A New Computational Geography

1. A New Computational Geography Andy Evans

2. Today • Belief. • Behaviour. • Belief and Behaviour.

3. 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…

4. 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.

5. 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?

6. Vernacular Geography

7. 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”

8. 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.

9. 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.

10. 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.

11. 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.

12. 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.

13. 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.

14. Input GUI • Spraycan of different sizes. • Attribute information box. • Send button.

15. Output GUI • Click on map of combined areas. • Comments of the people who weighted that area as most important float to the top.

16. 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.

17. 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?

18. Crime and Understanding • Looked at crime ratings vs. confidence in local knowledge.

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?

20. 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

21. Unsure Definitely well defined Definitely not in definition Mereotopological calculi • Areas defined like fried-eggs. • You can make definite statements about some bits, and not about others. • Pros: Useful for qualitative relationships: A is next to B. • Cons: No real notion of complex gradients / 2nd order vagueness.

22. 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.

23. 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.

24. Statistics / Probability • A range of techniques for comparing the incomparable. • Confusion / Entropy indexes for comparison with real data? • 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. Belief

25. 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: Web-based 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.

26. 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.

27. 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.

28. 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

29. 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.

30. 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?

31. 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.

32. 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)

33. 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.

34. 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.

35. 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.

36. 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 otherand bluebirds and squirrelsdo 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.

37. 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/

38. 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.