Context
This 30-month, highly interdisciplinary MSCA Global Fellowship was dedicated exclusively to the concept of spatial familiarity. Spatial familiarity is a state of spatial cognition which is acquired primarily (but not exclusively) by finding one’s own way through the environment. And, vice versa, spatial familiarity has a large impact on wayfinding itself with respect to both, reasoning about it and performance. Despite this support for the importance of familiarity, there is no generally agreed upon definition of it nor is there an agreement on how to assess it – which is a major research gap due to the evidence that both aspects are highly interrelated in empirical studies. The FamConMe project contributed significantly to the understanding and implementation of familiarity in geographic information systems, adding to our knowledge on how to conceptualize familiarity and providing ways to assess it in-situ based on behavioural data. We were particularly interested in understanding the conceptualization and measurement of spatial familiarity in human wayfinders so far; finding a way to assess a person’s sense of spatial familiarity at different levels and relate it to the person’s knowledge; predict the degree of a person’s spatial familiarity from eye and full-body movements during wayfinding. Beyond the theoretical advancement, the outcomes of this project are an important first step towards personalization of navigation systems. Continuously monitoring how familiar a person is, provides one basis for a system to tailor route instructions specific to a user’s need. This improves the user experience of such systems and, even more importantly, helps to mitigate potentially adverse impacts navigation system use might have on our spatial orientation abilities.
Results so far
Data
First large (N=96) behavioural dataset on map-based, pedestrian wayfinding, collected in-situ, monitoring eye movements (@120Hz), full-body motions (@60Hz), and high-precision location data (@1Hz).
Database comprising 100 papers dealing with spatial familiarity in wayfinding, coded in 19 different categories and 140+ codes.
First large (N=216) online study dataset, combining self-report familiarity measures with knowledge tasks and simultaneously studying landmarks, areas and routes.
Methodological
The combination of full-body motion capture, mobile eye tracking, and high-precision GNSS is a methodological blueprint for studies beyond wayfinding (e.g., to study perceived safety and comfort among cyclists).
Framework for exploiting behavioural correlates related to eye movements and IMU-based behavioural data for ML-based classification experiments to account for subtle behavioural differences (including feature engineering).
Coding system for wayfinding papers dealing with spatial familiarity papers useful for assessing conceptualization, measurement, research design, and environments used in empirical, conceptual, and computational model papers.
State-of-the-Art of spatial familiarity research in wayfinding
We have identified strengths and weaknesses w.r.t. conceptualization, measurement, research design, and study setting of papers dealing with spatial familiarity and wayfinding (empirical and computational model papers). The weaknesses provide important insights into urgent next steps (e.g. multi-level measurements of familiarity for different geographical features); we have worked on some of these already throughout this project.
Classification of spatial familiarity
Provide a research agenda for future studies on how sketchmap content reflects different levels of spatial familiarity based on the result that our data does not reflect these differences.
Provide evidence that the two extrema (familiar vs. unfamiliar) of spatial familiarity in pedestrian wayfinders can be highly accurately (96%) distinguished based on a single, head-mounted IMU. Personalization of route guidance can be based on this result.
MANY MORE TO COME 😉