Spatiotemporal heterogeneous perspective for analyzing influencing factors, identifying key drivers, and making dynamic predictions, all within a unified ‘full-map’ framework.

- Install the BSTVC R package

The package is currently in the internal testing phase. At present, it only supports local installation from GitHub.

# Install using the devtools package
# install.packages("devtools")
devtools::install_github("songbi123/BSTVC")

# Install using the remotes package
# install.packages("remotes")
remotes::install_github("songbi123/BSTVC")

- Install the dependency package - the INLA R package

When installing the BSTVC package in RStudio, the system will prompt you to install additional R packages that come with it. However, since INLA is a larger package, installing the BSTVC package might lead to a failure. To avoid this issue, we provide a separate method for installing the INLA package for your reference.

If the installation of the BSTVC package in the previous step failed, please install the BSTVC package after successfully installing the INLA package. If you have successfully installed the INLA package while installing the BSTVC package, you can skip this step.

## To install the INLA package, more information can be found at <https://www.r-inla.org/download-install>.
# Extend the overtime duration to 5 minutes
options(timeout = 300)
install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)

## Another approach, using an older version of INLA, is to download the compressed package of the INLA R package to your local machine and then proceed with the installation.

The BSTVC R package is designed to provide a comprehensive suite of functionalities for advanced spatiotemporal heterogeneous analysis. Here’s what our package can do for you:

• Targeting multiple types of response variables: It supports three mainstream types of response variables: continuous (log-Gaussian regression), binary (logistic regression), and count (Poisson regression), accommodating various analytical scenarios.

• Detecting spatiotemporal heterogeneous impact mechanisms: By fitting spatiotemporal regression coefficients, it reveals local spatiotemporal differences between explanatory variables (X) and response variables (Y), facilitating an in-depth analysis of context-specific patterns and exploring the impact mechanisms brought by spatiotemporal heterogeneity.

• Identifying spatiotemporal driving factors: On the basis of identifying spatiotemporal heterogeneous impact mechanisms, it clarifies key driving factors by calculating the spatiotemporal explainable percentage, providing strong evidence for geographical spatiotemporal attribution.

• Improving spatiotemporal prediction accuracy: Considering the spatiotemporal heterogeneity of local variable relationships, it significantly improves model fitting and prediction accuracy, which can be used for spatiotemporal missing value imputation, spatiotemporal smoothing, and future forecasting.

• Bayesian model assessment: It provides a comprehensive evaluation of Bayesian regression models, including model fitting (DIC, WAIC), complexity (pd), and prediction accuracy (LS) indicators, helping users fully understand model performance.

• Rich visualization outputs: It provides a variety of spatiotemporal visualization tools and codes to help users intuitively understand model results, enhance the interpretability of data analysis, and promote innovation in your applied research.

Bayesian STVC model is a powerful analytical tool with many advantages that other similar tools lack, such as a “full-map” modeling framework, parameter uncertainty, friendliness to missing values, and support for more spatial weight matrices, among others.

To help you quickly and fully get started with our R package for complex data analysis, we have prepared several detailed and comprehensive usage guides, as follows:

In the near future, we will continue to refine our documentation and provide new help documents.

View detailed changelog: CHANGELOG.md

We welcome and encourage user contributions, including reporting issues, requesting new features, or submitting code changes. If you encounter any problems when using the BSTVC package or need further assistance, you can get support through the following means:

1. GitHub issues: Report issues or request new features in theGitHub repository, please visit Issues.
2. Email contact: tangxxxxt@163.com(Tang Xianteng, related to R package usage); chaosong.gis@gmail.com (Song Chao, related to statistical theory)
3. Bayesian STVC model: https://chaosong.blog/bayesian-stvc/

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• [Bayesian STVC series models] Song, Chao, Yin, Hao, Shi, Xun, Xie, Mingyu, Yang, Shujuan, Zhou, Junmin, Wang, Xiuli, Tang, Zhangying, Yang, Yili, & Pan, Jay. (2022). Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities. International Journal of Disaster Risk Reduction, 77, 103078.

• [STVPI] Wan, Qin, Tang, Zhangying, Pan, Jay, Xie, Mingyu, Wang, Shaobin, Yin, Hao, Li, Junmin, Liu, Xin, Yang, Yang, & Song, Chao. (2022). Spatiotemporal heterogeneity in associations of national population ageing with socioeconomic and environmental factors at the global scale. Journal of Cleaner Production, 373, 133781.

• Song, Chao, Shi, Xun, & Wang, Jinfeng. (2020). Spatiotemporally Varying Coefficients (STVC) model: a Bayesian local regression to detect spatial and temporal nonstationarity in variables relationships. Annals of GIS, 26(3), 277-291.

• Song, Chao, Shi, Xun, Bo, Yanchen, Wang, Jinfeng, Wang, Yong, & Huang, Dacang. (2019). Exploring Spatiotemporal Nonstationary Effects of Climate Factors on Hand, Foot, and Mouth Disease Using Bayesian Spatiotemporally Varying Coefficients (STVC) Model in Sichuan, China. Science of The Total Environment, 648, 550-560.