  Disentangled representation learning offers useful properties such as
dimension reduction and interpretability, which are essential to modern deep
learning approaches. Although deep learning techniques have been widely applied
to spatio-temporal data mining, there has been little attention to further
disentangle the latent features and understanding their contribution to the
model performance, particularly their mutual information and correlation across
features. In this study, we adopt two state-of-the-art disentangled
representation learning methods and apply them to three large-scale public
spatio-temporal datasets. To evaluate their performance, we propose an internal
evaluation metric focusing on the degree of correlations among latent variables
of the learned representations and the prediction performance of the downstream
tasks. Empirical results show that our modified method can learn disentangled
representations that achieve the same level of performance as existing
state-of-the-art ST deep learning methods in a spatio-temporal sequence
forecasting problem. Additionally, we find that our methods can be used to
discover real-world spatial-temporal semantics to describe the variables in the
learned representation.
