  Modeling human mobility helps to understand how people are accessing
resources and physically contacting with each other in cities, and thus
contributes to various applications such as urban planning, epidemic control,
and location-based advertisement. Next location prediction is one decisive task
in individual human mobility modeling and is usually viewed as sequence
modeling, solved with Markov or RNN-based methods. However, the existing models
paid little attention to the logic of individual travel decisions and the
reproducibility of the collective behavior of population. To this end, we
propose a Causal and Spatial-constrained Long and Short-term Learner (CSLSL)
for next location prediction. CSLSL utilizes a causal structure based on
multi-task learning to explicitly model the
"\textit{when$\rightarrow$what$\rightarrow$where}", a.k.a.
"\textit{time$\rightarrow$activity$\rightarrow$location}" decision logic. We
next propose a spatial-constrained loss function as an auxiliary task, to
ensure the consistency between the predicted and actual spatial distribution of
travelers' destinations. Moreover, CSLSL adopts modules named Long and
Short-term Capturer (LSC) to learn the transition regularities across different
time spans. Extensive experiments on three real-world datasets show promising
performance improvements of CSLSL over baselines and confirm the effectiveness
of introducing the causality and consistency constraints. The implementation is
available at https://github.com/urbanmobility/CSLSL.
