---
id: splot
version: "1.1.7"
license: 3-Clause BSD
license_treatment: permissive
maintenance: aging
---

# splot — Visual analytics for spatial analysis with PySAL.

License: permissive · Maintenance: aging · Downloads: 99.2K/mo

## 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 above — verify before relying.

splot creates static and interactive visualizations for spatial analysis workflows, connecting PySAL objects to matplotlib and other visualization toolkits.

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

pip install splot
uv add splot
poetry add splot

## Installing splot

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.

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)

Requires geopandas 0.9.0 or later and matplotlib 3.3.3 or later; designed for Python 3.8+.

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: unspecified
- Install friction: low
- Maintenance: aging
- Downloads: 99.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

spatial analysis visualization, pysal plotting, choropleth maps, spatial autocorrelation plots, geopandas visualization, spatial statistics charts, interactive spatial maps, spatial-analysis, geospatial-viz, pysal-ecosystem

[View on SkillFed](https://skillfed.io/packages/splot) · [View on PyPI](https://pypi.org/project/splot/)