Bearman, N

Lovett, A

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University of East Anglia digital repository

Using Sound to Represent Uncertainty in Address LocationsNick Bearman1 and Andrew Lovett11School of Environmental Science, University of East Anglia, Norwich, NR4 7TJTel: +44 (0)1603 591346, www.nickbearman.me.ukEmail: n.bearman@uea.ac.uk, a.lovett@uea.ac.ukKEYWORDS: positional accuracy, sound, sonification, tone, ArcGIS1. IntroductionAll spatial data have positional uncertainty, which sometimes users ignore. There are a number ofdifferent ways of representing these data visually, but they have limitations which can be addressed byutilising non-visual methods. This study uses piano notes to represent spatial uncertainty in OrdnanceSurvey Address Layer 2 (AL2) data, and was evaluated by 49 spatial data users using computer basedevaluations and discussion sessions. 2. Literature ReviewThe AL2 data set has different status flags within it indicating various factors including positionalquality accuracy. This is important because greater uncertainty may mean addresses are shown up toseveral km away from their true location and ignoring this could have serious consequences (e.g. forrouting applications in emergency services usage). Despite this, a number of interviews with OrdnanceSurvey Account Managers and Pre and Post Sales staff suggested that often status flags are notproperly considered when the data is used by external organisations.The representation of uncertainty has significant coverage within the literature and visual methodssuch as colour, blurring or multiple maps are generally effective (Appleton et al., 2004; Ehlschlaegeret al., 1997). However these methods can obscure underlying data or limit the amount of informationshown. The use of other senses has been explored to address these limitations. Haptic (touch) maps arebeing developed, but they require specialised hardware and training to use (Golledge et al., 2005).Sound is also being researched and the hardware required (sound card and headphones/speakers) isreadily available, but user training is required (Pauletto & Hunt, 2009).  One way of showing uncertainty information more effectively is to use sound in combination withvision, and this has been addressed from a theoretical and practical point of view (Krygier, 1994;Fisher, 1994). MacVeigh & Jacobson (2007) developed a prototype which sonified three different landuses (sea, land and harbour). They found participants understood the map quickly and that the soundenhanced their experience of the map.  They suggested that an extension to an industry standard GIS(e.g. ESRI's ArcGIS) could be created which would use sound to represent spatial data. Thisintegration into a commercial application would enable greater use and easier evaluation. Few of theseideas have gone beyond the proof of concept stage or had significant user testing. This field ofresearch is still in an early stage of development and comparison is often difficult due to differentterminologies and research frameworks (Frauenberger & Stockman, 2009). 3. MethodsAn ArcScript (custom extension to ArcGIS) was created to allow evaluation of both visual and sonictechniques to represent positional uncertainty with Ordnance Survey MasterMap Topography and AL2data from Norwich. The AL2 Positional Quality Accuracy status flag (PQA) was linked with theTopography data using the associated TOIDs (topographic identifiers). The AL2 PQA status flag values are: Surveyed (most accurate), Approximate, Postcode Unit Mean,Estimate and Postcode Sector Mean (least accurate). In a pilot study, participants said that five soundswere too many to understand effectively, as they were unable to discriminate between the differentpiano notes and found it difficult to relate the notes to the status flag values. The five categories werereduced to three by recoding Postcode Sector Mean to Estimate and Approximate to Postcode UnitMean. The data presented to participants was altered to allow different proportions of Surveyed values(see below). One section of the evaluation required a second dataset to be presented; this wasfabricated “Council Tax bands” information.The three categories were represented using piano notes because the scale on a piano is very easy tovisualise (i.e. participants can easily understand the difference between a 'high' and 'low' note) and themajority of people are familiar with this instrument. The notes chosen were E5, G4 & C4, which werebased on the CEG triad split over two octaves. A triad was chosen because triads are sets of noteswhich sound harmonious together (Burrus, 2009) and CEG was the favoured option in the pilot study.The highest note (E5) represented the highest level of accuracy and the lowest note (C4) representedthe lowest. These were played as the participant moved the mouse over the buildings, allowing them toeither query a specific building or scan an area of data to get an overall view. A legend was provided,to allow the participant to link specific notes to specific values. The data set was shown to theparticipant using four different presentation methods (see Table 1). Table 1. The four different presentation methods (in the order they were shown to theparticipants) and which data were shown visually or sonically. The topography layer wasalways shown visually. Presentation Method Visual Data Sonic DataSonic only (see Figure 1) Topography outlines only AL2 Positional AccuracyVisual only (see Figure 2) AL2 Positional Accuracy NoneVisual and Sonic representing thesame variable (VS Same)AL2 Positional Accuracy AL2 Positional AccuracyVisual and Sonic representingdifferent variables (VS Different)Council Tax bands AL2 Positional AccuracyFor each presentation method, the participant was asked to identify the proportion of Surveyed values,from options of 25%, 50% or 75%. The data were randomly assigned one of these values using astratified random method for each presentation method. Other background questions were also asked,which allowed analysis depending on musical experience, learning preference and a number of othervariables. This task was chosen because it combined a simple principle (i.e. what proportion of thevalues are Surveyed) with the need to utilise sound in a way that visual representations are oftenemployed. Figure 1. Screen shot of ArcGIS showing the data represented sonically. Note the topography layershown visually, and the sound legend in the top right-hand corner. Figure 2. Screen shot of ArcGIS showing the data represented visually. Note the AL2 data shownvisually, and the legend on the top left-hand side. The topography layer is also visible. A total of 49 participants completed the assessment, consisting of 19 from Ordnance Survey, 23 fromUEA and 7 from Local Authorities. All the participants had at least a basic knowledge of GIS, spatialdata and ArcGIS and used these on a regular basis, although experience with AL2 varied. Headphoneswere used to provide auditory stimuli, with adjustable volume. The evaluation took place in groups ofthree to six, and was followed by a facilitated discussion for around 20 to 30 minutes which coveredthe participants’ views and feelings and the potential uses of this technique. 4. Results & DiscussionNearly all participants (46 out of 49) identified the correct proportion of Surveyed values for 3 or moreof the presentation methods. Figure 3 shows how the mean answer for participants (correct = 1, notcorrect = 0) varies between proportion and presentation method, which are the two main influencingfactors. The general trend was for more correct answers with 25% and 75% data proportions and lesswith 50% proportion. The exception to this was the Sonic presentation method, which performed asbadly with 25% as it did with 50%. Visual and VS Same (see Table 1 for definitions) performedreasonably well, while Sonic and VS Different had lower correct frequencies. This could occurbecause the nature of the program makes it very easy to find the sounds when they are common, butdifficult when they are sparse. This would impact Sonic and VS Different and potentially result inparticipants over estimating the proportion. Both proportion (p < 0.005) and presentation method (p <0.05) had a significant influence on whether a participant identified the correct proportion. A higher knowledge of the data set being sonified increased the likelihood of the participants choosingthe correct proportion. However, this trend was not especially strong and although including it in alogistic regression model with proportion and presentation method improved the model (see Table 2)the addition was not statistically significant. Table 2. Factors added to the Logistic Regression Model and their impactFactors added to Model -2 Log Likelihood Cox & Snell R2Proportion 182.01 0.043Presentation Method 169.579 0.11Address Knowledge 167.319 0.116The free text answers showed that some participants found the sonification very useful and that itadded a large amount to the interpretation of the data, while others said the sound was very difficult toFigure 3. Answers for all participants, split by presentation method and proportion25% 50% 75%00.10.20.30.40.50.60.70.80.91VisualSonicVS SameVS Dif ferentProportion of Values with 'Surveyed'Mean answers (0 = incorrect, 1 = correct)understand and when combined with vision, distracted them from the visual interpretation. Sonic andVS Different were considered harder to use than Visual and VS Same. Sonic only had a very lowsuccess rate and seems unlikely to ever be successful with this type of interface. The discussion sessions after each evaluation session provided further qualitative information andgave participants a chance to suggest changes and improvements to the technique. Preferences for thetypes of sounds used were subjective, and are likely to vary depending on the data set and the analysistaking place. A wider range of audio clips coupled with user choice could allow easier differentiationof sounds and potential for representation of a larger number of variables. Possibilities includedifferent piano notes, different instruments, or completely different sounds, such as environmental oranimal sounds. Colour-blind users were highlighted as a group who might find this sonification useful,however a larger sample size is required to effectively evaluate this. The task chosen may limit the wider applicability of the results but there are few existing evaluationsin this area so there is very little comparative data. The task needed to be easy enough to ensure thatsome of the participants managed to answer most/all of the questions correctly but not too difficult sothat it would result in exclusively incorrect answers. Possible future options include more complextasks (such as clustering exercises) and more comparisons of different presentation methods, utilisingboth sound and vision. This research has highlighted specific characteristics that influence the ability of users to interpretsound to make proportion judgements. The proportion of the data the the user is interested in and thepresentation method are the two factors that have the most impact on whether a person will be able tounderstand the proportion correctly. Knowledge of the data set being sonified also appears to havesome impact, but this is not so clearly apparent with these results. These issues will be explored infuture research when the author's PhD research evaluates the use of sound to represent uncertainty inUK climate scenario data. 5. AcknowledgementsThis study has been conducted as part of an ESRC/NERC PhD Studentship. The authors would alsolike to thank all the participants in the study; the Ordnance Survey for their additional financialsupport, particularly Jenny Harding and Glen Hart for their advice, cooperation with data andassistance with arranging the evaluation sessions; and Katy Appleton for providing valuable commentswhile writing this extended abstract. I would like to thank the GISc Research Group of the RGS-IBG for financial support towards the costof presenting at the GISRUK 2010 conference in London.ReferencesAppleton, K., Lovett, A., Dockerty, T. & Sünnenberg, G. (2004) Representing Uncertainty inVisualisations of Future Landscapes. In: Proceedings of the XXth ISPRS Congress.Burrus, C. (2009) There's Math behind the Music! [Internet]. Available from:<http://www.charlieburrus.com/MathInMusic/Index.htm> [Accessed 17 February 2010].Ehlschlaeger, C.R., Shortridge, A.M. & Goodchild, M.F. (1997) Visualizing spatial data uncertaintyusing animation. Computers & Geosciences, 23 (4), pp.387-395.Fisher, P.F. (1994) Hearing the Reliability in Classified Remotely Sensed Images. Cartography andGeographic Information Systems, 21 (1), pp.31-36.Frauenberger, C. & Stockman, T. (2009) Auditory display design-An investigation of a design patternapproach. International Journal of Human-Computer Studies, 67 (11), pp.907-922.Golledge, R.G., Rice, M. & Jacobson, R.D. (2005) A Commentary on the Use of Touch for AccessingOn-Screen Spatial Representations: The Process of Experiencing Haptic Maps and Graphics.The Professional Geographer, 57 (3), pp.339-349.Krygier, J.B. (1994) Sound and Geographic Visualization. In: Visualization in Modern Cartography.Oxford, UK, Elsevier Science, pp.149-166.MacVeigh, R. & Jacobson, R.D. (2007) Increasing the dimensionality of a Geographic InformationSystem (GIS) Using Auditory Display. In: Proceedings of the 13th International Conferenceon Auditory Display. Available from:<http://www.music.mcgill.ca/icad2007/proceedings.php> [Accessed 25 May 2008].Pauletto, S. & Hunt, A. (2009) Interactive sonification of complex data. International Journal ofHuman-Computer Studies, 67 (11), pp.923-933.Biography3ick Bearman completed his MSc GIS at University of Leicester in 2008 and is currently studying fora PhD at UEA in Environmental Science, researching different methods of representing uncertainty ina variety of spatial data environments. 

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