B. Krzyzanowski |
teaching resources & course materials
Spatial Data in r
Course Overview: We are living in a Big Data Era and finding that there is a deluge of data available for exploring questions about our world. With this much data comes a heightened need for efficient data management practices, powerful processing techniques, and streamlined analytical and visualization strategies. Working with vast quantities of complex spatial data has become commonplace within geographical information science (GIS). This course will introduce students to basic data management techniques that can be used to process Big Spatial Data within R Project (an open-sourced data management and statistical software). The data manipulation tasks that students tackle in this course will likely not be anything new to them. Lab assignments cover basic data transformation tasks such as collapsing tables, converting data types, and performing spatial joins. Most of these tasks can be easily accomplished outside of R with software such as ArcMap or Excel. However, when working with multiple inputs that contain thousands of records, these such tasks become impractical in standard GIS platforms. A well-written script in R can handle these tasks with just a click of a button.
Goals and Outcomes: Students who successfully complete this course will be able to use, manipulate, and visualize spatial data within R and apply these scripts to most big spatial datasets.
Structure: Each week we will focus on a few common data analysis or manipulation techniques. Most weeks, the data manipulation tasks will be related to one another (such as creating maps and saving images), but other weeks the tasks will seem quite disparate (such as exporting tables and rescaling variables). Optional readings and video lectures will be made available on Canvas on the day that the Lab assignments are released (usually on Wednesdays). Online discussion forums will be assigned 4 times during the semester, occurring on the weeks between Lab assignments. Discussion forums will become active on Thursdays mornings and close on Friday evenings (discussion forums are live for 36 hrs). Online office hours are held twice weekly. Overall, this course primarily necessitates independent learning with a few instances of collaborative discussion forum-based activities. The Lab assignments will require trial and error on your part which may exact much frustration. A successful student will be curious, motivated, and patient in the face of this frustration.
Goals and Outcomes: Students who successfully complete this course will be able to use, manipulate, and visualize spatial data within R and apply these scripts to most big spatial datasets.
Structure: Each week we will focus on a few common data analysis or manipulation techniques. Most weeks, the data manipulation tasks will be related to one another (such as creating maps and saving images), but other weeks the tasks will seem quite disparate (such as exporting tables and rescaling variables). Optional readings and video lectures will be made available on Canvas on the day that the Lab assignments are released (usually on Wednesdays). Online discussion forums will be assigned 4 times during the semester, occurring on the weeks between Lab assignments. Discussion forums will become active on Thursdays mornings and close on Friday evenings (discussion forums are live for 36 hrs). Online office hours are held twice weekly. Overall, this course primarily necessitates independent learning with a few instances of collaborative discussion forum-based activities. The Lab assignments will require trial and error on your part which may exact much frustration. A successful student will be curious, motivated, and patient in the face of this frustration.
BrittanyKrzyzanowski© 2021
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