Automated Map Content for Perfect Seams: Cognitive Engineering in Mixed-Scale and Mixed-Space Applications

Lucas Godfrey*1, William Mackaness†1

1School of GeoSciences, The University of Edinburgh

January 13, 2017

Summary

We present an approach to extending previous work related to non-lateral geospatial queries on graph structures such that we can surface single journey views for complex multimodal routes. The focus is on automating the provision of map content to support cognitively smooth transitions between scales and levels of schematisation on mobile devices. The aim is to reduce the cognitive load of wayfinding

information while maintaining an element of ‘seamfulness’; that is to say a user experience of navigational applications that is not entirely passive and encourages engagement with the overall

structure and character of the geography.

KEYWORDS: spatial cognitive engineering; automated cartography; mixed-space; multimodal travel; wayfinding.

1. Introduction As we travel across complex cities, we use a variety of information types, often accessed on a small screen device. Each of these ’views’ are essentially discrete, and it is left to the cognitive effort of users to ‘stitch’ the information together so they can make spatial decisions and effectively complete their journey. This information may include maps at different levels of detail, and maps of alternate network schematisations that must be accessed by changing to an alternate app or navigating to a different web page. We propose that the cognitive effort of making effective spatial decisions could be reduced by automating map content such that varying information types are integrated into heterogeneous views of the geography to provide a balance of functional and contextual information, taking account of the current context of the map user. In this paper, we propose applying insights from spatial cognition to the challenge of surfacing heterogeneous views of the geography across both mixed-scale and mixed-space contexts. By mixed-scale we mean the integration of multiple levels of detail into single map views. By mixed-space, we mean the integration of topological and topographic representations. 1.1. Automated cartography: seamlessness vs perfect seams When we consider automation, a central consideration is whether we are aiming for a ‘seamless’ experience or a ‘seamful’ experience (Weiser, 1994). Seamlessness implies a passive role for the user, where computation is almost entirely hidden from view and the user is essentially directed through a set of instructions (or a process takes place without any interaction at all); in wayfinding, this can be seen by way of support for a simple route orientation strategy as opposed to a higher-level survey orientation strategy (Skagerlund et al., 2012). ‘Seamfulness’ implies that the underlying data is exposed to the user such that the user has some conception of the mechanics behind why a particular piece of advice or information was given by the system (Jipp et al., 2016). In relation to spatial cognition, Richter et al. (2015) discuss ‘perfect seams’, whereby a system for assisting in spatial decision-making balances the ‘two components of navigation’ (Montello, 2005): “the near automatic, subconscious locomotion, and the goal-directed, conscious planning component of wayfinding…”

* lucas.godfrey@ed.ac.uk † william.mackaness@ed.ac.uk

(Richter et al., 2015). In the context of the graphical display of geospatial data, this ambition provokes a range of conceptual and technical challenges which indicate a need to re-think both how spatial data may be formalised in a database, and how this data model may then be surfaced to the map user in such a way as to support spatial decision-making for complex journeys (e.g. journeys that traverse multiple forms of transport). It should be noted that the notion of ‘seamfulness’ (Barkhuus et al., 2011) in the context of automated services usually implies a heterogeneous assemblage of data and processes that most likely involves multiple underlying databases. In the research presented here however, a key assumption is the possibility of creating a single database that may be queried to surface geospatial data from varying transport networks, for example the geometries of both the metric Cartesian view of the topography, and the topological schematic view that is used to simplify network representation. 1.2. From mixed-scale to mixed-space: creating perfect seams for multimodal wayfinding The prevailing approach to storing, querying and ultimately visualising geospatial information is based on a striated view of space in which objects are generically grouped together based on scale. This approach is based on ‘lateral’ queries through our hierarchical conceptualisation of geography – from global through to local, with a lateral query retrieving a sub-set of geographic objects generically based on a given level of detail. Here then, ‘perfect seams’ may be realised by providing information that supports cognitively smooth transitions between varying levels of detail and varying forms of representation, while maintaining geographic context.

Figure 1 The Event Horizon: a non-lateral ‘cut’ through a hierarchical data structure, supporting the visualisation of both global context and local detail in a single view (Quigley, 2001)

Quigley’s ‘event horizon’, illustrated in Figure 1, addresses this challenge with a non-lateral ‘cut’ through the underlying graph, selecting a set of objects from varying levels of detail relative to a single heterogeneous context. This work was applied more specifically to geographic information by Mackaness et al. (2011), however questions remain around the actual structure and content of the underlying graph, and crucially, around the logic of both the event horizon query and the ultimate method of visualisation.

Figure 2 Discrete spaces: from the topological to the topographic. Shown here: a) Open Street Map

Public Transport routes for Edinburgh and b) Edinburgh bus and tram network schematisation (courtesy of Transport for Edinburgh)

The application of the event horizon concept to the visualisation of geospatial information is also linked to research such as van Oosterom et al.’s work on ‘tGAP’ (topological generalized area partitioning) and the ‘SSC’ (space-scale cube), which represents an approach to surfacing ‘mixed-scale’ geographic information both in terms of the data model and the ultimate visualisation (van Oosterom et al., 2011).

Figure 3 Edinburgh street and public transport networks: mixed-scale and mixed-space information

(OSM/ Transport for Edinburgh)

While researchers such as van Oosterom have investigated the problem of ‘continuous generalisation’ and the representation of features from different scales in single views, we argue that to achieve ‘perfect seams’ in applications to support urban wayfinding, it is also necessary to integrate the Cartesian metric space defined by a geographic coordinate system, with non-metric schematisations that better reflect simplified representations of public transport networks (i.e. map views that are often based on a normalisation of routes to either a horizontal, vertical or 45 degree position, with true north also adjusted based on the particular layout of the city in question - see Figure’s 2 and 3). 1.3. The functional graph: formalising insights from spatial cognition We propose the functional graph as an approach to storing a set of objects and relations that include, in addition to the usual content of a geospatial database, explicitly defined objects and relations that

relate to the functional elements of the geography, i.e. aspects of the geography that ‘specify action’ (Freksa et al., 2005). The parameters of this additional data are bound by our use case - multimodal public transport. Here our focus is on formalising transfer-points, turning-points and anchor-points such that we are able to derive a heterogeneous set of objects and a geometry for the route, being mindful of the average size and proportions of mobile devices. We then propose the event horizon as the query logic for retrieving a sub-set of the objects and relations based on three contextual parameters: the start location, the destination and the possible modes of travel.

Figure 4 Metric (WGS-84) multimodal journey extent relative to mobile device view

Figure 5 Basic functional graph output, visualised with polyfocal Cartesian distortion

In Figure 5 we show a mixed-scale, mixed-space view of a journey across central Edinburgh making use of the tram network. Here polyfocal Cartesian distortion simply serves as a way to visually demonstrate the concept as opposed to being proposed as a viable end solution for visualisation. This research can be seen as extending Tomko and Winter’s work on describing the functional spatial structure of urban environments (2013). While Tomko and Winter built on Lynch’s Image of the City

(1960) to create graphs based on the accessibility afforded by varying forms of urban transport, the work presented here focuses on the likely cognitive salience of aspects of the route that can be used to automatically derive a heterogeneous set of objects and geometries for visualisation. Drawing on insights such as the anchor-point hypothesis (Couclelis et al., 1987), the functional graph offers an underpinning data structure from which to ultimately surface single journey views of multimodal routes optimised for small screen devices. 1.4. Conclusions and next steps The paper concludes with a set of next steps centred around user testing to compare the efficacy of our approach with established approaches, and to ascertain the optimal level of distortion in the visualisation, for example the number of objects to be ‘drawn’ to the screen, the visual treatment of the objects (e.g. how best to represent intermediate spaces that join different scales, while maintaining context), and the extent to which relative distances and angles may be expanded/ collapsed/ rotated without affecting a user’s recognition of the underlying character of the geography. 2. Acknowledgements The authors would like to thank the Ordnance Survey and the Engineering and Physical Sciences Research Council for supporting this research. 3. Biography Lucas Godfrey is a PhD Researcher in the School of GeoSciences at the University of Edinburgh. With a focus on spatial cognitive engineering and applied computational design, Lucas’s research centres around how to model and visualise spatial information to support decision-making, particularly in the context of complex urban transport networks. William Mackaness is a Senior Lecturer in the School of GeoSciences at the University of Edinburgh. William’s core expertise lies map generalisation and automated cartography, in particular, multiscale map generalisation and geovisualisation. 4. References Barkhuus, L. and Polichar, V.E. (2011). Empowerment through seamfulness: smart phones in

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