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Using Sound to Represent Positional Accuracy of
Address LocationsNick Bearman and Andrew Lovett
PhDSchool of Environmental Science
University of East Anglia
[email protected]; [email protected]
IntroductionPositional
Accuracy & AL2
Why Sound? Methods Results Next Steps
Positional Accuracy of Address Locations
• Geo-coding is ‘address’ → ‘location’
• Two main uses
• Spatial analysis & Routing
• Why is it important?
• Address Layer 2 is used for geo-coding• not all addresses have correct locations
• assuming all entries are correct can impact the analysis or routing
Merrifield CottageTR16 5DA
IntroductionPositional
Accuracy & AL2
Why Sound? Methods Results Next Steps
OS MasterMap ® Address Layer 211B Clarendon Road
NorwichNR2 2PN
IntroductionPositional
Accuracy & AL2
Why Sound? Methods Results Next Steps
Value Description
Surveyed Within the building that the address refers to.
Approximate Usually within 50m.
Postcode Unit Mean Mean position calculated from correctly located points within the postcode unit (e.g. NR4 6AA is a postcode unit).
Estimate Usually within 100m.
Postcode Sector Mean Mean position calculated from correctly located points within the postcode sector (e.g. NR4 6__ is a postcode sector).
Positional Accuracy - which some users ignore
Why?
• it's not relevant• users don't think it's relevant (when it is)• users can't access the information within the data• users can't display the information• the information isn't available
• Vision can be saturated• Alternatives to Vision
• Vision can be used more effectively – but there are limits
• Sound is the next most powerful sense (haptic/touch)• Little Previous Research
• These were pilot studies, custom coded. Need to move these to a generic, easily usable environment (GIS)
• No user testing & no existing research frameworks• Piano Notes
• Easy to understand – clear order (implicit assumption?)• CEG Triad, preferred option• Possibly could have chosen any sound though
Why Sound?
http://kbark.wordpress.com/2006/12/17/where-am-i/ (18/03/2009)
Finnish Town
http://www.politics.co.uk/news/policing-and-crime/new-police-crime-maps-confusing--$1272816.htm (18/03/2009)
London Crime
Fisher (1994)
Fisher (1994) Uncertainty of Classification
MacVeigh & Jacobson (2007)
MacVeigh & Jacobson (2007) Harbour, Sea and Land
IntroductionPositional
Accuracy & AL2
Why Sound? Methods Results Next Steps
Value
Surveyed
Approximate
Postcode Unit Mean
Estimate
Postcode Sector Mean
Methods• Created extension to ArcGIS
• Written in Visual Basic
• Prototype – tested and improved
• Users asked to choose proportion• 25%, 50% or 75% (stratified random method)
• Four methods
• Demo
IntroductionPositional
Accuracy & AL2
Why Sound? Methods Results Next Steps
• Proportion – (p < 0.005)
– Logical, but no evidence of this in GI literature
• Presentation Method– (p < 0.05)
– As expected
ResultsProportion Mean Score
75% 0.88
25% 0.74
50% 0.62
Presentation Method Mean Score
Visual Sonic Same 0.86
Visual 0.82
Sonic 0.67
Visual Sonic Different 0.65
+-
+-
+
-+
IntroductionPositional
Accuracy & AL2
Why Sound? Methods Results Next Steps
• Logistic Regression• Also Knowledge of the Data appeared to have an impact
– but when this was added into the logistic regression, it wasn’t significant.
• Discussion Sessions• Greater differences between sounds• Colour blind users
Results
Factors added to Model -2 Log Likelihood Cox & Snell R2
Proportion 182.01 0.043
Presentation Method 169.579 0.105
Knowledge of the Data 167.319 0.116
IntroductionPositional
Accuracy & AL2
Why Sound? Methods Results Next Steps
Next Steps• Summary
• Sound can work
• Significant Factors• Proportion of Data and Presentation Method
• Knowledge of Data
• Improvements• Task & Sounds
• Next case study• UKCP09
• web interface
• more focus on prior knowledge of data set
• VR Visualisations of Future Landscapes
IntroductionPositional
Accuracy & AL2
Why Sound? Methods Results Next Steps