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Class 1

Intro to Spatial Data Science

GEOG 215

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AGENDA

Today’s Class …

  • Instructor Information
  • Syllabus & Logistics
  • Software requirements
  • File and Folder Organization
  • Introduction to Spatial Data

During Class …

  • Download Software
  • Set up Folder Structure for Class

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Next Class …

  • Introduction to R

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Your Instructor

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  • 5th year PhD student
  • Research the impacts of weather-related road closures

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Course Logistics

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Meeting Time

M-F 9:45am-11:15am

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Instructional Format

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Options: in-person, remote synchronous

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Classroom or Zoom

Carolina Hall 220 or https://unc.zoom.us/j/95500549755

Office Hours

T, TH 11:45-1:30, other days by appointment

Coates Building (across from planetarium) 201B

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GEOG 215

After taking this course, students will be familiar with using the programming language R for exploring spatial data. By the end of the semester, students will be able to:

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  1. Develop spatially oriented research questions.
  2. Identify different types of publicly available spatial data and upload these data into R.
  3. Clean and organize spatial data for analysis.
  4. Perform and interpret exploratory spatial data analysis techniques.

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GEOG 215

  • R based class
    • Programming is a highly valuable skill
    • Helps you think logically
    • Increases reproducibility
    • Will make you more efficient
  • Math and statistics
    • Necessary for effective use of data
  • Data visualization
    • Focus on effective data visualization

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Website and Canvas Walkthrough

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File Organization and Data Management

  • Data science requires you to be VERY comfortable with reading, processing, and writing data
  • A huge portion of learning this is learning how to manage and organize data

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Download Software

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Set Up Folder Structure

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Lecture 1

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What is Data Science?

Two definitions to consider

    • Data science is using data to gain information or knowledge about the world
    • Data science is the development of methods and techniques to extract information or knowledge from data

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What is Data Science?

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    • facts and figures

Data

    • data organized such that it is useful

Information

    • accumulated and integrated information on a topic over a period and across a broad range of situations

Knowledge

    • application of universal principles, reason, and knowledge to discern what is true and right

Wisdom

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What is Data Science?

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What is Spatial Data Science?

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Focus on observations/data that are georeferenced to a location on Earth's surface

    • Have a defined location
    • Focus on answering and understanding spatial/geographic questions, e.g.,
        • Where?
        • Why there?

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Components of Spatial Data

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    • Often defined in 2-D space, but can be 3-D

Location

    • Some measurable or observable property

Attribute

    • Often a snapshot, but may include variable time

Time

    • Describes the data and assumptions

Metadata

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Spatial Data

  • Course will focus data that can be stored in tabular format
  • Rows are the observations
    • Every row is unique
    • Also called records
  • Columns contain attributes for each observation
    • Also called fields or variables

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Spatial Data Types

  • Raster
    • Cells
  • Vector
    • Points, lines, polygons

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Observation Units

  • What does each observation represent?
  • For example:
    • An individual
    • A county
    • A city
    • A census tract
    • A facility
    • A cell

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