The next generation of dashboards: a spatial online analytical processing (SOLAP) platform for COVID-19

The health and societal impacts of COVID-19 have created tremendous interest in the scientific community, resulting in interdisciplinary research teams that combine their expertise to provide new insights into the epidemic. However, spatial computation, exploratory data analysis, and spatial data exploration tools have yet to be integrated into these dashboards. We present a Spatial Online Analytical Platform that integrates spatial analysis tools that enable users to explore and learn more about spatial patterns of COVID-19. We present three interaction classes to support users needs. Our first class allows users to apply user-defined data classifications and custom map color choices. The second class applies a risk index across the time series, informing users of the recent temporal trends. The third class allows users to hypothesize about the presence of spatial clusters and receive results on demand. Our SOLAP platform supports the data analysis and exploration needs of big spatial-temporal data.

新冠疫情（COVID-19）对公共健康与社会造成的冲击，引发了科学界的广泛关注，由此催生了多学科研究团队，他们整合各自专业专长，为此次疫情防控提供全新的认知视角。然而，空间计算、探索性数据分析以及空间数据探索工具，尚未被整合至此类疫情监测仪表板中。本研究提出一款空间在线分析平台（Spatial Online Analytical Platform，以下简称SOLAP），该平台整合了空间分析工具，可帮助用户探索并深入了解新冠疫情的空间分布模式。我们设计了三类交互模式以满足用户的分析需求：第一类交互模式支持用户自定义数据分级规则，并可自选地图配色方案；第二类交互模式可基于时间序列计算风险指数，向用户展示疫情的近期时序变化趋势；第三类交互模式允许用户针对空间聚集性提出假设，并可按需获取分析结果。本SOLAP平台可满足大规模时空大数据的分析与探索需求。