Describing Temporal Correlation Spatially in a Visual Analytics Environment

Abstract

In generating and exploring hypotheses, analysts often want to know about the relationship between data values across time and space. Often, the analysis begins at a world level view in which the overall temporal trend of the data is analyzed and linear correlations between various factors are explored. However, such an analysis often fails to take into account the underlying spatial structure within the data. In this work, we present an interactive visual analytics system for exploring temporal linear correlations across a variety of spatial aggregations. Users can interactively select temporal regions of interest within a calendar view window. The correlation coefficient between the selected time series is automatically calculated and the resultant value is displayed to the user. Simultaneously, a linked geospatial viewing window of the data provides information on the temporal linear correlations of the selected spatial aggregation level. Linear correlation values between time series are displayed as a choropleth map using a divergent color scheme. Furthermore, the statistical significance of each linear correlation value is calculated and regions in which the correlation value falls within the 95% confidence interval are highlighted. In this manner, analysts are able to explore both the global temporal linear correlations, as well as the underlying spatial factors that may be influencing the overall trend.

Explanation and Analysis of Spatio-Temporal Correlations—Towards a Conceptual Approach of a Semantic Comparison Visualization in a Use Case of Carparks in Mainz, Germany

“…Area-based approaches use different choropleth maps with a divergent color scheme for positive or negative correlations [37], or in combination with interactive views with multivariate, geographically weighted boxplots and scalograms [38]. The use of additional visual variables (e.g., highlighted borders as a result of tests of statistical significance) [39], hotspot analyses [40], or Linked Views (e.g., topological network visualizations, triad configurations, correlation matrices) [41] extend choropleth maps in spatio-temporal correlation analyses. In contrast, point-based approaches examine spatiotemporal correlations on a micro-level.…”