pygwmodel Documentation

pygwmodel is a Python package providing conscious and easy-to-use interfaces to high-performance C++ implementations of geographically weighted (GW) models, based on libgwmodel and GeoPandas.

GW models are a branch of spatial statistics suited to situations where data are not well described by some global model, but where spatial regions exist where a suitably localized calibration provides a better description.

Implemented Models

  • GWRBasic — Basic Geographically Weighted Regression with a single bandwidth.

  • GWRMultiscale — Multiscale GWR (MGWR) with per-variable bandwidths and backfitting algorithm.

  • GTWR — Geographically and Temporally Weighted Regression.

  • GWAverage — Geographically Weighted Summary Statistics (mean, std dev, etc.).

  • GWCorrelation — Geographically Weighted Correlation coefficients.

Quick Start

from pygwmodel import GWRBasic, GWRMultiscale, BandwidthWeight, CRSDistance

# Basic GWR
algorithm = GWRBasic(data, y, x,
                     weight=BandwidthWeight(36.0, adaptive=True),
                     distance=CRSDistance()).fit()
print(algorithm.diagnostic['RSquare'])

# Multiscale GWR
mgwr = GWRMultiscale(data, y, x,
                     weights=[BandwidthWeight(36.0, adaptive=True) for _ in range(4)]
                     ).fit()
print(mgwr.diagnostic)

Contents:

Indices and Tables