## 1 Introduction  
Geographic automata systems (GAS) are a longstanding theoretical framework used to represent and model complex dynamic geospatial phenomena [1]. Since its introduction, this spatiotemporal modeling paradigm continues to be an important research theme within the domain of geographic information science (GIScience) [2]. By portraying local-level processes and interactions, GAS models support investigation of change mechanisms that produce larger-scale patterns [3]. Additionally, their capacity to depict complex systems behaviors like non-linearity, emergence, and feedback loops offers benefits over top-down statistical modeling strategies [4]. Within the GAS paradigm, cellular automata (CA) and agent-based modeling (ABM) approaches are used for simulating local and individual dynamics, respectively, from the bottom up [1]. Of the two strategies, CA modeling is utilized extensively due to lower technical barriers, less stringent data requirements, flexibility of expression, and theoretical simplicity [5]. Such characteristics also make the paradigm more accessible to multi-disciplinary researchers beyond the field of geography. Geographic CA extends traditional elements of CA conveyed in earlier studies [6, 7] to include grids of regularly or irregularly spatially tessellated cells, discrete cell states, neighborhood functions, transition rules, and discrete increments of time [8, 9, 10, 11]. Simpletransition rules capturing local interactions are used to simulate the behaviors of geospatial systems [12], examine processes [10], explore \"what-if\" scenarios [13], assist in spatial and environmental planning endeavors [14], and support spatial decision-making practices [15]. The nature of CA also allows seamless integration of geospatial data acquired from remote sensing (RS) imagery or raster GIS datasets [16; 17]. Although adjustments to essential model elements can facilitate representation of innumerable real-world systems [6], studies leveraging the geographic CA paradigm mainly focus on land change simulation [2; 18], especially urban growth [3; 19; 20]. Even so, CA were successfully used to model a range of geospatial phenomenon like human epidemics [21], deforestation [22], forest fires [23], insect infestation [24; 25], snow cover extent [26], seismicity [27], sand dune dynamics [28], and avalanches [29]. However, the commonality among the listed pursuits is their programmed implementations. Whether code for each study is openly available or not, programming is a recurring barrier in developing CA models that are explicitly spatial and compatible with geospatial data. Several efforts to create standalone and integrated geographic CA modeling tools equipped with graphical user interfaces (GUIs) sought to expedate model implementation and reduce technical obstacles. Reflecting the dominant usage of geographic CA models in urban land change applications, numerous GUI-based CA software applications specialized for this phenomenon have included _SLEUTH_[30], _DUEM_[31], _UrbanSim_[32], _Metronamica_[33], and _FLUS_[34]. However, standalone CA tools typically encapsulate fragments of model-building activities, requiring tasks like data preparation, geospatial analyses, and cartography to be conducted using other solutions [35]. For example, previous studies employed ad hoc model-building procedures spanning multiple software and programmatic solutions [36; 37; 38]. To alleviate tool-switching bottlenecks, studies proposed new extensions or \"add-ins\" created for familiar GIS software environments. An early example of this is _SimLand_, which was created to extend Esri's ArcInfo software [39]. Since then, several works offered specialized extensions for Esri's ArcGIS Desktop software such as _iCity_[35], _GeoSOS_[40], _BNID-CA_[41], and _WDUNE_[28]. Other CA add-ins extended functionality of Idrisi Kilimanjaro [42] and QGIS [43] for modeling forest fires and land change, respectively.