GIScience 2021

Abstract

Many classic exploratory data analysis tools in quantitative geography, designed to measure global 15 and local spatial autocorrelation (eg Moran’s I statistic), have become standard in modern GIS 16 software. However, there has been little development in amending these tools for visualization and 17 analysis of patterns captured in spatiotemporal data. We design and implement a new open-source 18 Python library, VASA, that simplifies analytical pipelines in assessing spatiotemporal structure of 19 data and enables enhanced visual display of the patterns. Using daily county-level social distancing 20 metrics during 2020 obtained from two different sources (SafeGraph and Cuebiq), we demonstrate 21 the functionality of the developed tool for a swift exploratory spatial data analysis and comparison 22 of trends over larger administrative units.

Evgeny Noi

Spatial Stats and Data Mining Nerd

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