  Knowledge graphs are a key technique for linking and integrating cross-domain
data, concepts, tools, and knowledge to enable data-driven analytics. As much
of the worlds data have become massive in size, visualizing graph entities and
their interrelationships intuitively and interactively has become a crucial
task for ingesting and better utilizing graph content to support semantic
reasoning, discovering hidden knowledge discovering, and better scientific
understanding of geophysical and social phenomena. Despite the fact that many
such phenomena (e.g., disasters) have clear spatial footprints and geographical
properties, their location information is considered only as a textual label in
existing graph visualization tools, limiting their capability to reveal the
geospatial distribution patterns of the graph nodes. In addition, most graph
visualization techniques rely on 2D graph visualization, which constraints the
dimensions of information that can be presented and lacks support for graph
structure examination from multiple angles. To tackle the above challenges, we
developed a novel 3D map-based graph visualization algorithm to enable
interactive exploration of graph content and patterns in a spatially explicit
manner. The algorithm extends a 3D force directed graph by integrating a web
map, an additional geolocational force, and a force balancing variable that
allows for the dynamic adjustment of the 3D graph structure and layout. This
mechanism helps create a balanced graph view between the semantic forces among
the graph nodes and the attractive force from a geolocation to a graph node.
Our solution offers a new perspective in visualizing and understanding spatial
entities and events in a knowledge graph.

Human-Computer Interaction