Geospatial Automation Scripting Vs Modelbuilder

Describe a situation when a geospatial professional would automate using a script rather than use a tool like ModelBuilder.

Geospatial professionals use two fundamental methods to automate geoprocessing tasks: ModelBuilder and scripting. ModelBuilder allows a geospatial professional to visually create, run, and edit workflows by dragging and dropping variables, data, and tools, and using arrows to show interconnections between inputs and outputs (Zhou, 2021). On the other hand, scripting entails writing codes using programming languages such as Python to access and run geoprocessing functions (Zhou, 2021). Each method has its share of advantages and disadvantages. A primary advantage of the Modelbuilder is that it is visual, and thus clearly shows the workflow structure and logic (Zhou, 2021). Moreover, it is simple to use and can easily be used by people with little coding knowledge (Zhou, 2021). However, there are situations where a geospatial professional may prefer to use script rather than ModelBuilder in their automation.

One such situation is when the professional is dealing with a complex non-deterministic model that does not directly flow from inputs to outputs (De Smith et al., 2018). A deterministic model is characterized by a well-defined input-process-output flow, where the result is specific and generated through the same standard process (De Smith et al., 2018). Conversely, non-deterministic models are more complex and are often characterized by repetitive processes (iterative processes), where outputs from one iteration become inputs for subsequent iterations, resulting in multiple outcomes (De Smith et al., 2018). For instance, a geospatial professional may be interested in developing a model that predicts areas of likely wildfire. Using the FlamMap wildfire modeling system, the model may take on a range of inputs, including crown bulk density, canopy height, canopy cover, fuel model, aspect, slope, and elevation, among others (De Smith et al., 2018). These inputs are then combined with other optional components, including wind files, weather, and fuel moisture level (De Smith et al., 2018). Within the model, a combination of other models are applied. These use different input combinations to produce outputs that are then used as inputs in subsequent processes to yield outcomes that inform fire management planning and impact/risk assessments (De Smith et al., 2018).