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Spaghetti plot

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Spaghetti diagram of flow within a facility

Spaghetti plots are a method of viewing data to visualize possible flows through systems. Flows depicted in this manner appear like noodles, hence the coining of this term.[1] This method of statistics was first used to track routing through factories. Visually flow in this manner can reduce inefficiency within the flow of a system. Sometimes, as with animal migration and weather buoys drifting through the ocean, they are drawn to study migration patterns. Within meteorology, these diagrams can help determine confidence in a specific weather forecast, as well as positions and intensities of high and low pressure systems.

History

Spaghetti diagrams were first used to track to track routing through a factory.[2]

Within nature

Spaghetti diagrams have been used to study why butterflies are found where they are, and to see how topographic features (such as mountain ranges) limit their migration and range.[3] Within mammal distributions across central North America, these plots have correlated their edges to regions which were glaciated within the previous ice age, as well as certain types of vegetation.[4]

Within meteorology

Spaghetti plot of ten NCEP global ensemble members at the 500 hPa pressure level for a 3.5 day forecast. Areas of greatest uncertainty are circled in red

Within meteorology, spaghetti diagrams are drawn from ensemble forecasts. A meteorological variable e.g. pressure, temperature, or precipitation amount is drawn on a chart for a number of slightly different model runs from an ensemble. The model can then be stepped forward in time and the results compared and be used to gauge the amount of uncertainty in the forecast. If there is good agreement and the contours follow a recognizable pattern through the sequence, then the confidence in the forecast can be high. Conversely, if the pattern is chaotic i.e resembling a plate of spaghetti then confidence will be low. Ensemble members will generally diverge over time and spaghetti plots are quick way to see when this happens.

Spaghetti plots can be a more favorable choice compared to the mean-spread ensemble in determining the intensity of a coming cyclone, anticyclone, or upper level ridge or trough. Because ensemble forecasts naturally diverge as the days progress, the projected locations of meteorological features will spread further apart. A mean-spread diagram will take a mean of the calculated pressure from each spot on the map as calculated by each permutation in the ensemble, thus effectively smoothing out the projected low and making it appear broader in size but weaker in intensity than the ensemble's permutations had actually indicated. It can also depict two features instead of one if the ensemble clustering is around two different solutions.[5]

Within the field of climatology and paleotempestology, spaghetti plots have been used to correlate ground temperature information derived from boreholes across central and eastern Canada.[6]

Like in other disciplines, spaghetti diagrams can be used to show the motion of objects, such as drifting weather buoys over time.[7]

Within business

Spaghetti chart which shows nurse movement through a facility in the search for a glucometer

Spaghetti plots are a simple tool to visualize movement and transportation.[8] Analyzing flows through systems can determine where time and energy is wasted, and identifies where streamlining would be beneficial.[1] This is true not only with physical travel through a physical place, but also during more abstract processes such as the application of a mortgage loan.[9]

References

  1. ^ a b Theodore T. Allen (2010). Introduction to Engineering Statistics and Lean Sigma: Statistical Quality Control and Design of Experiments and Systems. Springer. p. 128. ISBN 9781848829992. Retrieved 2011-02-17.
  2. ^ William A. Levinson (2007). Beyond the theory of constraints: how to eliminate variation and maximize capacity. Productivity Press. p. 97. ISBN 9781563273704. Retrieved 2011-02-17.
  3. ^ James A. Scott (1992). The Butterflies of North America: A Natural History and Field Guide. Stanford University Press. p. 103. ISBN 9780804720137. Retrieved 2011-02-17.
  4. ^ J. Knox Jones and Elmer C. Birney (1988). Handbook of mammals of the north-central states. University of Minnesota Press. pp. 52–55. ISBN 9780816614202. Retrieved 2011-02-17.
  5. ^ Environmental Modeling Center (2003-08-21). "NCEP Medium-Range Ensemble Forecast (MREF) System Spaghetti Diagrams". National Oceanic and Atmospheric Administration. Retrieved 2011-02-17.
  6. ^ Louise Bodri and Vladimír Čermák (2007). Borehole climatology: a new method on how to reconstruct climate. Elsevier. p. 76. ISBN 9780080453200. Retrieved 2011-02-17.
  7. ^ S. A. Thorpe (2005). The turbulent ocean. Cambridge University Press. p. 341. ISBN 9780521835435. Retrieved 2011-02-17.
  8. ^ Lonnie Wilson (2009). How to Implement Lean Manufacturing. McGraw Hill Professional. p. 127. ISBN 9780071625074. Retrieved 2011-02-17.
  9. ^ Rangaraj (2009). Supply Chain Management For Competitive Advantage. Tata McGraw-Hill. p. 130. ISBN 9780070221635.

http://www.ncl.ucar.edu/Applications/tigge.shtml TIGGE Project at NCAR

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