• Data visualization 1 presentations
• Introduction to maps and spatial humanities
• Practical aspects of mapping
• Hands-on session with choice of mapping tools
3. Visualisation Presentations
• If presenting: walk us through your visualization:
• What is the data?
• How should we read it?
• What decisions did you make and why?
• What conclusions do you think we can draw from it?
• Prepare one question for each presenter, I’ll ask people at random
4. What is Spatial Humanities?
• The process of including space and place in humanities research or
• Examples include
• Historical maps
• Social geography
• Mapping fictional places
• Mapping in journalism
• “Space is not simply the setting for historical action but is a significant
product and determinant of change. It is not a passive setting but the
medium for the development of culture.” Bodenhammer et al, Deep
Maps and Spatial Narratives
8. ‘Thematic Maps’
• “Representations of attribute data
(quantitative and qualitative) on a
base map.” Meirelles, 116.
• Can be economic, social, political
• Aim is often to discover the
geographic nature of the subject
or data, for example
understanding spatial patterns in
voting or demographics.
9. ‘Deep Maps’
• Typical maps can portray a positivist viewpoint of the world, they
claim to represent the world in a very objective form, as a series of
points, lines, polygons.
• “A deep map is a finely detailed, multimedia depiction of a place
and the people, animals, and objects that exist within it and are
thus inseparable from the contours and rhythms of everyday
life. Deep maps are not confined to the tangible or material, but
include the discursive and ideological dimensions of place, the
dreams, hopes, and fears of residents—they are, in short,
positioned between matter and meaning.”
11. How do we create our own maps?
• Usually of some/all of the following elements:
• A basemap: a background map on which we wish to draw data
• Geographic data: for example, points, lines, or polygons
• In many cases, data (for example quantitative), mapped to these
• Needed to anchor the geographic data in recognizable space.
• Often provided by third-party, e.g. Google or Open Street Map.
• Think about scale: how closely does the data need to be read?
13. Geographic Data
• Points: set of latitude/longitude
• Lines: Multiple sets of lat/lon
coordinates, taken together
create a line.
• Polygons: map shapes, such as a
country border or city boundary.
14. Visualising Geographic Data
• These can be visualized using the
same aesthetics we discussed
last week: size, shape, color, etc.
• The same rules apply: numerical
data should use a continuous
colour scheme, and so forth.
15. Today’s Hands-on session
• Three options
• Map geographic data using Palladio
• Create a narrative map using StoryMap
• Download and map some data using Open Street Map
• Instructions available at https://yann-ryan.github.io/assignment0-2
18. Mapping with R and Open Street Map
• Use MyBinder service to run a code notebook
• Experience coding useful but feel free to try it out!
• Be warned it can be a bit unreliable so have a plan B
19. For next week:
• Finish your map, upload to Brightspace
• Isabel Mereilles, Design for Information, ‘Chapter 2 (Relational Structures:
Networks)’ (ebook available through Library catalogue)
• Other texts:
• Chapter 4, ‘Visualising Networks’, in Ahnert, R., Ahnert, S., Coleman, C., &
Weingart, S. (2021). The Network Turn: Changing Perspectives in the
Humanities (Elements in Publishing and Book Culture). Cambridge:
Cambridge University Press. doi:10.1017/9781108866804
• Venturini, T., Jacomy, M., & Jensen, P. (2021). What do we see when we
look at networks: Visual network analysis, relational ambiguity, and force-
directed layouts. Big Data & Society, 8(1).