  Language models now constitute essential tools for improving efficiency for
many professional tasks such as writing, coding, or learning. For this reason,
it is imperative to identify inherent biases. In the field of Natural Language
Processing, five sources of bias are well-identified: data, annotation,
representation, models, and research design. This study focuses on biases
related to geographical knowledge. We explore the connection between geography
and language models by highlighting their tendency to misrepresent spatial
information, thus leading to distortions in the representation of geographical
distances. This study introduces four indicators to assess these distortions,
by comparing geographical and semantic distances. Experiments are conducted
from these four indicators with ten widely used language models. Results
underscore the critical necessity of inspecting and rectifying spatial biases
in language models to ensure accurate and equitable representations.
