Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK

Data sources

We received data via the Facebook Data for Good program¹⁰ describing the daily number of trips starting within a tile, and the sum of the length of all trips starting within a tile, where a tile is defined by a grid that Facebook applies to the Earth’s surface in order to aggregate the movement of its users¹¹. Each tile is approximately 25 km². A trip in the Facebook data is defined as the movement of a handset from one tile to another between two subsequent updates of the geolocation data (the time period between updates is defined by mobile phone operating systems). The latitude and longitude of the centroid of each tile was provided and these were used to aggregate data from the tile level to the local authority district (LAD) level for consistency with the mobile network data (described below). The data were used for 10th March - 22nd May 2020 inclusive for Facebook and for 1st February - 5th May 2020 inclusive for the mobile network.

Facebook also provided data on the daily number of active users of their app, with GPS location services enabled, within different locations. We also aggregated these data to UK LADs and calculated the Facebook population size in each LAD relative to the mean in the first week of data (10th - 16th March 2020 inclusive). To adjust for changing Facebook population size over time (Extended data, Figure S2⁹) the number of daily trips per LAD and the daily total distance travelled per LAD were divided by the Facebook population size per LAD relative to the first week of data.

In addition to the Facebook data, we received anonymised and aggregated crowd level mast data from O2 (which is further modelled to the UK population) detailing the number of trips starting within each UK local authority district for 1st February - 5th May 2020 inclusive. A trip is created when a device moves from one overlapping group of cells to another (where a cell is an area of coverage provided by a single mobile network antenna) and remains with the group of cells for long enough to indicate the device is stationary. As these are mast data, all mobile phones registered to the data provider’s network are recorded, not just smartphones with GPS tracking enabled.

Data comparison and normalisation

To enable comparison of the mobile network data with the Facebook data, we defined a baseline level of mobility as the mean daily number of trips within the week 10th - 16th March 2020 per LAD. We then calculated the percentage change in mobility for the Facebook and mobile network data compared to this baseline.

We inspected the data on mean daily number of trips within the following spatial regions: individual countries of the UK, specific cities (defined by the boundaries of their constituent LADS) which were selected because they are the largest city in each of the four countries of the UK respectively (London, Cardiff, Glasgow, and Belfast), LADs, and finally we grouped the LADs into quartiles of population density (throughout, the quartile of lowest population density is referred to as 1, whilst quartile 4 is the quartile of greatest population density). The location of population density quartiles is shown in Extended data, Figure S1⁹. Data describing the population per LAD were provided by LandScan¹².

Model fitting

We fitted a segmented-linear model¹³ to the percentage relative to baseline for the nationally aggregated Facebook data against time. The segmented-linear model divides the data into multiple time components split by “breakpoints”, then, using linear regression fits a straight line to each separate component. The dates of each breakpoint are included as parameters of the model. The minimum size of a component allowed was chosen to be three data points to minimise overfitting.

Due to the categorical difference between levels of mobility on weekdays, Saturdays, and Sundays, we only fitted the model to weekdays. Bank holidays were also omitted from the data during the fitting as they were markedly different to the other weekdays but were not considered to be representative of any longer-term trends in the data.

We investigated segmented-linear models with 0, 1, 2, 3, 4, 5 and 6 breakpoints. For each model, all possible permutations of breakpoints were considered subject to the constraint of there being a minimum of three data points per component. For each permutation the other parameters of the model were fitted assuming log-normal errors for the number of trips as a percentage compared to baseline. The relative weighting of points was estimated using inverse-variance weighting, where relative variance was estimated as the daily number of trips. The permutation with the highest likelihood was then considered the best fit for the model. Comparisons of the segmented-linear models (with different numbers of breakpoints) were made using the corrected Akaike information criterion (AICc), with the most parsimonious model being the one with the lowest AICc.

Finally, we also investigated differences in the distribution of distance travelled per journey in areas of high and low population density, and how these distributions changed following the introduction of social distancing measures. The shape of these distributions was defined by smoothing over the data points using a Gaussian kernel.

All the analysis was conducted using the R statistical programming language¹⁴; the code used to plot the figures and run the analyses is publicly available at https://github.com/mrc-ide/covid-uk-mobility-report and has been archived with Zenodo⁹.

Changes in mobility over time

The mean number of trips per local authority district (LAD) made in the UK by users of the Facebook mobile phone app dropped substantially between the 18^th and 26th of March inclusive (Figure 1), from around 99% of the baseline to around 37%. The number of trips was still close to the baseline on Friday 20th March (the last day in which schools were open, other than to vulnerable children and the children of key workers, see Extended data, Table S1⁹) at approximately 87% of the baseline, then dropped substantially over the weekend of 21st - 22nd March to about 30% of the baseline number of daily trips per LAD. This was the first weekend during which all pubs, clubs, bars, gyms, etc. were closed (Extended data, Table S1⁹). We see a slight increase in the mean number of trips per LAD on Monday 23rd to 67% of the baseline. On the evening of Monday 23rd of March the government messaging changed from “we advise you to stay home” to “you must stay home”. From Tuesday 24th to Thursday 26th of March we then see a greater decrease in the number of daily journeys made per LAD to approximately 40% of the baseline across the UK on working days, and approximately 15% of the baseline on the following weekend. As a percentage of the number of daily trips made per LAD, this difference between weekdays and weekends remains approximately constant from the 26th of March until the last day of data we considered: the 22nd of May (Figure 2 and Figure 3).

Figure 1.

Rapid reduction in mobility in the UK from the 18th March 2020 (A) through to the 26th March (I). Colour shows percentage change in daily number of journeys compared to the mean in the week 10th-16th March 2020 inclusive, by origin tiles that consistently reported data each day. Sufficient data were not available for tiles in the grey area. Note that (C) and (D) are weekend days and there was an increase in overall mobility on the 23rd March (see also Figure 2).

Figure 2. Consistent changes in mobility observed between Facebook data and mobile phone data.

Change in movement over time as a percentage of baseline movement for the four home countries within the UK and their largest city for Facebook data (blue) and mobile phone data (red). Baseline movement defined as the mean number of journeys starting within a small unit within each city from 10th-16th March 2020 inclusive. The dashed vertical line at 23rd March indicates when the most stringent lockdown measures were imposed.

Figure 3. Fit of the segmented-linear model with 5 breakpoints to the percentage relative to baseline of number of trips against time (top panel).

Weekdays (blue), Saturdays (red), Sundays (orange) and bank holidays (purple). Bottom panel shows univariate 95% confidence regions for each breakpoint.

Mobility variation by location and data source

There was little variation in mobility reductions either by data source, city (London, Cardiff, Glasgow, Belfast), or country of the UK (Figure 2). There were clear weekend effects in all areas (see the previous paragraph) and small variations from day to day (Figure 2); however, trends across different locations were highly consistent. For example, on the Wednesday of the first full week post-lockdown (the 31st of March 2020) the city with the greatest number of daily trips relative to the baseline was Belfast according to the Facebook data and Glasgow according to the mobile phone data (30% and 35% of the baseline respectively), and the city with the fewest number of daily trips relative to the baseline on the same day were Glasgow according to the Facebook data and Belfast according to the mobile phone data (29% and 33% of the baseline, respectively). On average, the Facebook data suggested fewer trips were made following the social distancing interventions than the mobile phone data.

Following the initial reduction in mobility coinciding with the introduction of social distancing measures, there has been a gradual increase in the number of daily trips made by Facebook and mobile phone users, starting on approximately the 1st of April and continuing until the 22nd of May (Figure 2–Figure 4). Using the Facebook data, we fitted multiple segmented-linear models (see Methods) to the number of daily journeys made per LAD as a percentage relative to the baseline (mean number of daily trips per tile in the week 10th – 16th of March). A segmented-linear model with five breakpoints was the most parsimonious (Figure 3) with breakpoints on 13th March, 23rd March, 27th March, 20th April, and 27th April. However, a four-breakpoint model had only a marginally higher AICc (-300.68 vs -299.86) and so could not be rejected (Extended data, Table S2⁹). For the period between the 27th March and 20th April there has been a statistically significant increase in the number of daily trips (p-value < 0.001), with mobility (as a percentage relative to the baseline) increasing at a rate of 0.15 percentage points per day. For the period since the 27th April this rate of increase has been 0.51 percentage points per day. We conclude that since the beginning of April there has been a gradual increase in the number of daily trips made per LAD and that this rate has increased recently.

Figure 4. We ranked each UK local authority district by population density and determined the corresponding quartile for each local authority district, with lower population density in quartile 1 and higher population density in quartile 4.

The shaded region is the range of percentage differences in journeys made at each time point for both lower quantile (low population density) and upper quantile (high population density) using the mobile data. Solid lines are the median difference from baseline within each quartile. The dashed line on 23rd March is when the most stringent lockdown measures were imposed.

There was a greater decrease in the median number of trips made within high-density populations compared with low-density populations (Figure 4), from 91% to 47% of the baseline in low-density populations, and 90% to 40% of the baseline in high-density populations between the 18th and the 26th of March. Prior to the beginning of lockdown on the 24th of March, there was a higher median mobility, more pronounced weekend effects (greater decrease in mobility at the weekend than on weekdays), and a greater range in mobility within high-density populations compared with populations living in regions of lower population density. However, after the lockdown the reduction in mobility in high- and low-density populations was comparable on weekends, and slightly higher on weekdays in low-density populations (e.g. 43% and 38% of the baseline in low- and high-density populations respectively on the 31st of March 2020). Also, the range of reductions in mobility in high-density populations was not symmetric about the median: some high-density populations reduced their levels of mobility by almost 100% compared to baseline levels, whereas the range in low-density population was narrower and approximately symmetric about the median pre and post-lockdown.

We observed similar changes in the distribution of journey distances (Figure 5) to those described above for the number of journeys. In the two lowest quartiles of population density (Figure 5A, B), the decrease after lockdown was less and the median journey distance was lower at all time points than in the most densely populated LADs. However, the range of journey distance was greater both before and after the lockdown in the lowest density populations (Figure 5A) than in high-density populations (Figure 5D).

Figure 5. Distribution of mean journey lengths per LAD on each Wednesday in the Facebook data for areas of increasing population density.

(A–D) Journey length distributions for each quartile of population density from lowest (A) to highest (D). The y axis is on a log10 scale and the median and interquartile range for each distribution is shown by the horizontal lines. A Gaussian kernel is used to define the shape of the distributions which are truncated at the minimum and maximum point in the data.

Underlying data

The raw data needed to replicate these analyses has not been made public by the data providers, meaning we are forbidden from sharing it with this paper. However, the reader can apply for access to the Facebook data through the Geoinsights platform here: https://dataforgood.fb.com/, or by emailing disastermaps@fb.com or diseaseprevmaps@fb.com. Details on how to access these data are available at https://dataforgood.fb.com/tools/population-density-maps/.

Mobile phone operator data was provided by O2 Motion. More details on how to apply to access these data can be found at https://www.o2.co.uk/business/solutions/data-mobile/o2-motion.

Extended data

Zenodo: mrc-ide/covid-uk-mobility-report: Initial Release. https://doi.org/10.5281/zenodo.3865406⁹.

This project contains the following extended data:

Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).