  Individual trajectories, rich in human-environment interaction information
across space and time, serve as vital inputs for geospatial foundation models
(GeoFMs). However, existing attempts at learning trajectory representations
have overlooked the implicit spatial-temporal dependency within trajectories,
failing to encode such dependency in a deep learning-friendly format. That
poses a challenge in obtaining general-purpose trajectory representations.
Therefore, this paper proposes a spatial-temporal joint representation learning
method (ST-GraphRL) to formalize learnable spatial-temporal dependencies into
trajectory representations. The proposed ST-GraphRL consists of three
compositions: (i) a weighted directed spatial-temporal graph to explicitly
construct mobility interactions in both space and time dimensions; (ii) a
two-stage jointly encoder (i.e., decoupling and fusion), to learn entangled
spatial-temporal dependencies by independently decomposing and jointly
aggregating space and time information; (iii) a decoder guides ST-GraphRL to
learn explicit mobility regularities by simulating the spatial-temporal
distributions of trajectories. Tested on three real-world human mobility
datasets, the proposed ST-GraphRL outperformed all the baseline models in
predicting movement spatial-temporal distributions and preserving trajectory
similarity with high spatial-temporal correlations. Analyzing spatial-temporal
features presented in latent space validates that ST-GraphRL understands
spatial-temporal patterns. This study may also benefit representation learnings
of other geospatial data to achieve general-purpose data representations and
advance GeoFMs development.
