Coronavirus: The Road to Economic and Social Recovery
By: Descartes Labs
Tags: Analytics, Coronavirus, Geospatial, Emergency, Data Science

Coronavirus: The Road to Economic and Social Recovery

Updated with aggregated mobility data from Friday, June 5th, 2020

Review our aggregated mobility tracking methodology in our technical paper: Mobility Changes in Response to COVID-19. We are making views of this mobility data freely available at the US admin1 (state) and admin2 (county) level under a Creative Commons Attribution (CC BY 4.0) license at the following GitHub repository.

Coronavirus is having a profound impact on everyday life — canceled schools, closed restaurants, working at home, no gatherings of more than 10 people, etc. — with one commenter saying “it’s like we’re pushing the pause button on the economy.”

Despite the gloom and significant personal impacts, there’s also widespread belief we’ll get through this — the question is when?

Remote sensing refers to data collected from satellites, aircraft, and distributed sensors that can provide information about the earth and help us understand human activities at a macro scale. While it is possible to understand the economic consequences of coronavirus through news reports and surveys, remote sensing provides direct observations that can be aggregated on a large scale and automatically processed for real-time insights.

Using remote sensing at Descartes Labs, we’ve developed a set of tracking and monitoring tools that can be used by businesses to understand consumer and supply chain activities that are critically important to revenues. Here are four types of signals that our customers are already leveraging to better navigate the global crisis:

Aggregated mobility tracking

By combining multiple data sources, we can observe motions of everyday life — going to school, commuting to work, heading to the park — to see patterns comparing activity today with activity six months ago, or last week until today.

Changes in social activity are a leading indicator of economic activity. Fewer movements, less growth; more movements, more growth. By monitoring and understanding these activity patterns on a global scale, we can provide a first-hand view of stabilization moving toward eventual growth.

In the example below, we look at aggregated mobility at the country level. Our methodology looks at a collection of mobile devices reporting consistently throughout the day. We calculate the maximum distance moved in kilometers (excluding outliers) from the first reported location. Using this value, we calculate the median across all devices in the sample to generate a mobility metric for each country.

Some interesting insights immediately appear:

Mobility in the US appears to decline only as recently as March 14th, corresponding roughly with the start of widespread school closings and social distancing.

Mobility in Hong Kong declines around January 25th from roughly three kilometers per day to less than one. This continues throughout all of February until a gradual return in March, though far below levels in January.

Mobility in Iran appears to decrease substantially during the last week in February and almost flatline thereafter.

Cyclicality is clearest in the US data with peaks on Friday (Saturday and Sunday are not shown on these graphs). Distance traveled is also the highest in the US compared to other countries.

Running this same analysis over counties in New York and Washington state and converting to a “mobility index” shows major declines occurring just within the past week (100 = median weekday mobility, week of Feb 16th).

And here’s the same chart for select US states:

Location-specific activity tracking

For companies that want to track very specific kinds of activities, we also have the capability to observe aggregated activity on a per-location basis — airports, malls, casinos, restaurants, and industrial parks — to better understand the relative differences for very targeted locations across unique geographies and timelines.

In this example, we show a percent change in total device counts per airport on March 9th-13th relative to February 10th-14th (four-week delta). Airports on the West Coast, and California in particular, showed decreases of 50% or more. The decrease in device counts at most other airports ranged from 20% or 40%. Only a handful of airports showed no change or small increases in activity.