  Named geographic entities (geo-entities for short) are the building blocks of
many geographic datasets. Characterizing geo-entities is integral to various
application domains, such as geo-intelligence and map comprehension, while a
key challenge is to capture the spatial-varying context of an entity. We
hypothesize that we shall know the characteristics of a geo-entity by its
surrounding entities, similar to knowing word meanings by their linguistic
context. Accordingly, we propose a novel spatial language model, SpaBERT, which
provides a general-purpose geo-entity representation based on neighboring
entities in geospatial data. SpaBERT extends BERT to capture linearized spatial
context, while incorporating a spatial coordinate embedding mechanism to
preserve spatial relations of entities in the 2-dimensional space. SpaBERT is
pretrained with masked language modeling and masked entity prediction tasks to
learn spatial dependencies. We apply SpaBERT to two downstream tasks:
geo-entity typing and geo-entity linking. Compared with the existing language
models that do not use spatial context, SpaBERT shows significant performance
improvement on both tasks. We also analyze the entity representation from
SpaBERT in various settings and the effect of spatial coordinate embedding.
