Oop'Narf and Up The Junction: Capturing the Vernacular

1. Oop'Narf and Up The Junction: Capturing the Vernacular Andy Evans

2. Thanks • Steve Carver (Leeds Uni) • Richard Kingston (Manchester Uni) • Tim Waters (Bradford Council) • Chris Jones (Leeds Uni) • Kevin Cressy (City Uni) Without whom…

3. What’s it all about? • Work on democracy • How people relate to the world • Capturing vernacular geography • Using vernacular geography

4. What’s wrong with the political process? • 2001 average election turnout : 59.4% • Nottingham East: 45.5% • Nottingham South: 50.11% • Nottingham North: 46.74% • Most of the missing are in the 18-34 age bracket. • This is 12% lower than anything since WWI. • Turnout for local elections ~30% or less • Yet, people aren’t content: • We have ~1.5 Million people marching through London. • Support for local causes seems to be on the up.

5. Public participation and apathy • People may feel: • The system doesn’t cover the things they are interested in. • All politicians are the same: the system doesn’t have the breadth of representation to express their views. • The politicians who hold their views on one thing, may not on others. • When a by-election (Leeds Central) has a turnout of 20%, representational democracy has failed. • Yet participatory democracy is increasing.

6. The dangers of participation • Tends to be dominated by a few. • Agenda can often be over-constrained. • Tends to be difficult to get involved. • E.g. Drive to meetings. • Information often difficult. • But, people do want to be involved.

7. GIS and the Web • GIS can help: • Frame questions in relationship to where people live. • Allow people to explore scenarios and data. • The web can help: • Access better than meetings. • Less domination by small groups. • More time for consideration. • Easier data access (for public) and analysis (for decision makers).

8. Example: Virtual Slaithwaite • Planning for Real. • Took over village fair as well. • Allowed input of problems attached to locations. • Easy analysis: Built community understanding of level of concern about locations and issues.

9. Example: Multi-Criteria Evaluation • Where is Wild in Britain? • Where should the Yorkshire Dales have more trees? • Where should we dispose of Nuclear Waste? • Rank a number of factors and constraints. • Allowed analysis of how people respond to data and opportunities for change.

10. Fear of the great unwashed • Analysed factors important to people in nuclear waste sites before and after they saw: • The geography • Where their best sites were relative to their homes. • Geography did effect the chosen location/factors. • But didn’t effect how close to their homes. • People made rational choices based on their preferences.

11. Deeper understanding of risk and location • Current project with Wakefield Council. • Allows people to zoom into a map and comment on a problem. • Burnt out car, graffitti, dead animals, noise etc. • We hope it will show: • the way people navigate data. • the scale at which people understand different problems.

12. Problems • Access: 53% of homes online: • strong age skew. • 7% thought too expensive. • Taking systems to the people. • More generally we need to balance hard to understand data vs. completeness.

13. How people relate to the world • Work on democracy • How people relate to the world • Capturing vernacular geography • Using vernacular geography

14. Normal Human Beings? The General Populous are darned 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 General Populous use geographical coordinates and specific data layers like Normal Human Beings?

15. Vernacular Geography

16. 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. • 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.

17. 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”

18. 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.

19. Capturing vernacular geography • Work on democracy • How people relate to the world • Capturing vernacular geography • Using vernacular geography

20. Are fuzzy boundaries useful? Fuzzy boundaries occur because of: Continuousness (Ontic vagueness): • When the data’s not discrete. Aggregation (Prototyping): • Where discrete boundaries represent the average location of continuous or discrete variables binned together for descriptive convenience, usually categorized by comparison with a prototype. Averaging (Scale dependent vagueness): • Where discrete boundaries average a single time or scale varying geographical boundary. Imprecision (Epistemological vagueness): • Where we cannot know a boundary because we can’t measure it accurately enough. Ambiguity (Semantic vagueness): • Where boundaries are tied to linguistic factors.

21. Tools in three interfaces • We’ve been developing a set of tools to capture fuzziness in a GIS. • Input: • A spraycan interface for a online GIS, that allows comment attributes to be attached. • Administration: • For decompression and combination. • Query: • A way of representing all users’ data and searching for the comments in order of users’ perceived importance.

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

23. Type of Spraycan • Continuous vs. dot • Gaussian vs. constant • Users preferred constant fills. • Easier to use where boundaries a mix of sharp and fuzzy. • Dots are easier for beginners. • Easier to grasp the intensity the spray is at in white space areas. • Harder to convert into a density map.

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

25. It’s not a perfect world • Transferring data across the net. • Combining and searching many user responses. • Need to balance the accuracy of our representation with the technical difficulties.

26. User dots Density map Shrunk to 1/5 size GIF style compression GZip Sent The compression Waveform compression algorithms might improve on this. Tests suggest a typical compression rate is two orders of magnitude. For example, a combined image and data object of 859Kb was compressed to 67Kb just using the GZip algorithm, and further compressed to 14Kb with the addition of the shrinking process.

27. Technicalities • User tests suggested a 9x9 pixel averaging kernel best represented the areas users had drawn using the dots. • Tests suggested this could be shrunk to 5 times the size and re-inflated without users noticing a significant change in the image.

28. Recent developments • New system to capture these areas in Arc. • New system to allow you to use a ‘pencil’ in Arc to draw boundaries. • New server-side system which speeds up implementation and scalability. http://www.ccg.leeds.ac.uk/software/tagger

29. Capturing vernacular geography • Work on democracy • How people relate to the world • Capturing vernacular geography • Using vernacular geography

30. 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.

31. 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.

32. 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 fuzzy neighbourhoods.

33. How might we handle it: Fuzzy logic • The notion that something is more or less something can be handled by fuzzy logic. • Scientists love it so much… “Fuzzy theory is wrong, wrong, and pernicious. What we need is more logical thinking, not less. The danger of fuzzy logic is that it will encourage the sort of imprecise thinking that has brought us so much trouble. Fuzzy logic is the cocaine of science.” Prof William Kahan

34. Fuzzy rules • 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) and (REALCRIME > average) then INVESTMENT is HIGH • Pros: Gives you some degree something is true. • Cons: Hard to know how to use. E.g. Union of sets.

35. How might we handle it • 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.

36. 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.

37. Problems • In fact, there’s nothing to stop us breaking our data down in all these ways, once it is in. • More pressing problems: • What do the numbers represent? • What is the maximum in this situation? • How do we combine data from one person? • Contrasting: e.g. levels of RURAL vs. URBAN? • Different categories: e.g. MOUNTAIN vs. FOREST? • How do we combine data from multiple people? • Variety of connectors in fuzzy logic and decision support theory, but often seem arbitrary.

38. Problems • Can we disaggregate into different types of fuzziness/causes? • Do we need to? • Is data fit-for-purpose if not collected for a specific use? • Does supervaluation etc. sets allow us to get round this? • How do we compare this with real data? • Confusion / Entropy indexes? • Could treat it as a set of beliefs (or beliefs about memberships): • Doxastic Modal logics, Evidence set theory. • Could treat it as a probability and use Bayesian statistics: • Though this may mis-represent one person, it may model combinations of areas.

39. Problems • How do we cope with resolution? • We may collect at one scale and use at another. • Can we quantify or predict the erroneousness of scale changes?

40. Example analyses • How does fear of crime vary with: • personal victimhood? • media exposure? • conditions (summer vs. winter)? • Current models based on aspatial demographic, psychological and temporal factors only accounted for ~1/3 aspatial fear levels.

41. More generally • Policy – “Where should we invest to improve perceptions?” • Psychology – “What is the relationships between things in the real world and perceived areas?” • Is there a predictable relationship? • Are they at the same place? • Does perception of some things have a wider geographical spread than others? • How to people get an understanding of areas?

42. Future • Most work has focused on: • Storing data so qualitative spatial relationships can be generated (next to, touching, within, etc.). • Capturing quantitative spatial relationships using fuzzy logic (close to, far from). • How often are these used in policy making? • Is it better to concentrate on how we relate this data to the real world and similar datasets? • Vernacular geography is vastly more complex though. • All lines are fuzzy (measurement / labels) we’ve just hidden it.

43. Further information • www.geog.leeds.ac.uk/people/a.evans/ • www.ccg.leeds.ac.uk/democracy/ • www.ccg.leeds.ac.uk/software/tagger/