giddy

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

Giddy provides methods for exploratory spatiotemporal data analysis, including directional LISA, spatial Markov chains, mobility measures, and sequence analysis to study how geospatial distributions evolve over time.

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

pip install giddy

uv add giddy

poetry add giddy

Installing giddy

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.

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 input

Requires Python 3.12 or later; depends on scipy, libpysal, mapclassify, esda, and quantecon.

Verify 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

Evidence: giddy-2.3.9-py3-none-any.whl

Keywords: spatial statistics, spatiotemporal analysis

Tags

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