## 5 Discussion  
This study introduces the first version of the _Geographic Automata_ add-in for ArcGIS Pro. The add-in was developed to address the lack of general-purpose GUI-based CA technologies available in contemporary GIS software. Two case studies illustrated how the add-in (1) provides general-purpose functions capable of supporting binary or multi-class CA models of various geospatial systems and (2) facilitates end-to-end model-building activities that can be intertwined with ArcGIS Pro functionality. In the first case study, five MPB infestation scenarios presented the incremental effects of combining and layering different transition rule types to implement a multi-class CA model. The various scenario settings show the capabilities of the add-in tools to support as many generative or limiting rules as a user decides. For instance, each scenario applied additional rule mechanisms that produced increasingly complex spatial patterns. The outcomes demonstrate the flexibility of transition rule types for use with real-world geospatial datasets and for capturing the characteristics of documented insect infestation behavior. For example, Scenario 3 showed a possible integration of behaviors drawn from previous studies, while Scenarios 4 and 5 exemplified how dispersal could be guided with an expert-derived susceptibility map and real-world physical constraints. Although some previous studies describing CA models of MPB infestation have excluded detailed information about the software or programming required to implement model behaviors described [55], the _Geographic Automata_ add-in tools enable researchers to encode documented dispersal  
Figure 14: Error components of simulated urban areas for 2020 obtained from the _Model Evaluation_ tool report. mechanisms or investigate other possible processes that propel changes observed over space and time. The second case study demonstrates an end-to-end model-building activity involving built-in ArcGIS Pro functionality and _Geographic Automata_ add-in tooling to compare the effects of ML-generated transition potential maps on CA model outcomes. The comparison centered on comparing urban growth forecasts of traditional ML-CA models with those produced by a novel AutoML-CA approach. The simulated outputs of each model type adhered to trends reported in scientific literature using ML-CA routines [38; 50]. However, both studies relied on patchworks of software and custom programmed functionality to implement and execute ML-CA routines. For example, [PERSON] and [PERSON] [38] relied on an assortment of tools including Esri's ArcMap, the Dinamica EGO software, various ML packages available in R, and the Map Comparison Kit. The methodology presented by [PERSON] et al. [50] similarly included ArcMap, a standalone NN-based land transformation modeling tool, and other ML models coded with MATLAB, R, and Java. In contrast to the mosaics of disparate tooling described in preceding studies, the second case study demonstrates the capacity of the _Geographic Automata_ add-in to support a simple, streamlined model-building workflow within an established GIS software. The presented workflow shows the capability of the _Geographic Automata_ add-in tools to support researchers in implementing rapid comparisons of CA models enhanced with outcomes of other analyses. At this point, there are numerous possible trajectories for enhancing and extending the inaugural version of the _Geographic Automata_ add-in. Several enhancements of existing CA functionality could include adding a transition rule fallback routine, implementing support for dynamic variables and rule applications at specific temporal intervals, adding explicit distance decay or weighted neighborhood function options, conducting more rigorous performance benchmarking, expanding model parameter reports and rule tracing options, and improving UI components. As tool development progresses, so should a library of technical resources such as video tutorials and documentation web pages for researchers, educators, and students.