This talk was given to the Demographics User Group on the 1st December 2016

1. Guy Lansley
Department of Geography, UCL
g.lansley@ucl.ac.uk
@GuyLansley
The Demographics User Group
Annual Away Day
1st December 2016
New Analytical Methods for
Geocomputation

2. Geocomputation

3. Big Data and
Software
New software has had to
adapt to the growing size
and complexity of data
Kira Kowalska

4. Big Data shifts
• The concept of Big Data is changing and
becoming more challenging
• Emphasis on place rather than space
• Challenges to representing the dynamics of
real world phenomena

5. Open Source Software

6. Coding Languages

7. R and Rstudio
• Command line interface.
• Object oriented.
– You create things with names
using the “<-” symbol.
• Ten <- 5*2
• Two <- Ten/5
• Write a script of functions.
• The standard installation has
relatively few functions but more
have been made available via
open source downloadable
packages
R Scripts Workspace
Console
Multi-tab
(includes plots)
• Can also be run through
Rstudio which provides a
more user friendly GUI

8. Why should we conduct analysis in code?
• Accessibility
• Unrestrictive
• Automation and Consistency
• Skills development

9. Accessibility
Coding techniques for
spatial analysis are now
more accessible than ever
before

10. Using R as a GIS
• Free online training resources coming soon to the
CDRC website
• www.cdrc.ac.uk/training-capacity-building/online-courses
Slides on slideshare

11. Fundamentals
• Data scientists still need to understand basic
fundamentals
• i.e. Circular statistics
– Commonly overlooked

12. Automation and
repetition
Coding can make insight
generating more efficient and
less time-consuming

13. 2011 Open Atlas Project
• A manual map might
typically take 5 minutes
to create - thus:
– 5 minutes X 134,567
maps = 672,835 minutes
– Or 467.2 days (no
breaks!)
www.alex-singleton.com
• Produced by Prof. Alex Singleton (CDRC, University of
Liverpool)
• R was used to automate the production of 134,567 into a
collection of PDF atlases
• This included downloading and formatting the data from the
ONS websites

14. 2011 Open Atlas Project
• Code available here:
rpubs.com/alexsingleton/openatlas
• E.g. Step 1: Download the data
E.g. archive =
http://www.nomisweb.co.uk/output/census/2011/ks101ew_2011_oa.zip

15. Algorithms
Alyson Lloyd
• Use a pipeline of
methods and
decisions to analyse
data
• i.e. data cleaning
Cleaning the registered
locations of customers
based on their store
visits

16. Bespoke techniques
New techniques can take
advantage of advancements
in computer science

17. Simulating the dynamic world

18. New Computing Methods
• Neural Networks
– Self Organising Maps
• Machine learning
Seth Spielman

19. Text Mining
New techniques for
analysing unstructured data

20. Textual Data
Unstructured data is difficult
to quantify

21. Text source: https://en.wikipedia.org/wiki/Tag_cloud
Word Frequencies

22. Text source: Wikipedia
Word Frequencies
But it is still difficult to compare and
contrast several documents

23. Topic Modelling
Blei et al. (2003) Latent Dirichlet Allocation (LDA):
In this example, I have applied an
LDA to 1.3 million geotagged
Tweets from Inner London
transmitted in 2013

24. 20 Twitter Groups
1 Photography and Sights
2 Optimism, Kindness and Positivity
3 Leisure and Attractions
4 TV and Film
5 Humour and Informal Conversations
6 Transport and Travel
7 Politics, Beliefs and Current Affairs
8 Sport and Games
9 Anticipation and Socialising
10 Business, Information and Networking
11 Pessimism and Negativity
12 Music and Musicians
13 Routine Activities
14 Food and Drink
15 Body, Appearances and Clothes
16 Social Media and Apps
17 Slang and Profanities
18 Place and Check-Ins
19 Wishes and Gratitude
20 Foreign and Other
Identifying Patterns

25. Time Distribution Hour
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Photography and Sights
Optimism, Kindness and Positivity
Leisure and Attractions
TV and Film
Humour and Informal Conversations
Transport and Travel
Politics, Beliefs and Current Affairs
Sport and Games
Anticipation and Socialising
Business, Information and Networking
Pessimism and Negativity
Music and Musicians
Routine Activities
Food and Drink
Body, Appearances and Clothes
Social Media and Apps
Slang and Profanities
Place and Check-Ins
Wishes and Gratitude
Foreign and Other
All Tweets
-1.5 -1 -0.5 0 0.5 1 1.5
Identifying Patterns

26. Identifying patterns
This map shows the density of Tweets from
the Education subtopic, relative to the
density of all Tweets in London.
UCL
University of
Westminster
Imperial College
London
London South
Bank
Kings College
Queen Mary
London Metropolitan
University
University of
Greenwich
City
Goldsmiths
SOAS
Birkbeck
LSE
UAL
University of
Roehampton
University of East
London
Identifying Patterns

27. Data to Information
All data which is not random
is useful to someone for
some purpose

28. Names
Indicators of gender

29. Forenames – Age (Males)
5 clusters of forenames based on their
age distributions

30. Most Big Data are by-
products of activities
Big Data as Exhaust

31. Retail Data
• Using data to infer
wider mobility
patterns
Alyson Lloyd
Data from stores near to London’s main stations

32. Twitter Catchments
Lloyd, A. and Cheshire, J. (2016). Mining Consumer Insights
from Geo-Located Social Media Datasets

33. Consumer Registers
2013 2014Matches
Comparing registers to identify household change

34. Consumer Registers
Modelling migration?

35. Interactivity
Interactive outputs make the
sharing of information
easier and more accessible

36. A Basic Shiny Map
ui.R server.R
Population density (2011 Census)

37. On CDRC Maps
• Geodemographics
– OAC, COWZ, IUC
• Retail
– Value, Sector, Change
• Metrics
– IMD, IMD Components
– Population Density
– Population Change
• Top Metric Maps
– Dwelling Ages
– Country of Birth
– Occupation
– Mode of Commute
CDRC Maps
Oliver O’Brien

38. The Demographic Toolkit
• Analytical web mapping system (Web GIS)
– Self-hosted raster and vector map tiles
– Open source packages (OpenStreetMap, Mapnik &
Leaflet)
• Create and analyse spatial and temporal profiles
– Standard and bespoke functional regions
– MAUP (Modifiable areal unit problem) in public policy
• Aims to be available in mid-2017
Tian Lan

39. The Demographic Toolkit
Tian Lan

40. Summary
• We have to become more comfortable with coding in
order to unlock the full potential of machines
• We are exploring new techniques to unlock new insights
from Big Data
• We are also harnessing data in novel ways to gain
insights about the population and their dynamics
• However, converting big data into wisdom is still
challenging and new techniques still need to be made
more accessible

41. Guy Lansley
Department of Geography, UCL
g.lansley@ucl.ac.uk
@GuyLansley
Acknowledgements
Tian Lan
Wen Li
Alyson Lloyd
Oliver O’Brien
Seth Spielman
www.cdrc.ac.uk