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Selecting coefficient ranges from final calibration

This is an example of one decision algorithm for selecting a coefficient set from a final calibration of Demo_city using the demo200 input images.

Using file control_stats.log:

  1. Sort file in descending order using the Lee Sallee metric
  2. For the top score: select the corresponding coefficient values
    It is possible for more than one run to have the same score, creating a tie. In this case select the lowest value.
  3. For each coefficient: in the scenario file to be used to derive forecasting coefficients, using the best performing coefficient value, set the _START and _STOP values.
  4. For each coefficient: set the _STEP value to one (1).

Top 3 scores from demo200 final calibration, sorting only on the Lee Sallee metric:

sort value
initial coefficient values
Run Product Compare Pop Edges Clusters Cluster
Size
Leesalee Slope %Urban Xmean Ymean Rad Fmatch Diff Brd Sprd Slp RG
696 0.09178 0.70813 0.9958 0.99999 0.62293 0.92098 0.50055 0.965 0.9846 0.65794 0.99831 0.99859 0.7273 1 3 11 87 1
697 0.09178 0.70813 0.9958 0.99999 0.62293 0.92098 0.50055 0.965 0.9846 0.65794 0.99831 0.99859 0.7273 1 3 11 87 10
698 0.09178 0.70813 0.9958 0.99999 0.62293 0.92098 0.50055 0.965 0.9846 0.65794 0.99831 0.99859 0.7273 1 3 11 87 20
435 0.11017 0.69032 0.9969 0.99991 0.62174 0.95949 0.49951 0.91687 0.98605 0.89465 0.91054 0.99921 0.7301 1 2 11 81 30
933 0.10363 0.77654 0.99716 1 0.5881 0.85638 0.49902 0.95635 0.98816 0.78703 0.98132 0.99911 0.73023 1 4 11 78 30

Coefficient values used to derive forecasting coefficients:

coefficient type {_START - _STOP, _STEP}
dispersion {1 - 1, 1}
breed {3 - 3, 1}
spread {11 - 11, 1}
slope {87 - 87, 1}
road gravity {1 - 1, 1}