Data, code, and frameworks we develop will be published on this page.

COSIT2022: Spatial Familiarity Prediction by Turning Activity Recognition

Published Dataset:

Abstract: Spatial familiarity plays an essential role in the wayfinding decision-making process. Recent findings in wayfinding activity recognition domain suggest that wayfinders’ turning behavior at junctions is strongly influenced by their spatial familiarity. By continuously monitoring wayfinders’ turning behavior as reflected in their eye movements during the decision-making period (i.e., immediately after an instruction is received until reaching the corresponding junction for which the instruction was given), we provide evidence that familiar and unfamiliar wayfinders can be distinguished. By applying a pre-trained XGBoost turning activity classifier on gaze data collected in a real-world wayfinding task with 33 participants, our results suggest that familiar and unfamiliar wayfinders show different onset and intensity of turning behavior. These variations are not only present between the two classes –familiar vs. unfamiliar– but also within each class. The differences in turning-behavior within each class may stem from multiple sources, including different levels of familiarity with the environment.

Free Choice Navigation

The results of the simulation study can be found here:
The corresponding source code will be published soon.

Using navigation assistance systems has become widespread and scholars have tried to mitigate potentially adverse effects on spatial cognition these systems may have due to the division of attention they require. In order to nudge the user to engage more with the environment, we propose a novel navigation paradigm called Free Choice Navigation balancing the number of free choices, route length and number of instructions given. We test the viability of this approach by means of an agent-based simulation for three different cities. Environmental spatial abilities and spatial confidence are the two most important modeled features of our agents. Our results are very promising: Agents could decide freely at more than 50% of all junctions. More than 90% of the agents reached their destination within an average distance of about 125% shortest path length.


UrbanCore Mailinglist:

Effect of Currentness of Spatial Data on Routing Quality

Code and Data:

Route Selection Framework

A docker container will be soon published