A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary
  Modifiable Areal Units
Liyue Chen Jiangyi Fang Tengfei Liu Shaosheng Cao Leye Wang
Spatio-Temporal (ST) prediction is crucial for making informed decisions in
urban location-based applications like ride-sharing. However, existing ST
models often require region partition as a prerequisite, resulting in two main
pitfalls. Firstly, location-based services necessitate ad-hoc regions for
various purposes, requiring multiple ST models with varying scales and zones,
which can be costly to support. Secondly, different ST models may produce
conflicting outputs, resulting in confusing predictions. In this paper, we
propose One4All-ST, a framework that can conduct ST prediction for arbitrary
modifiable areal units using only one model. To reduce the cost of getting
multi-scale predictions, we design an ST network with hierarchical spatial
modeling and scale normalization modules to efficiently and equally learn
multi-scale representations. To address prediction inconsistencies across
scales, we propose a dynamic programming scheme to solve the formulated optimal
combination problem, minimizing predicted error through theoretical analysis.
Besides, we suggest using an extended quad-tree to index the optimal
combinations for quick response to arbitrary modifiable areal units in
practical online scenarios. Extensive experiments on two real-world datasets
verify the efficiency and effectiveness of One4All-ST in ST prediction for
arbitrary modifiable areal units. The source codes and data of this work are
available at https://github.com/uctb/One4All-ST.