Spatial networks are a unique form of graph. Unlike in community analysis, where layout is instrumental in revealing how nodes are connected, nodes in a spatial network have pre-existing physical characteristics that are preserved, relying on link visualization alone to convey connections.
Spatial networks occur in nature, and graphs can be useful in understanding complex networks of this kind. Neuroscientists use graph visualization and analysis to learn more about how our brains are wired. In the business world, however, spatial networks are often equated with infrastructure. For example, supply chains have a spatial component that may be highly relevant to supply optimization. Oil pipelines, electrical power networks, transportation infrastructure, and computer networks are a few of the many other examples of spatial networks where graph visualization and analysis are of value.
Seeing how elements are connected physically and logically provides insight into the structure of a system. It also provides important context in understanding the health and performance of a system, the impact of failures, and how to plan for and mitigate them.
System routes and flows are often of central interest in spatial networks. One of the primary challenges of visualizing spatial networks is the limited number of links that can be successfully displayed at once. Links that cross each other or other nodes in their path introduce perceptual confusion. So, when ...