Spatio-temporal Visualization of Interpolated Particulate Matter (PM2.5) in Beijing

Abstract

People in growing urban areas are more and more influenced by emissions coming from numerous vehicles and factories. In this paper we inspect the concentration of particulate matter (PM2.5) visually over time. This information stems from a data set of air quality measurements from 36 static sensors in Beijing over one year (from 8.02.2013 till 8.02.2014). One possibility for creating an overview for 36 positions with varying PM2.5 measurements in time is the use of interpolation techniques. In our approach, we generate surfaces of PM2.5 concentration using inverse distance weighting (IDW). The resulting surfaces represent interpolated PM2.5 values, based on averaged PM2.5 information (e.g. average of one day). We create simple interactive visualizations using points as surface representations. Each surface point within the 3D visual analysis display exhibits its PM2.5 value by differing coloration and z-value (height component). The interactivity consists of using selection circles for stacked 3D displays of interpolated PM2.5 surfaces for different times (time series). The aim of this visual information analysis is the possible detection of periodical hotspots of high PM2.5 concentrations, which might be useful for people with respiratory diseases. For the detection of dynamic PM2.5 hotspot variations, we introduce thresholds for querying only the highest PM2.5 values of the surfaces. Afterwards, these points are aggregated into convex hulls (polygons), with the idea of comparing the size and shape of the PM2.5 hotspots in each created surface. The change of position and size of these polygons over time may be an indicator for air quality changes within an urban environment. Considering the above, this may be a starting point for the conception of a personalized routing solution for pedestrians or vehicle drivers with respiratory diseases, who want to avoid these hotspots of high PM2.5 concentrations.