Calibration Mode Process Flow

Calibration is the most complex of the different mode types. Each coefficient set combination created by the coefficient START_, STOP_ and STEP_ values will initialize a run (R). Each run will be executed MONTE_CARLO_ITERATIONS number of times. The RANDOM_SEED value initializes the first monte carlo simulation of every run.

The run initializing seed value is set in the scenario file with the RANDOM_SEED flag. The number of monte carlo iterations is set in the scenario file using the MONTE_CARLO_ITERATION flag. Coefficient sets are defined in the scenario file with the CALIBRATION_* flags, where "*" indicates a coefficient type.

Several statistic (*.log) and image files may be generated in calibrate mode by setting preferences in the scenario file. However, due to the computational requirements of calibration, it is recommended that these write flags are set to OFF. Instead, once a few top coefficient sets are identified, statistics and image files for these runs may be generated in test mode. For a description of mode output see our data page.


Initial Conditions

Each run of a calibration job is initialized with a permutation of the coefficient ranges. Each run will be executed MONTE_CARLO_ITERATIONS number of times. The first monte carlo of each run is initialized with the RANDOM_SEED value. After a simulation is completed, the initializing seed that began that simulation is reset and a new monte carlo simulation is run. This process continues MC number of times. When the number of monte carlo iterations for that run has been completed, a coefficient value will be incremented and a new run initialized. This will continue until all possible coefficient permutations have been completed.


Generate Simulations

It is assumed that one growth cycle represents a year of growth. Following this assumption:

number of growth cycles in a simulation = stop_date - start_date.

As growth cycles (or years) complete, time passes. When a cycle completes that has a matching date from the urban input layers, a gif image of simulated data is produced and several metrics of urban form are measured and stored in memory. When the required number of monte carlo simulations has been completed the measurements for each metric are averaged over the number of monte carlo iterations (see avg.log). These averaged values are then compared to the input urban data, and Pearson regression scores are calculated for that run. These scores are written to the control_stats.log file and used to assess coefficient set performance.


Conclude Simulation

When the required number of growth cycles has been generated, the simulation concludes.