This module explores the use of social media data - specifically Twitter data to better understand the social impacts and perceptions of natural disturbances and other events. Working with social media requires the use of API's to access data, text mining to extract useful information from non-standard text and then finally analysis using text-mining workflows
Leah WasserLeah Wasser has contributed to the materials listed below. Leah is the director of the Earth Analytics Education Initiative at Earth Lab and maintains this website.
Course material modules are sets of materials developed to teach specific learning objectives in a course setting.
In this module, we introduce various ways to access, download and work with data programmatically. These methods include downloading text files directly from a website onto your computer and into R, reading in data stored in text format from a website, into a data.frame in R and finally, accessing subsets of particular data using REST API calls in R.
This module will overview the basic principles of DRY - don't repeat yourself. It will then walk you through incorporating functions into your scientific programming to increase efficiency, clarity, and readability.
In this module we will learn more about dealing with clouds, shadows and other elements that can interfere with scientific analysis of remote sensing data.
This tutorial set covers some basic things you can do to refine your plots in Rmarkdown document. It covers plotting in grids, adding titles to plotRGB() plots and refining the width and height of plots to optimize space.
This teaching module overviews the use of spectral remote sensing data to better understand fire activity. In it we will review spectral remote sensing as a passive type of remote sensing and how to work with space-borne vs airborne remote sensing data in R. We cover raster stacks in R, plotting multi band composite images, calculating vegetation indices and creating functions to make the processing more efficient in R.
In this module, we will discuss the concept of uncertainty as it relates to both remote sensing and other data. We will also explore some metadata to learn how to understand more about our data.
Learn how to create maps with custom colors and legends in both base R and with ggplot in R.
This tutorial covers the basic principles of LiDAR remote sensing and the three commonly used data products: the digital elevation model, digital surface model and the canopy height model. Finally it walks through opening lidar derived raster data in R / RStudio
This module introduces the raster spatial data format as it relates to working with lidar data in R. We will cover how to open, crop and classify raster data in R. Also we will cover three commonly used lidar data products: the digital elevation model, digital surface model and the canopy height model.
This tutorial covers the basic principles of lidar remote sensing and the three commonly used data products: the digital elevation model, digital surface model and the canopy height model. Finally, it walks through opening lidar derived raster data in R / RStudio
This module covers using ggmap to create basemaps in r / rmarkdown and how to overlay raster data on top of a hillshade.
This module covers how to work with, plot and subset data with date fields in R. It also covers how to plot data using ggplot.
This module introduces the R scientific programming language. We will work with precipitation and stream discharge data for Boulder County to better understand the R syntax, various data types and data import and plotting.
This module covers how to write easier to read, clean code. Further is covers some basic approaches to getting help when working in R. Finally it reviews how to install QGIS - a free and open source GIS tool - on your computer.
This module uses time series data to explore the impacts of a flood. Learn how to use Google Earth imagery, NOAA precipitation data and USGS stream flow data to explore the 2013 Colorado floods.
This module reviews how to use R Markdown and knitr to create and publish dynamic reports that both link analysis, results and documentation and can be easily updated as data and methods are modified / updates.
This module walks you through getting R and RStudio set up on your laptop. It also introduces file organization strategies.
A hands-on activity where students review a project for readability, organization, etc and identify key elements that would make it more usable and readily reproducible.