LSAPy

[baptistehamon]

Submitting Author: Baptiste Hamon (@baptistehamon)
Package Name: LSAPy
One-Line Description of Package: Package to help and ease Land Suitability Analysis (LSA) workflow.
Repository Link (if existing): https://github.com/baptistehamon/lsapy
EiC: TBD

---

Code of Conduct & Commitment to Maintain Package

• I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
• I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.

Description

LSAPy stands for Land Suitability Analysis (LSA) in Python. Its objective is to make conducting LSA in Python easier and more accessible to users. It provides a set of objects built around xarray and operating together, making LSA's workflow straight forward and easy to understand.

Community Partnerships

We partner with communities to support peer review with an additional layer of
checks that satisfy community requirements. If your package fits into an
existing community please check below:

• Astropy: My package adheres to Astropy community standards
• Pangeo: My package adheres to the Pangeo standards listed in the pyOpenSci peer review guidebook

Scope

• Please indicate which category or categories this package falls under:

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

Domain Specific

• Geospatial
• Education

---

• Explain how and why the package falls under these categories (briefly, 1-2 sentences). For community partnerships, check also their specific guidelines as documented in the links above. Please note any areas you are unsure of:

LSAPy helps user to perform Land Suitability Analysis (LSA). In this sense, it falls into the category data processing/munging by performing a series of geospatial operations on input data, converting the criteria indicators into suitability values according to user-defined rules, and then proposing several ways of aggregating them to obtain an overall suitability value.

• Who is the target audience and what are the 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 similar things? 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.

• Any other questions or issues we should be aware of:

P.S. Have feedback/comments about our review process? Leave a comment here