The goal of tpSVG is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, tpSVG introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to currently available count-based model for spatially resolved data analysis, the tpSVG models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis.