Proximizing

Proximizing is a term used to describe the process of increasing proximity between two or more entities, either in physical space or in an abstract feature space. It refers to actions or methods that bring items closer together, reduce effective distance, or strengthen perceived relatedness through layout, design, or reconfiguration.

In urban planning, architecture, and logistics, proximizing involves siting facilities, routes, or amenities to shorten travel distances, improve accessibility, and cluster related functions. In data visualization and network analysis, it is achieved by layout algorithms and embedding techniques that position related nodes nearer to one another, using proximity measures such as Euclidean distance, cosine similarity, or shared attributes to guide placement.

In optimization and machine learning, proximity concepts appear in two related ways. Some describe proximizing as procedures that increase closeness in a dataset or model representation, while more formal literature uses proximal operators and methods to solve optimization problems by incorporating proximity constraints. The term is often informal, and its precise meaning can vary by field.

Applications span facility location, supply chain design, social science research on interactions and networks, and recommender systems, where items are prioritized based on closeness in feature or behavioral space. Metrics for proximity can be spatial (distance, travel time) or conceptual (similarity, shared properties).

Limitations include trade-offs with other objectives (cost, congestion, privacy) and the dynamic nature of many systems, where increasing proximity in one dimension may degrade it in another. Overall, proximizing is a cross-domain concept describing attempts to make entities more closely related or spatially proximate through deliberate design and computation.