AirVis: Visual Analytics of Air Pollution Propagation

Deng, Zikun; Weng, Di; Chen, Jiahui; Liu, Ren; Wang, Zhibin; Bao, Jie; Zheng, Yu; Wu, Yingcai

doi:10.1109/tvcg.2019.2934670

Cited by 53 publications

(51 citation statements)

References 69 publications

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“…For instance, we did not consider elaborate animations such as that by Dudas et al [26] or propagation graphs (e.g. [66,21]) as they explicitly show computationally extracted propagation trajectories, making assumptions about what are important/interesting paths. Instead, we chose techniques that use raw data, as they are applicable to more analysis contexts since they leave the interpretation of the pattern to the analyst.…”

Section: Selection and Design Of Visualization Techniquesmentioning

confidence: 99%

A Comparison of Geographical Propagation Visualizations

Peña-Araya

Bezerianos

Pietriga

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Geographical propagation phenomena occur in multiple domains, such as in epidemiology and social media. Propagation dynamics are often complex, and visualizations play a key role in helping subject-matter experts understand and analyze them. However, there is little empirical data about the effectiveness of the various strategies used to visualize geographical propagation. To fill this gap, we conduct an experiment to evaluate the effectiveness of three strategies: an animated map, smallmultiple maps, and a single map with glyphs. We compare them under five tasks that vary in one of the following dimensions: propagation scope, direction, speed, peaks, and spatial jumps. Our results show that small-multiple maps perform best overall, but that the effectiveness of each visualization varies depending on the task considered.

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Section: Selection and Design Of Visualization Techniquesmentioning

confidence: 99%

A Comparison of Geographical Propagation Visualizations

Peña-Araya

Bezerianos

Pietriga

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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“…When analyzing the scientific works [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] as well as legal provisions [14], it can be stated that there is currently incomplete information regarding the air quality when it comes to carbon monoxide, which results from a few reasons:…”

Section: Discussionmentioning

confidence: 99%

“…Detailed studies of urban areas with division into estates where excessive levels of air pollution with carbon monoxide caused by road transport may occur were not found in the analyzed literature. Although studies pertaining to CO pollution [27,58,59] do exist, they pertain mainly to models estimating air pollution as well as analyses of spatial data with the use of the dispersion model [60]. Research on the influence of carbon monoxide air pollution on the condition of the environment without accounting for meteorological parameters was presented in the present work.…”

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confidence: 99%

Mapping Carbon Monoxide Pollution of Residential Areas in a Polish City

Kwiecień

Szopińska

2020

Remote Sensing

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Road traffic is among the main sources of atmospheric pollution in cities. Maps of pollutants are based on geostatistical models using a digital model of the city along with traffic parameters allowing for ongoing analyses and prediction of the condition of the environment. The aim of the work was to determine the size of areas at risk of carbon monoxide pollution derived from road traffic along with determining the number of inhabitants exposed to excessive CO levels using geostatistical modeling on the example of the city of Bydgoszcz, a city in the northern part of Poland. The COPERT STREET LEVEL program was used to calculate CO emissions. Next, based on geostatistical modelling, a prediction map of CO pollution (kg/year) was generated, along with determining the level of CO concentration (mg/m3/year). The studies accounted for the variability of road sources as well as the spatial structure of the terrain. The results are presented for the city as well as divided into individual housing estates. The level of total carbon monoxide concentration for the city was 5.18 mg/m3/year, indicating good air quality. Detailed calculation analyses showed that the level of air pollution with CO varies in the individual housing estates, ranging from 0.08 to 35.70 mg/m3/year. Out of the 51 studied residential estates, the limit value was exceeded in 10, with 45% of the population at risk of poor air quality. The obtained results indicate that only detailed monitoring of the level of pollution can provide us with reliable information on air quality. The results also show in what way geostatistical tools can be used to map the spatial variability of air pollution in a city. The obtained spatial details can be used to improve estimated concentration based on interpolation between direct observation and prediction models.

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“…Forvizor [WXW∗18] leverages a flow chart based on juxtaposition to show the formation changes of two soccer teams. AirVis [DWC∗19] compares different propagation modes based on juxtaposition, and StreamExplorer [WCS∗18] shows different topic maps with juxtaposition for comparing topics of events at different timestamps. Seo and Shneiderman [SS05] proposed a rank‐by feature approach that consists of a ranked list and scores with ordered bars.…”

Section: Related Workmentioning

confidence: 99%

Warehouse Vis: A Visual Analytics Approach to Facilitating Warehouse Location Selection for Business Districts

Li¹,

Liu²,

Tang³

et al. 2020

Computer Graphics Forum

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Selecting a proper warehouse location serving to satisfy the demands of the goods from a certain business area is important to a successful retail business. However, the large solution space, uncertain traffic conditions, and varying business preferences impose great challenges on warehouse location selection. Conventional approaches mainly summarize relevant evaluation criteria and compile them into an analysis report to facilitate rapid data absorption but fail to support a comprehensive and joint decision‐making process in warehouse location selection. In this paper, we propose a visual analytics approach to facilitating warehouse location selection. We first visually centralize relevant information of warehouses and adapts a widely‐used methodology to efficiently rank warehouse candidates. We then design a delivering estimation model based on massive logistics trajectories to resolve the uncertainty issue of traffic conditions of warehouses. Based on these techniques, an interactive framework is proposed to generate and explore the candidate warehouses. We conduct a case study and a within‐subject study with baseline systems to assess the efficacy of our system. Experts ‘feedback also suggests that our approach indeed helps them better tackle the problem of finding an ideal warehouse in the field of retail logistics management.

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AirVis: Visual Analytics of Air Pollution Propagation

Cited by 53 publications

References 69 publications

A Comparison of Geographical Propagation Visualizations

A Comparison of Geographical Propagation Visualizations

Mapping Carbon Monoxide Pollution of Residential Areas in a Polish City

Warehouse Vis: A Visual Analytics Approach to Facilitating Warehouse Location Selection for Business Districts

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