gnnwr 0.0.9

A pytorch implementation of the Geographically Neural Network Weighted Regression (GNNWR)

gnnwr

A pytorch implementation of the Geographically Neural Network Weighted Regression (GNNWR) and its derived models

This repository contains:

1. Source code of GNNWR, GTNNWR model and other derived models
2. Tutorial notebooks of how to use these model
3. Released Python wheels

Table of Contents

• Models
• Install
• Usage
• Reference
• Contributing
• License

Models

GNNWR

GNNWR (Geographically neural network weighted regression) is a model address spatial non-stationarity in various domains with complex geographical processes. A spatially weighted neural network (SWNN) is proposed to represent the nonstationary weight matrix and to achieve local estimation by weighted least squares with this weights.

With the superior fitting ability of the neural network, GNNWR has a well-constructed nonstationary weight matrix, which makes it possible to better describe the complex geo-processes in environment and urban research.

GTNNWR

GTNNWR (Geographically and temporally neural network weighted regression), is a model for estimating spatiotemporal non-stationary relationships. Due to the existence of spatiotemporal non-stationary, the spatial relationships of features exhibit significant differences with changes in spatiotemporal structure. The calculation of spatiotemporal distance is an important aspect of solving spatiotemporal non-stationary problems. Therefore, this model introduces spatiotemporal distance into the GNNWR model and proposes a spatiotemporal proximity neural network (STPNN) to accurately calculate spatiotemporal distance. Collaborate with SWNN in the GNNWR model to calculate the spatiotemporal non-stationary weight matrix, thereby achieving accurate modeling of spatiotemporal non-stationary relationships.

Install

⚠ If you want to run gnnwr with your GPU, make sure you have installed pytorch with CUDA support beforehead:

For example, a torch 1.13.1 with cuda 11.7:

> pip list | grep torch
torch                   1.13.1+cu117

You can find install support on Pytorch's official website for installing the right version that suits your environment.

⚠ If you only want to run gnnwr with your CPU, or you have already installed the correct version of pytorch:

Using pip to install gnnwr:

pip install gnnwr

Usage

We provide a series of encapsulated methods and predefined default parameters, users only need to use to load dataset with pandas , and call the functions in gnnwr package to complete the regression:

from gnnwr import models,datasets
import pandas as pd

data = pd.read_csv('your_data.csv')

train_dataset, val_dataset, test_dataset = datasets.init_dataset(data=data,
                                                                 test_ratio=0.2, valid_ratio=0.1,
                                                                 x_column=['x1', 'x2'], y_column=['y'],
                                                                 spatial_column=['u', 'v'])

gnnwr = models.GNNWR(train_dataset, val_dataset, test_dataset)

gnnwr.run(100)

For other uses of customization, the demos can be referred to.

Research Cases

Environment

Air quality

Air pollution, especially the measurement of PM2.5, has recently been a popular researching topic in China. Using the data of PM2.5 which is collected by separated station and Considering the complicated terrain variability and wide geographical scope in China, the work of estimating and mapping the PM2.5 distributions across China with high accuracy and reasonable details is challenged by significant spatial nonstationary and complex nonlinearity. GNNWR model can obtain spatially continuous PM2.5 estimates in China with the processed data of AOD, DEM, as well as some climate factors. The estimation of PM2.5 is closer to the ground observation than the results of traditional regression model, and it is more accurate in the high value zone and more detailed.

Water quality

Transferring dissolved silicate (DSi) from land to coastal environments is vital for global biogeochemical cycling. Yet pinpointing coastal DSi distribution is complex due to spatiotemporal variability, nonlinear modeling, and low sampling resolution. GTNNWR model, by converting the spatiotemporal relationships between sparse sampling points and unknown points into space-time distances and weights, and using neural networks to determine the nonlinear distances and nonstationary weights, outperforms traditional regression models in both fitting accuracy and generalization ability. This effective data-driven-based method can help explore the fine-scale dynamic changes in coastal seas like surface DSi.

Urban

House price

Housing prices are closely related to the lives of new urban residents, and they also comprise a vital economic index to which the government needs to pay close attention. Compare with traditional regression models, GNNWR and GTNNWR model can improve the accuracy of real estate appraisal with the help of neural networks, which are practical and powerful ways to assess house prices.

Further, these model can be applied to other valuation problems concerning geographical data to promote the prediction accuracy of socioeconomic phenomena.

Reference

algorithm

1. Du, Z., Wang, Z., Wu, S., Zhang, F., and Liu, R., 2020. Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. International Journal of Geographical Information Science, 34 (7), 1353–1377.
2. Wu, S., Wang, Z., Du, Z., Huang, B., Zhang, F., and Liu, R., 2021. Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. International Journal of Geographical Information Science, 35 (3), 582–608.

case study demo

1. Jin Qi, Zhenhong Du, Sensen Wu, Yijun Chen, Yuanyuan Wang, 2023. A spatiotemporally weighted intelligent method for exploring fine-scale distributions of surface dissolved silicate in coastal seas. Science of The Total Environment, 886 , 163981.

Contributing

Contributors

License

GPLv3 license