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<!DOCTYPE html>

<head>

<title>GEO503: Spatial Data Science</title>

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<a class="navbar-brand" href="index.html">GEO 503: Spatial Data Science</a>

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<li>

</li>

<li>

<a href="Syllabus.html">Syllabus</a>

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<li>

<a href="Schedule.html">Schedule</a>

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Content

</a>

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<a href="CourseContent.html">About Course Content</a>

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<li class="dropdown-header">Module 1: Introduction to R</li>

<li>

<a href="00_CourseIntroductionFrame.html">00 Course Introduction</a>

</li>

<li>

<a href="01_Rintro.html">01 First Steps</a>

</li>

<li>

<a href="02_graphics.html">02 Graphics</a>

</li>

<li>

<a href="03_DataWrangling.html">03 Data Wrangling</a>

</li>

<li>

<a href="03b_DataWrangling.html">03 Data Wrangling 2</a>

</li>

<li class="dropdown-header">Module 2: Spatial Analyses</li>

<li>

<a href="04_Spatial_with_sf.html">04 Spatial Data with sf</a>

</li>

<li>

<a href="05_Raster.html">05 Spatial Raster Data</a>

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<li class="dropdown-header">Module 3: Advanced Programming</li>

<li>

<a href="06_CreatingWorkflows.html">06 Creating Workflows</a>

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<li>

<a href="07_Reproducibile.html">07 Reproducible Research</a>

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<a href="08_WeatherData.html">08 Weather Data Processing</a>

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<li>

<a href="09_RemoteSensing_appeears.html">09 Satellite Data Processing</a>

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<a href="11_ParallelProcessing.html">11 Parallel Processing</a>

</li>

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<a href="12_DynamicVisualization.html">12 Dynamic Visualization</a>

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<a href="13_SDM_Exercise.html">13 Species Distribution Modeling</a>

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Assignments

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Resources

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<a href="Projects_2017.html">2017 Final Projects</a>

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<a href="Resources.html">Resources</a>

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<a href="GitSSHNotes.html">Setting up Github</a>

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</div>

</div>

</div>

<h1 class="title toc-ignore">GEO503: Spatial Data Science</h1>

<h3 class="subtitle"><em>Introduction to Spatial Data Science with R</em></h3>

</div>

<p>Professor <a href="http://adamwilson.us"> Adam M. Wilson </a><br> Department of Geography, & Graduate Program in Evolution, Ecology and Behavior <br> University at Buffalo, Buffalo, NY</p>

<h2>Background</h2>

<p>The quantity and quality of data available for ecological and environmental research has exploded over the past few decades. These ‘big data’ now allow us to address important questions (both old and new) with unprecedented rigor and generality. Leveraging these new data streams requires new tools and increasingly sophisticated workflows. The free and open-source R programming language has become a lingua franca for ecological, epidemiological, and statistical research. The course will use a combination of lecture and hands-on exercises to provide a gentle introduction to programming in R with a focus on spatial data processing. The use of ‘literate programming’ (code embedded within text) to generate dynamic, reproducible research output (figures, manuscripts, websites, etc.) will also be addressed. The course includes an extensive project for students to conduct spatial analysis related to their research. Familiarity with basic GIS concepts (raster, vector, geographic projection, etc.) will be assumed, but no prior experience with R is necessary.</p>

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