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  • About DS Learn
  • Tutorials
    • ¶ Digital Exhibits
      • Getting Started with Digital Exhibits
        • Considerations
        • Basic Steps
          • Site Organization
          • Usability & Accessibility
        • Platforms
    • ¶ Digital Storytelling
      • Introduction to ArcGIS StoryMaps
        • Getting Started
        • Using Content Blocks
        • Importing Maps from David Rumsey
      • Introduction to KnightLab StoryMap JS
      • TimelineJS
    • ¶ 3D Modeling & Immersive Technology
      • Adding 3D Models in Omeka
      • Intro to Photo Processing with Agisoft Metashape for 3D Model Making
      • Tips and Tricks for Taking Photos for 3D Model Creation
      • An Introduction to Apple's Reality Composer AR
      • Importing SketchFab Models into AR for the iPad or iPhone
      • Creating Basic 3D Objects for AR in Blender
      • Introduction to Meshlab
    • ¶ Data Visualization
      • Introduction to Tableau
        • Download and Install Tableau
        • Using Tableau to Visualize COVID-19 Data
        • Tableau DH
        • Resources
      • Beyond Simple Chart in Tableau
        • Beyond Simple chart Examples
      • Google Colab
        • Get Started
        • Data Import
        • Data Wangling
        • Visualization
        • Results Export
      • Out of Box Data Visualization Tools
        • How to use Google Data Studio with Google Sheets
        • Google Data Studio Interface
        • Creating Visualizations in Google Data Studio
    • ¶ Mapping
      • Tiling High-Resolution Images for Knightlab StoryMapJS
      • Hosting and Displaying Zoomable Images on Your Webpage
      • Georectifying Historical Maps using MapWarper
      • Making a Starter Map using Leaflet
    • ¶ REST API
      • How does REST API work?
      • JSON File
      • Get Started with Google Sheets Script Editor
      • Example 1: Extract Data by One Cell
      • Example 2: Extract Data by A Cell Range
    • ¶ Text Analysis
      • Introduction to Text Analysis
        • Step 1: Exercise One
        • Step 2: What is Text Analysis?
        • Step 3: Important Considerations
        • Step 4: Why Voyant and Lexos?
        • Step 5: Exercise Two
      • Text Repositories
      • Text Analysis in JSTOR
        • Overview of Constellate
        • Build A Dataset
        • Create A Stopwords List
        • Word Frequency
  • Digital Scholarship Incubator
    • Schedule
    • Getting Started
    • People
    • Project Guidelines
    • Topics
      • 3D Modeling and Immersive Technologies
        • Part 1: 3D Photogrammetry & Laser Scanning
          • Exercise: Experiment with 3D creation tools
        • Part 2: An Introduction to Apple's Reality Composer AR
          • Exercise: Experiment with Apple RealityComposer AR
      • Anatomy of a DS Project
        • Parts of a DS Project
        • Some DS Project Examples
        • Exercise: Evaluating a DS Project
      • Pedagogy
      • Data and Data Visualization
        • Introduction to Data
        • Introduction to Data Visualization
        • Introduction to Tableau
          • Download and Install Tableau
        • Introduction to Network Visualization
      • Digital Exhibits
        • Exercise 1: Exploring Exhibits
        • Exercise 2: Exhibit.so
      • DS Intro & Methodologies
      • User Experience
        • Usability Exercise
      • Mapping and GIS
        • An Introduction to Mapping, GIS and Vector Data
          • Workshop: Exploring and Creating Vector Data
          • Quick Review: Spatial Data
        • An Introduction to Raster Data and Georeferencing Historical Maps
          • Workshop: Finding and Georeferencing an Historical Map
          • Tutorial: Georectifying Historical Maps using MapWarper
        • Presentation + Workshop: Putting it together in ArcGIS Online
        • Workshop: A Brief Introduction to QGIS
          • Adding Base-maps and Raster Data
          • Adding and Creating Basic Vector Data
          • Styling your data and preparing it for exporting
      • Story Maps
        • Story Map Exercise
      • Text Analysis
        • Exercise 1: Voyant
        • Exercise 2: Python
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  • Vector Data
  • Raster Data

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  1. Digital Scholarship Incubator
  2. Topics
  3. Mapping and GIS
  4. An Introduction to Mapping, GIS and Vector Data

Quick Review: Spatial Data

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Last updated 2 years ago

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Spatial data adds a geographic dimension to a qualitative and/or quantitative data set, situating it in a particular location within a coordinate system relative to other data points. (The coordinate system can be a real-world system or a locally created one used to meet the needs of a particular project.)

Spatial datasets, in general, come in two distinct forms, (points, lines, and polygons) and . Raster and vector data can come together in the creation of a wide variety of mapping projects, from a traditional figure with an explanatory legend and caption, such as might appear in an academic text, to an online interactive platform that allows for the searching or filtering of thousands of pieces of spatial data or hundreds of historical maps.

Vector Data

Vector data includes points, lines, or polygons (shapes made up of straight lines) containing spatial information that represent some sort of feature or event in a physical or imagined landscape and may contain other types of qualitative or quantitative information, called attributes. A point may represent a tree, a city, or a moment in time. Lines might indicate the street grid of a town, the path someone traveled across the world, or a social link between two communities. Polygons can mark the boundaries of a country or voting district, the catchment area of a river, or a single city block.

For example, the relatively simple and ongoing project from the Burn's Library collection pictured below uses vector point data to offer a selection of images and accounts from individuals and their observations about how the cities and landscapes they visited appeared. Users can filter the point data by data or search for particular location names in the search bar.

Raster Data

Raster consists of "cells" of data covering a specific area (its extent), with attribute values in each cell representing a particular characteristic. It may still consist of points, lines, and polygons, but these shapes are themselves composed of pixels (the way a jpeg or other image file type is).

Data of this type may take many forms, such as satellite imagery containing vegetation or elevation data, precipitation maps, or even an historical map, which has been given a spatial reference. Unlike vector data, raster data has a particular resolution, meaning each pixel represents a particular geographic region of a specific size.

Most projects combine various forms of vector and raster datasets.

vector data
raster (or pixel data)
World Travel and Description
Screenshot of World Travel and Description
Vector vs Raster data