The Toolbox requires data to be in a specific format. Some of this formatting happens in the data preparation phase within the data preparation subfolder or repo, and some of this formatting is done by the Toolbox.
ohicore functions involved in this automated data formatting include [ ]. For more details on usage and workings of these functions, see the Reference pages.
Finalized datasets from the data preparation phase are saved to the ‘layers’ subfolder in csv format. These contain two or three columns: region identification number (rgn_id) and a column with the data value which will be labeled differently depending on what dimension (pressure, trend, resilience) the data layer relates to, and possibly a year column if multiple years are to be used in the relevant goal model(s).
Once created, prepared data layers must be registered in the the layers.csv spreadsheet. Each row in layers.csv represents one data layer. The 8 columns that must be updated are [ ]. The information in this spreadsheet directs ohicore to appropriate data layers during calculations.
What datasets to include depends on the models developed for the OHI assessment, and coded in the
functions.R script. Deciding on relevant data to include is a significant and time-consuming part of completing an OHI assessment. Considerations regarding whether it makes sense to include a dataset: (1) scientific, sociological or economic conceptual relevance and a signal distinguishable from background noise or confounding factors, (2) data-completeness sufficient that gapfilling can be done in a reasonably realistic manner, and in some cases (3) redundancy or multicollinearity of variables. The team must also consider whether the data fits ideal spatial boundaries for the assessment. We encourage stakeholder and expert engagement on OHI+ teams; the core OHI team is also happy to provide consultation on these matters.