Democratizing Multi-Granularity Spatio-Temporal Intelligence with Multi-Agent Systems
Name
3764915.3770718.pdf
Size
1.43 MB
Format
Adobe PDF
Checksum (MD5)
44b93e4ba6665e65afe671bec410a49a
Author(s)
Wu, Che-Cheng
Huang, Syuan-Bo
Song, Yu-Lun
Lin, Po-Han
Lin, Michael
Lin, Yu-Ta
Date Issued
November 2, 2025
Publisher
ACM|The 1st ACM SIGSPATIAL International Workshop on Generative and Agentic AI for Multi-Modality Space-Time Intelligence
Citation
Che-Cheng Wu, Syuan-Bo Huang, Yu-Lun Song, Po-Han Lin, Michael Chia-Liang Lin, and Yu-Ta Lin. 2025. Democratizing Multi-Granularity Spatio-Temporal Intelligence with Multi-Agent Systems. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Generative and Agentic AI for Multi-Modality Space-Time Intelligence (GeoGenAgent '25). Association for Computing Machinery, New York, NY, USA, 22–26.
Version
Final published version
Abstract
We propose a system that democratizes multi-granularity spatio-temporal analysis by integrating a Discrete Global Grid System (DGGS) data pipeline with a Multi-Agent System (MAS). Unlike existing single-agent spatial AI solutions that primarily target experts and lack support for heterogeneous data, persistent memory, and validation, our platform converts diverse datasets into standardized H3-indexed cells, enabling consistent analysis across scales. To enhance usability for non-experts, the system interactively guides users to refine queries, which are decomposed into sub-tasks managed by specialized agents for data retrieval, transformation, analysis, and visualization. Agents communicate through a decentralized framework with shared memory, supporting persistent reasoning and multi-turn dialogue. Reflection modules and human-in-the-loop validation further strengthen robustness. Demonstrated through real-world scenarios, such as analyzing the relationship between aging rate patterns and average income to inform social welfare policy in Taiwan, the system illustrates how natural language queries, combined with intuitive map- and chart-based visualizations, can support evidence-based decision-making.
Description
GeoGenAgent ’25, Minneapolis, MN, USA
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3764915.3770718
