  Large language models (LLMs) exhibit emerging geospatial capabilities,
stemming from their pre-training on vast unlabelled text datasets that are
often derived from the Common Crawl corpus. However, the geospatial content
within CC remains largely unexplored, impacting our understanding of LLMs'
spatial reasoning. This paper investigates the prevalence of geospatial data in
recent Common Crawl releases using Gemini, a powerful language model. By
analyzing a sample of documents and manually revising the results, we estimate
that between 1 in 5 and 1 in 6 documents contain geospatial information such as
coordinates and street addresses. Our findings provide quantitative insights
into the nature and extent of geospatial data within Common Crawl, and web
crawl data in general. Furthermore, we formulate questions to guide future
investigations into the geospatial content of available web crawl datasets and
its influence on LLMs.
