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Choices and Principles
Q: Why does Forest Foresight work on a 0.004°x0.004° (about 400x400m) resolution
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A: An Early Warning System is used as a general term in two instances: to alert before an event happens or to alert as soon as possible when an event has happened. Though the term Early Warning System is used way more often than our Predictive Warning System we have experienced that the term is mostly used to describe the latter (report as soon as possible after the fact). By changing the name of our System we want to differentiate ourselves from that.
Technical Questions
Q: what hardware specifications are required to run the Forest Foresight package?
A: Since we use R we almost never have memory issues. Regardless we would advise to have at least 16GB of RAM to process an average-sized country (32-64GB for a country like Brazil). No GPU acceleration is used so not important. Any moderately sized CPU will do (i5-i7). Storage depends on the area size but for an average-sized country you need about 2GB of storage space. Note that this is only required for training, for predicting using an existing model you can use almost any computer.
Q: Why was R chosen as a scripting language for Forest Foresight?
A: In our experience it is very easy to package and install an R package, as opposed to for instance a python module with all its conflicting dependencies. We wanted to prevent the need of containerization because this would also increase the required level of programming skills needed to run the package.
Q: Why does the Forest Foresight package not have a GUI?
A: We find it more important that the user has full control over the scripts to make it their own than to give the easiest experience possible. For non-technical people we provide the pre-made predictions monthly that can be downloaded with a GUI. Do note that it requires very little scripting skills to get started with building and running a model, as explained here.
Q: Do you need to have scripting skills to add new features?
A: No, our package was built data-agnostic. In our case this means that the process will fetch all data from the data input folder, which does not require changing any of the functions or code of Forest Foresight.