DP Mobility Report

The following table shows the key figures of the dataset.

The allocated privacy budget for this statistic is shown below and noise is applied accordingly to compute the estimate and the 95% confidence interval.

The following table shows the number of missing values for each column of the dataset.

The allocated privacy budget for this statistic is shown below and noise is applied accordingly to compute the estimate and the 95% confidence interval.

The following map shows the spatial distribution of the dataset according to the provided tessellation. Records outside the tessellation are indicated as number of outliers below the map.

The legend below shows the number of visits per tile ranging from 0 to the maximum number of visits per tile.

The allocated privacy budget for this map is shown below and noise is applied accordingly onto the counts. The confidence interval is indicated below.

Tiles below a certain threshold are grayed out: Due to the applied noise, tiles with a low visit count are likely to contain a high percentage of noise. For usability reasons, such unrealistic values are grayed out. More specifically: The threshold is set so that values for tiles with a 5% chance (or higher) of deviating more than 20 percentage points from the estimated value are not shown.

This table shows the mean number of visits per tile as well as the five-number summary consisting of: the most extreme values in the dataset (the maximum and minimum values), the lower and upper quartiles, and the median.

These values are computed from the counts visualized above. Thus, no extra privacy budget is used.

The following visualization shows the cumulated relative number of visits. This means that the tiles are sorted according to the number of visits in descending order and the relative number of visits are added tile by tile. Thus, you can use the graph to evaluate how many tiles are needed to cover a certain share of the visits.

If all tiles are visited equally, the cumulated sum follows a straight diagonal line (gray line). The larger the share of single tiles in the total number of visits, the steeper the curve.

These values are computed from the counts visualized above. Thus, no extra privacy budget is used.

The following visualization shows the ranking of most frequently visited tiles.

The y-axis shows the tile name (if provided) and tile ID in order of the ranking. The x-axis shows the number of visits per tile.

These values are computed from the counts visualized above. Thus, no extra privacy budget is used. The 95% confidence interval of the visits per tile indicated above also applies here and is visualized with error bars.

Each map shows the arrivals (destinations) for the respective time window for each tile, split by weekday and weekend, as absolute counts and as the deviation from tile average. The tile average is defined as the mean number of visits in one tile across all time windows. Thus, the deviation from tile average indicates higher or lower number of visits for this tile during certain time windows of a day.

Tiles below a certain threshold are grayed out: Due to the applied noise, tiles with a low visit count are likely to contain a high percentage of noise. For usability reasons, such unrealistic values are grayed out. More specifically: The threshold is set so that values for tiles with a 5% chance (or higher) of deviating more than 20 percentage points from the estimated value are not shown.

The following map shows the origin-destination (OD) flows between the tiles according to the provided tessellation, meaning the number of trips between respective start and end tiles.

The origin of the OD flows is indicated by a small circle and by clicking on one OD connection, information on the origin and destination cell name as well as the count of for this OD connection will show up.

The legend for the intra-tile flows is below. The intra-tile flow is defined as an OD connections that starts and ends in the same tile.

The allocated privacy budget for this map is shown below and noise is applied accordingly onto the counts. The confidence interval is indicated below.

Flows below a certain threshold are not displayed (grayed out for intra-tile flows): Due to the applied noise, flows with a low count are likely to contain a high percentage of noise. For usability reasons, such unrealistic values are not displayed/grayed out. More specifically: The threshold is set so that values for flows with a 5% chance (or higher) of deviating more than 20 percentage points from the estimated value are not shown.

The following histogram shows the distribution of number of trips per user, i.e. how many trips a user contributed to the dataset.

The y-axis indicates the relative number of users and the x-axis shows the range of the histogram bins according to the user configured maximum of trips per user.

The allocated privacy budget for this statistic is shown below and noise is applied accordingly to compute the estimate (bars) and the 95% confidence interval (error bar).

The following histogram shows the distribution of time between consecutive trips of a user, i.e. the time that passes between the end of one trip and the beginning of the following trip of one user.

The y-axis shows the relative number of trips and the x-axis shows the range of histogram bins in hours between trips of the same user according to the user configured bin size and maximum value.

The allocated privacy budget for this statistic is shown below and noise is applied accordingly to compute the estimate (bars) and the 95% confidence interval (error bar).

The following histogram shows the distribution of the radii of gyration. The radius of gyration is the characteristic distance traveled by an individual during a period of time.

The y-axis shows the relative number of users and the x-axis shows the range of histogram bins in kilometers according to the user configured bin size and maximum value.

The allocated privacy budget for this statistic is shown below and noise is applied accordingly to compute the estimate (bars) and the 95% confidence interval (error bar).

The following histogram shows the distribution of how many distinct tiles a user has visited. It describes the diversity of locations a user visits.

The y-axis shows the relative number of users and the x-axis the number of distinct tiles according to the user configurated bin size and maximum value.

The allocated privacy budget for this statistic is shown below and noise is applied accordingly to compute the estimate (bars) and the 95% confidence interval (error bar).

The following histogram shows the distribution of the mobility entropy.

The mobility entropy characterizes the heterogeneity of the users visitation patterns and can be interpreted as a measure for the predictability of a users location. If a user only visits a single tile, the entropy is 0, i.e., their location is highly predictable. If a user visits, e.g., four different tiles each 10 times, the entropy is 1, i.e., their location is not predictable as every of the four tiles has the same probability to be visited by the user. Intuitively, the more trips per user are entailed in the data, the more meaningful the mobility entropy.

The y-axis shows the relative counts of users and the x-axis shows the range of histogram bins of the mobility entropy.

The allocated privacy budget for this statistic is shown below and noise is applied accordingly to compute the estimate (bars) and the 95% confidence interval (error bar).