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GIS and Humanities lecture

Outline

  • Introduction to the course
  • Basic theory behind GIS
  • Motivation
  • Practical Example
  • Homework
  • Discussion

Introduction to the course

Main Idea

This webpage is an auxiliary material to the lecture Spatial data in Humanities. This lecture is part of the course ARTS020 - Digital Humanities that took place at Masaryk University, Brno, in 2020. This course aims to explain the theoretical basics behind Geographic Information Systems, discuss some examples of their use in Humanities, and provide a short practical example.

Requirements

  • QGIS application installed
    • STANDALONE STABLE version is recommended

Basic theory behind GIS

GIS, Cartography, Geoinformatics, Remote Sensing, Map

  • GIS

    • An equivalent to "Geospatial"

    • Geographical Information SCIENCE

      • Scientific discipline
      GIS illustration. Taken from ESRI blog.

    • Geographical Information SYSTEMS

      • Software / General tools - ArcGIS, QGIS
      • Built-in systems
      • Frameworks / libraries for data manipulation, visualization...
      ArcGIS software random screenshot. Taken from Youtube.

  • Cartography

    • "Drawing" and understanding maps - a depiction of the geographical space

    • More historical term

    • Mathematical cartography (how to transform globe to the map), visualization (what is the best way to "draw" our data), theoretical cartography

      Map of Margraviate of Moravia, made by Komensky in ~1624. Taken from Wikipedia.

  • Geoinformatics

    • An equivalent to "Geomatics"

    • Heavy use of IT

    • Scripting the methods

    • Spatial data analysis (processing geospatial data), web-based maps (map web applications), Localization services (GPS)

      Areas of Geoinformatics. Taken from zgis.at.

  • Remote sensing

    • Processing images from satellites or airplanes

    • Application in ecology, agriculture, forest management...

    • Detection of changes in forest health, estimation damages after a natural disaster

      Illustration of Remote Sensing process. Taken from www.flaticon.com and Remote sensing from space.

GIS data sources

  • Two types of data

    • Raster data

      • Pixel-based / image like datasets
      • Formats - png, tiff, jpg...
      • Spatially continuous information - satellite images, elevation, land cover, temperature
      • Space is divided into pixels, each pixel holds a value / set of values

    • Vector data

      • Defined by coordinates / mathematically
      • Formats - shp, geojson, pdf, svg
      • Topology - points, lines, polygons
      • Spatially discrete information - points of interest, rivers / streets / borders, regions
    A graphical depiction of vector and raster layers.

  • Layers

    GIS layers.

Spatial Analysis

  • Manipulating (filtering, extending, combining) of spatial datasets to extract new information
  • Detection of hotspots of crime in the city, predicting temperatures in a set of regions

Map by Dr. John Snow of London, showing clusters of cholera cases in the 1854 Broad Street cholera outbreak. Taken from wikipedia.

Visualization of Spatial Data

  • Attribute types

    • Categorical / Qualitative - dominant crop in the region, name of the local mayor, type of transportation
    • Numerical / Quantitative - average annual temperature, population number
    • Boolean - areas with snow
    • Ordinal - position in a ranking

  • Graphical variables

    • Size
    • Shape
    • Color hue / value / saturation
    • Orientation

Visual variables and their ideal use, by Bertin.
  • Point symbols
    • Simple - mostly just a symbol that communicates a single value through its size, color, or shape
    • Glyphs - one symbol "communicates" more variables; a complex system of symbolization

Demography of USA visualized by point symbols. Taken from https://demographics.virginia.edu/DotMap/.

Example of a map using a multivariate point symbols. Taken from A.-M. Guerry’sMoral Statistics of France: Challenges for MultivariableSpatial Analysis.

  • Line symbols

    • Identifications - the feature is generalized by the line; railroads, trails, rivers
    • Borders - the feature is enclosed by the line; regions, areas
    • Movement - the line symbol (arrow) shows the angle that important for the understanding of the feature; wind movement, migration
    Map of metro in Bratislava. Taken from Reddit.

  • Polygons

    • Regions - all features are on the same level of importance, we just need to define their position; a simple political map
    • Choropleth - features are visualized to represent a quantitative variable

Changes in population within European regions; an example of a choropleth map. Taken from BBSR.

Europe according to Culinary horror 2013; example of polygons as regions. Taken from Atlas of Prejudice.

  • Heatmap

    • display the intensity of the phenomenon in space

  • 3D

World of cows. Taken from Twitter.

Mathematical Cartography, Geographical Coordinates

  • Transformation of the Earth's globe surface into a flat plane with the system of mathematical equations

  • This way, we can define every position on the planet / in a region

  • WGS (World Geodetic System) - a system of XY coordinates

  • Cartographic projections playground

Motivation (Why to be interested in GIS?)

GIS + X

  • Every phenomenon that relates to the geographical space
  • There are many fields of human activity that are dependent on GIS.
    • Meteorology
    • Transportation
    • Ecology
    • Urban planning
    • Agriculture ...

Real time prediction of public transport in the city of Cologne; an example of use of GIS in transportation. Taken from company.ptvgroup.com.

Visualization of the Aladin model on CHMU website; an example of an use of GIS in meteorology. Taken from chmu.cz.

GIS + Humanitites = Spatial Humanities (Digital Humanities)

  • Long tradition of sketching maps in all kind of sciences
  • Constraints:
    • Data quality (validity, uncertainty, incompleteness)
    • Preferences in qualitative approaches
    • Untrust to the process of generalization (?)
    • Statistical / Mathematical / Programming skills required
    • ...

A map of central Europe at the beginning of the 13th century, an example of a map created by a researcher in humanities. Taken from Zemlicka 1990.

ORBIS application that calculates travelling costs and times in the Roman times.

Practical Example

1. Create point-layer dataset and export it as .csv

  • Create a new table in MS Excel, LibreOffice Calc, Google Spreadsheets...
  • Rows are records; columns are variables
  • Two columns for geographic coordinates - X and Y
  • Define value domains for each column - e.g., column "label" is a text, column "certainty" is a boolean (TRUE, FALSE values), and column "price" is a number
  • Fill the values for each record based on the defined domains

2. Import .csv dataset into QGIS

  • Run QGIS and create a new Project
  • Open Data Source Manager (Ctrl + L) and choose Delimited Text
  • Select the .csv file into the first input (File Name)

3. Add base layers

4. Select symbolization

  • Right-click on the layer in the Layers panel -> Properties -> Symbolization
  • There are several visualization methods to use
    • most basic methods: Graduated (Quantitative data) and Categorized (Qualitative Data)
  • Different possibilities for Vector and Raster data

5. Labels

  • Right click on the layer in the Layers panel -> Properties -> Labels
  • Choose Single labels and select the label attribute in the Value form

6. Export and create the map composition

  • New layout (Ctrl + P) -> Enter name
  • Add map components to the layout - title, texts, images, scales, legend...
    • Try to explain, "What information do you want to give to the readers." Ideally, try to show something that would be "hidden" without the map

Homework

  • Create a map composition in QGIS to display your spatial (point-based) dataset
    • Its recommended to use any graphical editor (yes, also MS Paint) to finish the composition
    • Data should come from your study, research, your interest - anything that you have some insight into. This insight is crucial for interpreting the map.
    • Before creating the map, think about the main idea (spatial pattern) you want to communicate. And based on this, choose the visualization method and other map components wisely
    • After you create the map - show it to someone without any explanation to see whether he/she understands your "message". If not, think about a better way to communicate that "message."
    • At the end - add a free-form text to the map composition where you describe the map, the dataset, and the reasoning behind that process (3-5 sentences...).

Discussion

Aditional information

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