VASA: an exploratory visualization tool for mapping spatio-temporal structure of mobility – a COVID-19 case study

ABSTRACT

Exploratory data analysis tools designed to measure global and local spatial autocorrelation (e.g. Moran’s $\mathit{I}$ statistic) have become standard in modern GIS software. However, there has been little development in amending these tools for visualization and analysis of patterns captured in spatio-temporal data. We design and implement an exploratory mapping tool, VASA (Visual Analysis for Spatial Association), that streamlines analytical pipelines in assessing spatio-temporal structure of data and enables enhanced visual display of the patterns captured in data. Specifically, VASA applies a set of cartographic visual variables to map local measures of spatial autocorrelation and helps delineate micro and macro trends in space-time processes. Two visual displays are presented: recency and consistency map and line-scatter plots. The former combines spatial and temporal data view of local clusters, while the latter drills down on the temporal trends of the phenomena. As a case study, we demonstrate the usability of VASA for the investigation of mobility patterns in response to the COVID-19 pandemic throughout 2020 in the United States. Using daily county-level and grid-level mobility metrics obtained from three different sources (SafeGraph, Cuebiq, and Mapbox), we demonstrate cartographic functionality of VASA for a swift exploratory analysis and comparison of mobility trends at different regional scales.

KEYWORDS:

• COVID-19
• human mobility
• visual analytics
• Moran’s I
• spatial autocorrelation
• spatio-temporal analysis

Acknowledgement

The authors gratefully acknowledge the support from the National Science Foundation (awards BCS #2043202 and #1853681), and the University of California Santa Barbara Vice Chancellor for Research COVID-19 Seed Program to conduct this research.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2022.2156388

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the third parties:

• SafeGraph – free of charge under the “Data for Good” initiative for academic purposes.

• Cuebiq – proprietary data, which requires licensing fee for access.

Restrictions apply to the availability of these data, which were used under license for this study.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1. https://github.com/move-ucsb/VASA Github.

2. A Google Scholar query for “covid and visualization” yielded around 343,000 at the time of writing this paper.

3. See https://loquacious-frangipane-b89dda.netlify.appinteractive examples on a Lab’s website.

Additional information

Funding

The work was supported by the NSF [1853681,2043202]; University of California Santa Barbara Vice Chancellor for Research COVID-19 Seed Program.