Landmarks are inevitable means for people to memorize and communicate routes (Denis et al. 1999; Michon and Denis 2001; Tom and Denis 2003). However, the concept of a landmark is not clearly defined. Being a landmark is generally seen as a quality of a feature (Lynch 1960; Arthur and Passini 1992; Deakin 1996; Lovelace et al. 1999; Sorrows and Hirtle 1999). First approaches exist that quantify the “landmarkness”, or salience, of a feature (Raubal and Winter 2002; Elias 2003).

Landmarks have different qualities. Sorrows and Hirtle (1999) distinguish between visual, semantic, and structural landmarks. Salience of features can be computed based on measures of these qualities, given databases that provide all these properties of spatial features.

Current commercial wayfinding services (Location Based Services providing route directions) construct their directions based on geometry (length or duration), orientation (cardinal or egocentric directions), and street names. In cases where they additionally provide references to landmarks these landmarks are pre-selected. The selection processes as well as the selection criteria are not published. Often the process is driven more by marketing interests than by the success rate in wayfinding.

Wayfinding services can profit from a computable and context-sensitive model of salience. Wayfinding directions can be enriched with cognitively plausible salient features. Thus we can expect a better success rate in wayfinding and a better user satisfaction by a reduction of the users’ cognitive workload.

A first model quantifying the salience of facades could be shown to be highly correlated to features selected by humans, i.e., the model is cognitively plausible (Raubal and Winter 2002). This model generates a series of new questions – like for the salience of other feature types, integration of salience, aggregation of salience, or context-sensitive weighting of measures – which can be subsumed to a general theory of salience of spatial features.

In this project the research in quantifying the salience of spatial features shall be extended to a level where a larger grant on this general theory can be acquired. For that reason the salience of other features, especially network, natural, or artificial features shall be formalized. The hypothesis of this project is that we can formalize a cognitive plausible model of salience for network features, artificial and natural features. Having a basic methodology to determine salience, the next step would be to integrate these measures, to adapt them to different contexts, and to model wayfinding communication with salient features. The longer term goal is a cognitive plausible model of salience for different modes of travel.
 

Developing a model consists of identifying the physical, functional and social parameters defining the properties of the features. The parameter values have to be merged to a measure of salience. The development process is critical with respect to both available data that has to support the measurement, and the correlation with human cognition. The results from the human subject test can help to improve the proposed model.

The implementation of the model shall be done to a level where a mobile location-based service can query for the most salient feature at the current location. In the proposed architecture, the implementation concerns three parts: One component deals with spatial analysis to derive measures of individual features (e.g., visibility). Another component combines measures and determines overall salience. A third component provides for a given location the most salient feature within a range.

The user of the mobile service can compare the proposed feature for her current position with the real environment, grade the selection, and propose alternatives. The human subject test requires the development of questionnaires, and a standard stochastic analysis.
 

Deakin, A., 1996: Landmarks as Navigational Aids on Street Maps. Cartography and Geographic Information Systems, 23 (1): 21-36.

Denis, M.; Pazzaglia, F.; Cornoldi, C.; Bertolo, L., 1999: Spatial Discourse and Navigation: An Analysis of Route Directions in the City of Venice. Applied Cognitive Psychology, 13: 145-174.

Elias, B., 2003: Extracting Landmarks with Data Mining Methods. In: Kuhn, W.; Worboys, M.F.; Timpf, S. (Eds.), Spatial Information Theory. Lecture Notes in Computer Science, Vol. 2825. Springer, Berlin, pp. 398-412.

Lovelace, K.L.; Hegarty, M.; Montello, D.R., 1999: Elements of Good Route Directions in Familiar and Unfamiliar Environments. In: Freksa, C.; Mark, D.M. (Eds.), Spatial Information Theory. Lecture Notes in Computer Science, Vol. 1661. Springer, Berlin, pp. 65-82.

Lynch, K., 1960: The Image of the City. MIT Press, Cambridge, 194 pp.

Michon, P.-E.; Denis, M., 2001: When and Why are Visual Landmarks Used in Giving Directions? In: Montello, D.R. (Ed.), Spatial Information Theory. Lecture Notes in Computer Science, Vol. 2205. Springer, Berlin, pp. 292-305.

Raubal, M.; Winter, S., 2002: Enriching Wayfinding Instructions with Local Landmarks. In: Egenhofer, M.J.; Mark, D.M. (Eds.), Geographic Information Science. Lecture Notes in Computer Science, Vol. 2478. Springer, Berlin, pp. 243-259.

Sorrows, M.E.; Hirtle, S.C., 1999: The Nature of Landmarks for Real and Electronic Spaces. In: Freksa, C.; Mark, D.M. (Eds.), Spatial Information Theory. Lecture Notes in Computer Science, Vol. 1661. Springer, Berlin, pp. 37-50.

Tom, A.; Denis, M., 2003: Referring to landmark or street information in route directions: What difference does it make? In: Kuhn, W.; Worboys, M.; Timpf, S. (Eds.), Spatial information theory. Lecture Notes in Computer Science, Vol. 2825. Springer, Berlin, pp. 384-397.