Center for Spatial Studies

Many classic exploratory data analysis tools in quantitative geography, designed to measure global and local spatial autocorrelation (e.g. Moran’s 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 spatiotemporal data. We design and implement a new open-source Python library, VASA, that simplifies analytical pipelines in assessing spatiotemporal structure of data and enables enhanced visual display of the patterns. Using daily county-level social distancing metrics during 2020 obtained from two different sources (SafeGraph and Cuebiq), we demonstrate the functionality of the developed tool for a swift exploratory spatial data analysis and comparison of trends over larger administrative units.