## 4 Implementing Models with the Geographic Automata Add-In: Two Case Studies
### Case Study 2: Comparing ML-CA Models of Urban Growth
#### 4.2.2 Model Implementation  
Binary cell states are specified as follows: (0) non-urban areas and (1) urban areas. Training ML Models and Generating Change Probability Maps  
For the ML subroutine, the training label is created by reclassifying the land use data for 2010 (\(T_{1}\)) such that urban areas and non-urban areas are signified by ones and zeros, respectively. Next, the training dataset imbalance is addressed by retaining equal numbers of changed and persistent samples [73]. Of the 352,803 cells comprising the study area, \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Description** & **Data Source** \\ \hline \multirow{3}{*}{**Land Use Data**} & 2000 (Initial) & \\  & 2010 (Calibration) & AAFC Land Use \\  & 2020 (Validation) & \\ \hline \multirow{6}{*}{**Driving Factors**} & (1) Current land use type & AAFC Land Use \\ \cline{2-3}  & (2) Elevation & ASTER Digital Elevation Model \\ \cline{2-3}  & (3) Slope & \\ \cline{2-3}  & (4) Euclidean distance to railways & \\ \cline{2-3}  & (5) Euclidean distance to streets & BC Data Catalogue \\ \cline{2-3}  & (6) Euclidean distance to highways & \\ \cline{2-3}  & (7) Euclidean distance to conservation areas & \\ \cline{2-3}  & (8) Euclidean distance to parks & \\ \cline{2-3}  & (9) Euclidean distance to commercial areas & Township of Langley Open Data Portal \\ \cline{2-3}  & (10) Euclidean distance to industrial areas & \\ \cline{2-3}  & (11) Euclidean distance to institutional areas & \\ \cline{2-3}  & (12) Euclidean distance to rivers & Government of Canada Open Data Portal \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Datasets supplying initial, calibration, validation, and driving factor information. Figure 12: An overview of the ML-CA comparative modeling procedure. 46,825 cells transitioned to urban between 2000 and 2010. To create the balanced dataset, all changed samples are included in the training dataset, while 46,825 persistent cells are randomly sampled from non-urban unchanged locations. The final training dataset contains 93,650 samples. The LR and RF models are implemented in an ArcGIS Pro Notebook calling on scikitlearn functionality, while the ensemble model is executed using the graphical AutoML tool available in ArcGIS Pro's GeoAI toolbox. All models are trained with 80% of the balanced training dataset, while 20% is withheld for ML validation purposes. Next, the LR, RF, and AutoML models are applied to estimate the urban development probability values across the study area. Model Types and Transition Rule Specification  
Four model types are configured using the _Basic CA_ tool: LR-CA, RF-CA, AutoML-CA\({}_{1}\), and AutoML-CA\({}_{2}\). Each model type differs in the _Allocation and Quantity_ rule implementation shown in Table 5. Additionally, the AutoML-CA\({}_{2}\) configuration implements an alternative ML-CA model structure using demand limits instead of setting a minimum suitability value [94]. Model Execution  
Using the _Basic CA_ tool, the initial raster layer represents urban areas in the year 2000 (\(T_{0}\)). The number of timesteps is set to two, where one iteration represents a 10-year temporal interval. The actual datasets for \(T_{1}\) and \(T_{2}\) are used for CA model calibration and validation, respectively. Model Evaluation  
In CA model calibration, the goal is to adjust model parameters to reduce differences between simulated outcomes and observed data. In model validation, simulated outcomes are compared to real-world data independent from model calibration. Using the _Model Evaluation_ tool, agreement of real and simulated maps is quantified for calibration and validation stages. To calibrate the model in this case study, the FOM value is primarily used to determine agreement of changed locations. This three-map comparison measure requires an initial raster layer, a reference layer, and a simulated layer.