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Modern cities are increasingly incorporating advanced technologies that are transforming the way we live, work and interact with our urban environment. Transport, public lighting, waste collection, video surveillance... no sector is immune to the massive data collection, algorithms and artificial intelligence that are now being used. Meet them all at the Datawalk.
What is a Datawalk?
Datawalk is a unique experience that invites citizens to explore their city or neighbourhood from a new angle. Walking through the streets, participants discover how data is collected and used to improve urban life, from traffic management to optimising public services. These walks not only highlight the technical aspects of the smart city; they also prompt reflection on ethical issues and the protection of personal data.
The Datawalk methodology is essentially based on the concept of a guided tour, where a guide prepares in advance a route along which participants will be asked questions about various data collection devices and their use made of them. Among the devices commonly found in a city, and of interest for a Datawalk, are the following:
Weather stations: Small stations available in several cities map elements such as rainfall, wind speed and temperature with great precision. By analysing this data, the city can fine-tune its local climate policy, for example by anticipating heat islands or flooding.
Mobile phone towers: Your mobile phone in combination with cell towers enables telephone companies to collect a great deal of personal information, such as your location, your movements, your working hours and so on. This information can be combined (at an aggregated level) with socio-demographic and payment data. For example, a city can map traffic flows, get an idea of visitors' spending habits and adapt its policy accordingly.
Public WiFi: Each device equipped with a WiFi module (such as a smartphone) has a unique identifier. Anyone within range of a WiFi transmitter with a smartphone automatically exchanges a connection request, to which this code is sent. WiFi transmitters, with which you can connect to public WiFi, often also function as WiFi sniffers (a device that maps all WiFi devices in the area). In this way, the location of your device and general traffic patterns in the city can be mapped, even if you're not (actively) using the WiFi network.
Shared scooters and bikes: Companies that offer shared scooters and bikes that you can hire via an app also collect various forms of data. They receive information on where and when customers use their service. This enables them to analyse which routes or pick-up and drop-off points are the most popular, for example.
Cameras: Their function is to increase security in the public domain. They can be used in a variety of ways to count vehicles, detect suspicious behaviour and identify traffic offences.
Because of the simplicity and flexibility of its implementation, the data walk is a particularly effective educational tool for engaging citizens in discussions about the use of intelligent technologies in public spaces.
Examples of Datawalks
The Datawalk concept was born in 2017 in England with the 'Data Walking' project and has since been exported to Germany, Denmark, Canada and Belgium, among others.
In particular, the Spectre project in Belgium has published various articles on the methodologies developed and feedback from the field. Fourteen workshops were held in the Belgian cities of Brussels, Ghent and Leuven between September 2021 and March 2022. The workshops focused on citizens' experiences and perceptions of the collection and processing of (personal) data in the public space.
The pre-planned routes led to a variety of technologies, depending on what was present in each city: different types of CCTV cameras, cellphone towers, parking sensors, public Wi-Fi, bike or e-scooter sharing systems, Bluetooth and Wi-Fi trackers, and more. The routes followed are illustrated on the maps below.
The moderators highlighted information on the smart city concept, data protection concepts and the sensors encountered during the walk. The conversations were guided so that the different perspectives, advantages and disadvantages of data processing activities could be discussed. In all, more than a hundred members of the public took part.
More recently, we should also mention the Datawalks set up by FARI in Brussels since 2022, which illustrate the flexibility allowed by these walks depending on the educational angles targeted. While Spectre was mainly concerned with surveillance and privacy, FARI's Datawalks also focused on promoting projects developed in Brussels by various public administrations and citizen' associations.
On these walks, it is interesting to note that participants were often surprised (and unaware) of the amount of technology present in their daily environment and the scale of the data involved. For some participants, the Datawalk was even a real revelation, allowing them to discover a new facet of their city.
The SteamCity: AI Odyssey approach
The AI Odyssey is the name chosen for the Datawalk activity that we are currently preparing as part of the SteamCity project. The activity is built around the investigative approach that is central to the SteamCity project, whereby young people are encouraged to organise their own Datawalk, put it into practice and then analyse it critically.
Organising a Datawalk for young people has a number of significant educational advantages. Firstly, this activity engages pupils in an active process of gathering information about the technologies present in their urban environment. By preparing for the walk, they learn to identify and understand the role of sensors and other data-gathering devices. This stage is also designed to encourage students to take a critical look at their environment and question the way these technologies work and their purpose.
Then, during the Datawalk, the students explore their city by looking for the sensors identified in class. The aim is both to develop their ability to recognise and understand the integration of technologies into the urban fabric, and to make the concept of the smart city tangible, by making learning more concrete and meaningful. In addition, the physical dimension of the walk contributes to a more dynamic and engaging learning experience.
Finally, the classroom analysis stage, after the walk, aims to consolidate the experience of the walk into an opportunity for reflective learning. The learners compare their discoveries with their initial predictions and look more closely at how the data collected by the sensors could be used, particularly in terms of artificial intelligence applications. Young people are encouraged to reflect on the ubiquity of sensors in their daily lives and the ethical implications, particularly in terms of data protection and privacy.
By questioning data collection devices and their use, the Datawalk helps to develop a deeper understanding of the technologies that surround us and teaches us to ask pertinent questions about their societal impact. This active learning process encourages the development of critical thinking, which is essential in an increasingly complex and technological society.
References
Create your own Datawalk. Knowledge Center Data and Society
Spectre project. Smart city Privacy: Enhancing Collaborative Transparency in the Regulatory Ecosystem
Policy Brief #57: Walkshops. Innovative methodology for citizen involvement in Smart Cities
Join us for a Datawalk in Brussels! FARI - AI for the Common Good in Brussels.