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Space Planning AI

AI that optimizes spatial layouts based on program requirements and constraints.

Definition

Space Planning AI uses algorithms to generate and optimize floor plan layouts based on program requirements, adjacency relationships, building constraints, and design goals. These systems can explore thousands of possible arrangements to find layouts that maximize efficiency, natural light, or other objectives while respecting spatial constraints and code requirements.

In Depth

Space planning arranges program elements — offices, conference rooms, support spaces, circulation — within a floor plate to satisfy adjacency requirements, daylight access, circulation efficiency, and code compliance. AI generates layout options that meet all of these criteria, enabling the designer to evaluate and refine options rather than building each one manually.

The constraint satisfaction approach considers that the executive offices need daylight and proximity to the reception area, that the conference rooms need proximity to the office zones they serve, that the break room needs to be accessible but not disruptive, and that the circulation must provide adequate egress and accessible routes. AI produces layouts that satisfy all constraints simultaneously.

Examples

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Generating hospital department layouts based on adjacency requirements

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Optimizing office floor plans for collaboration

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Creating efficient warehouse layouts

Nomic Use Cases

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Frequently Asked Questions

Space Planning AI uses algorithms to generate and optimize floor plan layouts based on program requirements, adjacency relationships, building constraints, and design goals. These systems can explore thousands of possible arrangements to find layouts that maximize efficiency, natural light, or other objectives while respecting spatial constraints and code requirements.

Generating hospital department layouts based on adjacency requirements. Optimizing office floor plans for collaboration. Creating efficient warehouse layouts.

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