1. Visualising Geographic Variations
Dr Orian Brook
University of Stirling
Firstly we need to think about what we mean by big data. It’s a popular term, widely used in the media, but it can mean different things to different people. There’s no one agreed-upon definition, so I’m going to talk about some of the characteristics of big data and what they mean.

2. Inescapable problems of mapping data
Maps are simplifying complex reality: “to present a useful and truthful picture, an accurate map must tell white lies”
“A single map is but one of an indefinitely large number of maps that might be produced for the same situation or from the same data”
from p1-2 of Monmonier , M (1996) How to Lie With Maps
See also: Dorling, D (2012) The Visualisation of Spatial Social Structure

3. Issues and Tips for Visualising Geographic Variations
Problems and challenges presented by:
Variations in population density
Choice of data ranges
Differences that aggregation level/choice can make
Colours/symbology
Isn’t necessarily one right answer. But be aware of pitfalls and possible alternatives.

4. Population Density

5. Urban-Rural Indicator 2013-2014
Rural Areas dominate map, but only 18% of Scotland live in them
70% live in Urban areas
Can give misleading impression
Difficult to see detail of other areas

6. Adjust areas according to population? - Cartogram
In theory, displays two dimensions – population size and rurality
In practice, has disadvantages

8. Cartograms Can make maps very difficult to relate to known geographies
Are options as to degree of rescaling
But then hard to know what you’re looking at?
Lots more eg.s at
Eg infant mortality

9. Other options
Local Council Election Results visualisation.org
Still hard to identify places
Requires specialist tools

10. Solutions? Think carefully how you interpret a map!
Think about zooming in on areas of interest

11. Data Ranges

12. Options for Summarising Data
Three common approaches:
Quantiles – where a fixed proportion of areas falls into each group
Quartiles – 4 groups, quintiles - 5 groups, deciles – 10 groups
Equal Interval
Eg 11-20%, 21-30%, 31-40%
Manual
Choose your own on the basis of what fits the data/tells the story
Best approach will depend on the data & what you want to say
Which provides most useful discrimination & comparison?

13. Quantiles Advantages Relatable – the 25% most deprived areas
Always get map with visual range
Consistency of approach - good for comparing different data
eg 25% highest crime areas, 25% lowest income areas
Cons
Not good for narrow data ranges or clusters
Eg % Chinese, male/female ratio

14. Equal Interval Advantages Relatable – 10%, 20%,
Good for showing contrasts of same measure
Eg areas with 30% or more graduates vs with 30% or more no qualifications
Cons
No consistency of approach for comparing different data
Eg mapping counts of burglaries vs arson
Dependent on variability of data
Doesn’t work well when it’s skewed

15. From www.petitionmap.unboxedconsulting.com
Stop spending .7% GNP on Foreign Aid
Parliament to sit on Saturdays

16. SIMD Crime Domain
Quartiles vs Equal Interval

17. Areal Units

18. Different Areas=Different Results
Technically known as modifiable areal unit problem
As data is aggregated to different (esp larger) units, proportions will change (variances are smoothed out)
Different areas in England lay claim to “most deprived”, based on datazone, ward, local authority, constituency …

19. John Snow’s Cholera Map

20. Colours

21. Greater colour contrast isn’t always better – can be confusing
Won’t print in black and white/photocopy,
May look different depending on screen/projector
Remember colour blindness (8% of men)
Range of similar colours may work better

23. Make one yourself?
Try indiemapper.com