A very spatial session

Meg Miller - GIS & Research Visualization Librarian


workspace: bit.ly/uml_map

Outline


  1. What is GIS?
  2. Things to consider when working on a mapping project
  3. Research visualization and Campus Libraries
  4. Let's make a map! (workshop)

What is GIS?

Geographic Information System

Any system used to analyze, capture or work with information you would display with a map (aka geospatial data).

1. What data types are used in GIS


Data Types: Tabular

Screenshot of a table

Data Types: Vector (point, line, polygon)


points, lines and areas

Each point has an x,y (lat/long) coordinate pair associated with it (.shp, .svg, .dxf...)

Data Types: Raster

screenshot of a raster image

A grid made of cells/ pixels(.jpg, .tiff, .grid, .rast, .ecw…)

2. Then what is 'doing GIS'


Using a program specific example:


"GIS enabled historical consciousness"

Sasha Mullally- UNB History Dept.

3. What is visual encoding (and why should I care)


Mapping data variables to visual cues


In data visualization/ mapping, this means constructing our data to be represented by visual elements like size, colour, length and placement.

Colour- Unconscious perceptions

  1. Red is bad, green is good;

  2. Light blue shapes on maps are water;

  3. Light colours represent less, dark more.

Note:

Be engaged (and beware of 'universal truths').






Thanks to Vanessa Lillie, Cary Miller, Lyle Ford and Elder Valdie Seymour
for their insight in the following section.

Be engaged (1)

  1. Data visualization theory is a colonial construct;

  2. Colour has connotation outside of your own world view;

Be engaged (2)

  1. "Maps have killed more people than guns ever have" Dayrit (2020)

  2. Who are you focussing on? Who is being erased?

Popular processes:

  1. Digitization of maps/images
  2. Reconstruction of features from the past or present
  3. Georeferencing microdata
  4. Revealing trends

3. How can Libraries support me


Library Support:

Librarian Support

Subject Guides

Esri (ArcGIS) software access

Training opportunities

Tool Choice


points, lines and areas

Key Points:

  • Your expertise - be kind to your future self (time etc.)
  • Purpose - exploratory vs. explanatory
  • Audience - expertise, needs, support
  • Data requirements - size, residency, privacy...

Hands-on: From there to here – Making a meaningful map without a computer

Workshop content

Questions


meg.miller@umanitoba.ca

workspace: bit.ly/uml_map