Smoothing the math of spatial networks
A new statistical approach could make modeling spatial networks more accurate and efficient.
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Every day, we rely on networks. Roads connect places, power lines connect homes and cities, and transport networks link people and goods. These are spatial networks, and modeling them accurately is not straightforward. Researchers from KAUST and Lund University in Sweden have now developed a new statistical approach that could make this task more accurate and efficient[1].
“Networks are intrinsically difficult to model using conventional spatial approaches,” says Alexandre Simas from KAUST’s Stochastic Processes and Mathematical Statistics lab.
Statistical modeling of spatial data, such as weather observations, has advanced over the past decade as datasets have become larger and more detailed. However, relatively little attention has been given to linear networks, such as roads and other connected systems.
“A road network, a river system, or a set of connected pipes is not the same as a two-dimensional region,” Simas explains. “Imagine two sensors that are very close in ordinary distance but are located on opposite sides of a road or highway — one may record high vehicle speeds because traffic is flowing freely, while the other may record lower speeds because of congestion. A classical spatial model could incorrectly average these two situations together, even though they should be observed separately.”
Read the full story on KAUST Discovery.
