MC2 -通过空间-非空间流动方法挖掘工厂污染数据(视觉传达清晰度荣誉奖)

Joshua Castor, J. Borowicz, A. Burks, Manumol Thomas, T. Luciani, G. Marai
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引用次数: 0

摘要

2017年VAST挑战赛的迷你挑战2集中在虚构的Mistford保护区以南的一个小工业区,特别是四个制造工厂周围。我们的主要目标是开发一个可视化的分析工具来探索时空化学读数和风数据。具体来说,我们想要确定哪些工厂排放了哪些化学物质,并确定该地区9个传感器的性能。为了帮助实现这一目标,我们开发了一个基于web的应用程序,该应用程序利用交互式可视化和路径线分析来揭示传感器错误和化学读数峰值,以及精确定位化学读数峰值的可能来源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MC2 - Mining Factory Pollution Data through a Spatial-Nonspatial Flow Approach (Honorable Mention for Clarity in Visual Communication)
Mini Challenge 2 of the VAST Challenge 2017 focused on a small industrial area south of the fictional Mistford preserve, specifically around four manufacturing factories. Our main goal was to develop a visual analytics tool to explore the spatio-temporal chemical readings and wind data. Specifically, we wanted to determine which factories were responsible for emitting which chemicals and to determine the performance of the nine sensors in the area. In order to help achieve this goal, we developed a web-based application that utilizes interactive visualizations and path line analysis for revealing sensor errors and chemical reading spikes, along with pinpointing the possible sources of chemical reading spikes.
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