## 4 Implementing Models with the Geographic Automata Add-In: Two Case Studies
### Case Study 1: Multi-Class CA Modeling of a Forest Insect Infestation
#### 4.1.3 Results  
The model execution time for each scenario was 31 to 36 s. The respective outcomes are displayed in Figure 10. Rule types defining Scenarios 1 through 3 are exclusively generative, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rule ID** & **Rule Type** & **Purpose** & **Transition Rule Description** & **Scenario Application** \\ \hline \multirow{4}{*}{A} & \multirow{4}{*}{_Neighborhood-Based_} & & _IF_ 15 to 122 infested cells are & \\  & & Propagating the & within a 11 \(\times\) 11 circular & \\  & & hypothetical insect & neighborhood of an uninfested & \\  & & infestation to nearby areas. & cell, _THEN_ the central cell has an & \\  & & & 80\% chance of transitioning to & \\  & & & light-moderate infestation. & \\ \hline \multirow{4}{*}{B} & \multirow{4}{*}{_Cell-Level_} & & _IF_ a cell is currently undergoing & \\  & & Randomly increase & light-moderate infestation, & \\  & & infestation severity. & _THEN_ the cell has a 20\% chance & \\  & & & of becoming severely infested. & \\ \hline \multirow{4}{*}{C} & \multirow{4}{*}{_Neighborhood-Based_} & & _IF_ 4 to 25 cells are undergoing & \\  & & Hypothetical “infilling” & severe infestation within the & \\  & & behavior, where new & 5 \(\times\) 5 circular neighborhood of a & \\  & & infestation occurs within & light-moderate infested location, & \\  & & gaps between existing & _THEN_ the central cell has a 100\% & \\  & & patches. & chance of becoming severely & \\  & & & infested. & \\ \hline \multirow{4}{*}{D} & \multirow{4}{*}{_Stochastic Disturbance_} & & Short-distance dispersal & _IF_ an uninfested cell is located & \\  & & behavior, where MPB & within 1 and 9 cells (up to 270 m) & \\  & & flights can occur randomly & _THEN_ the uninfested cell has a & \\  & & up to a specified distance. & 10\% chance of becoming a & \\  & & & light-moderate infestation. & \\ \hline \multirow{4}{*}{E} & \multirow{4}{*}{_Allocation and Quantity_} & & _IF_ a potential new infestation & \\  & & Imposing an arbitrary & location is among the 10,000 & \\  & & quantity limit on & most susceptible locations, & \\  & & infestation spread. & _THEN_ the cell is permitted to & \\  & & & transition; _ELSE_, the cell & \\  & & & maintains its previous state. & \\ \hline \multirow{4}{*}{F} & \multirow{4}{*}{_Allocation and Quantity_} & & _IF_ a potential new location for & \\  & & Imposing an arbitrary & severe infestation corresponds & \\  & & minimum susceptibility & with a susceptibility value of 0.1 & \\  & & value required for a & or higher, _THEN_ the cell is & \\  & & location to host severe & permitted to transition; _ELSE_, & \\  & & infestations. & the cell maintains its previous state. & \\ \hline \multirow{4}{*}{G} & \multirow{4}{*}{_Constraint_} & & _IF_ a potential new infestation & \\  & & Infestation locations are & location is not located within a & \\  & & prevented from & restricted area, _THEN_ the cell is & \\  & & propagating to water & permitted to transition; _ELSE_, & \\  & & bodies and rivers. & the cell maintains its previous & \\  & & state. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Transition rules used to parameterize MPB infestation scenarios. with hypothetical MPB dispersal and intensification propelled with _Neighborhood-Based_, _Cell-Level_, and _Stochastic Disturbance_ rule types. Meanwhile, Scenarios 4 and 5 introduce limiting or refining mechanisms via the _Allocation and Quantity_ and _Constraint_ rules. The simulated outcomes for Scenarios 4 and 5 also present the capacity of the _Geographic Automata_ add-in to incorporate auxiliary datasets like susceptibility maps and constraints during model execution. For instance, the insect infestation simulation outcomes of Scenario 5 are prevented from spreading to locations with water bodies or rivers (Figure 11e). After obtaining model outputs, other data layers pertaining to constraints or other relevant layers can be overlayed with simulation outcomes using ArcGIS Pro's existing geoprocessing functions.