In order to acknowledge the previous state of the pandemic, an interactive system that displays Covid-19 cases in the Chicago area in 2021 is developed based on Processing language. The projection mapping visualizes Covid-19 cases and related clinic data, and this is a highly immersive experience that engages the senses and emotions of the users. The simulation of k- means clustering identifies the hotspots of Covid-19 cases based on the location and indicates the spread of the virus grouped by each month during the year, which provides the users a way to physically interact with the data to understand the patterns and predict future trends. One toio is used as a controller for the users to slide to change the time frame from left to right, rotate to change the color opacity of each area, and shake to change the format of the data points from black dots to red circles. The other seven toios are representations of a variety kind of hospitals around each area according to the cluster center of Covid-19 cases.
When the user places a toio on the mat, a k cluster would be formed from a partition of a set of data points, and the toio would move to the center of that cluster as a hospital place for people. The users can place the toios as much as they want to see the tendency of Covid-19 cases and allocate the hospital resources accordingly. Meanwhile, the users can slide the controller to set a time frame and recognize the trends of Covid-19 cases. Through rotating the controller, the users can notice the change of the color opacity of every k clustering area. Also, the users can shake the controller to adjust the format of data points with a beep sound indicator, which exhibits the data possibly in a more comprehensible way.
References:
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Melin, P., Monica, J. C., Sanchez, D., & Castillo, O. (2020). Analysis of spatial spread relationships of coronavirus (COVID-19) pandemic in the world using self organizing maps. Chaos, Solitons & Fractals, 138, 109917.
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Yoo, H., & Kim, H. (2014). A study on the media arts using interactive projection mapping. Contemporary Engineering Sciences, 7(23), 1181-1187.
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Zubair, M., Asif Iqbal, M. D., Shil, A., Haque, E., Moshiul Hoque, M., & Sarker, I. H. (2021). An efficient k-means clustering algorithm for analysing covid-19. In Hybrid Intelligent Systems: 20th International Conference on Hybrid Intelligent Systems (HIS 2020), December 14-16, 2020 (pp. 422-432). Springer International Publishing.