This paper proposes MapGPT which is a novel approach that integrates the capabilities of language models, specifically large language models (LLMs), with spatial data processing techniques. This paper introduces MapGPT, which aims to bridge the gap between natural language understanding and spatial data analysis by highlighting the relevant core building blocks. By combining the strengths of LLMs and geospatial analysis, MapGPT enables more accurate and contextually aware responses to location-based queries. The proposed methodology highlights building LLMs on spatial and textual data, utilizing tokenization and vector representations specific to spatial information. The paper also explores the challenges associated with generating spatial vector representations. Furthermore, the study discusses the potential of computational capabilities within MapGPT, allowing users to perform geospatial computations and obtain visualized outputs. Overall, this research paper presents the building blocks and methodology of MapGPT, highlighting its potential to enhance spatial data understanding and generation in natural language processing applications.

翻译：本文提出MapGPT，这是一种将语言模型（特别是大型语言模型（LLM））的能力与空间数据处理技术相结合的新方法。本文介绍了MapGPT，旨在通过突出相关核心构建模块，弥合自然语言理解与空间数据分析之间的鸿沟。通过结合LLM与地理空间分析的优势，MapGPT能够对基于位置的查询提供更准确且更具上下文感知的响应。所提出的方法论重点在于利用针对空间信息的标记化和向量表示，在空间数据和文本数据上构建LLM。本文还探讨了生成空间向量表示的相关挑战。此外，研究讨论了MapGPT内计算能力的潜力，允许用户执行地理空间计算并获得可视化输出。总体而言，本研究呈现了MapGPT的构建模块和方法论，凸显了其在自然语言处理应用中增强空间数据理解与生成的潜力。