# Quantifying Geospatial in the Common Crawl Corpus

arXiv: https://arxiv.org/abs/2406.04952 · HTML: https://mlsanity.com/paper/2406.04952/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus](https://mlsanity.com/paper/2104.08758/) (2021)
- [Multimodal datasets: misogyny, pornography, and malignant stereotypes](https://mlsanity.com/paper/2110.01963/) (2021)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [Large Language Models are Geographically Biased](https://mlsanity.com/paper/2402.02680/) (2024)
- [On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence](https://mlsanity.com/paper/2304.06798/) (2023)
- [Empirical Study of Zero-Shot NER with ChatGPT](https://mlsanity.com/paper/2310.10035/) (2023)
- [Are Large Language Models Geospatially Knowledgeable?](https://mlsanity.com/paper/2310.13002/) (2023)
- ['I think I discovered a military base in the middle of the ocean' -- Null Island, the most real of fictional places](https://mlsanity.com/paper/2204.08383/) (2022)
- [CC-GPX: Extracting High-Quality Annotated Geospatial Data from Common Crawl](https://mlsanity.com/paper/2405.11039/) (2024)

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

- [Understanding the Geospatial Reasoning Capabilities of LLMs: A Trajectory Recovery Perspective](https://mlsanity.com/paper/2510.01639/) (2025)
- [The Scenic Route to Deception: Dark Patterns and Explainability Pitfalls in Conversational Navigation](https://mlsanity.com/paper/2603.14586/) (2026)
- [Coordinates from Context: Using LLMs to Ground Complex Location References](https://mlsanity.com/paper/2510.08741/) (2025)
- [Much of Geospatial Web Search Is Beyond Traditional GIS](https://mlsanity.com/paper/2605.11336/) (2026)

## Abstract (arXiv)

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 (CC) 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 1.5, a powerful language model. By analyzing a sample of documents and manually revising the results, we estimate that 18.7% of web documents in CC contain geospatial information such as coordinates and addresses. We find little difference in prevalence between Enlgish- and non-English-language documents. Our findings provide quantitative insights into the nature and extent of geospatial data in CC, and lay the groundwork for future studies of geospatial biases of LLMs.