COVID-19 mobility report

The effect of the COVID-19 measures on mobility in California

The coronavirus is challenging societies around the world. In the USA, many states have issued stay-at-home orders to limit the spread of the virus. In California, a state of emergency was issued on March 4 and a stay-at-home order has been in effect since March 19. Even before these state wide measures, individual counties reacted with bans on large gatherings, school closures and other restrictions. What are the effects of these measures?

The chart below is an overview of traffic flows in California relative to their January averages. What is measured is the number of daily trips between and within cities compared to the average number of daily trips in January. Select one of the bars to get the geographic breakdown of the mobility reduction for that week in the map below.

The map shows the remaining mobility per region in the selected week relative to January traffic levels. Each colored region on the map represents a corresponding urban area. The lower the percentage value, the greater the reduction in mobility. The colored arcs represent mobility between urban centers. Hover over the colored areas to see details.

The chart shows a gradual drop of mobility in the state of California starting from the week of March 7. Initially, a reduction of mobility was observed in the Bay Area. This trend continues in the week preceding the state-wide stay-at-home order during which mobility in the bay area already dropped to 47% below January levels. In the week of March 21, a significant reduction in mobility can be observed in the entire state of California. Inner city residual mobility ranged from 23% (Palo Alto) to 59% (Bakersfield) of January levels. It is clearly visible that while the Mexican border city of Tijuana still showed some residual mobility during the week, cross border traffic was significantly reduced to 34% of January levels.

The two graphs below show the distribution of residual mobility in the selected week within and between the selected cities. The first graph depicts the percentage of the selected cities whose residual mobility falls inside the bucket noted on the x axis. The second graph shows the percentage of city connections whose residual mobility falls inside the respective range of percentage points in the given week.

What went into this analysis?

For this particular analysis we asked ourselves the following questions: Do we see an overall reduction of mobility in California following the implementation of restrictions in response to COVID-19? If yes, how strong was the reduction? How quickly did people comply? Furthermore, we were wondering if we would find regional differences. It also seems reasonable to expect that local trips to the supermarket stay relatively frequent while longer distance travel between cities would drop off sharply.

Lastly, we were curious about cross-border traffic. Each state adopted a slightly different response to COVID-19 and it seems reasonable to investigate whether these differences are visible in people's mobility patterns.

The data for this analysis stems from the TomTom Origin/Destination API. This API processes huge amounts of de-identified floating car data to provide estimates of traffic flows between and within geographic regions.

We want to be clear that while these results are interesting to study, we should be careful with their interpretation. If a city shows less of a mobility reduction than another city, this does not mean that there is less compliance with the restrictions. It might just as well mean that this city hosts more essential industry and simply cannot reduce mobility further. To make the most out of this data, it should be correlated and combined with other sources and carefully interpreted.

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