# On the Inclusion of Spatial Information for Spatio-Temporal Neural Networks

arXiv: https://arxiv.org/abs/2007.07559 · HTML: https://mlsanity.com/paper/2007.07559/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction](https://mlsanity.com/paper/2304.14343/) (2023)
- [Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting](https://mlsanity.com/paper/2302.09956/) (2023)
- [Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations](https://mlsanity.com/paper/2212.12932/) (2022)
- [A HEART for the environment: Transformer-Based Spatiotemporal Modeling for Air Quality Prediction](https://mlsanity.com/paper/2502.19042/) (2025)

## Abstract (arXiv)

When confronting a spatio-temporal regression, it is sensible to feed the model with any available prior information about the spatial dimension. For example, it is common to define the architecture of neural networks based on spatial closeness, adjacency, or correlation. A common alternative, if spatial information is not available or is too costly to introduce it in the model, is to learn it as an extra step of the model. While the use of prior spatial knowledge, given or learnt, might be beneficial, in this work we question this principle by comparing spatial agnostic neural networks with state of the art models. Our results show that the typical inclusion of prior spatial information is not really needed in most cases. In order to validate this counterintuitive result, we perform thorough experiments over ten different datasets related to sustainable mobility and air quality, substantiating our conclusions on real world problems with direct implications for public health and economy.