Geographically-situated networks, in which nodes are placed in space at their actual geographic locations, offer an incredibly rich new analytic framework that blends spatial and non-spatial connectivity in a single visual. A graph is the mathematical abstraction of a network. It consists of vertices and edges connecting those vertices. A geographic graph is a graph where vertices are associated with locations.
What: Discover Interconnections
Why: Contextualize Relationships in Space
History of Geographic Network Diagram
Spatial networks have been studied intensively during the 1970s in quantitative geography, while networks and graphs were studied for a long time in mathematics, physics, mathematical sociology and computer. Objects of studies in geography are inter alia locations, activities and flows of individuals, but also networks evolving in time and space. Network diagrams sought to address many problems like the location of nodes of a network, the evolution of transportation networks and their interaction with the population. However, many important points still remain unclear, partly because at that time datasets of large networks and larger computer capabilities were lacking. Recently, being a subject in studies of statistics, spatial networks are being used to connect probabilities and stochastic processes with networks in the real world.
When to Use a Geographic Network Diagram?
1When required to compare the strength of links in a network
Use Geographic network diagrams to represent networks like transportation and mobility networks, internet, mobile phone networks, power grids, social, contact, and neural networks as into these the underlying space is relevant and the graph’s topology alone does not contain all the information. The network diagram can assist in positioning nodes based on connection strength, with countries that appear more frequently together being placed closer together in the network. Nodes are then sized based on degree strength and color-coded by modularity finding.
2Decipher the structure of the network- make regional flows more visible
Use Geographic network diagrams to decode the structure of a network through the maps’ color-codes where each line connecting the two locations is separated by the average “tone” of all. This can yield fascinating conflict/cooperation insights, however, can make it difficult in high-density areas to understand the micro-level connectivity due to all of the lines. Using colors appropriately in terms of the background and links the interconnected regions can be made immediately clear.
3Map as a dashboard and monitor the health of a network
Use geographic network maps to survey the entire landscape, notice key features and ignore details that aren’t so important from a distance. Dashboards offer the “big picture” at a glance, and a mapping element is an insightful component common for cyber threat intelligence, managing system performance by monitoring geography-based faults quickly and efficiently. The dashboard can show the health of a network on a world map, with links representing the status of connections.
Types of Geographic Network Diagrams
1. Area cartograms
A type of thematic map where the regions of the map are scaled such that their areas correspond to the underlying data. In the context of vertex-weighted graphs, the vertices are encoded as regions and their edges as adjacencies between those regions: the cartogram is a contact representation of the graph.
2. Linear Cartograms
Used to visualize edge-weighted geographic graphs. Whereas area cartograms scale regions to encode vertex weights, linear cartograms deform the map such that the distance between locations corresponds to edge weights.
When Not to Use a Network Diagrams?
1Overly complex and hard for understanding for larger datasets
Network Diagrams have a limited data capacity and start to become hard to read when there are too many nodes and can resemble “hairballs”.With no obvious pattern represented, the figure gets cluttered and becomes unreadable.
2When geographic positioning does not reveal the network structure with inadequate data information on proximity
The map is essentially providing geographic context and implicit labeling, but if the underlying data is not driven by geographic proximity important details can be obscured. Also if plotting the network on a full world map, there might be cases when the shortest arc goes “behind” the map – e.g. exits it on the left side and enters back on the right, not displaying the entire network completely.