Geohackweek Tutorials for Freshwater Research and Education

Freshwater research using large data with computationally intensive analyses is an area of ongoing development.  CUAHSI staff and University of Washington Freshwater researchers are partnering with eScience Geohackweek organizers  to provide ongoing access to tutorials developed for Geohackweek 2017, as well as helpful links available on HydroShare Help pages.  [sentence about content and how to contribute here].

Geohack Python Basics

Introduction to Conda  What is Conda?  How can I use conda in managing all the libraries for my projects? (Don Setiawan, Applied Physics Laboratory, University of Washington)

Getting Started with Git     What is Git and Github? Why are we using Git? (Ben Weinstein, Oregon State University)

Cloud Computing in Geospatial Sciences     How can cloud computing help (Amanda Tan, eScience Institute, UW IT.)

Cloud Computing on HydroShare        Use a NSF sponsored platform service (Tony Castronova, CUAHSI)

Introduction to JupyterHub         What is JupyterHub? (Amanda Tan eScience Institute, UW IT, Catherine Kuhn, 

School of Forest & Environmental Sciences, University of Washington)

Introduction to Docker   How to use Docker? (Amanda Tan, eScience Institute, UW IT.)

Vector Data Processing using Python Tools

Contacts: Emilio Mayorga and Don Setiawan, Applied Physics Laboratory, University of Washington

Create a Vector Environment on your computer

Geospatial Concepts  What is ‘vector’ geospatial data all about?

Encodings, Formats and Libraries What are common ways to encode vector geospatial data in Python, and how do they relate to broader encoding standards?

GeoPandas Introduction  What is GeoPandas? What functionality and advantages does GeoPandas offer over other Python geospatial tools? What geospatial storage, analytical and plotting capabilities does it include? What is its relationship to Pandas?

GeoPandas Advanced Topics  What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features?

Raster Processing with Python Tools

Contacts: James Douglass, Natural Capital Project & David Shean, Applied Physics Laboratory, University of Washington

Introduction: Working with Raster Data  

Geospatial Concepts: Raster Data  What is a raster? What sorts of information does a raster typically model? What are the major characteristics of a raster dataset? What assumptions does the format imply?

Encodings, Formats and Libraries  What sorts of formats are available for representing raster datasets?

Working with Raster Datasets   How can I extract pixel values from a raster dataset? How might I write pixel values out to a new raster file?  What raster dataset formats (reading and writing) are supported?

Rainier DEM Example  How can I work with rasters from different sources, with different projection, extent, resolution, etc.?

Google Earth Engine  Jill Deines Earth and Environmental Sciences, Michigan State University & Catherine Kuhn, School of Forest & Environmental Sciences, University of Washington