splot - visual analytics for spatial statistics
splot - visual analytics for spatial statistics - Published in JOSS (2020)
Science Score: 95.0%
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Found 7 DOI reference(s) in README and JOSS metadata -
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Published in Journal of Open Source Software
Keywords from Contributors
Scientific Fields
Repository
Lightweight plotting for geospatial analysis in PySAL
Basic Info
- Host: GitHub
- Owner: pysal
- License: bsd-3-clause
- Language: Jupyter Notebook
- Default Branch: main
- Size: 76.7 MB
Statistics
- Stars: 101
- Watchers: 22
- Forks: 27
- Open Issues: 21
- Releases: 9
Metadata Files
README.md
splot is in the process of being archived. It's functionality is being integrated into associated PySAL projects.
splot
Visual analytics for spatial analysis with PySAL.
What is splot?
splot connects spatial analysis done in PySAL to different popular visualization toolkits like matplotlib.
The splot package allows you to create both static plots ready for publication and interactive visualizations for quick iteration and spatial data exploration. The primary goal of splot is to enable you to visualize popular PySAL objects and gives you different views on your spatial analysis workflow.
If you are new to splot and PySAL you will best get started with our documentation and the short introduction video of the package at the Scipy 2018 conference!
Installing splot
Installing dependencies
splot is compatible with Python 3.8+ and depends on geopandas 0.9.0 or later and matplotlib 3.3.3 or later.
splot also uses
numpyseabornmapclassifyIpywidgets
Depending on your spatial analysis workflow and the PySAL objects you would like to visualize, splot relies on:
- PySAL 2.0
or separate packages found in the PySAL stack:
- esda
- libpysal
- spreg
- giddy
Installing splot
There are two ways of accessing splot. First, splot is installed with the PySAL 2.0 metapackage through:
$ pip install -U pysal
or
$ conda install -c conda-forge pysal
Second, splot can be installed as a separate package. If you are using Anaconda, install splot via the conda utility:
conda install -c conda-forge splot
Otherwise you can install splot from PyPI with pip:
pip install splot
Usage
Usage examples for different spatial statistical workflows are provided as notebooks:
- for creating value-by-alpha maps
- for assessing the relationship between neighboring polygons
- for the visualization of space-time autocorrelation, also documented in giddy
- for visualizing spatial autocorrelation of univariate or multivariate variable analysis
You can also check our documentation for examples on how to use each function. A detailed report about the development, structure and usage of splot can be found here. More tutorials for the whole PySAL ecosystem can be found in our notebooks book project.
Contributing to splot
splot is an open source project within the Python Spatial Analysis Library that is supported by a community of Geographers, visualization lovers, map fans, users and data scientists. As a community we work together to create splot as our own spatial visualization toolkit and will gratefully and humbly accept any contributions and ideas you might bring into this project.
Feel free to check out our discussion spaces, add ideas and contributions:
- Idea collection which PySAL objects to support and how new visualizations could look like
- Discussion about the splot API
- Ideas how to integrate other popular visualization toolkits like
BokehorAltair
If you have never contributed before or you are just discovering what PySAL and splot have to offer, reading through """Doc-strings""" and correcting our Documentation can be a great way to start. Check for spelling and grammar mistakes or use pep8 and pyflakes to clean our .py files. This will allow you to get used to working with git and generally allows you to familiarize yourself with the splot and PySAL code base.
If you have already used PySAL and splot and you are missing object-specific views for your analysis feel free to add to our code-base or discuss your ideas. Please make sure you include unit test, documentation and examples or (create an issue so someone else can work together with you). The common splot API design discussed here can help you to decide how to best integrate your visualization prototype into splot.
Beyond working on documentation and prototyping new visualizations, you can always write a bug report or feature request on Github issues. Whether large or small, any contribution makes a big difference and we hope you enjoy being part of our community as much as we do! The only thing we ask is that you abide principles of openness, respect, and consideration of others as described in the PySAL Code of Conduct.
Road-map
We are planning on extending splot's visualization toolkit in future. Functionality we plan to implement includes:
- visualisations for density methods (mapping density estimations)
- cross-hatching fill styles for maps (to allow choropleth visualizations without class intervals)
- legendgrams (map legends that visualize the distribution of observations by color in a given map)
If you are interested in working on one of these or any other methods, check out the linked issues or get in touch!
Community support
Owner
- Name: Python Spatial Analysis Library
- Login: pysal
- Kind: organization
- Repositories: 37
- Profile: https://github.com/pysal
JOSS Publication
splot - visual analytics for spatial statistics
Authors
Department of Forest Resource Management, University of British Columbia, Center for Geospatial Sciences, University of California Riverside
Geographic Data Science Lab, Department of Geography & Planning, University of Liverpool
Center for Geospatial Sciences, University of California Riverside
Department of Geography, Pennsylvania State University
Department of Forest Resource Management, University of British Columbia
Center for Geospatial Sciences, University of California Riverside
Department of Geographical Sciences, University of Maryland, College Park
School of Geographical Sciences, University of Bristol, Alan Turing Institute
Center for Geospatial Sciences, University of California Riverside
Editor
Leonardo Uieda
Tags
visualization spatial analysis spatial statisticsPapers & Mentions
Total mentions: 1
Automatising the analysis of stochastic biochemical time-series
- DOI: 10.1186/1471-2105-16-S9-S8
- OpenAlex ID: https://openalex.org/W1592521008
- Published: June 2015
GitHub Events
Total
- Issues event: 9
- Watch event: 3
- Issue comment event: 14
- Push event: 1
- Pull request event: 2
- Fork event: 1
Last Year
- Issues event: 9
- Watch event: 3
- Issue comment event: 14
- Push event: 1
- Pull request event: 2
- Fork event: 1
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Serge Rey | s****y@g****m | 1,133 |
| Phil Stephens | p****s@g****m | 399 |
| Stefanie Lumnitz | s****z@g****m | 320 |
| Charles Schimdt | s****c@g****m | 260 |
| Taylor Oshan | t****n@g****m | 241 |
| Jay | j****a@a****u | 149 |
| Dani Arribas-Bel | d****l@g****m | 142 |
| ljwolf | l****f@g****m | 103 |
| ljwolf | l****2@a****u | 101 |
| David Folch | d****h@g****m | 91 |
| Myhungha Hwang | m****4@g****m | 87 |
| Dani Arribas | d****e@g****m | 57 |
| James Gaboardi | j****i@g****m | 50 |
| Nick Malizia | n****a@g****m | 44 |
| pedrovma | p****a@g****m | 39 |
| Luc Anselin | l****n@g****m | 26 |
| Wei Kang | w****9@g****m | 24 |
| Martin Fleischmann | m****n@m****t | 18 |
| Marynia | m****k@g****m | 10 |
| Qunshan | q****o@a****u | 10 |
| James Gaboardi | j****i@f****u | 10 |
| renanxcortes | r****s@g****m | 8 |
| Xinyue Ye | x****e@g****m | 7 |
| Andrew Winslow | a****w@g****m | 6 |
| David Folch | d****h@t****l | 5 |
| kritin sai | k****1@g****m | 4 |
| Stuart Lynn | s****n@g****m | 3 |
| Sergio Rey | s****e@b****l | 2 |
| bohumul | b****a@a****u | 2 |
| Serge Rey | s****y@g****m | 2 |
| and 23 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 63
- Total pull requests: 61
- Average time to close issues: over 1 year
- Average time to close pull requests: 24 days
- Total issue authors: 21
- Total pull request authors: 8
- Average comments per issue: 3.33
- Average comments per pull request: 2.26
- Merged pull requests: 56
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 4
- Pull requests: 5
- Average time to close issues: about 12 hours
- Average time to close pull requests: about 10 hours
- Issue authors: 2
- Pull request authors: 3
- Average comments per issue: 2.75
- Average comments per pull request: 4.4
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- jGaboardi (21)
- slumnitz (12)
- martinfleis (8)
- knaaptime (4)
- ljwolf (2)
- LuYan5203 (1)
- olenaboiko303 (1)
- sjsrey (1)
- stevenlis (1)
- Babakjfard (1)
- digital-idiot (1)
- alsace-research (1)
- LSYS (1)
- darribas (1)
- SamComber (1)
Pull Request Authors
- slumnitz (34)
- jGaboardi (15)
- martinfleis (8)
- weikang9009 (4)
- arfon (1)
- sjsrey (1)
- MgeeeeK (1)
- tirkarthi (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 3
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Total downloads:
- pypi 26,329 last-month
- Total docker downloads: 218
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Total dependent packages: 7
(may contain duplicates) -
Total dependent repositories: 73
(may contain duplicates) - Total versions: 17
- Total maintainers: 3
pypi.org: splot
Visual analytics for spatial analysis with PySAL.
- Homepage: https://github.com/pysal/splot
- Documentation: https://splot.readthedocs.io/
- License: 3-Clause BSD
-
Latest release: 1.1.7
published over 1 year ago
Rankings
conda-forge.org: splot
splot connects spatial analysis done in PySAL to different popular visualization toolkits like matplotlib. The splot package allows you to create both static plots ready for publication and interactive visualizations for quick iteration and spatial data exploration. The primary goal of splot is to enable you to visualize popular PySAL objects and gives you different views on your spatial analysis workflow.
- Homepage: http://pysal.org
- License: BSD-3-Clause
-
Latest release: 1.1.4
published over 4 years ago
Rankings
anaconda.org: splot
splot connects spatial analysis done in PySAL to different popular visualization toolkits like matplotlib. The splot package allows you to create both static plots ready for publication and interactive visualizations for quick iteration and spatial data exploration. The primary goal of splot is to enable you to visualize popular PySAL objects and gives you different views on your spatial analysis workflow.
- Homepage: https://pysal.org
- License: BSD-3-Clause
-
Latest release: 1.1.7
published 5 months ago
Rankings
Dependencies
- esda *
- geopandas >=0.4.0
- giddy *
- libpysal *
- mapclassify *
- matplotlib *
- numpy *
- seaborn *
- spreg *
- bokeh * development
- codecov * development
- coverage * development
- ipywidgets * development
- jupyter * development
- nbconvert * development
- numpydoc * development
- pytest * development
- pytest-cov * development
- sphinx >=1.4.3 development
- sphinx_bootstrap_theme * development
- sphinxcontrib-bibtex * development
- actions/checkout v3 composite
- actions/github-script v6 composite
- actions/setup-python v3 composite
- pypa/gh-action-pypi-publish master composite
- actions/checkout v3 composite
- actions/setup-python v4 composite
- codecov/codecov-action v2 composite
- mamba-org/provision-with-micromamba main composite
- pre-commit/action v3.0.0 composite