GeoSEE: Regional Socio-Economic Estimation With a Large Language Model
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arXiv
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| Format: | Preprint |
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2024
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| author | Han, Sungwon Ahn, Donghyun Lee, Seungeon Song, Minhyuk Park, Sungwon Park, Sangyoon Kim, Jihee Cha, Meeyoung |
| author_facet | Han, Sungwon Ahn, Donghyun Lee, Seungeon Song, Minhyuk Park, Sungwon Park, Sangyoon Kim, Jihee Cha, Meeyoung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09799 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GeoSEE: Regional Socio-Economic Estimation With a Large Language Model Han, Sungwon Ahn, Donghyun Lee, Seungeon Song, Minhyuk Park, Sungwon Park, Sangyoon Kim, Jihee Cha, Meeyoung Computers and Society 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. |
| title | GeoSEE: Regional Socio-Economic Estimation With a Large Language Model |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2406.09799 |