Metadata-Version: 2.1
Name: DynamicESF
Version: 0.1.1
Summary: DynamicESF: fast spatially and temporally varying coefficient model
Home-page: https://github.com/hayato-n/DynamicESF
Download-URL: https://github.com/hayato-n/DynamicESF
Author: Hayato Nishi
Author-email: hnishiua@gmail.com
License: BSE 3-Clause
Keywords: spatial statistics
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.8
License-File: LICENSE

# DynamicESF

Author implementation of DynamicESF model, a computationally efficient spatially and temporally varying coefficient model.
DynamicESF extends SVC (Spatially Varying Coefficient) models for space-time analysis.

## Install

```
pip install DynamicESF
```

See https://pypi.org/project/DynamicESF/ for detail.

## Examples

Check out [jupyter notebooks](https://github.com/hayato-n/DynamicESF/blob/main/examples).

## Reference

Please cite the following article.

- Nishi, H., Asami, Y., Baba, H., & Shimizu, C. (2022). Scalable spatiotemporal regression model based on Moran’s eigenvectors. *International Journal of Geographical Information Science*, 1–27. https://doi.org/10.1080/13658816.2022.2100891

I recommend checking the following paper, which proposed the approximation method of Moran's eigenvectors.

- Murakami, D., & Griffith, D. A. (2019). Eigenvector Spatial Filtering for Large Data Sets: Fixed and Random Effects Approaches. *Geographical Analysis*, 51(1), 23–49. https://doi.org/10.1111/gean.12156

And R package `spmoran` (https://cran.r-project.org/web/packages/spmoran/index.html) will be helpful for R users.


