Modeling the Economic Impact of COVID-19 with EO-Based Technologies

DESCRIPTION: The economic impact of the COVID-19 outbreak will be modeled using existing data sources, classifications of EO data based on crowd-sourced data, and a Computable General Equilibrium (CGE) model adapted from earlier work at the University of Southern California. CIESIN will assist in the sample design and crowd-sourcing

Task 1: Stratified Sampling of Sectoral Impacts. CGE modeling begins from the premise that impacts in a given industry have rippling effects throughout the economy as subsequent demand for products and services are affected. CIESIN will work with ImageCat staff to set up a sampling protocol to identify land parcels associated with key economic sectors (manufacturing, retail, education, etc.) specifically for the interpretation of EO data. This requires developing a statistically robust way to cross reference GIS data and EO data (moderate resolution data from Low Earth Observation satellites and higher resolution optical image available from commercial providers) to interpret economic activity. This task is primarily setting up the sampling of EO data. Deciding on a sampling approach is a critical decision that needs to be addressed upon award. We will be careful to consider a reasonably limited effort, given the budget limitations of
a RAPID grant.

Task 2: Crowd-Sourced Interpretation. This task will require the interpretation of the specific sites selected in the sampling from task 1, primarily the distribution of 1) automobiles in neighborhoods, parking lots, and streets and 2) shipping containers in ports. Crowd participation will be highly informed by the sampling strategy and limited to a handful of known contributors given budget. This task is also primarily an interpretation of EO data. CIESIN staff will aid with the implementation of the crowd participation system, testing the system, and evaluation of crowd participation inputs for quality control.