giddy
PySAL-giddy for exploratory spatiotemporal data analysis
Decision gist · record as of 2026-08-14
Yes. Giddy is actively maintained, has low install friction, carries a permissive BSD license, and fills a specific niche in spatiotemporal analysis. It's suitable if you work with regional or geographic time-series data and need methods that explicitly model spatial structure in distribution dynamics. Requires Python 3.12+.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later; depends on scipy, libpysal, mapclassify, esda, and quantecon.
- Low friction install with a pure-Python wheel.
- Active maintenance—last release 19 days ago with ongoing development.
License · maintenance · safety
BSD 3-Clause (permissive) — BSD 3-Clause permissive license allows commercial and private use with minimal restrictions, requiring only license and copyright notice retention.
last release 2026-07-26 (19 days) · last repo commit 2026-07-26 · 77 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 157,061 downloads/mo, #10,766 on PyPI
Alternatives
Verify before relying
pip install giddy
import giddy
from giddy.directional import DirectionalLISA
# Requires spatial weights and time-series data as input- Whether directional LISA, spatial Markov, and mobility methods are suitable for your specific spatiotemporal research question.
- Performance characteristics and scalability limits for large spatial datasets or long time series.
What it is and what it does
Giddy is a PySAL library for analyzing how geospatial distributions change over time. It implements statistical methods that explicitly account for spatial structure when studying temporal evolution—directional LISA for detecting directional spatial autocorrelation in income or other variables, spatial Markov chains to model transitions between spatial regimes, and mobility measures to quantify rank changes across regions. It's built on scipy, libpysal, mapclassify, esda, and quantecon, and includes visualization support via matplotlib and seaborn.
You use giddy when you have time-stamped spatial data (e.g., regional income over decades, population density across years) and want to understand not just how values change, but how spatial patterns and regional relative positions shift. It's designed for researchers and analysts in economic geography, regional science, and spatial statistics who need to move beyond simple time-series or cross-sectional analysis.
Use it for
- Analyze how regional income inequality evolves spatially over time using spatial Markov and directional LISA methods.
- Detect directional spatial autocorrelation in economic or demographic variables to visualize spatial trends as rose diagrams.
- Decompose regional mobility and rank changes to understand inter- and intra-regional economic dynamics.
- Study sequence alignment in spatiotemporal trajectories to compare regional development paths.
- Measure global and local indicators of mobility association to quantify how regions move relative to each other.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Giddy is actively maintained, has low install friction, carries a permissive BSD license, and fills a specific niche in spatiotemporal analysis. It's suitable if you work with regional or geographic time-series data and need methods that explicitly model spatial structure in distribution dynamics. Requires Python 3.12+.
Install
giddy on PyPI
Before you install
Low friction install with a pure-Python wheel. Active maintenance—last release 19 days ago with ongoing development. Requires Python 3.12 or later.
Requires Python 3.12 or later; depends on scipy, libpysal, mapclassify, esda, and quantecon.
License in practice
BSD 3-Clause permissive license allows commercial and private use with minimal restrictions, requiring only license and copyright notice retention.
Quickstart
pip install giddy
import giddy
from giddy.directional import DirectionalLISA
# Requires spatial weights and time-series data as inputVerify before relying
- Whether directional LISA, spatial Markov, and mobility methods are suitable for your specific spatiotemporal research question.
- Performance characteristics and scalability limits for large spatial datasets or long time series.
Package facts
| License | BSD 3-Clause permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesesdalibpysalmapclassifyquanteconscipyseabornmatplotlib |
| Maintenance | Actively maintained 19 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 157,061 / month, #10,766 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: GIS |
Evidence: giddy-2.3.9-py3-none-any.whl
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