Use Cases: How a Civic Digital Trust Could Work

In this section, we explore particular uses of data within a smart city that can create public value. The purpose of creating imagined scenarios of use is to have a conversation about data sharing that is more relatable to everyday life. The details of each scenario draw on earlier discussions in the Primer (including different technical architectures and business models), putting the theory into practice.

The scenarios are illustrative, rather than realistic. They are not endorsements or recommendations of any particular civic digital trust model. We hope they get you thinking about what data you personally are and are not willing to share, for what uses, and how a trust could help.

We developed four use cases around key elements of smart cities to imagine how a civic digital trust could function in each scenario. Participants in our civic digital trust workshop created a fifth scenario to provoke discussions around the vast amount of data that is already being collected about us from our use of digital technology.

Utilities and Consumption
Adaptable Building
Mobility
Smart Health Assistance
Enabled Citizen

Currently, the utility consumption and appliance data within citizen's homes and the broader building data is primarily siloed and not shared broadly to utilize network effects of the data. In a connected world, how can we create a trusted sharing environment for better use of this data to improve building efficiency, increase safety for tenants and hit environmental targets?

With smart cities integrating elements of live, work and play into unified spaces, we are challenged to better understand how to balance these elements. With the use of technology we can capture the utilization of spaces like never before. How can we balance the need for privacy with generating the data needed to improve the spaces we live in?

As cities continue to densify, the challenge of moving people across the city and neighbourhoods becomes evident. In a hyper-connected world there are opportunities to stitch together multiple types of data to create deep insights into the movement of humans. Data from our travel habits, transit utilization, heat mapping the travel patterns of the city can be used to better move through the city, how can we enable this?

There is a rise of preventative medicine, wearable technologies and access to information. We have great insights into our bodies and health that typically stays within our health apps and associated companies. Connecting that data to health services, emergency services can yield improvements to speed and efficiency of receiving treatment. How might we foster this collaboration to improve citizen health?

We are currently living a life that generates mass amounts of data that is utilized by major technology companies. Our location, habits and preferences are well known and documented and that data is being used to influence our behaviours, enhance current products and services as well as develop new ones. How can we enable citizens to regain control and manage this data effectively?

These use cases can help have a focused conversation around what data is needed to make these scenarios, to further understand how the citizen will be affected and what the composition of civic digital trust will be. Through the development of the use cases, we have challenged our interviewee's to think through these dynamic situations and test the limits of what types of data are needed, how they will be collected, how this information will be used and who will have access to it. Overlaying potential civic digital trust models on top of these data uses allows for discourse around the strength of the model, where it fails and where it adds value. The use cases forthcoming, have gone through initial iterations but are still applicable to continue to test and have discussions around.

We encourage you to ask tough questions and provide input around the viability of the data trust in these contexts, the ownership and enforcement models that would allow for unbiased innovation, and to question the types of data needed and how its captured.

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