https://github.com/ziqi-li/fastgwr
Fast Geographically Weighted Regression (FastGWR)
https://github.com/ziqi-li/fastgwr
gwr spatial spatial-statistics
Last synced: 7 months ago
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Fast Geographically Weighted Regression (FastGWR)
- Host: GitHub
- URL: https://github.com/ziqi-li/fastgwr
- Owner: Ziqi-Li
- License: mit
- Created: 2018-04-20T22:38:16.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-03-17T10:18:31.000Z (almost 4 years ago)
- Last Synced: 2025-07-14T19:56:26.232Z (7 months ago)
- Topics: gwr, spatial, spatial-statistics
- Language: Jupyter Notebook
- Homepage: https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1521523
- Size: 14.6 MB
- Stars: 57
- Watchers: 5
- Forks: 18
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# FastGWR
A command line tool for fast parallel computation of Geographically Weighted Regression models (GWR and MGWR).
### New feature:
Multi-scale GWR model added!
### Installation:
The `fastgwr` program is dependent on `mpi4py` package and a working MPI implementation. The easiest way to install both dependencies is to use `conda`:
```bash
$ conda install mpi4py
```
By installing `mpi4py`, `conda` will also install an MPI implementation based on your computer system (OpenMPI for Mac/Linux; MPICH/MS-MPI for Windows). Users may want to check whether the MPI implementation is successfully installed and is on your path by running the `mpiexec` command. Then the `fastgwr` program can be installed from PyPi:
```bash
$ pip install fastgwr
```
After sucessful installation, users can test the functionalities from the command line by running:
```bash
# Using zillow sample data for testing MGWR model fitting.
$ fastgwr testgwr
```
or
```bash
# Using zillow sample data for testing MGWR model fitting.
$ fastgwr testmgwr
```
## Examples
Example call to the `fastgwr` to fit GWR model:
```bash
$ fastgwr run -np 4 -data input.csv
```
Example call to the `fastgwr` to fit MGWR model:
```bash
$ fastgwr run -np 4 -data input.csv -mgwr
```
where:
```bash
-np 4 Number of processors (e.g. 4).
-data input.csv Input data matrix. (e.g. input.csv)
Can also be an URL (e.g. https://raw.github.com/
Ziqi-Li/FastGWR/master/Zillow-test-dataset/zillow_1k.csv)
-out results.csv Output GWR results matrix including local parameter
estimates, standard errors and local diagnostics.
-adaptive/-fixed Adaptive Bisquare kernel (defualt) or Fixed Gaussian kernel.
-bw 1000 Pre-defined bandwidth parameter. If missing, it will
search (golden-section) for the optimal bandwidth and use
that to fit the GWR model.
-minbw 45 Lower bound in golden-section search. (e.g. 45)
-mgwr Fitting an MGWR model.
-chunks Number of chunks for MGWR computation (set to a larger
number to reduce memory footprint).
-estonly Allowing MGWR to output parameter estimation only.
```
The input needs to be prepared in this order:
| | X-coord | y-coord | y | X1 | X2 | X3 | Xk |
|---|---------|---------|------|-----|-----|-----|-----|
| | ... | ... | ... | ... | ... | ... | ... |
| | ... | ... | ... | ... | ... | ... | ... |
| | | | | | | | |
```
where:
X-coord: X coordinate of the location point
Y-coord: Y coordinate of the location point
y: dependent variable
X1...Xk: independent variables
```
See the example Zillow datasets in the repository.
## Results Validation
The results are validated against the [mgwr](https://github.com/pysal/mgwr), which can be seen in the [notebooks here](https://github.com/Ziqi-Li/FastGWR/tree/master/validation%20notebook).
## Citations
This program is developed based on these two papers:
[FastGWR](https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1521523)
Li, Z., Fotheringham, A. S., Li, W., Oshan, T. (2019). Fast Geographically Weighted Regression (FastGWR): A Scalable Algorithm to Investigate Spatial Process Heterogeneity in Millions of Observations. International Journal of Geographic Information Science. doi: 10.1080/13658816.2018.1521523.
[FastMGWR](https://www.tandfonline.com/doi/abs/10.1080/13658816.2020.1720692)
Li, Z., & Fotheringham, A. S. (2020). Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science, 34(7), 1378-1397.