Capturing and using vernacular geography - obstacles and rewards

1. Capturing and using vernacular geography - obstacles and rewards Andy Evans

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

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

4. How do people engage with the world? • Interested in how people engage with decisions. • In particular how they engage with spatial data. • How does it effect their opinions? • How do they understand a problem? • How do they understand the world?

5. 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.

6. People and Spatial Data • People “walked” themselves around the map. • The most useful thing about the spatial data was finding other people were concerned about locations. • The kids did the mouse-work. What it doesn’t show is how those comments were different from any that might have been given if they’d just had a box to type in – or if we’d used different data.

7. Example: Multi-Criteria Evaluation • 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.

8. How are users effected? • We have: • Weights applied before and after seeing map. • Location for the risk picked and home location. • Therefore: • Distance between top sites before and after seeing maps. • Distances from picked site and top site. • Distances from these risks to their homes. • Also, where the general population lives.

9. How are users effected? • The possible distances are effected by the shape of the UK, but a random population of distances (for example between homes and random points) can be constructed for significance testing. • We can also compare the distances with each other: E.g. After seeing the maps, are the points picked generally further from their homes?

10. Yes, in my back yard • Are the users just clicking randomly? No. • Are the users spatially representative of the population? No. • Is the geographical data effecting where they are picking sites? Yes. • Are their home locations effecting where they pick? No. • Do the factors change after seeing the data? All rated as more important – though population less so. • Do the home locations effect how they change the factors to weight areas away from homes? No.

11. Deeper understanding of risk and location • Current project with Wakefield Council, UK. • 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. Formal vs. Popular • So, we can disaggregate the data to look at motivation. • But this ties people to decisions based on formal, scientific data. • Wouldn’t it be better to see the world as the see it, and see the behaviours and decisions based on that? • Background work on democracy • How people relate to the world • Capturing vernacular geography • Using vernacular geography

13. 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?

14. Vernacular Geography

15. 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.

16. 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”

17. 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.

18. Capturing vernacular geography • Work on democracy • How people relate to the world • Capturing vernacular geography • Using vernacular geography • A major feature of vernacular geography is that the boundaries tend to be poorly defined or diffuse.

19. Diffuse boundaries are useful when there is… Continuousness (branch of 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.

20. 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.

21. 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.

22. Tools • 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.

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

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. 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.

27. 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

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

29. 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.

30. 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.

31. 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?

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

33. 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?

34. Problems • Many of these problems are familiar from formal datasets. • What is lacking is experience in dealing with them. • Many of the assumptions we need to make are already accepted in standard techniques. • Many techniques are available from more clear-cut areas. • Mereotopological calculi • Supervaluation semantics • Fuzzy Logic • Statistical / Probabilistic techniques

35. 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.

36. 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.

37. 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) and (REALCRIME > average) then INVESTMENT is HIGH • Pros: Gives you some degree something is true. • Cons: A little arbitrary in places. Makes large assumptions about comparability.

38. Statistics / Probability / Logic • 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 action without necessarily going through understanding.

39. 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.

40. 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?

41. 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.

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