  This paper focuses on the integration of generative techniques into
spatial-temporal data mining, considering the significant growth and diverse
nature of spatial-temporal data. With the advancements in RNNs, CNNs, and other
non-generative techniques, researchers have explored their application in
capturing temporal and spatial dependencies within spatial-temporal data.
However, the emergence of generative techniques such as LLMs, SSL, Seq2Seq and
diffusion models has opened up new possibilities for enhancing spatial-temporal
data mining further. The paper provides a comprehensive analysis of generative
technique-based spatial-temporal methods and introduces a standardized
framework specifically designed for the spatial-temporal data mining pipeline.
By offering a detailed review and a novel taxonomy of spatial-temporal
methodology utilizing generative techniques, the paper enables a deeper
understanding of the various techniques employed in this field. Furthermore,
the paper highlights promising future research directions, urging researchers
to delve deeper into spatial-temporal data mining. It emphasizes the need to
explore untapped opportunities and push the boundaries of knowledge to unlock
new insights and improve the effectiveness and efficiency of spatial-temporal
data mining. By integrating generative techniques and providing a standardized
framework, the paper contributes to advancing the field and encourages
researchers to explore the vast potential of generative techniques in
spatial-temporal data mining.
