Game-like 3D visualisation of air quality data

The data produced by sensor networks for urban air quality monitoring is becoming a valuable asset for informed health-aware human activity planning. However, in order to properly explore and exploit these data, citizens need intuitive and effective ways of interacting with it. This paper presents CityOnStats, a visualisation tool developed to provide users, mainly adults and young adults, with a game-like 3D environment populated with air quality sensing data, as an alternative to the traditionally passive visualisation techniques. CityOnStats provides several visual cues of pollution presence with the purpose of meeting each user’s preferences. Usability tests with a sample of 30 participants have shown the value of air quality 3D game-based visualisation and have provided empirical support for which visual cues are most adequate for the task at hand.info:eu-repo/semantics/publishedVersio

Un enfoque de visualización espacio-temporal para la exploración de datos de concentración de PM10 en Lima Metropolitana

Lima is considered one of the cities with the highest air pollution in Latin America. Institutions such as DIGESA, PROTRANSPORTE and SENAMHI are in charge of permanently monitoring air quality; therefore, the air quality visualization system must manage large amounts of data of different concentrations. In this study, a spatio-temporal visualization approach was developed for the exploration of data of the PM10 concentration in Metropolitan Lima, where the spatial behavior, at different time scales, of hourly concentrations of PM10 are analyzed using basic and specialized charts. The results show that the stations located to the east side of the metropolitan area had the highest concentrations, in contrast to the stations located in the center and north that reported better air quality. According to the temporal variation, the station with the highest average of biannual and annual PM10 was the HCH station; for this season, the highest PM10 concentrations were registered in 2018, during the summer, highlighting the month of March with daily averages that reached 435 μg/m3. During the study period, the CRB was the station that recorded the lowest concentrations and the only one that met the Environmental Quality Standard for air quality. The proposed approach exposes a sequence of steps for the elaboration of charts with increasingly specific time periods according to their relevance, and statistical analysis, such as the dynamic temporal correlation, that allows to obtain a detailed visualization of the spatio- temporal variations of PM10 concentrations; furthermore, it was concluded that the meteorological variables do not indicate a causal relationship with respect to PM10 levels, but rather that the concentrations of particulate material are related to the urban characteristics of each district.LIMAEscuela Profesional de Ingeniería AmbientalGestión Ambienta

Visualization of real-time monitoring datagraphic of urban environmental quality

Abstract Quality of urban environment directly affects people health, and it is important to understand the real-time status of urban air quality. Air quality monitoring, data analysis, and visualization can grasp the concentration data of air pollutants in cities. In view of the current air quality monitoring using digital displays, it is difficult for users to intuitively determine the air pollution level with unsatisfied interaction mode of the data query. Using the real-time monitoring data of 23 observation points in Beijing, the work based on Google Earth applied Keyhole Markup Language (KML) for the visualization of air monitoring data. The interactive query makes it easier for users to query air quality, and gradually varied color can visually highlight the air quality level. Visualization of data has stronger expression (more images and more intuitive) than the original data table, which is beneficial for further analysis of data