作者：ShengkaiWang

Abstract: Hand-foot-and-mouth disease (HFMD) is a highly contagious viral infection, and real-time predicting of HFMD outbreaks will facilitate the timely implementation of appropriate control measures. By integrating a susceptible-exposed-infectious-recovered (SEIR) model and an ensemble Kalman filter (EnKF) assimilation method, we developed an integrated compartment model and assimilation filtering forecast model for real-time forecasting of HFMD. When applied to HFMD outbreak data collected for 2008–11 in Beijing, China, our model successfully predicted the peak week of an outbreak three weeks before the actual arrival of the peak, with a predicted maximum infection rate of 85% or greater than the observed rate. Moreover, dominant virus types enterovirus 71 (EV-71) and coxsackievirus A16 (CV-A16) may account for the different patterns of HFMD transmission and recovery observed. The results of this study can be used to inform agencies responsible for public health management of tailored strategies for disease control efforts during HFMD outbreak seasons.

To site this paper:

Zhan, Z.; Dong, W.; Lu, Y.; Yang, P.; Wang, Q.; Jia, P. Real-time forecasting of hand-foot-and-mouth disease outbreaks using the integrating compartment model and assimilation filtering. Scientific Reports 2019, 9, 2661.

doi:10.1038/s41598-019-38930-y

Abstract: While population maps are important tools for people to perceive the regularities of population distribution, different scales of population maps cause map readers’ cognitive difference in the regularities of spatial distribution of population. In this paper, eye movement parameters such as number of fixations, fixation duration and number of correct answers were selected in the population map cognitive experiment by eye movement tracking to test the significance of the difference, and the results were analyzed from the perspective of spatial differentiation regularities. By exploring the influence of different scales including province and county (city) on map readers’ cognition of the distribution regularities of population, it is concluded that different scales of population maps have a significant impact on readers’ perception based on the significant difference analysis. When perceiving the characteristics of spatial distribution of population and the population quantity, more details and information are provided by county (city)-scale population maps, which is beneficial to readers’ understanding of the spatial differentiation regularities of population, with less average number of fixations, shorter average fixation duration, more correct number of answers for each question and higher cognitive efficiency. The impact of scale on the cognition of the population spatial distribution and the population size was discussed. The acquired cognitive rules of the scale can be used in designing the demographic maps and shortening readers’ cognition time, which is convenient for readers to extract valid information from the demographic maps, thus to improve the map usability. Besides, through the analysis of eye movement parameters like the fixations points, fixation time and number of correct answers, as well as the significance test, quantitative researches of the scale effects on the population distribution were carried out. The perspective drawing of the fixations hotspot can be used to visualize the cognitive spatial differentiation of the readers. And the results are no longer limited to the simple qualitative expression, which is of great significance for the use of different scales of demographic maps to express population distribution characteristics and regularities. In addition to adopting the hierarchical mapping method to draw the population maps, this thesis also has conducted experiments on the readers’ cognition of the spatial distribution regularities of population with different population density maps at different scales. Since it can reflect the population distribution more precisely and more visually, the results of this research may be further improved. And in the further work, the above population map needs further studying.

To site this paper:

Zhang, W.; Zhao, S.; Zhang, D.; Dong, W. Influence of scale on cognition of spatial differentiation regularities of population maps. Journal of Geoinformation Science, 2018,20(10):1396-1402.

doi:10.12082/dqxxkx.2018.180080

Abstract: As part of geography education, geography courses play an important role in the development of spatial ability. However, how geography courses affect map-based spatial ability has not been well documented. In this study, we use an eye-tracking method to explore the impact of geography courses on map-based spatial ability. We recruited 55 undergraduates from Beijing Normal University (BNU) to attend the map-based spatial ability test before and after six-month geography courses arranged by the Faculty of Geographical Science, BNU. The results show that the participants’ map-based spatial ability significantly improved after taking the geography courses; specifically, accuracy increased by 22.3% and response time decreased by 14.7% after training. We analysed two types of eye-movement behaviour; in terms of processing measures, the fixation duration of the topographic map decreased by 18.4% and the fixation distribution was more concentrated after training, and in terms of matching measures, participants have more switch times per second for both photographed scenes and topographic maps. Switch times between options decreased by 48.2%, which is a notable decrease. These empirical results are helpful for the design of geography courses that improve map-based spatial ability.

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Dong, W.; Ying, Q.; Yang, Y.; Tang, S.; Zhan, Z.;  Liu, B.; Meng, L. Using Eye Tracking to Explore the Impacts of Geography Courses on Map-based Spatial Ability. Sustainability 2019, 11(1), 76.

doi: https://doi.org/10.3390/su11010076

Abstract: Map reading is an important skill for acquiring spatial information. Previous studies have mainly used results-based assessments to learn about map-reading skills. However, how to model the relationship between map-reading skills and eye movement metrics is not well documented. In this paper, we propose a novel method to assess map-reading skills using eye movement metrics and Bayesian structural equation modelling. We recruited 258 participants to complete five map-reading tasks, which included map visualization, topology, navigation, and spatial association. The results indicated that map-reading skills could be reflected in three selected eye movement metrics, namely,the measure of first fixation, the measure of processing, and the measure of search. The model fitted well for all five tasks, and the scores generated by the model reflected the accuracy and efficiency of the participants’ performance. This study might provide a new approach to facilitate the quantitative assessment of map-reading skills based on eye tracking.

To site this paper:

Dong, W.; Jiang, Y.; Zheng, L.; Liu, B.; Meng, L. Assessing Map-Reading Skills Using Eye Tracking and Bayesian Structural Equation Modelling. Sustainability 2018, 10, 3050.

doi:https://doi.org/10.3390/su10093050

Abstract: In this article, we use eye-tracking methods to analyze the differences in spatial ability between geographers and non-geographers regarding topographic maps, as reflected in the following three aspects: map-based spatial localization, map-based spatial orientation, and map-based spatial visualization. We recruited 32 students from Beijing Normal University (BNU) and divided them into groups of geographers and non-geographers based on their major. In terms of their spatial localization ability, geographers had shorter response times, higher fixation frequencies, and fewer saccades than non-geographers, and the differences were significant. For their spatial orientation ability, compared to non-geographers, geographers had significantly lower response times, lower fixation counts and fewer saccades as well as significantly higher fixation frequencies. In terms of their spatial visualization ability, geographers’ response times were significantly shorter than those of non-geographers, but there was no significant difference between the two groups in terms of
fixation count, fixation frequency or saccade count. We also found that compared to geographers, non-geographers usually spent more time completing these tasks. The results of this study are helpful in improving the map-based spatial ability of users of topographic maps.

To site this paper:

Dong, W.; Zheng, L.; Liu, B.; Meng, L. Using Eye Tracking to Explore Differences in Map-Based Spatial Ability between Geographers and Non-Geographers. ISPRS Int. J. Geo-Inf. 2018, 7, 337.

doi:https://doi.org/10.3390/ijgi7090337

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.