Explorative pedestrian mobility GPS data from a citizen science experiment in a neighbourhood

Abstract

Pedestrian GPS data are key to a better understanding of micro-mobility and micro-behaviour within a neighbourhood. These data can bring new insights into walkability and livability in the context of urban sustainability. However, pedestrian open data are scarce and often lack a context for their transformation into actionable knowledge in a neighbourhood. Citizen science and public involvement practices are powerful instruments for obtaining these data and take a community-centred placemaking approach. The study shares some 3000 GPS recordings corresponding to 19 unique trajectories made and recorded by groups of participants from three distinct communities in a relatively small neighbourhood. The groups explored the neighbourhood through a number of tasks and chose different places to stop and perform various social and festive activities. The study shares not only raw data but also processed records with specific filtering and processing to facilitate and accelerate data usage. Citizen science practices and the data-collection protocols involved are reported in order to offer a complete perspective of the research undertaken jointly with an assessment of how community-centred placemaking and operative mapping are incorporated into local urban transformation actions.

Figures (7)

• Figure 1: Map of "Barri Primer de Maig". The neighbourhood "Primer de Maig" in the city of Granollers (Catalonia, Spain) is limited by the streets: "Carrer d'Enric Prat de la Riba", "Carrer de Roger de Flor", "Carrer de Pius XII" and "Carrer d'Isabel de Villena". The red crosses represent the location of the three sites related to the communities involved: the school "Escola Ferrer i Guàrdia (EFiG)", the neighbourhood association site "Associació de Veïns Sota el Cami Ral (AAVV)", and the space for older people "Espai Actiu de la Gent Gran (EAGG)". The meeting point was located at the "Plaça de la Font" and this square is also displayed in red. The neighbourhood has an area of approximately 3 ha (300$\times$100 metres) and the distance between the three communities' locations and the neighbourhood is less than 70 metres. The satellite image was retrieved from the Spanish National Geographic Institute (https://www.ign.es/web/ign/portal) with data from the European Copernicus satellites.
• Figure 2: Schematic description of the citizen science experiment. The data files available are reported jointly with the participatory process underpinning the whole research. The direct participation of citizens is highlighted in green and the data sets being created in grey.
• Figure 3: Wikiloc App screenshots (in Catalan) during a test carried out in the University facilities (Faculty of Physics, UB). (a) Initial screen when opening the Wikiloc App. Participants had to click on "Comena̧r a Gravar" (Start recording, in Catalan) to start recording the trajectory. (b) A real time recording of the journey; the recording time is displayed at the top of the screen, and the recording can be paused by pushing the red lower button. (c) The screen after pausing the recording of the journey; the participants had to click on "Finalitza" (End, in Catalan) to end the experiment and save the GPS data. (d) The screen after finalizing the recording. In this step, the participants returned the tablet to one of the researchers, who in turn saved the GPS data by clicking on "Desar ruta" (Save route, in Catalan), after entering the trajectory's name "Nom" (Name, in Catalan) which was the code of the respective group.
• Figure 4: GPS trajectories and stops of all participating groups. (a) Heat-map of the GPS locations (in motion). (b) Display of all trajectories in the neighbourhood. (c) All the GPS locations labelled as stopped (the size of the yellow circle depends on the duration of the stop: the larger the circle, the longer the stop).
• Figure 5: Example of a trajectory clean-up. The trajectory corresponds to a group from the school EFiG. (a) Map representation of the original GPS location data from a single trajectory. (b) Map representation after the clean-up process, with the removal of the records outside the neighbourhood at the beginning and at the end of experiment.