ABSTRACT
Ahstract-The occurrence of seasonal natural phenomena depends on the conditions leading to it and not directly on the progression of time, meaning its context varies across time and space. Examples of this include comparing plant growth, insect development or wildfire risk during the same time period at different locations or in different time periods at the same location. However, visualizing and comparing such phenomena usually implies plotting it across the time axis as it's perceived as temporal data. Since it's not directly dependent of time, identifying patters of recurrence using this technique is inefficient. Because of this, we proposed transforming (when needed) the dependent function to a non-decreasing monotone one, in order to preserve the monotonic property of time progression. Then we used the resulting function as a time axis replacement to achieve an equal ground of comparison between the different contexts in which the phenomenon occurs. We applied this technique to real data from seasonal natural phenomena, such as plant and insect growth, to compare its progression in different temporal and spatial contexts. Since the dependent function of the phenomenon was scientifically known, we were able to directly use the technique to infer its seasonality patterns. Furthermore, we applied the technique to real data from the coronavirus worldwide pandemic by hypothesizing its dependent function and analysing if it was able to reduce the existing temporal misalignment between different contexts, like years and countries. The results achieved were positive, although not as remarkable as when the dependent function was known. © 2022 IEEE.
ABSTRACT
Air quality is an important issue that impacts who live in cities. During COVID-19 pandemic, it becomes clear that low air quality can increase the effects of the disease. It is very important that in this era of Big Data and Smart Cities we use technology to address health issues like air quality problems. Often, air quality is monitored using data collected using fixed selected stations in a region. Such approach only gives us a global notion of the air quality, but do not support a fine-grained comprehension about spots distant from the collector’s stations, specially in residential urban places. In this paper, we propose a visual analytics solution that provides city council decision-makers an interactive dashboard that displays air pollution data at multiple spatial resolutions, that uses real and predicted data. The real air quality data is collected using low-cost portable sensors, and it is combined with other environmental contextual data, namely road traffic mobility data. Estimated air quality data is obtained using a machine learning regression model, that is integrated into the interactive dashboard. The visual analytics solution was designed with the city council decision-makers in mind, providing a clutter-free interactive exploration tool that enables those users to improve the quality of life in the city, focusing on one of the most important cities’ health quality key issues. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.