DOI: 10.6084/m9.figshare.29326700.v1
title: <b>Regional Decision-Making Agent: Resourc</b><b>e </b><b>governance</b><b> </b><b>results & Project reproduction code</b><b> & </b><b>I</b><b>nstructions on how to</b><b> </b><b>reproduce each reported </b><b>finding</b>

description: <b>1.Overview</b>LLM-based agents can integrate geospatial data and models for automated regional decision-making, leveraging semantic reasoning and function calling. However, their effectiveness is limited by challenges in managing heterogeneous resources and unreliable outputs, hindering consistent, context-aware workflows for regional governance.This paper introduces a multi-agent collaborative framework, Regional Decision-Making Agent (RDMA), to integrate, manage and schedule geographic analysis resources to address regional decision-making tasks. RDMA supports multi-round interactive regional development and decision-making, rather than serving as a single-round Q&A tool for individual users. RDMA is organized into three key modules: <b>data governance</b>, <b>model governance</b>, and <b>agent scheduling</b>. Our research contributes a novel data and model description standard, an automated resource invocation strategy, and an adaptive agent scheduling framework.Two case studies around urban planning and stormwater management have validated the effectiveness of RDMA in solving regional decision-making tasks, recommending appropriate resources, and configuring models in an interactive, conversational manner with users. <b>2.</b><b>Resourc</b><b>e </b><b>governance</b><b> </b><b>results</b><b>(1) </b><b>Data description document</b>File <b>d</b><b>ata_description_document.json</b>: Provides a semantic and structural description of geospatial data to facilitate data selection and integration by LLMs, including:<b>Spatio-temporal Structure</b>: Defines time, place, event, and other contextual attributes .<b>Related Semantics Description</b>: Specifies concepts, units, application topics, and related hashtags .<b>Data Structure View</b>: Details data formats for flexible access.<b>(2) </b><b>Model description </b><b>documents</b>File <b>mod</b><b>el</b><b>_introduction</b><b>_document.json</b>: Outlines the scope, principles, and applicability of geographic analysis models to support semantic model selection, including ID, Description, Location, Geometry, Attributes, Relationships, Evolution, and Mechanisms.<br>File <b>computational_interface</b><b>_document.json</b>: Defines the input data, parameters, output requirements and API interface for invoking models, bridging the semantic-computational gap for LLMs.<br>File <b>related_resource_</b><b>document.json</b>: Captures contextual information to enhance long-term decision support, including related data, models, applications, and historical decision cases.<b>3.</b><b>Instructions for codes</b><b>(1) C</b><b>odes for a</b><b>gent scheduling</b>This code repository is organized into six files:File <b>create_chromadb.py</b>: Processes data and model description documents, generates embeddings, and stores them in a ChromaDB database for efficient similarity-based retrieval and management.File <b>generate_prompt.py</b>: Defines prompt generation functions for five proposed agents, enabling their reasoning and behavior for tasks such as intent recognition, tool selection, and parameter modification based on user queries. It formats responses in JSON to facilitate communication between agents for model recommendations, data input suggestions, and configuration adjustments.<br>File <b>ollama_api.py</b>: Configures the IP address for an Ollama server and defines functions to send prompts generated by generate_prompt.py to the Ollama API for text generation or chat interactions, returning JSON-formatted responses.<br>File <b>main_for_one_chat.py</b>: Processes user queries by interfacing with agents to handle intent recognition, model recommendations, and parameter modifications, generating responses and storing them along with conversation history in designated files for agent communication and persistence.<br>File <b>latest_model_config.json</b>: The configuration file of the recommended model which is generated by recommender and can be read and written by configurator.<br>File <b>history_chat.json</b>: Records a round of user-agent dialogue, capturing user queries and corresponding agent responses.<b>(2) </b><b>C</b><b>odes for p</b><b>rototype system</b>Folder <b>Backend</b>: This is a backend project structured with:<b>Microservices</b>: DataSceneContainer-Auth-Central, DataSceneContainer-common, DataSceneContainer-Gateway, and DataSceneContainer-SystemMS, each containing Dockerfile-related files for containerized deployment.<b>Docker compose files</b>: db-docker-compose.yml, middleware-docker-compose.yml, services-docker-compose.yml: <b>Maven-based build management </b><b>files</b>: pom.xml<b>Documentation</b>: README.md<br>Folder <b>Front</b><b>end</b>: This is a frontend project built using Vue3 with:<b>Main application code</b>: index.html, App.vue, and main.ts<b>Reusable components</b>: components<b>Page layouts</b>: pages<b>Static resources</b>: assets<b>Configuration files</b>: tsconfig.json and package.json<br>Folder <b>Images</b>: The docker images of geographical analysis model servers.<b>4.</b><b>Instructions on how to reproduce each reported findings</b><b>instructions_on_how_to_reproduce_each_reported_findings.md</b>: details the steps to reproduce the figures and the tables shown in the paper.<br>File <b>Figures.pptx</b>: All the figures produced by vector shapes in PowerPoint.<br>Folder <b>Videos</b>: Contains two video recordings of the operation processes of prototype system for two case studies demonstrating the RDMA.