THE IMPORTANCE OF BEING RELEVANT Tumasch Reichenbacher
Department of Cartography Technische Universität München Arcisstr. 21, 80333 München, Germany tel: ++49 89 289-22836 fax: ++49 89 280-9573 e-mail:
[email protected] web: www.carto-tum.de
Mobile cartography aims at producing appropriate presentations of geographic information for mobile users, for instance in the form of map based mobile services. Major influences come from new technologies like Location Based Services. Due to the small displays of mobile devices and specific mobile usage contexts selecting and presenting only the most relevant information is indispensable. The paper argues for considering further relevance types beyond positional relevance used in LBS. First general theories of relevance are discussed before showing the use of the relevance concept in mobile cartography. Based on the relation between mobile usage context and relevance a set of relevance types is derived. Approaches for computing compound relevance measures enable applying the relevance as a filtering criterion or as an attribute that can be visualised. Various graphical means for visualising differences in relevance demonstrate the practical use of relevance measures in mobile cartography and represent a point of departure for further research.
INTRODUCTION Mobile technologies have lately seen growing interest in cartography. Strong influences have come mainly from new technologies like Location Based Services (LBS). Ongoing research investigates methods of appropriate visualisations of geographic information on small displays of mobile devices. What is valid for cartography in general is even more so for mobile cartography, the primate of reduction to the relevant information, or positively, the adequacy of the information and its visualisation. Thus, a mobile map should show as much information as needed and as little as required. LBS, for instance, use the position to provide the user with more spatially relevant information. Hence, the concept of relevance is a vital concern in mobile cartography and map-based mobile services. Despite this importance research in the fields of adaptation of map presentations and relevance models for map services are sparse. Zipf (2003) proposes a method for calculating the relative dominance of geospatial objects to be used in so called focus maps. This paper describes the more fundamental concept of relevance and its application or significance to cartography in general and mobile cartography in specific. The justification of this approach can be expressed by the following two hypotheses: (1) relevance can be increased through adaptation of map presentations; (2) increased relevance leads to increased usability of map services. The underlying assumption for the advancing of these hypotheses is the presence of suitable representations of geographic information accessible by a human or machine agents through geographic information retrieval (GIR). Different situations produce information needs that are met by retrieving geographic information. The assessment of the relevance takes place in the information use, i.e. the utility of the retrieved information for the situation at hand. The presentation of the retrieved geographic information closes the information process circle. The utility of the information is one aspect of the overall usability. The aim of this paper is to claim the importance of the relevance concept for mobile cartography in an introductory style. Rather than offering final solutions it demonstrates possible applications of relevance by giving examples. These applications should illustrate the building of relevance for mobile usage situations that are not equally feasible with static map products. Many of these proposals heavily rely on assumptions and methods that might not yet be technically feasible. Still, the ambition is to stress the significance of relevance for a successful implementation of mobile cartography in real products and services. Therefore, the remainder of the paper will examine the concept of information relevance and its role in visualisation. First, general characteristics of relevance and its meaning in information retrieval are explained. Furthermore the relationship of relevance and context, which plays a major role in mobile cartography and map adaptation, are sketched. Second, the relevance concept will be applied to mobile cartography and the value of a compound relevance assessment beyond location relevance is elaborated. Finally, different techniques are presented how to map relevance values to graphical variable values in order to visualise the relevance to the user.
RELEVANCE THEORY Despite noticeable research interest relevance is still a fuzzy concept with roots in philosophy, cognition and information management. Yet, when using information we humans intuitively know what relevance is even without having a clear concept of it. The fuzziness of relevance is evidently reflected in an early definition by Rees (1966) in (Greisdorf 2000, p. 67) based on a list of synonyms: “the criterion used to quantify the phenomenon involved when individuals (users) judge the relationship, utility, importance, degree of match, fit, proximity, appropriateness, closeness, pertinence, value or bearing of documents or document representations to an information requirement, need, question statement, description of research, treatment, etc.” Saracevic (1996) gives a crisper definition derived from the general attributes of relevance: relation, intention, context, inference, and interaction. “ […] relevance involves an interactive, dynamic establishment of a relation by inference, with intentions toward a context. […] relevance may be defined as a criterion reflecting the effectiveness of exchange of information between people (or between people and objects potentially conveying information) in communicative relation, all within a context.” Depending on the relations established relevance manifests itself in different ways. Thus, there is not a single relevance, but a system of relevancies on different levels (Cosijn and Ingwersen 2000). Saracevic (1996) distinguishes five manifestations of relevance (see also Fig.2): (1) the system or algorithmic relevance is independent of the context and measures how well the query topic and document topic match, i.e. is objective and independent of context, (2) the topical or subject relevance (aboutness, topicality), (3) the cognitive relevance or pertinence (informativeness, novelty) (4) the situational relevance or utility (usefulness in decision making, reduction of uncertainty), and (5) the motivational or affective relevance (satisfaction, success). Although relevance is still a poorly understood concept, (Wilson and Sperber 2004) outline a basic relevance theory of communication. They consider the information inputs (any external stimulus or internal representation) to an individual. These inputs can be irrelevant or relevant to a certain degree. The degree of relevance affects the processing of inputs. With regard to processing limitations, the attention to specific inputs has to be chosen in some way. Only substantially relevant inputs deserve attention. They postulate two criteria for inputs to be relevant to an individual: effect and effort. Only an input that yields a higher positive cognitive effect than others should be processed, since the higher the degree of positive cognitive effects achieved by processing an input, the greater the relevance of the input to the individual at that time. Positive cognitive effects produce a significant difference to the individual’s representation of the world. This can be a true conclusion, a revision or a rejection of an assumption. For example the statement (1) ‘the conference takes place in the Congress Hall’ has a greater cognitive effect than (2) ‘the conference takes place in a building’ because conclusions from the latter can also be drawn from the former. Or to put it differently: (1) inherits the characteristics from (2) and holds additional specific information. Moreover the effort it requires to process should be as minimal as possible, because the greater the processing effort the lower the relevance of the input. The declaration (3) ’The conference takes place in a construction designed for the gathering of people communicating with each other’ has the same cognitive effect as (1), but requires more effort to process. From these points they conclude the cognitive principle of relevance that states that “human cognition tends to be geared to the maximisation of relevance” (Wilson and Sperber 2004). The concept of relevance is central to information retrieval (IR) where the goal is to “ … retrieve all the relevant documents [and] at the same retrieving as few of the non-relevant documents as possible” (van Rijsbergen 1979, p. 6). IR deals with the semantic similarity between query terms and the terms found in documents. Semantic similarity is basically understood as the closeness of hierarchical concepts expressed as semantic distances. IR literature is rich on methods to determine these distances, i.e. similarity measures. The most prominent is the graph-based semantic distance model. (Brooks 1998) makes a point that semantic distance and relevance are inversely correlated. He calls this correlation the semantic distance effect stating that the smaller the semantic distance between two terms, the greater the assessed relevance. Or in other words; the further apart a query term and a term in a retrieved document are semantically, the less relevant is the document. Based on binary relevance (relevant – not relevant) several measures – such as precision and recall – to determine the efficiency and effectiveness of search or retrieval processes are common in IR. Precision is the ratio between the number of retrieved relevant and the total number of retrieved documents. Recall is the ratio between the number of retrieved relevant and the possible relevant documents (Mizzaro 2001). As pointed out by many researchers (e.g. Saracevic 1996; Wilson and Sperber 2004; Cosijn and Ingwersen 2000), there are degrees of relevance rather than a binary relevance. Consequently, a step further than binary relevance is the introduction of ranks for the retrieved documents based on the similarity of the document and the query as implemented in most Internet search engines (e.g. Google). Such search engines assess the relevance of found documents and perform a ranking by expressing documents and queries as vectors representing the occurring words. The scalar product of each document vector against the query vector yields a very basic ranking value that defines the presentation order of the retrieved documents.
APPLICATION OF RELEVANCE THEORY TO MOBILE CARTOGRAPHY If we try to apply the relevance concept to mobile cartography, it becomes obvious that it would be necessary to introduce at least one more relevance criterion, the spatial relevance. The importance of relevance in the geospatial domain has been stressed by (Raper et al. 2002): “… understanding the individual ‘geographical relevance’ of information will be necessary for location-based services to provide appropriate information …”. This understanding is also shared in the field of Geographic Information Retrieval (GIR), where methods for spatial ranking of retrieved geospatial information are developed (e.g. Larson and Frontiera 2004). GIR extends standard IR by spatially aware retrieval functions. Apart from the spatial relation there are other challenges in the mobile environment. Mobile users of geographic information are likely to be engaged in additional activities. They wish to have access to information that is relevant to their context beyond the spatial dimension. Interaction and browsing possibilities are limited on mobile devices. And, most important to cartography is the lack of map space on portable displays. This leads to a sort of competition for that limited map space, which requires a selection of relevant objects. All these facts justify the contemplation of the relevance concept for mobile maps.
Mobile context and relevance: Becker and Nicklas (2004) present a classification of context-aware applications. Two types are of interest for our purposes: context-based selection and context-based presentation. Both are incorporated in the adaptations of a geovisualisation service with the objective of achieving a greater relevance for the user and hence usability of the services. In general different usage contexts imply different relevance moments and therefore require different adaptors. However, there is a difference in relevancy and appropriateness or adequacy of the presentation. For instance the presentation of directions to a car driver on a map could be relevant, but may not necessarily be the adequate form of presentation. In comparison to GIR mobile cartography, as outlined for example in (Reichenbacher 2003), relies on further contextual factors. The most important context dimensions are location, time, user, activity, information, and system. As explained earlier relevance is dependent on context. Thus relevance from the perspective of mobile cartography should incorporate these contextual factors going beyond positional relevance. Consequently, in a mobile usage context the presented geographic information must be relevant to the context of use, i.e. to the current location at the current time, to the user, to the activity or task at hand, to the question/topic or request, and to the infrastructure available and colocated geospatial objects. In short a mobile map should satisfy the user’s contextual information needs. Figure 1 outlines the relationship of important context factors and their possible values (for further details see for instance (Reichenbacher 2004; Nivala and Sarjakoski 2003).
Figure 1: Possible values of context factors and their relationships
Activities as depicted in Fig. 1 typically follow patterns in the sense of a sequence of staying at or travelling between activities. Some activities show regularities concerning space and time (e.g. locations, sequence, and frequency) and can be attached to specific geospatial objects (Wang and Cheng 2001). By clustering the value combinations a possible rough classification can be achieved reflecting context and activity patterns. Such a typology can then be associated with virtual adaptors. Virtual adaptors are combinations of presentation templates, symbol templates, and user profiles that enforce methods in order to generate adapted and relevant presentations. A step further there would be the establishment of a context ontology - or to begin with, an activity ontology – to describe typical usage situations. A good example of an activity ontology for driving situations is described by (Kuhn 2001). In a much simpler way McCullough (2001) proposes a preliminary typology of everyday ‘situations’ relevant for mobile services in a rather qualitative manner. He argues that design which is considering these activities will be more usable. The four main ‘situations’ are at work, at home, in the town, and on the road, each associated with activities such as collaborating, watching, cruising, eating, shopping, sporting, touring, driving, walking, etc. (see also Fig. 1). This typology underpins the notion of socially constructed activity patterns and areas.
Relevance types: By combining the manifestations of relevance mentioned above and the contextual factors of importance in mobile environments a set of relevance types for mobile cartographic applications and services can be derived (Fig. 2). Of major importance to mobile cartography is the spatio-temporal aspect of relevance that should be expressed explicitly as spatial relevance and temporal relevance. The relations of objects to space and time can be attached to the physical relevance (analogous to physical context). The relation to the query elements corresponds to the GIR system relevance with all three relevance types belonging to objective relevance. All other types of relevance can be subsumed under the term personal relevance. Two manifestations proposed by Saracevic (1996) are attached as subtypes of the newly introduced activity relevance, since they represent two different aspects of activities: the motivation and the embedment in a situation. Spatial relevance becomes apparent as (1) area of interest, (2) spatial distance to the user’s position, and (3) activity or social space. This notion of spatial relevance assumes a relation to the user’s current position, i.e. has an egocentric presentation in mind. Of course, for an allocentric presentation the spatial relation would be different and hence the spatial relevance would look different. Similarly to spatial relevance, temporal relevance (timeliness) can express itself as (1) temporal distance between the time point of usage and a time reference of an object or event (e.g. start/end time), (2) as a time period or interval (e.g. duration) in which a point of interest is relevant or an activity is practicable. Furthermore time references on different levels might be involved (e.g. day, month, season) as well as qualitatively or socially defined times (e.g. leisure time). To illustrate these abstract types of relevance let us consider the example of shopping clothes: if you go shopping clothes, you expect from a mobile map service to display shops – preferably your usual favourites (thematic relevance) –, ATMs, coffee shops and the like (activity relevance) close to your whereabouts (spatial relevance) that are open at the present time or in the near future (temporal relevance). It is obvious that the relevance assessment is a highly dynamic process and that it is not always possible to clearly separate these relevance types. In practice they often overlap to some extent.
Figure 2: Relevance types for mobile cartographic applications (based on Saracevic, 1996)
Compound relevance: Approaches to formalise relevance of geospatial information are rather rare. Zipf (2003) attempts a formalisation by calculating the “remarkableness” or importance of single objects of a certain abstraction level dependent on the user and the context with the goal to emphasise them adequately in so called focus maps. The proposed formula derived from an
information visualisation approach incorporates user and context parameters to determine dominance values based on the overall distance of a candidate object’s parameters against the query parameters. Zipf acknowledges that most variables in his formula would have to be determined experimentally due to a lack of empirical, cognitive, and theoretical insights. For that reason in most cases relevance is approached pragmatically to date. A model of context validity is presented by (Schmidt and Gellersen 2001). It uses fuzzy set theory to model the spatial and temporal decrease of validity of context in relation to its origin. Although this model has been introduced for context-aware applications, its basic elements can be transformed to the concept of geographic relevance. An example of calculating a compound relevance factor for events based on this approach is described in (Reichenbacher 2004). Individually calculated spatial, temporal, and thematic relevance values are summed up to a total relevance factor indicating the relevance of an event to the user with a specific topic interest at a certain location and time. Simplistically modelled the general relevance R for an object (or event) Oi can be expressed as the function: R(Oi) = Ó wj rj, where j is the relevance type (spatial, temporal, …), rj is the value for the relevance type j, and wj is the weight for value j. The setting of weights for the considered relevance types is dependent on context. In practice not all relevance types have to or can be considered in the function. Yet, the consideration of more than one relevance type produces a more differentiated picture of object relevancies. The activity relevance might for a start be modelled as an enumerative list of typical activities associated with feature types that are a priori defined as relevant for each activity in the list. The spatial range of the activity then sets an upper limit of the spatially relevant objects for that activity. Within that range the spatial relevance to the position applies, i.e. the closer the more relevant. Activity relevance can bee seen in a different way: clusters of relevant objects for a specific activity form areas of interest (AoI) or activity zones (Fig. 3). The relevance degree of these zones could be derived from the frequency of objects per cluster. For example, the clusters of shops form shopping zones, i.e. relevant areas for the activity shopping. Zones with a higher number of shops can be regarded as more relevant for the activity. If activity relevance is coupled with temporal relevance, further objects (or events) can be filtered, for instance beer gardens in winter or skiing facilities in summer. Apart from distance in space and AoI spatial relevance can also mean the visibility of objects or areas. The visibility criterion is for example crucial for the landmark selection task (Elias et al. 2005). However, even though visible objects might have a greater attraction potential and are generally closer to the viewer, invisible objects can have other attraction potentials dependent on intrinsic characteristics.
Figure 3: Interdependencies between activity and spatial relevance All the examples above make it clear that an isolated contemplation of relevance types might improve the relevance, but in most cases interdependencies exist and a compound relevance factor is advantageous.
Applications of relevance: Relevance can be applied to mobile cartography in two different ways: 1) relevance of geospatial features in relation to a specific usage situation may be used for selecting or filtering geographic information from vast geospatial databases.
2) relevance measures can be applied to the map graphics to visually encode the differences in relevance. Both applications should improve the usability substantially by increasing the relevance of the presented information. The ultimate goal of adaptive geovisualisation services is the production of syntactically functioning and usable presentations with the highest possible relevance to the user, especially to the user activity. A high value of information relevance alone does not necessarily lead to a high degree of relevance of visualisation, where cartographic knowledge is essential (see Fig. 4). The overall relevance of a presentation of geographic information might be below its possibilities. To achieve a higher total relevance, different adaptations can also be applied in the visualisation domain (see Fig. 4). It seems to be important to indicate to the user that through the adaptation process it might occur that not all the information available is displayed, i.e. the data set is not complete. It is rather the case that less relevant information is omitted on purpose.
Figure 4: Relevance increase through adaptation Ideally an adaptive service would dynamically learn from observing the user’s behaviour in specific contexts, his activities, interactions and information needs in order to respond adequately. However, in practice this procedure is still too complex. Therefore a pragmatic solution of combined static and dynamic adaptation could provide faster solutions. The user defines his interests, preferences and favourites beforehand. This relatively static information is stored in a virtual adapter (e.g. a XML document). At the moment of service usage the virtual adaptor is combined with the service’s dynamic adaptation capabilities that take spatial and temporal settings into account (Fig. 5). For further insights in the process of adapting presentations and examples of map adaptation methods refer to (Reichenbacher 2004).
Figure 5: Adaptation model We will first consider the application of relevance in the filtering of geographic information before we study different graphical design methods and techniques for visualising relevance values in the next section. As mentioned in the section about relevance types spatial relevance has different aspects such as distance or area of interest that apply differently in filtering. A first step of filtering is the pre-selection based on the inclusion in an AoI. The parameters for a geographical object query can be derived from the context parameter values. These derived values represent inherently context-relevance and therefore allow the retrieval of relevant objects. Another procedure is the computation of a normalised compound relevance measure for pre-selected objects. The filtering of relevant objects might then be
the setting of a relevance threshold value, e.g. retrieve all objects with R > 0.75. The OGC Filter Encoding Implementation Specification (OGC 2001) offers a wide range of operators to build filter expressions for queries. Spatial operators are used for comparing the spatial relations between the query and the features (e.g. Equals, Disjoint, Touches, Within, Overlaps, Crosses, Intersects, Contains, DWithin, beyond, BBOX). For non-spatial properties comparison operators (=, ≠, , >=, >=, is_like, between) are available and with both types of operators more complex filters can be composed by applying logical operators (AND, OR, NOT). Temporal relevance filtering can be based on feature information, if a temporal relation is stored as an attribute of the feature. Thematically relevant objects can be filtered by comparing feature attributes with query terms. Such methods for computing semantic similarity measures are used for instance in the SPIRIT project (Jones et al. 2001).
TECHNIQUES FOR RELEVANCE VISUALISATION Adaptive geovisualisation services should present geographic information with the highest possible relevance to the user. Yet, the mere selection of relevant information is not enough (cf. Fig. 4). First of all the selected objects need to be appropriately symbolised applying cartographic knowledge. However, even this symbolisation process could be enhanced by visualising additionally the relevance values of the objects. The underlying assumption is that synoptically visualising differences in relevance leads to more usable map services. The relevance symbolisation should form a separate and independent layer of information that can be switched on and off by the user. It is also thinkable to have different levels of relevant objects stored in separate layers. In analogy to level of details (LoD) this allows for a concept of level of relevancies (LoR). This would enable the service or the user to switch on and off separate layers of relevance. Furthermore it would allow the user to adjust the presentation by loading layers holding less relevant objects in the case of a too severe adaptation of the service. Even a function of relevance zooming analogue to LoD zooming might be feasible.
Graphical techniques to show relevance of point objects: Fig. 6 depicts different graphical variables and animation types where relevance values could be mapped to. The left column shows variables for binary relevance, i.e. relevant and non-relevant. The middle column refers to graphical means that visualise degrees of relevance or a relevance order. The right column depicts types of animations that could be used to attract the user’s attention, i.e. to show for instance the most relevant object among relevant objects.
Figure 6: Graphical variables for relevance visualisation of points of interest symbols
For points of interests and events we can also apply different metaphors. In Fig. 7 left the hotspot metaphor is graphically realised with dots of intensifying grades of orange/red to visualise the ‘hotness’ (i.e. degree of relevance) of the object. The fade-away metaphor shown in Fig. 7 on the right visualises the temporal relevance or validity of events or appointments through animating the opacity of map symbols in real-time. The opposite is possible as well, i.e. the fadein of approaching events or appointments.
Figure 7: Hotspot metaphor for points of interest or events (left) and fade-away metaphor of event validity (right). (© base data: Städtisches Vermessungsamt München)
Graphical techniques to show relevance of line objects: To visualise the relevance of line objects in the map most of the graphical variables described for point symbols can be applied just the same. This might be interesting if alternatives of routes are available that differ in their relevance (Fig.8).
Figure 8: Relevance visualisation for different shopping roads (© base data: Städtisches Vermessungsamt München)
Graphical techniques to show relevance of area objects: A possible way to intuitively convey the relevance of different AoIs or activity regions is the cold-hot metaphor. Different grades of blue and red hues are selected to present the hotness or coolness of an area (Fig. 9 left). Another method for showing the relevance of AoI or activity regions is the use of focussed areas within a blurred map (Fig. 9 middle). Similarly light spots on a map direct the attention to the lightened areas (Fig. 9 right). The rest of the map could even be dimmed to enhance the effect.
Figure 9: Graphical variables for relevance visualisation of AoI: hot-cold metaphor (left) focus and blur effect (middle) and lighting effect (right) (© base data: Städtisches Vermessungsamt München) The graphic examples in Fig. 6 – Fig. 9 are possible ways to visualise relevancies or focussing the user’s attention to more relevant parts of a presentation. Although the usability of presentations of that kind is likely to be improved, several problems can arise, in the worst case leading to inferior usability. First of all, not all variables are equally apt. The variable ‘size’ for example is not optimal, since display space is limited. The most prominent problem is how to communicate the meaning of relevance symbolisation to the user. Colours of map symbols and relevance symbolisation might disturb each other or colours might be already taken by map symbols and may no longer be available for relevance visualisation. Animated map objects that should draw the user’s attention might be disturbing in mobile environments and easily lead to undesired cognitive overload effects.
IMPLEMENTATION OF RELEVANCE-ENHANCED SERVICES The mapping of relevance values derived from context to graphical variables can be implemented within an adaptive geovisualisation service based on Scalable Vector Graphics (SVG) as proposed by (Reichenbacher 2004). The architecture of the prototypical service is based on open standards such as Simple Object Access Protocol (SOAP) for encoding contextual parameters, a Web Feature Server (WFS) providing GML encoded features, and Extensible Stylesheet Language Transformation (XSLT) for transforming GML into maps encoded in SVG. Most of the animations of point symbols depicted in the map examples above can be achieved with SVG elements for colour animations and for transformation animations . The SVG element allows for blurring and lighting effects. However, filter effects are not supported by the SVG Mobile Tiny profile.
CONCLUSIONS The integration of relevance concepts into the design of mobile geovisualisation services can enhance the service usability. Further research should focus on the definition of typical activities of mobile users, their embedment in contexts and which presentation forms are best suited for these activities. Additionally, the influence of changing context parameters on activities should be investigated with respect to the implications for the presentation and its relevance. This paper intends to unveil the benefits of a deeper understanding of the relevance concept for mobile cartography. It should be clear that the visualisation of relevance requires smart data structures allowing real-time information selection within a mobile map service. The necessary performance efficiency of relevance computation must be proved before an application to mobile services can make sense, since response time is a crucial factor in mobile applications. To fully exploit the potentials of the relevance concept many more steps need to be done: First, further studies of human activities and their patterns in a mobile context guided by activity theory have to be conducted and described in an ontology. Second, context and relevance modelling must be combined with the ontology approach as proposed by (Becker and Nicklas 2004). Third, the computational models to determine the relevance values need to be refined. Fourth, knowledge of cognitive processes in mobile usage situations has to be gathered in interdisciplinary studies. This knowledge is essential for the design of cognitively appropriate presentations under the aspect of mobile usage situations. And finally, there is a strong need for developing specific usability test methods for map-based mobile services taking the peculiarities into account. These tests would allow verifying the hypotheses stated in the introduction and further improve relevance computation model and the visualisation of relevance in map services.
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Biography: Tumasch Reichenbacher was born April 9, 1969 in Scuol (Switzerland). He received his basic education
in Zürich (Switzerland). From 1989 to 1995 he studied Geography at the University of Zürich (specialising in Geomatics and GIS) and Cartography at the Federal Institute of Technology in Zürich (ETHZ). He spent an academic exchange year from 1992 to 1993 at the University of Sheffield (U.K.). In 1995 he received his Diploma in Geography with a thesis about ‘Knowledge acquisition in cartographic generalisation by interaction logging and inductive learning’. From 1995 to 1996 he was research assistant with the Institute of Cartography at ETHZ. From 1996 to 1999 he was employed with the Zürich based mapping company Orell Füssli Kartographie AG. Since 1999 he has been a research assistant at the Department of Cartography, Technical University of Munich where he received his PhD in 2004 with a dissertation on ‘Mobile Cartography – Adaptive Visualisation of Geographic Information on Mobile Devices’.
