LandMatch: Using LLMs and social media algorithms to spatial planning

Social media and consumer product portals have successfully leveraged data analytics to match users with products, friends, or information, having a significant impact on lifestyle, economy, and politics. Central to these systems is the structured storage of heterogeneous data and the use of bespoke algorithms to enable context-specific search, ranking, and retrieval. This represents a potential opportunity for spatial planning and policy-making: can similar technologies be repurposed to support evidence-based policy-making and ecological management in rural landscapes?

We present LandMatch, an AI-based framework designed to support policymakers and agribusinesses in identifying partnerships, investment opportunities, and intervention strategies that jointly address economic performance and ecological sustainability in the UK countryside. LandMatch draws on techniques from social media analytics, information retrieval, and graph-based modelling, building a Spatial Knowledge Graph (SKG). It uses Large Language Models (LLMs) to summarise and structure this information into a form suitable for large-scale analysis and semantic retrieval. The spatial dimension of its graph structure enables analyses and recommendations that reflect both functional similarity and landscape-level ecological processes.

We have developed a prototype for LandMatch in the context of Chichester, West Sussex (UK). Through a series of tests, we demonstrate the feasibility of combining text-based retrieval augmented generation (RAG), automated data collection through web scraping and semantic mapping, as well as large-scale clustering and spatial graph analytics. Our work ultimately highlights a new approach to integrating social, economic, and geospatial data on a robust, interpretable, and design-ready platform.