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When & Where
Date: 
Fri, January 31, 2020 - 1:00 PM to 4:00 PM
Location: 
D-Lab Convening Room, 356 Barrows
Description
Type: 

Geospatial data are an important component data visualization and analysis in the social sciences, humanities, and elsewhere. The R programming language is a great platform for exploring these data and integrating them into your research. 

Geospatial Data in R, part 3: Working with raster data

Part three of this three-part workshop series will introduce tools and approaches for working with raster data. Raster data are used to represent geographic phenomena that are present and measurable anywhere in a study area, like elevation, temperature, rainfall, land cover, soil type, etc. These data are a valuable resource for social scientists, planners, and engineers, as well as natural scientists. This workshop will introduce basic raster concepts and methods for working with raster data in R. Participants will learn how to import and store raster data as spatial objects. We will explore methods for plotting rasters and manipulating raster data values. Basic methods of raster and raster-vector spatial data analysis will also be introduced. Additionally, the workshop will review coordinate reference systems and methods for reading, defining and transforming these with raster data.

Knowledge Requirements: Basic knowledge of geospatial data is expected. R experience equivalent to the D-Lab R Fundamentals workshop series is required to follow along with the tutorial.

Technology Requirements: Bring a laptop with R, RStudio and the following R packages installed: sp, rgdal, rgeos, raster, ggplot2 and tmap.

Training Keywords: 
Geospatial Data, Maps and Spatial Analysis
Primary Tool: 
R
Details
Training Host: 
D-lab Facilitator: 
Patty Frontiera
Format Detail: 
Hands-on, interactive
Participant Technology Requirement: 
Laptop

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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