data visualization

Impact of COVID-19 on Foot Traffic

Visualize changes in foot traffic across various industries, brands and regions due to COVID-19.

Here we show SafeGraph Patterns data on 2020 commercial activity compared to the same time period from previous years, revealing insights into how the ongoing pandemic impacted the US economy and disrupted daily life. We have a similar 2021 dashboard here, updated monthly.

About the Data

This dashboard is powered by SafeGraph Patterns data, an aggregated, anonymized, privacy-safe summary of foot traffic to millions of points of interest (POIs) in North America. Here, we aggregate the data by categories (like airports or supermarkets) or by brands (like Costco or McDonald's). The population sample is a panel of opt-in, anonymized smartphone devices, and is well balanced across USA demographics and geographies. We show data from both 2019 and 2020 to help contextualize 2020 data. Many categories have week-to-week seasonal variability that is apparent in both 2019 and 2020. In general, the 2020 data shows large deviations from 2019 during the same time period.

How You Can Get This Data

Academics can get this data for free by joining the SafeGraph Community. We’re proud to partner with thousands of researchers and be a part of the new studies being released every week.

F reach out with any questions. We are happy to help.

Data Methodology

To build these visualizations we start with a count of visits to individual points of interest by day (i.e., the SafeGraph Patterns column 'visits_by_day'). We then further aggregate by summing the data across either an industry (e.g., supermarkets), restaurant category (e.g., Chinese food), a particular brand (e.g., Costco), or within a metropolitan statistical area (MSA) (e.g., New York-Northern New Jersey-Long Island). For metropolitan statistical areas, we aggregate POI visits from all categories.

The visit data and the underlying panel is dynamic (growing and changing every day). Therefore, instead of showing raw measurement counts, for these graphs, we normalize, standardize, and smooth the data to enable better year-over-year (YoY) and change-over-time analysis.

First, we normalize daily by a measure of the total composition and activity of the panel. Therefore, the y-axis represents a normalized measure of foot traffic happening at this category or brand, given the sample size (which changes day to day).

Second, each year's data is standardized on the y-axis to early January (specifically the median value between the 2nd and 3rd Wednesday of January). This is to better visualize YoY differences and to abstract the semi-arbitrary units of normalized SafeGraph panel visit counts.

Third, we apply a lagging seven-day rolling-window smoothing algorithm. Most categories and businesses have large weekday vs weekend variability. We smooth over seven days to focus the analysis on the overall trend in a category or brand across many weeks, while still capturing important day-by-day changes.


Products we used to build this visualization:

Places Data

See how data leaders leverage Places as their source of truth for any location in the world.

Geometry Data

Incredibly accurate POI footprint data for a clear picture on the boundary of a place.

Patterns Data

Mobility and demographic aggregations that answer: how often people visit, where they come from, where else they go, and more.

Spend Data

See how and when people are spending their money at specific locations.

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