Supercomputing Enabled Deployable Analytics for Disaster Response

TL;DR

This paper presents a deployable analytics approach for disaster response that precomputes geo-spatial demographic data into files compatible with legacy hardware, enabling rapid analysis without network access.

Contribution

It introduces a method to generate and distribute precomputed analytics files for emergency responders, bypassing network and security constraints common in disaster scenarios.

Findings

Precomputed census data files enable quick access to demographic information.

The approach allows rapid analytics generation within minutes using supercomputing resources.

Tools like Excel and Google Earth facilitate intuitive data visualization for responders.

Abstract

First responders and other forward deployed essential workers can benefit from advanced analytics. Limited network access and software security requirements prevent the usage of standard cloud based microservice analytic platforms that are typically used in industry. One solution is to precompute a wide range of analytics as files that can be used with standard preinstalled software that does not require network access or additional software and can run on a wide range of legacy hardware. In response to the COVID-19 pandemic, this approach was tested for providing geo-spatial census data to allow quick analysis of demographic data for better responding to emergencies. These data were processed using the MIT SuperCloud to create several thousand Google Earth and Microsoft Excel files representative of many advanced analytics. The fast mapping of census data using Google Earth and Microsoft Excel has the potential to give emergency responders a powerful tool to improve emergency preparedness. Our approach displays relevant census data (total population, population under 15, population over 65, median age) per census block, sorted by county, through a Microsoft Excel spreadsheet (xlsx file) and Google Earth map (kml file). The spreadsheet interface includes features that allow users to convert between different longitude and latitude coordinate units. For the Google Earth files, a variety of absolute and relative colors maps of population density have been explored to provide an intuitive and meaningful interface. Using several hundred cores on the MIT SuperCloud, new analytics can be generated in a few minutes.