Segmentation using large language models: A new typology of American neighborhoods


Singleton, Alex D and Spielman, Seth (2024) Segmentation using large language models: A new typology of American neighborhoods EPJ Data Science, 13 (1). 34-. ISSN 2193-1127, 2193-1127

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Abstract

AbstractIn the United States, recent changes to the National Statistical System have amplified the geographic-demographic resolution trade-off. That is, when working with demographic and economic data from the American Community Survey, as one zooms in geographically one loses resolution demographically due to very large margins of error. In this paper, we present a solution to this problem in the form of an AI based open and reproducible geodemographic classification system for the United States using small area estimates from the American Community Survey (ACS). We employ a partitioning clustering algorithm to a range of socio-economic, demographic, and built environment variables. Our approach utilizes an open source software pipeline that ensures adaptability to future data updates. A key innovation is the integration of GPT4, a state-of-the-art large language model, to generate intuitive cluster descriptions and names. This represents a novel application of natural language processing in geodemographic research and showcases the potential for human-AI collaboration within the geospatial domain.

Item Type: Article
Uncontrolled Keywords: Geodemographics, Large Language Model (LLM), American Community Survey, Segmentation, Neighborhoods, Artificial Intelligence (AI), Demographics, Retreival Augmented Generation (RAG)
Depositing User: Symplectic Admin
Date Deposited: 02 Jul 2024 13:52
Last Modified: 16 Jun 2026 09:09
DOI: 10.1140/epjds/s13688-024-00466-1
Open Access URL: https://epjdatascience.springeropen.com/articles/1...
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URI: https://livrepository.liverpool.ac.uk/id/eprint/3182339
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