Unsupervised text encoding models have recently fueled substantial progress
in NLP. The key idea is to use neural networks to convert words in texts to
vector space representations based on word positions in a sentence and their
contexts, which are suitable for end-to-end training of downstream tasks. We
see a strikingly similar situation in spatial analysis, which focuses on
incorporating both absolute positions and spatial contexts of geographic
objects such as POIs into models. A general-purpose representation model for
space is valuable for a multitude of tasks. However, no such general model
exists to date beyond simply applying discretization or feed-forward nets to
coordinates, and little effort has been put into jointly modeling distributions
with vastly different characteristics, which commonly emerges from GIS data.
Meanwhile, Nobel Prize-winning Neuroscience research shows that grid cells in
mammals provide a multi-scale periodic representation that functions as a
metric for location encoding and is critical for recognizing places and for
path-integration. Therefore, we propose a representation learning model called
Space2Vec to encode the absolute positions and spatial relationships of places.
We conduct experiments on two real-world geographic data for two different
tasks: 1) predicting types of POIs given their positions and context, 2) image
classification leveraging their geo-locations. Results show that because of its
multi-scale representations, Space2Vec outperforms well-established ML
approaches such as RBF kernels, multi-layer feed-forward nets, and tile
embedding approaches for location modeling and image classification tasks.
Detailed analysis shows that all baselines can at most well handle distribution
at one scale but show poor performances in other scales. In contrast,
Space2Vec's multi-scale representation can handle distributions at different
scales.

提出了一个名为 Space2Vec 的表示学习模型，用于将地点的绝对位置和空间关系进行编码，并在地理数据和图像分类两个任务上得到了比径向基函数和多层前馈神经网络等传统机器学习方法更好的效果，其特点是具有多尺度表示能力。