Future Tech: June 2026

Reasoning with Maps:
The Era of Spatial Intelligence.

Moving beyond descriptive analytics. GeoSpatial AI combines Large Language Models (LLMs) with vector topology to reason, predict, and answer complex strategic questions for football development.

GeoAI Neural Network Map

Spatial-RAG Architecture

Semantic & Topological Reasoning Layer

What is GeoSpatial AI?

Traditional GIS tells you where things are. GeoSpatial AI tells you why they are there and what might happen next. By integrating Generative AI with spatial databases, we create a system that can understand natural language questions, perform complex geometric calculations, and infer relationships between disparate datasets—simulating the reasoning of an expert planner in seconds.

Potential for Reasoning in Football

Four key pillars where AI will drive decision making.

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Participation Growth

Optimizing locations for Comets, SquadGirls, and JustPlay. The AI finds the "sweet spot" by intersecting target demographic clusters (e.g., girls aged 12-16) with pitch availability and checking for a lack of existing competition within a 15-minute drive time.

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Referee Recruitment

Identifying geographic clusters of high Expression of Interest (EoI). By visualizing latent demand, County FAs can launch targeted recruitment drives in these hot-spots, maximizing course attendance and long-term retention.

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Funding Evidence

Generating robust support for planning applications. The system automatically collates multi-layer evidence—health deprivation, inactivity rates, and local demand—to prove the undeniable case for new facilities to funding bodies.

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Missed Opportunities

Uncovering "blind spots" in the network. Identifying areas with high youth density and demand but zero current provision—opportunities that manual analysis often overlooks due to administrative boundaries.

How It Works

Integrating AI into VisualEyes

We are building a Spatial-RAG (Retrieval-Augmented Generation) pipeline. This allows the VisualEyes system to "read" its own map data.

01.

Natural Language Input: User asks a complex question.

02.

Semantic Translation: LLM translates text into SQL/PostGIS spatial queries.

03.

Vector Retrieval: Database executes geometric calculations (buffers, intersections).

04.

Reasoned Output: AI synthesizes the data into a strategic answer.

GeoAI System Architecture

Enhanced Reasoning Decisions

Using natural language to extract quality insights instantly.

VisualEyes AI Agent v1.0

How many people could benefit from a 3G pitch in Oldbury?

sync Processing Natural Language Query...
🔹 Translating query to spatial parameters...
🚗 Action 1: Calculating 15-min drive catchment...
🚌 Action 2: Calculating 30-min transit catchment...
🏟️ Action 3: Assessing current facility saturation...
radio_button_unchecked Strategic Analysis

Based on multi-modal catchment analysis from Oldbury (B69):

Reachable Youth
12,540
Facility Saturation
Low (1 site)

Reasoning: High-impact opportunity identified. Despite high youth density, the area has a net deficit of 1.5 pitches. Projected utilization: 95%+ within 6 months.