Description

Currently, the Cell class (and by extension, the dynamically created GridCell subclasses) carries significant memory overhead that limits the scalability of Mesa models with large environments.
While Cell uses __slots__, it currently includes "__dict__" in the slots list. In Python, including __dict__ in slots defeats much of the memory benefit because it still initializes a dynamic dictionary for every instance. On modern Python versions, this adds approximately 250–300 bytes of overhead per cell.
In a model with 1,000,000 cells (e.g., a 1000x1000 grid), this translates to ~300MB of unnecessary RAM usage just for the empty dictionaries.

Issues

1. Redundant __dict__: Cell includes __dict__ in slots, likely to support cached_property and cache, but at a high memory cost.
2. Subclass Overhead: The Grid class creates dynamic GridCell subclasses. Since these subclasses do not define their own __slots__, Python automatically gives them a __dict__ even if the parent didn't have one.
3. Property Management: The empty attribute on Cell can clash with the empty property layer in Grid if slots are made strict, requiring a more robust setter/getter implementation.

Proposed Solution

1. Strict Slots: Remove __dict__ from Cell.__slots__.
2. Manual Caching: Replace functools.cache and cached_property in Cell with a manual _neighborhood_cache managed within a slot. This maintains performance for spatial queries without requiring a __dict__.
3. Optimize Subclasses: Ensure GridCell subclasses are created with slots = () to preserve the memory-efficient structure of the parent.
4. Manage Properties: Refactor the empty attribute into a managed property (_empty slot + empty property) to ensure compatibility with PropertyLayer descriptors used in the Grid system.

Expected Outcome

• A reduction of ~300 bytes per Cell instance.
• Improved scalability for high-resolution spatial models.
• Cleaner separation between core Cell state and Grid-level property layers.