Supplementary material (data & R script) for the CIEES 2022 manuscript authored by B Stoyanovski, T Iliev, R Cesarec, R Filjar

A single ZIP file contains 119 Android smartphone sensors trajectory observations on the path in the city centre of Krapina, Croatia, as well as the R script for data aggregation, trajectory formation, analysis, and machine learning-based prediction model development, for the CIEES 2022 Conference manuscript. Trajectory observations were taken using AndroSensors smartphone application (https://play.google.com/store/apps/details?id=com.fivasim.androsensor&amp;hl=en_US&amp;gl=US), which collects obseravtion on the move and stores them into a CSV file. Content depends on the available smartphone sensors. Data sets supplied consist of observations of the following variables: ACCELEROMETER X (m/s²) ACCELEROMETER Y (m/s²) ACCELEROMETER Z (m/s²) GRAVITY X (m/s²) GRAVITY Y (m/s²) GRAVITY Z (m/s²) LINEAR ACCELERATION X (m/s²) LINEAR ACCELERATION Y (m/s²) LINEAR ACCELERATION Z (m/s²) GYROSCOPE X (rad/s) GYROSCOPE Y (rad/s) GYROSCOPE Z (rad/s) LIGHT (lux) MAGNETIC FIELD X (μT) MAGNETIC FIELD Y (μT) MAGNETIC FIELD Z (μT) ORIENTATION Z (azimuth °) ORIENTATION X (pitch °) ORIENTATION Y (roll °) PROXIMITY (i) ATMOSPHERIC PRESSURE (hPa) SOUND LEVEL (dB) LOCATION Latitude : LOCATION Longitude : LOCATION Altitude ( m) LOCATION Altitude-google ( m) LOCATION Altitude-atmospheric pressure ( m) LOCATION Speed ( Kmh) LOCATION Accuracy ( m) LOCATION ORIENTATION (°) Satellites in range Time since start in ms YYYY-MO-DD HH-MI-SS_SSS <br> The analysis and prediction model R script is developed by authors in the R environment for statistical computing (https://www.r-project.org/), using additional R libraries: trajr (https://cran.r-project.org/web/packages/trajr/index.html) and caret (https://topepo.github.io/caret/), among the others.