Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.4%) to scientific vocabulary
Keywords from Contributors
geoparquet
topology
spatial-network
pysal
network-analysis
graph-theory
transportation
spatial-analysis
routing
facility-location
Last synced: 10 months ago
·
JSON representation
Repository
Multiscale Geographically Weighted Regression (MGWR)
Basic Info
- Host: GitHub
- Owner: pysal
- License: bsd-3-clause
- Language: Jupyter Notebook
- Default Branch: master
- Homepage: https://mgwr.readthedocs.io/
- Size: 58.5 MB
Statistics
- Stars: 396
- Watchers: 37
- Forks: 131
- Open Issues: 48
- Releases: 7
Created over 8 years ago
· Last pushed almost 2 years ago
Metadata Files
Readme
Changelog
License
README.md
Multiscale Geographically Weighted Regression (MGWR)
This module provides functionality to calibrate multiscale (M)GWR as well as traditional GWR. It is built upon the sparse generalized linear modeling (spglm) module.
Features
- GWR model calibration via iteratively weighted least squares for Gaussian, Poisson, and binomial probability models.
- GWR bandwidth selection via golden section search or equal interval search
- GWR-specific model diagnostics, including a multiple hypothesis test correction and local collinearity
- Monte Carlo test for spatial variability of parameter estimate surfaces
- GWR-based spatial prediction
- MGWR model calibration via GAM iterative backfitting for Gaussian model
- Parallel computing for GWR and MGWR
- MGWR covariate-specific inference, including a multiple hypothesis test correction and local collinearity
- Bandwidth confidence intervals for GWR and MGWR
Citation
Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6), 269.
Owner
- Name: Python Spatial Analysis Library
- Login: pysal
- Kind: organization
- Repositories: 37
- Profile: https://github.com/pysal
GitHub Events
Total
- Issues event: 4
- Watch event: 37
- Issue comment event: 18
- Fork event: 8
Last Year
- Issues event: 4
- Watch event: 37
- Issue comment event: 18
- Fork event: 8
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Taylor Oshan | t****n@g****m | 104 |
| Ziqi Li | c****0@g****m | 83 |
| Wei Kang | w****9@g****m | 37 |
| James Gaboardi | j****i@g****m | 16 |
| ljwolf | l****f@g****m | 11 |
| Martin Fleischmann | m****n@m****t | 8 |
| Ziqi Li | l****2@g****m | 6 |
| Philip Kahn | t****k@g****m | 2 |
| Ziqi | z****i@Z****l | 2 |
| Patricio Reyes | p****s@b****s | 1 |
| Matthew Tralka | m****9@g****m | 1 |
| Filipe Fernandes | o****f@g****m | 1 |
| Tyler Hoffman | 5****1 | 1 |
Committer Domains (Top 20 + Academic)
bsc.es: 1
martinfleischmann.net: 1
Issues and Pull Requests
Last synced: 12 months ago
All Time
- Total issues: 79
- Total pull requests: 58
- Average time to close issues: 2 months
- Average time to close pull requests: 8 days
- Total issue authors: 61
- Total pull request authors: 16
- Average comments per issue: 2.58
- Average comments per pull request: 1.36
- Merged pull requests: 45
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 7
- Pull requests: 0
- Average time to close issues: 4 days
- Average time to close pull requests: N/A
- Issue authors: 6
- Pull request authors: 0
- Average comments per issue: 3.14
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- jGaboardi (4)
- mattwigway (3)
- xyluo25 (3)
- ljwolf (3)
- PratyushTripathy (2)
- TehranGIS (2)
- kkyyhh96 (2)
- dickwxyz (2)
- weikang9009 (2)
- zlh1998ecnu (2)
- Ziqi-Li (2)
- ysm1nz (1)
- martinfleis (1)
- scardonau94 (1)
- HeyAy (1)
Pull Request Authors
- weikang9009 (15)
- Ziqi-Li (10)
- TaylorOshan (7)
- mehak-sachdeva (6)
- xyluo25 (5)
- ljwolf (4)
- trietmnj (2)
- jGaboardi (2)
- tdhoffman (1)
- hayato-n (1)
- ocefpaf (1)
- tigerhawkvok (1)
- mcvholloway (1)
- mtralka (1)
- pareyesv (1)
Top Labels
Issue Labels
enhancement (4)
bug (1)
doc (1)
question (1)
maintenance (1)
CI/testing (1)
Pull Request Labels
maintenance (5)
enhancement (3)
bug (1)
Packages
- Total packages: 3
-
Total downloads:
- pypi 22,919 last-month
- Total docker downloads: 218
-
Total dependent packages: 3
(may contain duplicates) -
Total dependent repositories: 52
(may contain duplicates) - Total versions: 19
- Total maintainers: 3
pypi.org: mgwr
multiscale geographically weighted regression
- Homepage: https://github.com/pysal/mgwr
- Documentation: https://mgwr.readthedocs.io/
- License: 3-Clause BSD
-
Latest release: 2.2.1
published over 2 years ago
Rankings
Docker downloads count: 1.9%
Downloads: 2.3%
Dependent repos count: 2.4%
Average: 3.2%
Stargazers count: 3.7%
Forks count: 4.4%
Dependent packages count: 4.8%
Maintainers (3)
Last synced:
11 months ago
conda-forge.org: mgwr
- Homepage: https://github.com/pysal/mgwr
- License: BSD-3-Clause
-
Latest release: 2.1.2
published almost 6 years ago
Rankings
Dependent repos count: 12.9%
Forks count: 18.1%
Average: 18.6%
Dependent packages count: 19.6%
Stargazers count: 24.0%
Last synced:
11 months ago
anaconda.org: mgwr
This module provides functionality to calibrate multiscale (M)GWR as well as traditional GWR. It is built upon the sparse generalized linear modeling (spglm) module.
- Homepage: https://github.com/pysal/mgwr
- License: BSD-3-Clause
-
Latest release: 2.2.1
published 12 months ago
Rankings
Forks count: 30.8%
Stargazers count: 36.8%
Average: 39.9%
Dependent repos count: 40.8%
Dependent packages count: 51.2%
Last synced:
11 months ago
Dependencies
requirements.txt
pypi
- libpysal >=4.0.0
- numpy >=1.3
- scipy >=0.11
- spglm >=1.0.6
- spreg *
requirements_docs.txt
pypi
- numpydoc *
- sphinx >=1.4.3
- sphinx_bootstrap_theme *
- sphinx_gallery *
- sphinxcontrib-bibtex *
- sphinxcontrib-napoleon *
requirements_tests.txt
pypi
- coverage ==4.5.4 test
- coveralls * test
- nose * test
- nose-exclude * test
- nose-progressive * test
- pandas * test
.github/workflows/unittest.yml
actions
- actions/checkout v2 composite
- codecov/codecov-action v2 composite
- mamba-org/provision-with-micromamba main composite
setup.py
pypi