A New Computational
Geography
Andy Evans
Today
Belief.
Behaviour.
Belief and Behaviour.
Thanks
Steve Carver, Richard Kingston
Tim Waters, Kevin Cressy, Chris Jones,
Mohammad Shan-A-Khuda
Alison Heppenstall, Hazel Parry, Mark Birkin
James Stott, Matt Turner, Richard Phillips,
Olga McFarland
Without whom…
Belief
Work on online democracy.
Try to capture how people feel about
problems.
First though, we need people to
understand how we talk about problems.
They can’t.
Normal Human Beings?
The Public are fools:
 They use geographical terms they
can’t define.
 They mix up their attribute datasets.
 They can rarely put anything
precisely on a map.
 Why, oh why, can’t The Public use
geographical coordinates and
specific data layers like Normal
Human Beings?
Vernacular Geography
Locational:
Loaded:
“Uptown”
“Our village”
“The shops”
“Everest”
“The West End”
“Down by the
docks”
“Up North”
“Across the river”
“Down by my
Grandmother’s”
“Dangerous end of
town”
“High crime area”
“Ugly bit of the
suburbs”
“Poor area around
the station”
“The Ghetto”
“The simply delightful
area around the park”
“Commutersville”
N.B.Places and
relations
Vernacular geography is good.
 Evolved to make things easy to remember and discuss.
 Gives us geographical references that include associated
environmental, socio-economic, and architectural data.
 “He lives in the grim area by the docks”
 “I’m going down to the shops”
 Gives us a connected socio-linguistic community with
shared understandings (and prejudices).
 “A poor little baby child is born… In the ghetto”
 “This is a local shop, for local people”
Vernacular geography is important.
 Represents psychogeographical areas in which we constrain
our activities.
 “I wouldn’t walk through the rough bit of town at night”
 Conveys to our socio-linguistic community that this constraint
should be added to their shared knowledge and acted upon.
 “That’s a pretty high crime area”
 This private and shared geography influences billions of
people every day.
 But it’s hard to tie directly to objective data so we can use it
to make policy or scientific decisions.
Defining vernacular geography
When asked, for example, to define and explain areas where
they are afraid to walk in the dark:
 The datasets people use are continuous and discrete, at
differing scales, historical, architectural, and mythological.
 A major feature of vernacular geography is that the
boundaries tend to be poorly defined or diffuse.
 The resultant areas linguistically ambiguous.
 May be bound by prominent landscape features for convenience,
but are more usually diffuse.
 Often have different levels of intensity within the areas.
Diffuse boundaries are useful when there is…
Continuousness (~ Ontic vagueness):

When we have no definition to help us place a boundary.
Imprecision (Epistemological vagueness):

Where we cannot know a boundary because we can’t measure it accurately
enough.
Multivariate classification (for example Prototyping):

Where discrete boundaries represent the average location of continuous or discrete
variables binned together for descriptive convenience.
Averaging (Scale dependent vagueness):

Where discrete boundaries average a single time or scale varying geographical
boundary.
Definitional disagreement (Semantic vagueness):

Where boundaries are tied to linguistic factors.
Typical problem
 Where is “downtown”.
 We don’t tend to understand it in terms
of boundaries.
Attempts to use it in this way are probably
misapplications of the definition.
If we’re in downtown, does one step take us
out of it?
 Sorites paradox
Exactly this kind of misapplication.
Infact, almost a tool for spotting these
misapplications.
Typical problem
 We therefore need to redefine “Downtown”.
 This becomes a semantic problem.
 How do you define something in space ostensively
defined without strong boundaries?
 Defuse or Fuzzy boundaries would seem to meet
the public half-way.
Capturing High Crime Areas
 2001/2002 British Crime Survey : people have a higher fear of crimes
than real victimhood.
 Believe crime rates are increasing, most actually falling.
 The fear of crime has a significant impact on peoples’ lives:




7% go out less than once a month because of the fear of crime.
29% of respondents say they didn’t go out alone at night.
6% said fear of crime had a “great effect” on their quality of life.
31% said it had a “moderate effect”.
 Concern about crime therefore represents a significant influence on
many peoples’ lives.
 Current models based on aspatial demographic, psychological and
temporal factors only accounted for ~1/3 aspatial fear levels.
Input GUI
 Spraycan of
different sizes.
 Attribute
information box.
 Send button.
Output
GUI
 Click on map of combined areas.
 Comments of the people who weighted that area as
most important float to the top.
Case study: Crime in Leeds
 Where do people think are
the “High Crime” areas in
Leeds?
 ~50 users drawn from
various socioeconomic
levels from all over the
area.
 Blue are areas ‘safer’ than
thought, red less safe.
 People could see how
others felt about areas.
First we need to understand the data
There are clear problems in this (toy) analysis.
 How can such entities be compared with traditional
scientific data?
 What kinds of algebra can be performed on such data,
alone and in combination with other datasets?
 How do we deal with neighbourhood influences both within
and between diffuse neighbourhoods?
 How can additional data sprayed by the users help?
Crime and Understanding
 Looked at crime ratings vs. confidence in local knowledge.
Found actual crime spots
23.81
Failed to find actual crime spots
4.15
Overestimated crime spots
76.19
Problems
 Fit for purpose
Individuals – are “High crime” areas collected for one
purpose usable in another?
 Contrasting: e.g. levels of HIGH vs. LOW?
 Different categories: e.g. HIGH CRIME vs. POOR AREAS?
Groups - are “High crime” areas collected for one person
usable with another’s?
 Accuracy
Resolution – are “High crime” areas collected at one scale
usable at another?
Confidence – do we understand the errors in both the
mechanics of collection and the “instrument of perception”?
 What do the numbers represent?
 What is the maximum in this situation?
Problems
 Many of these problems are familiar from formal
datasets.
 Many of the assumptions we need to make are already
accepted in standard techniques.
 What is lacking is experience in dealing with them.
 Many techniques are available from more clear-cut
areas.
 Mereotopological calculi
 Supervaluation semantics
 Fuzzy Logic
 Statistical / Probabilistic techniques
Mereotopological calculi
 Areas defined like fried-eggs.
 You can make definite statements about some bits, and not
about others.
Unsure
Definitely well defined
Definitely not in definition
 Pros: Useful for qualitative relationships: A is next to B.
 Cons: No real notion of complex gradients / 2nd order
vagueness.
Supervaluation logic
 Assumes all vagueness is linguistic.
 Attaches the same term to different distinct
boundaries.
i.e. We draw multiple examples of definite boundaries.
 Analysis examples:
Something is super-true if it is true for all definitions.
Something is definitely possible if it is true for one
definition.
 Pros: Gives definite maybes.
 Cons: Assumes definite boundaries can be drawn.
Fuzzy Logic
 Users’ sprays represent membership values for
each point of a fuzzy set, e.g. CRIMEFEAR.
 We can then build up rules:
if (CRIMEFEAR is HIGH) then
INVESTMENT is HIGH
 Pros: Gives you some degree something is true.
 Cons: A little arbitrary in places.
Makes large assumptions about comparability.
Statistics / Probability
 A range of techniques for comparing the incomparable.
 Confusion / Entropy indexes for comparison with real data?
Belief
 Could treat it as a set of beliefs (or, with additional information, beliefs
about memberships):
 Bayesian techniques
 Dempster-Shafer (Evidence) Theory
 Doxastic Logic
 Advantage in these is that the can be extended to deal with correct
actions
 Might allow us the possibility of skipping from belief to behaviour
without necessarily going through understanding.
Stuff
 Most work has focused on:
Storing and spatial relationships.
 How often are these used in policy making?
Better to look at how we relate this to sciency data.
 Evans and Waters, Mapping Vernacular Geography: Webbased GIS Tools for Capturing “Fuzzy” or “Vague” Entities,
Int. Jour. G I Science.
 Predicting Risk and the Fear of Risk: Enhancing Policy and
Decision Making, EPSRC, Shen, Evans, Stell and Hogg.
Behaviour of individuals
 All very well understanding what people believe about
areas, but we also need to understand the resulting
behaviours.
 Geographers love a good bin.
 Bin people
 Bin places
 Bin behaviour
 Driven by computational power.
 We have computational power.
 We don’t need to bin.
 What we need is new techniques.
Behaviour of individuals
We rarely think about behaviour in depth.
We look at the results of behaviour.
We look at correlations between these and
other things.
Crime vs. poverty
Migration vs. jobs
You have to dig deep to find the behaviour.
Complex dynamic systems
 Snapshots are easy.
 Easy to collect the data.
 Easy to understand.
 There’s now more and
more dynamic data.
 We don’t really have new
ways of dealing with it.
 Eg. Ring-road dynamics
SECSE: Spatially Embedded Complex Systems Engineering
 How do we extract patterns from
complex dynamic datasets.
 At the individual and small-group
scales.
 At the total-system scale.
 How do we extract behaviour
from such patterns?
 How do we alter behaviour to
create stability or otherwise?
 Heppenstall, Evans, and Birkin, Genetic Algorithm Optimisation of a
Multi-Agent System for Simulating a Retail Market. Environment and
Planning B.
What can we do with this
Model using it.
Why model?
Stores theory in a useful fashion.
Allows us to test for contradictions and/or
completeness.
Allows our puny minds to grasp the bigger
picture.
Mind games: what would be the ultimate
social-science testbed?
Behaviour
 Traditional models are about hard data and the results of
behaviour.
 Traditional models have no use for behavioural rules.
 Agent-based systems.
 Collect individual behaviour.
 Model emergent properties of the system.
 Advantages:
 History and individual life-path
 Combine behavioural, mathematical and statistical rules.
 We can now model quite large numbers.
Example
 R.Padi model (Central
Science Lab):
Aphid morphology and
behaviour density
controlled.
Suffers from small
number problems if we
aggregate.
Need to model 100
Million (possible on 25
node cluster)
Stuff
 Parry, Evans, and Morgan, Aphid population dynamics in agricultural
landscapes: an agent-based simulation model Ecological Modelling.
 Heppenstall, Evans, and Birkin, Application of Multi-Agent Systems
to Modelling a Dynamic, Locally Interacting Retail Market. JASSS.
 Heppenstall, McFarland, and Evans, Application of Multi-agent
Systems and Social Network Theory to Petrol Pricing on UK
Motorways Lecture Notes in Artificial Intelligence
 Distributed computing Grid service at CSL, Seedcorn Fund.
 3-D Farming initiative.
Problems
Still at the mucking-around stage.
 Don’t really know how to visualise the propagation of
errors.
 Don’t really know how to validate models correctly.
 Don’t really have a sound cross-scale theory about how
individual level entities effect the total system.
 Don’t really understand what behaviours lead to stability
and dampening of errors/intervention and which respond
to intervention well.
A new computational geography
 Bringing it together:
A system that extracts behaviour from complex
dynamic datasets at a variety of scales.
System which connects beliefs about areas to
people’s behaviours.
We don’t deal with the world, we deal with what
people believe about the world.
We don’t deal with results, we deal with behavioural
rules and look at the emergent results.
Belief – Desire – Action Agents.
Pros and Cons
Pros
 More philosophically and scientifically honest.
 Draws together qualitative and quantitative science in
one lovely big hug where we all love each other and
bluebirds and squirrels do all the nasty jobs like empting
the bins and filling in EU grant proposals.
 Closer to how real people think (well, the behaviour bit
doesn’t have to be conscious…).
Cons
It’s as mad as Barking asylum under a full moon.
More info
Papers in ASAP box
Tagger
http://www.ccg.leeds.ac.uk/ > Software
Agent and Complexity
http://www.geog.leeds.ac.uk/groups/mass/
Other Sabbatical stuff
 Porter, Evans, Lowe, and
Crabtree, Bryophyte
Colonisation on a
Temperate Glacier;
Falljökull, Iceland.
 Sediment aggregate SEM
Work currently underway.
 Evans, The mirabilibus
britanniae of “Nennius”,
Folklore.