.

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LSAPy was pre-submitted in the issue #249.

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[eliotwrobson]

Thanks for the full submission! I'll get to the EiC checks at some point this weekend 👍🏽