Fast spatio-temporally varying coefficient modeling with reluctant interaction selection

TL;DR

This paper introduces a fast, flexible spatio-temporal varying coefficient modeling method that efficiently selects interaction structures, demonstrated through simulations and a large-scale crime analysis application.

Contribution

It develops a computationally efficient approach combining pre-conditioning and reluctant interaction selection for flexible STVC modeling.

Findings

Outperforms existing methods in accuracy and efficiency

Successfully applied to large-scale crime data

Provides a new R package for implementation

Abstract

Spatially and temporally varying coefficient (STVC) models are currently attracting attention as a flexible tool to explore the spatio-temporal patterns in regression coefficients. However, these models often struggle with balancing computational efficiency and model flexibility. To address this challenge, this study develops a fast and flexible method for STVC modeling. For enhanced flexibility in modeling, we assume multiple processes in each varying coefficient, including purely spatial, purely temporal, and spatio-temporal interaction processes with or without time cyclicity. While considering multiple processes can be time consuming, we combine a pre-conditioning method with a model selection procedure, inspired by reluctant interaction modeling. This approach allows us to computationally efficiently select and specify the latent space-time structure. Monte Carlo experiments demonstrate that the proposed method outperforms alternatives in terms of coefficient estimation accuracy and computational efficiency. Finally, we apply the proposed method to crime analysis using a sample size of 279,360, confirming that the proposed method provides reasonable estimates of varying coefficients. The STVC model is implemented in an R package spmoran.