DOI: 10.5281/zenodo.17117291
title: Mobility, Physical Activity, Energy Use, and Sustainable Behaviour in Office Environments: A Pilot Study

description: This dataset describes the energy-related behavior of the participants. It includes quantitative data in the sectors of personal mobility and housing and energy use, collected by means of the SWICE mobile app and power meters installed in a living lab occupied by participants respectively.This data was collected as part of a pilot study to test the SWICE infrastructure in the context of an office space living lab between January and May 2025.

All the different data sources can be linked through the identifier of the participant they correspond to.

Details about the collected data are provided in the following.

 

Personal Mobility Data

An overview of people's personal mobility and physical activity is gathered by means of the SWICE mobile app (Android: https://play.google.com/store/apps/details?id=ch.swice.swiceapp.android , iOS: https://apps.apple.com/ch/app/swice/id6504611837 ), using the phone's sensors to estimate the movements of people and the relative mode of transport that was employed. Additionally, the number of steps each participant took per day is registered, as well as a heatmap of the locations visited by all participants with each mode of transport. Anonymization techniques are put in place to preserve the privacy of the users.

More precisely, the data relative to personal mobility is here described in the files `movements.csv`, `steps.csv`, and `paths.csv`.

movements.csv

This represents each segment of a person’s mobility patterns, anonymized to prevent recognizing the person this belongs to and sensitive locations, such as their home and work addresses.

Data:

    - `participant_id`: Participant pseudonym - used to identify a single user and connect the different data sources

    - `start_time`: Start time (and date) of the registered movement. Format: YYYY-MM-DD HH:MM:SS±HH:MM

    - `end_time`: End time (and date) of the registered movement. Format: YYYY-MM-DD HH:MM:SS±HH:MM

    - `start_geohash`: Start location of the movement, approximated to a 1.22km X 0.61km rectangle, using geohashes

    - `end_geohash`: Final location of the movement, approximated to a 1.22km X 0.61km rectangle, using geohashes

    - `distance(m)`: Distance covered in this segment, in meters

    - `mean_of_transport`: Mean of transport, after eventual correction by the participant

    - `original_mean_of_transport`: Original mean of transport, what was recognized by the app with the sensors

    - `gCO2`: The estimated CO2-equivalent emissions related to this movement. Computed for Switzerland based on the Mobitool v3 database (https://www.mobitool.ch/de/tools/mobitool-faktoren-v3-0-25.html)

    - `is_power_saving`: Indicates if the phoen was in power saving mode when the movement was registered

 

steps.csv

The activity of people, estimated with the number of steps they take daily (with their phone).

Data:

    - `participant_id`: Participant pseudonym - used to identify a single user and connect the different data sources

    - `day`: Date of the registered movement. Format: YYYY-MM-DD

    - `steps`: Number of steps detected by the phone

 

paths.csv

This information represents approximate locations visited by people during their movements. It can be used to create heatmaps representing the approximate paths that people use to reach a certain location (see figure for an example). Each point is disconnected from the others, it does not clearly belong to a movement. This is also anonymized on the time dimension.

 

Data:

    - `time_range`: Time range representing the period of the day when this position has been registered, allowing to see difference for example between morning and evening paths. This indicates the hour

    - `day_of_week`: Day of week, to tell week days from weekends for example.

    - `month`: Month, to explore seasonality of movements

    - `mode_of_transport`: Mode of transport, the mode of transport recognized by the app (no correction possible, for anonymity reasons)

    - `geohash`: the coordinate of the registered location, approximated with a ~100m2 rectangle using geohashes, location at the start and end of a movement are discarded, to prevent finding the position of one person’s home and other recurrent locations

    - `direction`: The direction in which the person was traveling when this location has been registered.

    

Housing and Energy Use Data

An overview of people's energy usage in the building context, notably in the tracked living labs, can be found in the `sensors.csv` file. The exact parameters that are collected depend on the installed sensors.

 

sensor_data.csv

This information represents the values of the various sensors in the building that is tracked in the current experiment.

 

Data:

    - `building`: A pseudonym representing the building where this sensor is spaced

    - `room`: A pseudonym representing the building where this sensor is placed

    - `sensor`: The name of the sensor

    - `timestamp`: The timestamp when the data was collected by the sensor

    - `value`: The value recorded by the sensor

    - `unit`: The units of the recorded value