pyinterpolate

[SimonMolinsky]

Submitting Author: (@SimonMolinsky )
All current maintainers: (@SimonMolinsky )
Package Name: Pyinterpolate
One-Line Description of Package: Spatial interpolation: Kriging and Variogram Analysis toolset
Repository Link: https://github.com/DataverseLabs/pyinterpolate
Version submitted: 1.0.1
EiC: TBD
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: https://doi.org/10.21105/joss.02869
Version accepted: TBD
Date accepted (month/day/year): 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

• Include a brief paragraph describing what your package does:

Pyinterpolate is the Python library for spatial statistics. The package provides access to spatial statistics tools (variogram analysis, Kriging, Poisson Kriging, Indicator Kriging, Universal Kriging, Inverse Distance Weighting).

Package is designed for: GIS experts, Geologists, Social scientists

The core functionalities of Pyinterpolate are spatial interpolation and spatial prediction for point and block datasets, and areal data disaggregation.

Pyinterpolate performs:

• Ordinary Kriging and Simple Kriging - spatial interpolation from points
• Centroid-based Poisson Kriging of polygons - spatial interpolation from blocks and regions
• Area-to-area and Area-to-point Poisson Kriging of Polygons - spatial interpolation and data deconvolution from areas to points
• Indicator Kriging - kriging based on probabilities
• Universal Kriging - kriging with trend
• Inverse Distance Weighting - benchmarking spatial interpolation technique
• Semivariogram regularization and deconvolution - transforming variogram of areal data in regards to point support data
• Semivariogram modeling and analysis - is your data spatially correlated? How do neighbors influence each other?

Scope

• Please indicate which category or categories.
  Check out our package scope page to learn more about our
  scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):

  • 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

Community Partnerships

If your package is associated with 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

• For all submissions, explain how and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):

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

The target audience of pyinterpolate are researchers using spatial interpolation techniques in their studies (in mining, geology, social sciences, ecology, public health). Emphasis on Area-to-Point Poisson Kriging makes pyinterpolate valuable especially for people working with population-based spatial data that need to transform areal aggregates into point-support models.

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

PyKrige - point kriging, supports 2D and 3D ordinary and universal kriging and variogram analysis. Users cannot perform areal-based kriging. Package seems to be not maitained in the last year.

Tobler (PySAL) - areal interpolation, uses different modeling techniques than pyinterpolate. Package is under active development.

• If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

n/a

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

• does not violate the Terms of Service of any service it interacts with.
• uses an OSI approved license.
• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions.
• contains a tutorial with examples of its essential functions and uses.
• has a test suite.
• has continuous integration setup, such as GitHub Actions CircleCI, and/or others.

Publication Options

• Do you wish to automatically submit to the Journal of Open Source Software? If so:

JOSS Checks

• The package has an obvious research application according to JOSS's definition in their submission requirements. Be aware that completing the pyOpenSci review process does not guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS.
• The package is not a "minor utility" as defined by JOSS's submission requirements: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria.
• The package contains a paper.md matching JOSS's requirements with a high-level description in the package root or in inst/.
• The package is deposited in a long-term repository with the DOI:

Note: JOSS accepts our review as theirs. You will NOT need to go through another full review. JOSS will only review your paper.md file. Be sure to link to this pyOpenSci issue when a JOSS issue is opened for your package. Also be sure to tell the JOSS editor that this is a pyOpenSci reviewed package once you reach this step.

Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?

This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.

• Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

Confirm each of the following by checking the box.

• I have read the author guide.
• I expect to maintain this package for at least 2 years and can help find a replacement for the maintainer (team) if needed.

Please fill out our survey

• Last but not least please fill out our pre-review survey. This helps us track
  submission and improve our peer review process. We will also ask our reviewers
  and editors to fill this out.

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

Editor and Review Templates

The editor template can be found here.

The review template can be found here.

Footnotes

1. Please fill out a pre-submission inquiry before submitting a data visualization package. ↩