We designed and applied interactive visualisation to help an urban study group investigate how suburban residents in the Tallinn Metropolitan Area (Estonia) use space in the city. We used mobile phone positioning data collected from suburban residents together with their socio-economic characteristics. Land-use data provided geo-context that helped characterise visited locations by suburban residents. Our interactive visualisation design was informed by a set of research questions framed as identification, localisation and comparison tasks. The resulting prototype offers five linked and coordinated views of spatial, temporal, socio-economic characteristics and land-use aspects of data. Brushing, sorting and filtering provide visual means to identify similarities between individuals and facilitate the identification, localisation and comparison of patterns of use of urban space. The urban study group was able to use the prototype to explore their data and address their research questions in a more flexible way than previously possible. Initial feedback was positive. The prototype was found to support the research and facilitate the discovery of patterns and relations among groups of participants and their movements.
KährikATammaruT.Population composition in new suburban settlements of the Tallinn Metropolitan Area. Urban Stud2008; 45(5–6): 1055–1078.
2.
TammaruTLeetmaaKSilmS. Temporal and spatial dynamics of the new residential areas around Tallinn. Eur Plan Stud2009; 17(3): 423–439.
3.
MäeRAntovDAntsoI. Urban sprawl: mobility potentials in suburban areas of Tallinn. In: PacettiMPasseriniGBrebbiaCA. (eds) The sustainable city VII: urban regeneration and sustainability. Southampton: WIT Press, 2012, pp. 967–977.
4.
AhasRAasaASilmS. Mobile positioning in space-time behaviour studies: social positioning method experiments in Estonia. Cartogr Geogr Inform2007; 34(4): 259–273.
5.
AndrienkoGAndrienkoNHeurichM.An event-based conceptual model for context-aware movement analysis. Int J Geogr Inf Sci2011; 25(9): 1347–1370.
6.
AndrienkoGAndrienkoNDykesJ. Geovisualization of dynamics, movement and change: key issues and developing approaches in visualization research. Inform Vis2008; 7(3–4): 173–180.
7.
KeimDKohlhammerJEllisG. Mastering the information age: solving problems with visual analytics. Goslar: Eurographics Association, 2010, p. 168.
8.
ToryMMollerT.Human factors in visualization research. IEEE T Vis Comput Gr2004; 10(1): 72–84.
9.
AhasRAasaASilmS. Daily rhythms of suburban commuters’ movements in the Tallinn Metropolitan Area: case study with mobile positioning data. Transp Res Part C Emerg Technol2010; 18(1): 45–54.
10.
TammaruT.Suburbanisation, employment change, and commuting in the Tallinn Metropolitan Area. Environ Plann A2005; 37(9): 1669–1687.
11.
AhasRLainesteJAasaA. The spatial accuracy of mobile positioning: some experiences with geographical studies in Estonia. In: GartnerGCartwrightWPetersonMP (eds) Location based services and telecartography. Berlin, Heidelberg: Springer, 2007, pp. 445–460.
12.
KangCGaoSLinX. Analyzing and geo-visualizing individual human mobility patterns using mobile call records. In: 18th international conference on geoinformatics, Beijing, China, 18–20 June 2010, pp. 1–7. New York: IEEE.
13.
AndrienkoGAndrienkoNBakP. A framework for using self-organising maps to analyse spatio-temporal patterns, exemplified by analysis of mobile phone usage. J Locat Based Serv2010; 4(3): 200–221.
14.
SlingsbyABeechamRWoodJ. Visual analysis of social networks in space and time. In: The Nokia data challenge workshop (Pervasive 2012), Newcastle, 18–19 June 2012.
Dal FioreFBeinatERietveldP. Exploring the relationship between land use and mobile phone usage: analysis of a dataset for the Amsterdam metropolitan region. In: Workshop on pervasive urban applications (PURBA), San Francisco, CA, 12–15 June 2011.
17.
SkupinAMillerHJ. Nokia MDC atlas: an exploration of mobile phone users, land cover, time, and space. In: Nokia data challenge workshop (Pervasive 2012), Newcastle, 18–19 June 2012.
18.
Di LorenzoGCalabreseF. Identifying human spatio-temporal activity patterns from mobile-phone traces. In: 14th international IEEE conference on intelligent transportation systems (ITSC), The George Washington Univeristy, Washington, DC, USA, 5–7 October 2011, pp. 1069–1074. Washington: Omnipress.
19.
MacEachrenAM.How maps work: representation, visualization and design. New York: The Guilford Press, 1995, p. 513.
20.
WehrendSLewisC. A problem-oriented classification of visualization techniques. In: Proceedings of the 1st conference on visualization ’90, San Francisco, CA, 23–26 October 1990, pp. 139–143. Los Alamitos, CA: IEEE Computer Society Press.
21.
KnappL. A task analysis approach to the visualization of geographic data. In: NyergesTLMarkDMLauriniR. (eds) Cognitive aspects of human-computer interaction for geographic information systems. Dordrecht: Kluwer Academic, 1995, pp. 355–371.
22.
BertinJ.Semiology of graphics: diagrams, networks, maps. Madison, WI: The University of Wisconsin Press, 1983, p. 415.
23.
AndrienkoNAndrienkoG.Exploratory analysis of spatial and temporal data: a systematic approach. Berlin: Springer, 2006, p. 703.
24.
FriendlyMKwanE.Effect ordering for data displays. Comput Stat Data An2003; 43(4): 509–539.
25.
SlingsbyADykesJWoodJ.Configuring hierarchical layouts to address research questions. IEEE T Vis Comput Gr2009; 15(6): 977–984.
26.
FabrikantSI.Building task-ontologies for geovisualization. In: ICA commission on visualization and virtual environments pre-conference workshop on geovisualization on the web. Beijing, China, 3–4 August 2001.
BrewerCA.Chapter 7: color use guidelines for mapping and visualization. In: MacEachrenAMTaylorMAP (eds) Visualization in modern cartography. Tarrytown, NY: Elsevier Science, 1994, pp. 123–147.
29.
KohLCSlingsbyADykesJ. Developing and applying a user-centered model for the design and implementation of information visualization tools. In: 15th international conference on information visualisation (IV), London, United Kingdom, 13–15 July 2011, pp. 90–95. Los Alamitos, CA: IEEE Computer Society Press.
30.
SlingsbyADykesJ.Experiences in involving analysts in visualisation design. Beyond time and errors: novel evaluation methods for visualization (BELIV). In: Visweek 2012, Seattle, WA, 14–15 October 2012.
31.
AignerWMikschSSchumannH. Visualization of time-oriented data. London, Dordrecht, Heidelberg, New York: Springer, 2011.
32.
LloydDDykesJ.Human-centered approaches in geovisualization design: investigating multiple methods through a long-term case study. IEEE T Vis Comput Gr2011; 17(12): 2498–2507.
33.
RobertsJC. State of the art: coordinated & multiple views in exploratory visualization. In: Fifth international conference on coordinated and multiple views in exploratory visualization (CMV ’07), Washington, DC, 2 July 2007, pp. 61–71. Los Alamitos, CA: IEEE Computer Society.
34.
RobertsonGFernandezRFisherD. Effectiveness of animation in trend visualization. IEEE T Vis Comput Gr2008; 14(6): 1325–1332.
35.
MorrisonJL.A theoretical framework for cartographic generalization with the emphasis on the process of symbolization. Int Yearb Cartogr1974; 1974(14): 115–127.
36.
WareC.Information visualization: perception for design. 2nd ed.Amsterdam: Elsevier Morgan Kaufmann, 2004, p. 486.
37.
SlingsbyADykesJWoodJ.Exploring uncertainty in geodemographics with interactive graphics. IEEE T Vis Comput Gr2011; 17(12): 2545–2554.
YiJSYa KangStaskoJT. Toward a deeper understanding of the role of interaction in information visualization. IEEE T Vis Comput Gr2007; 13(6): 1224–1231.
40.
ShneidermanB. The eyes have it: a task by data type taxonomy for information visualization. In: Proceedings of IEEE symposium on visual languages. Boulder, CO, 3–6 September 1996, pp. 336–343. Los Alamitos, CA: IEEE Computer Society.
41.
ShneidermanB.Tree visualization with tree-maps: 2D space-filling approach. ACM T Graphics1992; 11(1): 92–99.
42.
BeckerRAClevelandWSShyuM-J.The visual design and control of trellis display. J Comput Graph Stat1996; 5(2): 123–155.
43.
FriendlyM.Mosaic displays for multi-way contingency tables. J Am Stat Assoc1994; 89(425): 190–200.
44.
GleicherMAlbersDWalkerR. Visual comparison for information visualization. Infor Vis2011; 10(4): 289–309.
45.
DykesJWoodJSlingsbyA.Rethinking map legends with visualization. IEEE T Vis Comput Gr2010; 16(6): 890–899.
