The World Spatiotemporal Analytics and Mapping Project (WSTAMP): Further Progress in Discovering, Exploring, and Mapping Spatiotemporal Patterns Across the World’s Largest Open Source Data Sets

Abstract

ABSTRACT:Spatiotemporal (ST) analytics applied to major data sources such as the World Bank and World Health Organization has shown tremendous value in shedding light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. WSTAMP engages this opportunity by situating analysts, data, and analytics together within a visually rich and computationally rigorous online analysis environment. Since introducing WSTAMP at the First International Workshop on Spatiotemporal Computing, several transformative advances have occurred. Collaboration with human computer interaction experts led to a complete interface redesign that deeply immerses the analyst within a ST context, significantly increases visual and textual content, provides navigational crosswalks for attribute discovery, substantially reduce mouse and keyboard actions, and supports user data uploads. Secondly, the database has been expanded to include over 16,000 attributes, 50 years of time, and 200+ nation states and redesigned to support nonannual, non-national, city, and interaction data. Finally, two new analytics are implemented for analyzing large portfolios of multiattribute data and measuring the behavioral stability of regions along different dimensions. These advances required substantial new approaches in design, algorithmic innovations, and increased computational efficiency. We report on these advances and inform how others may freely access the tool.

Reanalysis of Water Withdrawal for Irrigation, Electric Power, and Public Supply Sectors in the Conterminous United States, 1950–2016

“…DTW overcomes this challenge by developing a “warping” path along the temporal axis, from which distance measures are minimized to align chronological patterns among different entities (Berndt & Clifford, 1994). We applied DTW to total irrigation water usage using the WSTAMP package in R (Piburn et al., 2017), which calculates a distance matrix based on dissimilarities in time‐series data. We standardized water use data for each county from 0 to 1 so that trends would be purely based on behavior and agnostic to magnitudes.…”