As an example, we’ll use the Global Potential Vegetation Dataset from the UW-Madison Nelson Institute Sage Center for Sustainability and the Global Environment. In a previous post, we’ve discussed importing GIS data into a NetLogo model, but of course this depends on our ability to get the data into a model-readable format. The following tutorial is going to walk through the process of getting a NetCDF file, manipulating it in R, and then getting it into NetLogo.įirst let’s locate a useful NetCDF dataset and import it to R. ArcGIS and QGIS support them, but it can be difficult to work them into simulations without converting to a more benign data type like an ASCII file. They’re great for managing lots of spatial data, but if you’ve never dealt with them before, they can be a bit of a bear to work with. It’s also a convenient way to keep track of data obtained from satellite measurements over time. Folks who work with climate and earth systems tend to store their data this way. These chunks of data can then be accessed as combined spatial coverages over time. Three variables are represented this way, and these are stored together in a single time step. The actual structure of the file might be simpler (that is, it might consist of a single variable and/or single time step) or more complex (with many more variables or where each variable is actually a set of coverages representing a range of values for that variable imagine water temperature readings taken at a series of depths). In this diagram, each table represents a gridded spatial coverage for a single variable. To make this a little more clear, here’s a diagram: The basic structure of a NetCDF file A NetCDF file (which stands for Network Common Data Form) is a multidimensional array, where each layer represents the spatial gridded distribution of a different variable or set of variables, and sets of grids can be stacked into time slices. NetCDF is one of the more notorious of these. Some government agency or environmental science department put together some brilliant time series GIS package and you want to find a way to import it into your model. But oftentimes the data may be in a format which isn’t readable by your modeling software, or takes some finagling to get the data in there. You found it: the holy grail of palaeoenvironmental datasets. UPDATE 22 SEPT 2016: Please see the updated version of this tutorial here. And as a bonus, it’s an opportunity to visit the Pfund Dresdener Molkerei: the most beautiful dairy in the world!įor more information or to apply, visit TUD’s course webpage. This is a great opportunity to learn from some well-known (and published!) folks in the field: Uta Berger, Steven Railsback, and Volker Grimm. The goals of this course seemed to be aimed less at teaching technical skills than productively making use of them. Of course, you could use some of the materials in our tutorials section to get up to speed. The course makes use of two software packages discussed regularly on this site, NetLogo and R, and you would be expected to have some familiarity with NetLogo. The course will provide a short introduction to model development and implementation, but its focus is on the steps after your first model version is running. This course is intended primarily for grad students and researchers who have at least an early version of their model and seek guidance on finishing, analyzing, and doing science with it. Guidance for publishing will be provided by instructors who edit prominent modeling journals. Analysis topics include debugging, parameterization, sensitivity analysis, and robustness analysis. Model application topics include handling uncertainties in data, designing simulation experiments, and statistical analysis of results. The course will develop skill and experience in the analysis and application of individual- and agent-based modeling to scientific problems. Looking for ways to make the most of your models? The Technical University of Dresden (TUD) is hosting a 9-day course called “ Learning and Gaining from your Model: A Course in Analysis, Application, and Publication of Individual/Agent-Based Models”, to be held in the eastern mountain town of Holzhau.
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