splot
Visual analytics for spatial analysis with PySAL.
Decision gist · record as of 2026-08-14
Yes, if you work with PySAL for spatial analysis and need to visualize results. The package is stable (Production/Stable status), has no known vulnerabilities, and installs cleanly. The aging maintenance status is a minor concern for active development, but the repository is not abandoned and the codebase is mature. Install it as part of your PySAL workflow or as a standalone visualization layer for spatial statistics.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires geopandas 0.9.0 or later and matplotlib 3.3.3 or later; designed for Python 3.8+.
- Low install friction with a pure-wheel distribution.
- Maintenance status is aging—last release was in September 2024, though the repository remains active with a recent commit in June 2025.
License · maintenance · safety
3-Clause BSD (permissive) — 3-Clause BSD is permissive; you can use, modify, and distribute splot freely in commercial and private projects with minimal restrictions.
last release 2024-09-09 (704 days) · last repo commit 2025-06-20 · 101 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 99,239 downloads/mo, #13,033 on PyPI
Alternatives
Verify before relying
pip install splot
import splot
from splot.esda import moran_scatterplot
import geopandas as gpd
# Visualize spatial autocorrelation on a GeoDataFrame
moran_scatterplot(moran_result, gdf)- Whether all 10 runtime dependencies (esda, giddy, libpysal, spreg, etc.) are required for basic use or only for specific visualization workflows.
- Current state of interactive visualization support and which interactive toolkits are fully integrated beyond matplotlib.
What it is and what it does
splot is a visualization layer for the PySAL spatial analysis ecosystem. It translates PySAL statistical objects—like Moran's I results, classification schemes, and space-time autocorrelation measures—into publication-ready static plots and exploratory interactive visualizations. The package wraps matplotlib, seaborn, and related tools to handle the domain-specific needs of spatial data, such as choropleth mapping, spatial autocorrelation scatterplots, and value-by-alpha visualizations.
You use splot when you've completed spatial statistical analysis in PySAL (esda, libpysal, spreg, giddy) and need to communicate or explore the results visually. It abstracts away boilerplate matplotlib configuration and provides sensible defaults for common spatial workflows, letting you focus on interpretation rather than plot construction.
Use it for
- Visualize univariate or multivariate spatial autocorrelation results from esda as Moran scatterplots and cluster maps.
- Create choropleth maps with value-by-alpha styling to show spatial patterns with statistical confidence.
- Plot space-time autocorrelation trends from giddy to assess how spatial relationships evolve over time.
- Explore neighboring polygon relationships and non-planar spatial joins using libpysal visualization functions.
- Generate publication-ready static maps and exploratory interactive plots for spatial regression diagnostics from spreg.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with PySAL for spatial analysis and need to visualize results.
The package is stable (Production/Stable status), has no known vulnerabilities, and installs cleanly. The aging maintenance status is a minor concern for active development, but the repository is not abandoned and the codebase is mature. Install it as part of your PySAL workflow or as a standalone visualization layer for spatial statistics.
Install
splot on PyPI
Before you install
Low install friction with a pure-wheel distribution. Maintenance status is aging—last release was in September 2024, though the repository remains active with a recent commit in June 2025.
Requires geopandas 0.9.0 or later and matplotlib 3.3.3 or later; designed for Python 3.8+.
License in practice
3-Clause BSD is permissive; you can use, modify, and distribute splot freely in commercial and private projects with minimal restrictions.
Quickstart
pip install splot
import splot
from splot.esda import moran_scatterplot
import geopandas as gpd
# Visualize spatial autocorrelation on a GeoDataFrame
moran_scatterplot(moran_result, gdf)Verify before relying
- Whether all 10 runtime dependencies (esda, giddy, libpysal, spreg, etc.) are required for basic use or only for specific visualization workflows.
- Current state of interactive visualization support and which interactive toolkits are fully integrated beyond matplotlib.
Package facts
| License | 3-Clause BSD permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesesdageopandasgiddylibpysalmapclassifymatplotlibnumpypackagingseabornspreg |
| Maintenance | Aging 704 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 99,239 / month, #13,033 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: GIS |
Evidence: splot-1.1.7-py3-none-any.whl
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See also matplotlib · plotbin · pysal · spreg · esda · libpysal · pointpats · Cartopy · highcharts-maps · inequality