VASA - Visual Analytics for Spatial Association

VASA exploration

The developed Python package, named VASA, will be accessible at To demonstrate the applicability of the designed visualizations, the variability in spatiotemporal structure of human mobility patterns during the COVID-19 pandemic in the United States is assessed. VASA offers three novel multivariate visualizations: A stacked recency and consistency map, a line-path scatter plot, and a categorical strip (dot) plot. All three techniques use LISA as the base and utilize local Moran’s I and permuted p-values. The techniques are best suited for analysis of areal data at two levels of analysis: the object-level and the summary-level. The object-level of analysis receives the data at the finest available scale (e.g. county, census blocks, etc.), whereas the summary-level (e.g. state) refers to the less granular spatial units that contain object-level units. The stacked recency and consistency map allows to ascertain the spatiotemporal structure of data at both object- and summary-level. The categorical strip plot allows for comparison of trends at the summary-level. The line-path visualization is better suited for a fine-detail analysis of individual object-level trajectories within a specified summary-level.

Evgeny Noi
Evgeny Noi
Spatial Stats and Data Mining Nerd

My research interests include spatio-temporal modeling, machine learning, data mining and visual analytics.