# mgwr

**Repository Path**: liqianguy/mgwr

## Basic Information

- **Project Name**: mgwr
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-02-02
- **Last Updated**: 2021-02-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

**M**ultiscale **G**eographically **W**eighted **R**egression (MGWR)
=======================================

[![Build Status](https://travis-ci.org/pysal/mgwr.svg?branch=master)](https://travis-ci.org/pysal/mgwr)
[![Documentation Status](https://readthedocs.org/projects/mgwr/badge/?version=latest)](https://mgwr.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/mgwr.svg)](https://badge.fury.io/py/mgwr)

This module provides functionality to calibrate multiscale (M)GWR as well as traditional GWR. It is
built upon the sparse generalized linear modeling (spglm) module. 

Features
--------

- GWR model calibration via iteratively weighted least squares for Gaussian,
  Poisson, and binomial probability models.
- GWR bandwidth selection via golden section search or equal interval search
- GWR-specific model diagnostics, including a multiple hypothesis test
  correction and local collinearity
- Monte Carlo test for spatial variability of parameter estimate surfaces
- GWR-based spatial prediction
- MGWR model calibration via GAM iterative backfitting for Gaussian model
- Parallel computing for GWR and MGWR
- MGWR covariate-specific inference, including a multiple hypothesis test
  correction and local collinearity 
- Bandwidth confidence intervals for GWR and MGWR

Citation
--------
Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6), 269.