GeoSEE: Regional Socio-Economic Estimation With a Large Language Model

• URL: http://arxiv.org/abs/2406.09799v1
• Date: Fri, 14 Jun 2024 07:50:22 GMT
• Title: GeoSEE: Regional Socio-Economic Estimation With a Large Language Model
• Authors: Sungwon Han, Donghyun Ahn, Seungeon Lee, Minhyuk Song, Sungwon Park, Sangyoon Park, Jihee Kim, Meeyoung Cha,
• Abstract summary: We present GeoSEE, a method that can estimate various socio-economic indicators using a unified pipeline powered by a large language model (LLM) The system then computes target indicators via in-context learning after aggregating results from selected modules in the format of natural language-based texts. Our method outperforms other predictive models in both unsupervised and low-shot contexts.
• Score: 17.31652821477571
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Moving beyond traditional surveys, combining heterogeneous data sources with AI-driven inference models brings new opportunities to measure socio-economic conditions, such as poverty and population, over expansive geographic areas. The current research presents GeoSEE, a method that can estimate various socio-economic indicators using a unified pipeline powered by a large language model (LLM). Presented with a diverse set of information modules, including those pre-constructed from satellite imagery, GeoSEE selects which modules to use in estimation, for each indicator and country. This selection is guided by the LLM's prior socio-geographic knowledge, which functions similarly to the insights of a domain expert. The system then computes target indicators via in-context learning after aggregating results from selected modules in the format of natural language-based texts. Comprehensive evaluation across countries at various stages of development reveals that our method outperforms other predictive models in both unsupervised and low-shot contexts. This reliable performance under data-scarce setting in under-developed or developing countries, combined with its cost-effectiveness, underscores its potential to continuously support and monitor the progress of Sustainable Development Goals, such as poverty alleviation and equitable growth, on a global scale.

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