Geospatial Cognition and Visual Analytics Research Center

Abstract: Flow maps allow users to perceive not only the location where interactions take place, but also the direction and volume of events. Previous studies have proposed numerous methods to produce flow maps. However, how to evaluate the usability of flow maps has not been well documented. In this study, we combined eye-tracking and questionnaire methods to evaluate the usability of flow maps through comparisons between (a) straight lines and curves and (b) line thicknesses and color gradients. The results show that curved flows are more effective than straight flows. Maps with curved flows have more correct answers, fixations, and percentages of fixations in areas of interest. Furthermore, we find that the curved flows require longer finish times but exhibit smaller times to first fixation than straight flows. In addition, we find that using color gradients to indicate the flow volume is significantly more effective than the application of different line thicknesses, which is mainly reflected by the presence of more correct answers in the color-gradient group. These empirical studies could help improve the usability of flow maps employed to visualize geo-data.

To cite this paper:

Dong, W *.; Wang, S. *; Chen, Y.; Meng, L. Using Eye Tracking to Evaluate the Usability of Flow Maps. ISPRS Int. J. Geo-Inf. 2018, 7, 281. doi: https://doi.org/10.3390/ijgi7070281

Abstract: Eye movement data convey a wealth of information that can be used to probe human behaviour and cognitive processes. To date, eye tracking studies have mainly focused on laboratory-based evaluations of cartographic interfaces; in contrast, little attention has been paid to eye movement data mining for real-world applications. In this study, we propose using machine-learning methods to infer user tasks from eye movement data in real-world pedestrian navigation scenarios. We conducted a real-world pedestrian navigation experiment in which we recorded eye movement data from 38 participants. We trained and cross-validated a random forest classifier for classifying five common navigation tasks using five types of eye movement features. The results show that the classifier can achieve an overall accuracy of 67%. We found that statistical eye movement features and saccade encoding features are more useful than the other investigated types of features for distinguishing user tasks. We also identified that the choice of classifier, the time window size and the eye movement features considered are all important factors that influence task inference performance. Results of the research open doors to some potential real-world innovative applications, such as navigation systems that can provide task-related information depending on the task a user is performing.

Abstract: An understanding of guidance, which means guiding attention, and constancy, meaning that an area can be perceived for what it is despite environmental changes, of the visual variables related to three-dimensional (3D) symbols is essential to ensure rapid and consistent human perception in 3D visualization. Previous studies have focused on the guidance and constancy of visual variables related to two-dimensional (2D) symbols, but these aspects are not well documented for 3D symbols. In this study, we used eye tracking to analyze the visual guidance from shapes, hues and sizes, and the visual constancy that is related to the shape, color saturation and size of 3D symbols in different locations. Thirty-six subjects (24 females and 12 males) participated in the study. The results indicate that hue and shape provide a high level of visual guidance, whereas guidance from size, a variable that predominantly guides attention in 2D visualization, is much more limited in 3D visualization. Additionally, constancy of shape and saturation are perceived with relatively high accuracy, whereas constancy of size is perceived with only low accuracy. These first empirical studies are intended to pave the way for a more comprehensive user understanding of 3D visualization design.

Cite this paper:

Liu, B.; Dong, W.; Meng, L. Using Eye Tracking to Explore the Guidance and Constancy of Visual Variables in 3D Visualization. ISPRS Int. J. Geo-Inf. 2017, 6, 274. doi: 10.3390/ijgi6090274

Abstract: LiDAR has been an effective technology for acquiring urban land cover data in recent decades. Previous studies indicate that geometric features have a strong impact on land cover classification. Here, we analyzed an urban LiDAR dataset to explore the optimal feature subset from 25 geometric features incorporating 25 scales under 6 definitions for urban land cover classification. We performed a feature selection strategy to remove irrelevant or redundant features based on the correlation coefficient between features and classification accuracy of each features. The neighborhood scales were divided into small (0.5–1.5 m), medium (1.5–6 m) and large (>6 m) scale. Combining features with lower correlation coefficient and better classification performance would improve classification accuracy. The feature depicting homogeneity or heterogeneity of points would be calculated at a small scale, and the features to smooth points at a medium scale and the features of height different at large scale. As to the neighborhood definition, cuboid and cylinder were recommended. This study can guide the selection of optimal geometric features with adaptive neighborhood scale for urban land cover classification.

Cite this paper: Dong, W., Lan, J., Liang, S., Yao, W. and Zhan, Z. 2017. Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification. International Journal of Applied Earth Observation and Geoinformation, 60, 99-110. DOI: 10.1016/j.jag.2017.04.003

Abstract: 3D representations in applications that provide self-localization and orientation in wayfinding have become increasingly popular in recent years because of technical advances in the field. However, human factors have been largely ignored while designing 3D representations in support of pedestrian navigation. This exploratory study aims to explore gender effects on using 3D maps for spatial orientation. We designed a 3D map that combines salient 3D landmarks and 2D layouts, and evaluated gender differences in their performance during direction-pointing tasks by administrating an eye tracking experiment. The results indicate there was no significant overall gender difference on performance and visual attention. However, we observed that males using the 3D map paid more attention to landmarks in the environment and performed better than when using the conventional 2D map, whereas female performance did not show any significant difference between the two types of map usage. We also observed contrary gender differences in visual attention on landmarks between the 3D and 2D maps. While males fixated longer on landmarks than females when using the 3D map, females paid more visual attention to landmarks than males when using the 2D map. In addition, verbal protocols showed that males had more confidence while make decisions. These empirical results can be helpful in the design of map-based wayfinding enhancement tools.

Cite this paper: Liao H, Dong W. An Exploratory Study Investigating Gender Effects on Using 3D Maps for Spatial Orientation in Wayfinding. ISPRS International Journal of Geo-Information. 2017; 6(3):60. DOI: http://dx.doi.org/10.3390/ijgi6030060

Abstract: Nowadays, studies of factors influencing geographical spatial orientation ability mainly concentrate on gender whereas relationships of field cognitive style and spatial terminology with spatial orientation ability have rarely been studied. This study used eye tracking technology to explore the influences of the three individual variables on spatial orientation ability. 86 people participated in the experiments with an average age of 21 (SD=2.67). 继续阅读“Study on the Influence of Field Cognitive Style, Gender and Spatial Terminology on Geographical Spatial Orientation Ability: based on Experiments in Virtual Space”

Abstract: Despite the now-ubiquitous two-dimensional (2D) maps, photorealistic three-dimensional (3D) representations of cities (e.g., Google Earth) have gained much attention by scientists and public users as another option. However, there is no consistent evidence on the influences of 3D photorealism on pedestrian navigation. Whether 3D photorealism can communicate cartographic information for navigation with higher effectiveness and efficiency and lower cognitive workload compared to the traditional symbolic 2D maps remains unknown. 继续阅读“Eye tracking to explore the impacts of photorealistic 3d representations in pedestrian navigation performance”