GoogleLocationHistoryDataShapefiles.zip

In order to compare GLH data to travel diary data, GLH data were captured passively across five smartphone devices while two researchers followed prescribed itineraries. While the same five smartphone devices were not all carried on each prescribed itinerary, resulting in an uneven number of samples between devices, two researchers traveled together along prescribed itineraries following identical characteristics of travel. Arrival and departure times from all prescribed itinerary locations were measured empirically by self-report, thus completing the prescribed itineraries as travel diaries. This method afforded a priori knowledge of what the GLH data should be reporting as expected from the prescribed itineraries. In all, 12 unique itineraries were prepared, accounting information that GLH data provide, and to which can be compared: positions of locations, arrival and departure times, location dwell times, trip duration times, trip distances, travel modes, and trip geometries throughout different urban forms. Once GLH data were captured, the downloadable Keyhole Markup Language (KML) files were retrieved from the desktop Google Maps “Your Timeline” feature. The attribute information associated to the point and line shapefiles (SHP) derived from the KML files were then matched to the itinerary data by arrival and departure times within an error of ten minutes. We accepted an error of ten minutes as a reasonable limit for which we could unambiguously assume that the GLH data were sensing our prescribed itinerary travel and assign a match. The following analysis were done for each variable interaction of interest between smartphone device, travel mode and urban form. Mean location position errors were calculated by taking the mean Euclidean distance between the expected locations and the matched GLH data locations. Arrival and departure times were assessed using the root mean square error (RMSE). Similarly, trip duration times, trip distances, and location dwell times were assessed by the normalized RMSE to account for the differences in range for each interaction subset. Finally, GLH data trip geometries were assessed using the mean Hausdorff distance. The Hausdorff distance is a measure of the maximum distance from a point in set A to its nearest point in set B. In other words, of all the shortest distances from each point in set A to points in set B, it is the maximum of those distances. Since GLH data trip geometries were given as linestrings, we converted the linestrings to point sets for computing Hausdorff distances. This was done by generating points along linestring geometries. An interval of one meter between points was chosen as a distance small enough to account for the geometric variation in trip geometry for all scales of travel. The mean Hausdorff distances were then calculated for each interaction subset of travel mode and urban form.