Background:Considering the widespread of coronavirus disease 2019(COVID-19)pandemic in the world,it is important to understand the spatiotemporal development of the pandemic.In this study,we aimed to visualize time-as...Background:Considering the widespread of coronavirus disease 2019(COVID-19)pandemic in the world,it is important to understand the spatiotemporal development of the pandemic.In this study,we aimed to visualize time-associated alterations of COVID-19 in the context of continents and countries.展开更多
We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index(LVI).Knowing in advance the data,the aggregation functions that are used for visualizatio...We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index(LVI).Knowing in advance the data,the aggregation functions that are used for visualization,the visual encoding,and available interactive operations for data selection,LVI allows to avoid time-consuming data retrieval and processing of raw data in response to user’s interactions.Instead,LVI directly predicts aggregates of interest for the user’s data selection.We demonstrate the efficiency of the proposed approach in application to two use cases of spatio-temporal data at different scales.展开更多
文摘Background:Considering the widespread of coronavirus disease 2019(COVID-19)pandemic in the world,it is important to understand the spatiotemporal development of the pandemic.In this study,we aimed to visualize time-associated alterations of COVID-19 in the context of continents and countries.
基金National Key R&D Program of China(2018YFC0831700)NSFC project(61972278)+1 种基金Natural Science Foundation of Tianjin(20JCQNJC01620)the Browser Project(CEIEC-2020-ZM02-0132).
文摘We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index(LVI).Knowing in advance the data,the aggregation functions that are used for visualization,the visual encoding,and available interactive operations for data selection,LVI allows to avoid time-consuming data retrieval and processing of raw data in response to user’s interactions.Instead,LVI directly predicts aggregates of interest for the user’s data selection.We demonstrate the efficiency of the proposed approach in application to two use cases of spatio-temporal data at different scales.
