Datacat · Process ReportData literacy for youth

Andreas Jonsson

Esben Grøndal

María Crucera

Lara Casciola

Abstract:This paper describes the design process of developing a service concept under the theme of smart cities and open data. The following project explores how to improve data literacy among youth in Copenhagen through academic research and co-creation methods. Youth are not naturally aware of the data they generate and how it is used, and are often overlooked and not included in decision-making in the development of cities. With the youth being future decision makers, this calls for a solution. To this, we designed Datacat. Datacat begins with a course on data literacy introduced in social science classes at high schools. Youth can link their online activities to the service, and share specific data in response to municipal challenges. Through Datacat, youth will become aware of the digital data they generate, and how it can be used to support and inform urban development projects in the municipality. This way the youth will improve their data literacy and become active citizens taking part in developing Copenhagen as a smart city.

Copyright © This report and/or appended material may not be partly or completely published or copied without prior written approval from the authors. Neither may the contents be used for commercial purposes without this written approval.

Copies: 3

Pages: 90

Finished: 27 May 2015

Programme: MSc Service Systems Design

Semester: 8th

Title: Datacat. Data literacy for youth.

Project Period: Feb 2015 - May 2015 Semester Theme: Smart cities & Open data

Project group no.: 3

Members: Lara Casciola Study no. 20142707

María Crucera Study no. 20141327

Esben Grøndal Study no. 20140951

Andreas Jonsson Study no. 20140972

Aalborg University CopenhagenA.C. Meyers Vænge2450 København SV, Denmark

Supervisor(s):

Amalia de Götzen

Nicola Morelli

Semester Coordinator: Amalia de Götzen

Secretary: Judi Stærk Poulsen

Introduction 5Overview 2Learning Goals 3Project Limitations 4Methodology 5Project Management 7Iterative Process Overview Diagram 8

Problem Definition 11What is a Smart City? 13What is Open Data? 14Initial Direction 15Smart Copenhagen 16Gathering the Citizen Perspective 17Copenhagen’s Data: Who Has Control? 18

Exploring the Smart City Proposals 18Insights 19

Revising the Problem Statement 20Association Game 20Narrowing the Demographic 20Revised Problem Statement 21

People Centered Data 22Co-Creating a Solution 23

Why Co-Creation? 25What is Co-Creation? 25Benefits of Designing With People 25

Observation at Ordrup High School 26Recruiting Design Partners 27

What Is a Smart Citizen? 29Data Literacy 30

What is Data Literacy? 30Data Literacy in Youth 30Current Danish Initiatives 31

Youth Cafés 32Youth Café 1 33

Insights 34Summary 35

Framing the Design Challenge 36Youth Café 2 37

Association Game 37Merging Ideas: Initial Concept 39

Analyzing 39Synthesizing 40

Defining the Service Provider 41Summary 42

Creating City Challenges 43From Personas to Schools 44Putting The Pieces Together 46High School Prototype Test 49Youth Café 3 52Process Reflections 56

Final Concept: Datacat 57Final Service Description 58Blueprinting 59Stakeholder Analysis 62

Stakeholder Map 62Power-Interest Matrix 63Motivation Matrix 64

Datacat Datatypes 66Service Outcomes 67

Strategic Goals 67Technical Analysis 68Datacat App Design 72Scenarios 75Similar Services 77

Data Marketplaces 77Municipal Improvement 77

Future Steps 78Scalability 79

Reflections 80Validity and Reliability 81Did We Meet the Learning Goals? 82Group Work Reflections 83

References 84Appendices 88

Appendix A 89Survey Responses 89

Appendix B 89Video Sketching Workshop 89

Appendix C 90Semi-Structured Interview Guide 90

Table of Contents

5

Introduction

2

This semester-long service design project was completed in the second semester of the Service Systems Design Master’s program at Aalborg University Copenhagen. From January - June 2015 we worked to design a service system concept based on the theme “smart cities and open data”. We approached this project hoping to explore various co-creation methods, and involve stakeholders in designing a solution. The final service concept is an educational experience aimed at Copenhagen’s youth combined with a citizen inclusion tool for the municipality of Copenhagen. Implemented in high schools through a mobile application, our service concept will allow young people to see an overview of the digital data they produce, and contribute some of this data to assist Copenhagen municipality with urban improvement projects. This report details our process. Steps are described chronologically, with relevant, supporting research introduced in the order we explored each topic.

Overview

3

A discussion of our learning goals was held prior to beginning the design process. Both the formal learning goals from the study program as well as personal learning goals were covered. This was a process of aligning ambitions and expectations to the project and process as a whole.

The formal study description states:“The objective of this project module is to give hands-on experience on the design of a complex service on the basis of a modular platform, in which actors and competences are clearly identified, organizational and interaction aspects are planned and user participation is planned and supported.” (Aalborg University, Faculty of engineering and science, Board of studies for Media technology, 2012)

In extension hereof, four study guide goals were selected which we deemed the most important and relevant:

• Must be able to understand the nature and structure of distributed systems• Must be expert in planning and supporting collaboration, participation and integration of different components in a service system (synthesis)• Must be able to plan and describe competences of different components/actors in a modular service architecture and to organize them appropriately (synthesis)• Must be able to integrate technical and human components on a service platform (synthesis).

Learning Goals

In addition to the formal learning goals, we developed four personal learning goals:

• To involve users in various project phases, from co-creating initial ideas to final prototype testing• To adapt our approach to user involvement to generate and facilitate meaningful information for each project phase, and to analyze and implement findings successfully• To get involved with concepts related to smart cities. To learn about current and future initiatives within urban centers worldwide, and begin to utilize these concepts• To improve our cross-disciplinary communication tools, both within our group and in communicating our ideas to a diverse external audience.

We will eventually reflect on these learning goals, assessing whether they have been achieved and whether new understandings or competences have been acquired throughout the process.

4

This project has evolved within some limitations. The first of these is time as the role of our involvement in the project was limited not only to the amount of time we could allocate to it during the semester, but also the limit of the length of the semester itself.

Closely related to this is the size of the project team. Being only four of us with two Danish speakers presented us with a limitation regarding how many of us could help facilitate interviews and the like.

As we were physically located within the City of Copenhagen, we placed our service concept within the city. This allowed us to explore the implications of our concept through first-hand research and observation.

Project Limitations

5

The approach to this project is that of service design thinking (Stickdorn & Schneider, 2011: p.18). In this project the notion of service design thinking is defined as applying an established design process, and utilizing the tools and techniques available to uncover a problem and design a solution (ibid.).

It is widely accepted in design and academia that qualitative research is key to uncover emotions, aspirations and beliefs (Blomberg et al., 1993), so we aimed to gather mostly qualitative information from our user group. Being in close contact with people makes it possible to uncover tacit knowledge, which is often valuable and difficult to bring forth.

The iterative nature of design thinking provides the freedom to research, rework ideas, prototype and test as needed, resulting in stronger and more well-rounded solutions that truly fit a need. Although an iterative framework was chosen to structure the project (Figure 1), we were still bound to various deadlines. To ensure we met these, a series of checkpoints, or stage-gates, as suggested by Karlstrom & Runeson (2005) has been defined. This allowed us some measure of keeping track of the progression.

The checkpoints are defined as process related sentences linked to the generic steps of the design model (Figure 2):

• Finish foundation research. Problem statement is clear and valid.• A range of concepts are clear and specified. Co-creation has been used to explore possible solutions.• Prototypes have been developed and tested, in various levels of fidelity.• Report written and compiled.

Methodology

It is important to note that a design process is not linear as the model may indicate, nor should it be. The nature of design is chaotic, and navigating through the chaos, balancing between various phases and involving people where need be, is one of the many tasks of a service designer.

Empathise Define Ideate Prototype Test

Empathise Define Ideate Prototype Test

09 April

07 May

28 May

12 March

Figure 1: Iterative design process (d.school, 2013)

Figure 2: Tentative stage-gates

6

As such, a more accurate model of project progression is depicted by The Squiggle (Figure 3).

The Squiggle shows the iterative nature of design project progression, and how one will go back and forth, up and down, and ultimately converge towards a final design. The front end of the squiggle is often referred to as the fuzzy front end (Herstatt & Verworn, 2001). Here, the notion of getting the right design (Buxton, 2007) evolves, as it is a matter of finding the problem and generating a lot of ideas. Then it converges towards a prototyping phase, where the idea is to get the design right (ibid.).

It is furthermore important to note, that a given design solution will never be the perfect solution, but rather a solution amongst many. A solution will often evolve over time, into an even better, or new solution, to either the same or a completely different problem.

Figure 3: The Squiggle (Stickdorn & Schneider, 2011, p.124)

7

Project Management

In order to plan and organize the project, and to keep on track of checkpoints, we utilized a large paper calendar, a written logbook, and a modified version of SCRUM (Figure 4).

The paper calendar provided a clear overview of the entire semester, deadlines, and other appointments that influenced the project or process.

The logbook served as a retrospective tool with notes and reflections on all activities throughout. Having the logbook was a great advantage when writing the reports in the concluding phases of the project.

The SCRUM methodology was modified in the sense that we used the scrum board with sections titled: backlog, to-do, in progress, and done. We did not, however, work with sprints or daily scrum meetings. It gave a good indication of progression seeing the sticky notes move across the board and end up in the “done” column.

Managing the project with these tools made it less stressful to navigate the landscape of service design, and made it more straightforward to keep the project on track.

Figure 4: SCRUM board (http://scrummethodology.com/)

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EmpathisE In-Street IntervIewS

+ OnlIne SurveySchOOl ObServatIOn

DEfinEtheme: Smart cItIeS

DeSk reSearch

Smart cIty InItIatIveS matrIx

DeSk reSearch

PrOblem Statement buIlDer

prototypE

iDEatE vIDeO wOrkShOP

tEst

InItIal DIrectIOn narrOwIng the DemOgraPhIc

Iterative Process Overview Diagram

Figure 5: This diagram shows the iterative nature of our process and outlines the structure of this report.

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yOuth cafe 1

cIec PreSentatIOnframIng DeSIgn challenge

PrOtOtyPIng

wOrkShOP

PrOtOtyPe key elementS ux elementS

yOuth cafe 2mOtIvatIOnal

cOnSIDeratIOnS

hIgh SchOOl teSt yOuth cafe 3

StrategIc gOal

Of Data lIteracy

eStablIShIng munIcIPalIty aS ServIce PrOvIDer

10

11

Problem Definition

12

The general theme for this project was “smart cities and open data”. To begin the process, we conducted research into what these terms actually mean, and thought about the opportunities and challenges they provide. Through discussion, research, and qualitative data collection we determined a direction for the project.

13

What is a Smart City?

‘Smart city’ is a broad and multi-faceted term. The central theme within the concept is that technology, implemented in an urban setting, should make urban life more efficient, more convenient, and more comfortable (De Waal, 2011). Integral to this is the paradigm of viewing cities as optimizable, ever-improving, complex services.

During the early modernist period, revolutionary inventions such as sewers rapidly changed the practical definition of a city. Cities were no longer just large collections of people, but areas in which there was specific, large-scale infrastructure designed to make human life better for all (De Waal, 2011).

The smart city concept builds up on this viewpoint. The seamless background computing integrated in smart city spaces will theoretically offer us a quality of life previously unattainable. The smart city should strive to improve almost every aspect of citizen life: governance, people, environment, mobility, economy and living (Figure 6). Using a vast and tightly interconnected network of cameras, sensors, and real-time analysis, smart cities of the future will be able to connect data streams to create meaningful information (Donath, 2011).

Despite having been discussed extensively, in practical terms smart cities are still “embryonic” (Donath, 2011). Though cameras cover many urban areas worldwide, nowhere has the interconnected network of data streams which underlies a ‘smart’ city, been fully implemented (ibid.; Greenfield, 2013; Townsend, 2014).

SmartCity

Smart Economy

Smart Mobility

Smart Environment

Smart Living

Smart People

Smart Governance

Figure 6: The potential impact of the smart city concept (adapted from Fisher et al., 2011)

14

What is Open Data?

Data can be defined as discrete facts, or things used “as a basis for inference or reckoning” (Jashapara, 2004, p. 14). It can be of a quantitative (dependent on context for meaning), or qualitative (dependent on perception) nature (ibid.). In the digital age, much of this data is created and stored online in machine language that can be read and interpreted by various technologies (Janssen, n.d.). Within digital data, elements of the physical world are captured and “simulated for technological use” (ibid.).

The modern citizen is producing digital data almost constantly (Dragland, 2013). For example, we create data passively when we move throughout CCTV monitored urban spaces or when we pay with our credit cards (ibid.). When moving around a city, our location data is often recorded on our mobile devices, mostly in data banks out of our control (Fuller & Harley, 2011). When we browse the internet we create data actively (ibid.). Within an urban context, Deahl (2014) argues that this ubiquitous data collection could make individual citizens be seen as simple means to produce the data, instead of being consulted in decisions that have a great impact on them.

This data, constantly collected in staggering volumes, can be immensely useful in a broad variety of applications. Examples include analyzing Google searches medical researchers have been able to discover previously undocumented side effects of drug combinations (Hildebrandt & O’Hara, 2013). By collecting the driving information of GPS users, navigation device manufacturer TomTom helped the Dutch government improve their road system - and determine where best to place speed cameras (ibid.).

This amalgamation of many individual data streams is called ‘big data’. By combining many different data sources, “the integrated data are often more valuable than the sum of their parts.” (Barbosa et al., 2014). Within an urban setting, this data can be, and often is, leveraged to improve city services. The city of Copenhagen, for example, has used large-scale traffic data to design the ‘green wave’ - a system by which green light timing is optimized for improved traffic flow (Copenhagen Connecting, 2013).

Increasingly, big data within urban settings is published unrestricted online - accessible to citizens, social innovators, and policy makers at all levels (Barbosa et al., 2014). In this case, it is defined as ‘open data’. Over the past few years, open data has been increasing at a rapid pace, as governments worldwide realize the benefits of both increased transparency and a greater opportunity for innovative data application (ibid.). The city of Copenhagen has many data sets available online, and has plans to make these more useful by implementing an improved hosting system (Copenhagen Connecting, 2013).

15

Initial Direction

Faced with such a broad theme, we first had to narrow our focus area through discussion. Each group member individually researched concepts and ideas related to smart cities, and we met to discuss the areas we each found interesting. In order to gain perspective, we discussed the topic from a ‘zoomed out’ viewpoint. Through this discussion we realized that for us the interaction between citizens and the smart city as a whole was very interesting, and we decided to focus our project in this area. Due to access, we decided to situate our solution within Copenhagen.

Based on this, we determined an initial problem statement:

“How can we make it easier for citizens of Copenhagen to become aware of, engage with, and contribute to the smart city?”

16

Smart Copenhagen

In order to understand the context we were designing within, we assessed the current state of smart city planning. This gave us an overview of Copenhagen as a smart city.

Copenhagen is actively taking steps towards becoming a smarter city. As a long-established urban area, building Copenhagen as a smart city from the ground up is an impossibility. Holistic integration will instead be reached by connecting previously established initiatives post-implementation (Copenhagen Connecting 2013).

To this end, in 2013 the City of Copenhagen released a detailed plan describing their future smart vision. The plan was produced by Copenhagen Connecting, part of the city’s innovation center (Copenhagen Solutions Lab). It describes the major focus areas of Copenhagen’s smart city projects, potential projects that could be developed within these areas, initial feasibility assessments, and future challenges.

The smart city vision details three core areas for smart city development (Copenhagen Connecting, 2013). First, an improved city grid would integrate sensors, RFID tags, and WIFI triangulation to create real-time data about the movement of people and assets throughout the city. Second, the plan describes ongoing research and development into both using this data and general improvement of city operation methods. Finally, Copenhagen’s smart city vision outlines a goal to create more value from smart city data. (Figure 7) This section is not very specific, but acknowledges that for this data to be useful, users need to be educated on how to interact with and use it (Copenhagen Connecting, 2013).

Figure 7: Copenhagen Connecting’s data usage (Copenhagen Connecting, 2013)

17

Gathering the Citizen Perspective

Having narrowed the theme and constructed an initial problem statement, we planned to gain insight into what living in a smart city actually means for citizens of Copenhagen. To do this, we decided to immediately get out into the city and begin the process of talking to people (Figure 8). We determined a series of three questions to help us focus on a problem area, and give an indication of the smart city knowledge level of the average Copenhagen resident.

1. What do you like about Copenhagen?2. What do you dislike about Copenhagen?3. Have you heard of the concept of smart cities?

We spent some hours walking around the Strøget area asking these questions to a wide variety of people. People were generally wary of being approached, but as soon as we asked them what they liked about Copenhagen most of them became more enthusiastic–generally, people seemed to really enjoy talking about their city. Most had very little or no negative things to say. Almost all of our interview subjects had not heard of smart cities. Many of our younger interviewees seemed very curious about the subject, asking us to clarify or define the concept.

To reach an even broader range of people, we prepared a very similar set of questions in an online format. We distributed this survey both through our personal social media networks and by physically walking around a student cafe collecting responses. This survey provided us with similar results. Most of our respondents had very little notion of what constitutes a smart city, but most seemed very eager to describe what aspects of Copenhagen they particularly liked (See Appendix A for survey responses).

Figure 8: Initial interviews in Copenhagen.

18

Copenhagen’s Data: Who Has Control?

Exploring the Smart City Proposals

To determine for ourselves the range of projects within Copenhagen that fall within the smart city umbrella, we carefully analyzed a publicly available spreadsheet (Copenhagen Solutions Lab, 2015) containing information on every smart city idea. We began by reading through the list, which contained over 60 discrete projects, to determine parameters for organizing them. We realized quickly that some of the smart city service suggestions produced a ‘that’s creepy’ response with regard to digital privacy. We decided to act on this, and produced a matrix that ranged from creepy to not-creepy on one axis. On the other axis we ranked projects according to how useful they would be in everyday life. Within this matrix we placed all 60 of the current initiative suggestions (Figure 9).

This produced some valuable insights. A cluster of services appeared at the creepy-useful intersection: services that would collect a huge amount of very personal data, and could use this data to create a personalized and convenient urban experience. As initiatives moved away from the ‘creepy’ side, they often moved downward (becoming ‘less useful’).

In these services, not as much data was collected and therefore services tended to be more of the ‘one size fits all’, generic variety. Very few of the initiatives mentioned involving citizens in the use and analysis of collected data.

CREEPY NOT CREEPY

BENEFICIAL

NOT BENEFICIAL

Intelligent street

lightning

Measure water

management with sensors

Tracking of dangerous waste of chemicals

Optimized traffic flows

Low-cost tracking of dementia patients

Automatic calculation and billing of waste

collection

Detection of illegal vehicle

use

Smart city proposals

Health monitor-ing of patients,

disabled & elderly in their home

App contests & Innovation

platforms spur innovation &

growth

Figure 9: Smart city proposals

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Insights

Through this analysis we were able to clearly see the complex moral issues surrounding data within the smart city. The initiatives described in the list treated citizens mostly as passive consumers, taking their data and using it to produce services that could be marketed back towards them. After some research, we realized that in general, it is a trend that citizens are often overlooked in the discourse on smart cities (Greenfield, 2013). Greenfield sums it up in his book from 2013:

“The sense that citizens themselves may wish to avail themselves directly of the information ostensibly being gathered on their behalf is almost surreally absent from the smart-city literature.”

Greenfield states that the corporate vision of the smart city focuses on behavioral patterns to optimise the flow of traffic, manage water, and save electricity (ibid). Although these are positive initiatives, Greenfield also argues that increased top-down monitoring could result in a power imbalance (ibid.).

Most citizens do not have control of which data is being collected, how, and when; let alone have the knowledge to understand and evaluate it. This places the citizens far away from being influential in any data related decision process related to the city. Citizens are certainly affected by the collected data, usually controlled by governments and corporations, but do not have the resources and skills to participate in the decision process (Deahl, 2014).

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Figure 10: Association game.

Revising the Problem Statement

Association Game

Building on the experiences from our street-interviews, online survey, and research we wanted to give the project more direction by revisiting the initial problem statement.

In order to approach the problem-definition as creatively as possible, we employed a brainstorming technique similar to the “Random Word brainstorming technique” (Frey, 2015). This introduced the element of chance to our

thinking, and forced us out of particular ways of thinking and dealing with particularly favourite demographics or problems.Rather than simply picking one random word, we made cards representing 1) stakeholders such as “the elderly”, “obese” or “kids”, 2) smart city keywords we had previously identified and 3) a modifying word or phrase such as “too little” or “too much”. We each took one card from each pile and after 5 minutes of brain-writing from the combinations, we presented our thoughts to the group.

Narrowing the Demographic

After repeating this exercise three times, we grouped all our ideas and discussed areas in which we could narrow our focus. One especially broad area was demographic, and we realized that we had to narrow down on a specific target group. Some of our more interesting ideas were related to youth in the smart city, so we began to discuss how this age group is currently involved. We realized that although young people probably create a large amount of digital data through their online activity, they were not mentioned specifically in any of the smart city ideas. Additionally, our initial interviews suggested that youth were not very informed about the smart city concept. Based on this, we chose ‘youth’ (defined by us as young people aged 15-20) as our target group.

We then conducted initial research on youth and urban development and realized that youth are often overlooked in civic inclusion efforts. This has democratic implications, and overlooks youth’s ability to contribute with new, unbiased insights (Power et al., 2009; Hart, 1992).

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Revised Problem Statement

We used this insight to build a more specific problem statement, by framing the design challenge using a structure similar to one proposed by D.School (2013):

[Stakeholder] needs a way to [need], because [insights].

Through this we arrived at the following problem statement:

“Copenhagen’s youth need a way to become more involved in the development of Copenhagen as a smart city, because they are not as included as other demographic groups even though they are the citizens of tomorrow.”

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People Centered Data

At this point in our process, we participated in a week-long video sketching workshop as part of the Designing The Experience course at Aalborg University (Figure 11). We used the workshop as an exploration of our topic. We mapped our research, and explored ideas through video (See Appendix B for more information on this workshop). The flexible medium allowed us analyze and synthesize various ideas within our problem sphere. As we created our video sketches, we realized that a common theme was conscious citizen data creation. Youth should not merely be tracked through urban spaces, they should deliberately generate data. Through rapidly created video sketches, we explored various implications of user-generated data.

Figure 11: Picture from the video sketching workshop

23

Co-Creating a Solution

24

At this point in the process our direction was firmly established: involving youth more in Copenhagen’s smart city initiatives. We then took steps to get in contact with and involve our target group. As we described in our learning goals, we prioritized involving users in various project phases. We planned, therefore, to design a solution together with youth.

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Benefits of Designing With People

Working with a co-design mindset provides immediate benefits as suggested by Skidmore, Bound and Lownsbrough (2006). Namely, it a) it leads to a better, more responsive service, b) it tackles disengagement from politics and democracy, and c) it builds social capital. Brandwell and Marr (2008) goes on to define co-design saying that, “In its purest sense, co-design implies that no viewpoint is afforded greater legitimacy than another.” and that ideally in co-design users are not only heard, but are given a specific position of influence, over the development and application of the service.

We approached creating our solution with co-design in mind. We wanted to build close relationships with our stakeholders, and involve them in various ways.

What is Co-Creation?

The user-centered design approach has since the 1970s given the user more influence in the early design phases, starting with participatory design led by Northern Europeans (Sanders & Stappers, 2008). As such, design has slowly drifted away from an expert perspective where the designer is considered the main driver of innovation and the user merely a passive entity, to a participatory approach where the user is seen as a partner, and the driver of innovation (Sanders & Stappers, 2008). Emerging from this came the terms of co-design and co-creation. Co-design and co-creation are, however, commonly confused terms with no agreed upon definitions neither in academia nor industry. For this project, the working definitions proposed by Sanders & Stappers (2008) are used:

Co-creation: “… refer to any act of collective creativity, i.e., creativity that is shared by two or more people.” (ibid., p.7)

Co-design: “… collective creativity as it is applied across the whole span of a design process …” (ibid., p.7)

Why Co-Creation?

26

Observation at Ordrup High School

As a first step, we reached out to a high school north of Copenhagen, Ordrup Gymnasium. (ordrup-gym.dk).

We were invited to visit the high school on a Friday morning, attending 4 hours of classes in total, discussing with the students during break and having lunch with the teachers. We aimed to find out more about how youth interact with data and technology in their everyday lives.

By conducting these initial, unstructured interviews, we experienced first-hand how many digital services the students are exposed to during their school time. The students have to mentally structure all their digital data, and we witnessed their awareness of online privacy. Particularly, with regards to the latter, how they don’t mind being connected to a wide variety of school-related groups on Facebook. We also learned that they are largely left to themselves when it comes to understanding how things work, for example, how they are simply expected to use the required software and organise their data from day one.

Figure 12: Observing the high school teenagers during a chemistry class

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Recruiting Design Partners

Next, we explored options for recruiting youth more directly into our design process. We became aware of the Copenhagen Innovation and Entrepreneurship Centre (http://ciec.nu/) which is an organisation that hosts classes in innovation and entrepreneurship for 37 high schools from all over Zealand. Students can apply for the course with a letter of motivation and get selected to participate for a whole year and their high school then arranges the credit-transfer as an elective. In the school year of 2014/2015 CIEC had 142 students.

Our group was invited to participate in a panel of judges of several students’ service design projects. During their presentations, we could see that the students were interested, creative and motivated, and so they were good candidates for participating in our project. After the presentations, we were allocated time to present our project to the participating students.

Figure 13: A youth presentation (left), and presenting our project to them (right)

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We had four youths sign up with name and email addresses - Klara, Oliver, Francesca and Louise.

To organise our future efforts with the youth we created a dedicated Facebook group appropriately named “Service Design for Youth x Smart Cities” and invited everyone to participate there.

The aim of the platform was firstly to provide a centralised channel for coordination of events. Secondly, the Facebook group provided a place to share the process and progress of the project with the youth so they could gain an understanding of the development of our thinking and provide comments.

Figure 14: A screenshot from the ‘Youth x Smart Cities facebook group (above), and our youth team (left)

FrancescaOliverKlaraLouise FrancescaOliverKlaraLouise

FrancescaOliverKlaraLouise FrancescaOliverKlaraLouise

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0. UNAWARENESS 1. AWARENESS 3. USE2. ADAPTATION

Unawareness that one is producing

data, or of smart city initiatives.

Knowledge that one is producing data

(and roughly when).

Ability to innovate and utilize data to

realize ideas.

Behavioural change according to what one

is aware of.

SMART CITIZENS

During the CIEC event, the youth were curious as to what exactly we meant by ‘participation in smart city initiatives’. In our understanding, in order for citizens to participate fully in smart cities they must have the knowledge to fully understand the implications of data within an urban context. We called these citizens ‘smart citizens’. Prompted by the youth’s questioning, and building on the progression outlined by Copenhagen Connecting in Figure 7, we decided to develop a framework to assess whether or not citizens can be classified as ‘smart’ (Figure 15).

Level 0 refers to citizens that are completely unaware of smart city initiatives or that they are producing any kind of data. Citizens at Level 0 are not considered smart citizens. People who fall in any of the other three levels are considered smart citizens with different levels of knowledge and interaction with data. Level 1 includes all the citizens that, at least, are aware that they are producing data, and roughly when. When people start changing their behavior according to what they are aware of, they jump to level 2. Level 3 requires a deeper understanding of data, and it includes people who have the ability and knowledge to actively manipulate data to innovate and realize ideas.

These four levels are somewhat connected, and citizens who fall into the third level could have the effect of pulling people up from level 0 to higher levels, creating a feedback loop that would slowly transform ‘regular citizens’ into smart citizens. We also discussed that, as we have been working on this project, we have probably moved some citizens onto the first level, which is interesting in that our process becomes also part of the solution.

Figure 15: Our smart citizen assessment framework

What Is a Smart Citizen?

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We researched the connection between smart citizens and data and we realized we were discussing the concept of data literacy. Further research in this area provided us with the vocabulary necessary to describe our ideas, and allowed us to explore existing initiatives.

What is Data Literacy?

The Data Journalism Handbook defines the term ‘data literacy’ as “the ability to consume for knowledge, produce coherently and think critically about data” (Kayser-Bril, n.d.).Another definition, emphasizing the diversity of the term, is proposed by Deahl: “data literacy is the ability to understand, find, collect, interpret, visualize, and support arguments using quantitative and qualitative data” (Deahl, 2014). A data literate society could create benefits for both the city and its citizens. Data literacy should empower and provide the citizens with the necessary means to understand and use data, enabling them to think critically about social and political issues and to get engaged with projects related to their own environment. In a data literate society, citizens would also be able to identify problems and propose meaningful solutions by applying their knowledge to new topics (Deahl, 2014; Pentland, 2013).

Data Literacy in Youth

Data literacy has special implications for younger generations who have grown up in a digital era (Calzada & Cobo, 2015). This group is often referred to as ‘digital natives’, young people with solid ICT experience who are drivers of the information society (Pentland, 2013). However, Jarvis (2011) discusses that the phrase, ‘digital natives’ has fallen into disfavor, as it implies that young people are born with the knowledge and skills to protect themselves online. But as Jarvis argues, they do need to learn those things, and adults might not necessarily be their best teachers (ibid.). Given the space, trust and respect, young people teach one another as they develop their new society’s norms (Jarvis, 2011).

Deahl (2014) also refers to youth as the data scientists of the future. They will be the future decision-makers, and the ones who will live and develop the city. Therefore, by supporting youth data literacy we are providing the youth with the right tools to understand and contribute to a data-driven society, which can also be considered an investment in the future of a city.

Data Literacy

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Current Danish Initiatives Within Denmark, several initiatives already exist to increase data literacy among youth. The blog ‘Digitale Unge’ posts articles and links relating to the social media use of Danish youth. On the blog there is advice on how to behave online, news on related topics, and research relating to online privacy and behavior (http://digitaleunge.dk/ ).

Another online resource available for Danish youth is the ‘Center for Digital Pædagogik’. This site focusses on combatting online bullying and providing information about how to protect one’s privacy online. They also provide in-school workshops on appropriate online behavior, and provide a hotline for emergency advice (http://cfdp.dk/).

Furthermore, a private organization has created a comprehensive resource for curating one’s digital identity, ‘License2Share’ (Tranberg & Steen, 2015). This 60-page e-book goes into detail on big data, personal data, digital security, and social media behavior. It is aimed as a resource for teachers to introduce these subjects within a classroom setting. Supporting the e-book is a website containing more information. (http://license2share.dk/)

Figure 16: A screenshot from Digitale Unge (2015) detailing this blog’s content

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Youth Cafés

Building on the previous research, we began our co-creation process by creating a strategy for involving the previously recruited youth team. We planned to meet at a neutral ground–we selected Studenterhuset (http://studenterhuset.com/) as there would be other students, music and a café-like atmosphere. We hoped to put the participants at ease and make them feel comfortable expressing themselves. We nicknamed these sessions ‘youth cafés’. We planned to facilitate several of these sessions throughout our process, and through various activities we planned to co-create first knowledge, then service concept ideas. The youth cafés can be considered instances of co-creation according to our working definition.

Figure 17: A selfie taken in Studenterhuset prior to one of the youth cafés

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Youth Café 1

As presented in previous sections, a need to educate youth on data within a smart city context had been identified. Our goal for our first youth café was to understand these issues from the perspective of the youth themselves.

The youth café was facilitated around “getting to know each other”, with the conversation revolving around a printed map of Copenhagen and the surrounding suburbs.

This facilitating technique was used in order to steer the conversation around how localized data is used and created, both in terms of education, private life and leisure activities.

We prepared a semi-structured interview guide (See Appendix C), which roughly revolved around where the participants hang out, what they do online, and how they feel about sharing, etc. The interview guide was open in order to follow possible relevant openings.

When the participants arrived we introduced ourselves and began the session by assembling the map printout with them. The workshop began with this activity in order to break the ice and eliminate possible awkwardness of being around people with whom you are unfamiliar.

Figure 18: Views of the first youth café

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Insights

In order to organize the insights from the youth café, sticky notes were used to make the first categorizations. Our insights are expressed in two diagrams. Figure 19 shows the progression of online identity building, and Figure 20 shows how the youth clearly perceived the levels of intimacy in their social media accounts.

As literature on ethnographic research suggests, we were careful to note discrepancies between what the participants said and what they actually did (Blomberg et al., 1993), e.g. one participant described in detail how she does not care about followers, neither who nor how many. However, as the discussion evolved she expressed how she had acquired an app that reveals who is following her, and more importantly, who unfollows.

Figure 19: Online identity-building as described by the youth

Digital Services Used

Data Created

Online Identity

Expected use of plat-forms was discussed which provided insights on how the youth amongst themselves and their peers estab-lish an online etiquette and various digital cultures connected to the services they use.

The youth had not previously questioned what data their digital presence generated, or

what kind

of data was

generated about them in the city. But there was an immediate interest in, and discus-sion about, who uses it

when for what and why.

There was a clearly felt

sense of identity in the youth’s online presence.As a form of privacy, they

were keenly

aware of what to use which platforms for, and how they themselves were

presented through what

kind

of behavior online.

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Summary

It became evident through the youth café that the participants are using a lot of different services and thus generate a great amount of data throughout their daily lives. It was clear that the underlying notion of ‘creating data’ was not something that was actively reflected upon before, during or after sharing. However, as the workshop progressed, and data was discussed more thoroughly, the interest of the participants rose.

After the workshop, a much clearer picture of our participant’s everyday lives had been established along with what digital tools and services they use, and for what.

FACEBOOK TUMBLR INSTAGRAM SNAPCHAT SMS

further

closer

Figure 20: The various levels of intimacy perceived by the youth in social media accounts

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Framing the Design Challenge

We returned to our studio after the youth café and used the findings to guide our direction. At this point in the process it was necessary to think both logically and creatively about the research information gathered thus far, in order to formulate the design brief to which it would be possible to generate a solution. As such, it was decided to use the framework put forward by IDEO, used to frame a design challenges (IDEO.org, 2015, p.31)

The framework defines a series of consecutive steps, in order to narrow in on a design challenge.

By following these steps, we revised our problem statement again:

How might we improve data literacy among youth in Copenhagen?

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Youth Café 2

The second youth café was all about co-creating ideas. To facilitate this session, an association game was developed with the aim of generating a large quantity of service concepts in a short amount of time in relation to the design challenge. We used our previously described (pg. 20) association game, and followed the brainstorm rules as described by IDEO (IDEO.org, 2015, p.95).

Association GameThe game consisted of several rounds of four minutes duration. In each round all participants, either in teams or solo, picked three random cards from three different categories. Various words in relation to smart cities and data had been identified prior to the workshop, and had been put into categories covering stakeholders, devices, popular services, etc. As such, each participant had to come up with as many service concepts as possible within the given time. They recorded their ideas on idea cards. After each round the best ideas were voted for and saved separately. The first few rounds included “funny” keywords such as puppies, party animals and selfies to put the participants at ease. A total of 62 ideas were generated and presented throughout this session.

Figure 21: These tables show the words used in the association game. Words highlighted in yellow were used in the final round.

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Figure 22: Images from the second youth café. The bottom-right image shows an example of the type of rough service ideas produced during this workshop.

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Figure 23: The idea-organization matrix, with selected service concepts from the co-creation workshop organized on it

Merging Ideas: Initial Concept

AnalyzingWe then returned to the studio to assess the results of this co-creation session. All idea cards from the workshop were analyzed individually in order to decide which could be immediately discarded due to being unrelated to the design brief.

A total of 31 ideas were discarded, primarily ideas based on the “funny” keywords from the first few rounds of the game.

The 31 remaining ideas were related to the scope of the project. Ideas from this group were pared down further based on affinity to the design challenge and scope of the project. This narrowed the field down to 13 ideas.

The remaining 13 ideas were then placed along the spectrum of ‘non-generating data’ to ‘generating data’. ‘Non-generating data’ ideas do not actively create data during the service, while ‘generating data’ ideas create real-time data.

Most of the ideas were placed towards the ‘non-generating’ end. One of Oliver’s ideas, for example, was a service in which real estate agents can gather the social network information of residents in a particular area, in order to show prospective house buyers who their neighbors will be. This idea would be classified as ‘non-generating data’, as it interacts with already existing data. Another concept was a bike-lane optimization system, in which real-time data about a cyclist’s speed could be used to guide them on the best route through the city. This service, as it creates and uses real-time data, was considered as ‘generating data’.

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SynthesizingAs a result of analyzing the idea cards from the workshop, we identified a ‘marketplace’ as a means to combine the two extremes on our scale. We discussed whether both aspects could be present in the same service, and realized that one possible solution could be a group of people creating data and another group utilizing said data. Building on this, with other elements from the co-creation workshop ideas, we began to create a concept structure.

Our initial structure was a data marketplace facilitated through an app. Youth would sign up for the service and link their digital accounts, then access an overview of the data they are creating. This would increase data awareness, and therefore hopefully increase data literacy.

Youth could contribute parts of their data to various projects, and receive some reward or feedback. We did not fully define what projects would be involved, or what kind of motivation would encourage teens to use the service.

Figure 24: Our initial concept with influence from the co-creation ideas shown

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Defining the Service Provider

To further define the service concept, we discussed the service provider aspect. Reflecting on our previous research, we realized that a municipality has both the resources and the motivation to implement our concept.

Alongside the goals of becoming a smarter city, the municipality of Copenhagen (from here on: the municipality) is also actively pursuing new ways to involve citizens in the early phases of urban planning. The most recent activities towards this effort are related to two urban projects concerning street renewal in populated areas at Amager and Østerbro. These two projects focus on unconventional means of collecting citizen data, and support the idea that the municipality could be interested in implementing the service concept.

Amagerbrogade (2013) As a part of the street renewal project concerning Amagerbrogade, a photo competition was planned and facilitated utilizing the hashtag feature of Instagram. People were prompted to answer three different challenges, asking for the funniest, the ugliest and the prettiest photos in relation to the street. A panel of judges would then each week choose a winner in each of the categories and award them with gift cards, and eventually a winner was awarded an iPad. This was an attempt to include people who are normally not a part of the more formal involvement steps such as public hearings. Furthermore, the idea was to use digital media and social platforms that people already use, to facilitate ubiquitous participation. (https://www.facebook.com/nyamagerbrogade?fref=ts)

Figure 25: Amagerbrogade Instagram collage

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Nordre Frihavnsgade (2015) A more recent project at Nordre Frihavnsgade at Østerbro also works with data generated by the citizens in relation to the project. Instagram is utilized through a similar photo competition where the citizens are prompted to respond with a photo showing something they want on the street in the future. When tagged with #Nordrefrihavnsgade and #Sætordpågaden the photo will become a part of the project. Similarly, the winner here will be rewarded with a prize. (https://www.facebook.com/fremtidensnordrefrihavnsgade?fref=ts )

In this project Facebook is also used to promote similar challenges, such as:

“Shall we place more pedestrian crossings at Nordre Frihavnsgade? Please respond in a comment with ‘yes’ or ‘no’ :-)” [own translation from Danish]

Furthermore, the municipality has established a ‘citizens shop’ for people to stop by and provide any insights, needs or wants that they may have in relation to the renewal of the street. Other activities such as city walks and events are also hosted for greater awareness and inclusion.

SummaryWhat can be said about both projects in terms of citizen involvement is the fact that the municipality has realized that inclusion must be adapted to the people, and not the other way around. The municipality is trying to adapt to current trends in the digital realm but it seems applied as an extension to the formal process of urban planning, rather than an integrated part of the urban planning processes. Meaningful feedback in relation to specific citizen contributions is lacking in the Facebook groups, and citizens do not get comprehensive updates on the project progress–the process is not transparent. Citizen involvement has to be in the fabric of the process, and not just function as an optional extension that may or may not influence the process.

Within our concept we want to address these issues by stressing the importance of feedback. Youth would be able to follow projects, learning exactly what their input was used for. In return, the municipality could gain access to a vast quantity of previously unattainable data, that could be used to inform citizen-oriented decision making.

Figure 26: Nordre Frihavnsgade Facebook challenge

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Figure 27: A use case describing City Challenges

Creating City Challenges

At this point in the process, we participated in a workshop as part of the Designing the Experience course at Aalborg University, which was designed to teach us to sketch our ideas in hardware. As our service concept structure was almost entirely digital, this was a challenging prospect.

We explored various options and eventually created a prototype through which a user receives a push notification that contains a request for information when they enter a certain geographic location (See video explaining prototype; https://vimeo.com/127357232, 2015 and Figure 27 for use case). We realized through this prototype that the municipality could place their requests for data within an urban context by using a similar setup. Copenhagen municipality could geotag projects (the potential site of a new park, for example), and ask youth very specific questions about that area. We named these data requests ‘City Challenges’ (referred to from here on as City Challenges or Challenges).

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From Personas to Schools

At this point in our process, we were ready to synthesise our ethnographic research into personas (Nielsen, 2014). We hoped to use the personas to gain overview, and explore our service through the target audience’s eyes. We began by defining key parameters such as interest in city improvements or use of social media. By using quotes and details from our interviews in the street, our observation in the high school classroom, and our youth cafés, we created various sets of parameters that developed into two different personas (see Figure 28, over).

The motivational differences between our two personas presented the following challenge: how to reach youth with different levels of interest and motivations? Since our service would require a large degree of involvement from the youth side, we discussed that it was very likely that it would only reach those youths who are somewhat interested and concerned about their city and its future, and willing to spend some of their time on these issues. This would limit the kind of data that the municipality would get, coming only from a very specific group of youths.

In order to reach a more diverse group, we discussed that the service could be integrated into the school environment, as we had noted this was a commonality between our personas. By having teachers introducing the service, all the students would become aware of it. The service could be part of a data literacy course, combining lessons about data with involvement in municipal projects, which could prompt a frame for discussion between teachers and students. That would, at least, move all the students to the first level of our smart citizens model, thus pushing towards a more data literate society. For the municipality, presenting this service through the schools would be beneficial. There are more than 40 high schools in the Copenhagen area, and if all of them introduce the service with the data literacy course, the municipality could get an enormous amount of qualitative data from thousands of students, who normally are more difficult to reach.

45Gender: femaleAge: 18Location: Amager ØstOccupation: student

Gender: maleAge: 15Location: FrederiksbergOccupation: student

Everyday lifeMette is a very active teenager, and her day-to-day revolves around her academic life. She is a good student, and also a member of the student council at her high school. She used to play soccer a few years ago, but now she is too busy for more extracurricular activities, she mostly does schoolwork in the evenings. She has a small group of very good friends, who have known each other since they were kids. Some of them go to the same school as her, so they hang out together and eat pizza in the park next to the school during the breaks. Some others go to a different school, so they only have time to meet during the weekends.

Everyday lifeJimmi is an impulsive teenager who cares about his friends. He has a big group of friends, some from school and some from his neighborhood. They often hang out together in the evenings and weekends, and they normally meet at someone’s place to play video games, go to the skate park or cook dinner together (it is cheaper than going to a bar). At school, he is an average student. He does not particularly enjoy studying, but it is just part of his routine and he knows it is something he has to do. During the breaks he usually eats pizza with his friends in a park right next to the school.

MotivationsShe wants to achieve good results at school, because she knows that that will give her more possibilities when she has to choose what to do in the future. Sometimes, when she is trying to study, she deliberately closes her computer to avoid distractions. She is very interested in biology, and participated in an urban garden project in her neighborhood last summer.

MotivationsHis friends are the central aspect of his life. He is mostly concerned about enjoying his everyday life, and does not think about his future yet. He loves skateboarding and biking, and he gets very annoyed by slow bikers.

Perception of the cityShe is aware of city involvement but she thinks it does not concern her directly at this moment –however she knows it will when she gets older.

Perception of the cityHe does not think about his city as an entity, it is just a place to live. He has never considered how the city influences his life, or how he can have an impact on the city.

METTE JIMMI

“Ugh, construction again. I wonder what they are building here”

“Ugh, construction again. That’s annoying”

Social media Social media

Figure 28: our two personas

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Putting The Pieces Together

Having arrived at a point where we could begin sketching a complete service concept from beginning to end, we crafted the following initial concept map and scenario. We named our service ‘Datacat’ as it could act as a catalyst for data literacy.

The initial scenario is here presented in a manner very similar to how we brainstormed it in the group - as a simple storyboard on post-its (Figure 29). The story is based on our understanding of one of our personas. The initial scenario illustrates how a student might experience the introduction to and use of the service.

To expand on this, we moved on to sketch out the service concept in a more holistic manner.

The initial concept map provides an overview of the service idea at a glance (Figure 30, over). Expanding on the initial scenario, the map highlights the municipality as a key element in the service concept, in order to emphasize the fact that the service is not merely a communication effort in high schools, but a complete service offering.

Figure 29: Initial scenario

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Figure 30: The initial concept map

Jimmi

Jimmi’s Datacat accountMunicipal ‘Challenges’

Tell us about your neighborhood

This is how we

used your data

Help us decide whereto place new bike lanes

Photograph your favorite spot

We’re looking for location data in Hellerup

TeacherMunicipal Worker

This is how your data was used

We will use

Datacat

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MU

NICIPALITY

SCHOOL

DATACAT

TEACHER

MUNICIPALWORKER

STUDENT

Data literacy

Educational tool

Challenges

Data

Data

Data awareness + Involvement in

city projects

Awareness of the service

(+ Participation)

S

MART

CIT

Y

Youth civic engagement

Data literate society

+ Smart citizens

Figure 31: Actors Map (Morelli, 2007) depicts the main actors in the service offering and their mutual relations with the service and each other. The arrows show the value that is transferred between actors, and Datacat.

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High School Prototype Test

In the final stages of our project we had a chance to simulate an approximation of our service at the Ordrup high school we had previously been in contact with.

We devised a data literacy course prototype, which consisted of a short lecture (simulating the teacher introducing the service) and a small-scale, non-digital Challenge.

We also used this forum to confirm our previous findings on the current level of data awareness among youth.

Existing KnowledgeAfter a short introduction, we handed out ‘idea-cards’ similar to the ones we had created for Youth Café 2, only this time they were focused specifically on the students’ conception of data. This was to gauge the student’s knowledge of data going into the course. After 5 minutes we collected the cards, and proceeded to the lecture. The cards confirmed our previous findings which pointed to young people having a limited vocabulary when it comes to expressing what they know about digital data.

Figure 32: The students fill out the idea cards, and (right) a sample card

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LectureFor the lecture we prepared a presentation of about 15 minutes duration. We wanted to determine their level of knowledge and willingness to engage in discussion on this topic.

City Challenge PrototypeWe then tested our city challenge prototype. We organised the class in pairs, and each pair received a so-called ‘data booklet’. With this, the students were supposed to answer a question that

simulated a challenge in the municipal setting of the service concept. The challenge was: how can we improve your school together?

The data booklet was divided into sections, each representing a data type that will likely be used in Datacat. For this simulation we chose text-input, drawings (instead of photos) and location to simulate the most common data inputs through mobile devices.

Figure 33: The students fill out the data books and (right) a databook page

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After 20 minutes we gathered the materials and engaged the students in a discussion about how they had experienced the simulation. The youth took our prototyping session seriously, and produced very detailed data booklets. They described issues and improvements for their school very visually, and appeared to enjoy the process of describing their thoughts. They engaged actively in our discussion on data, and expressed the need to have specific channels for certain content (for example, maintenance issues within their school which they want to highlight, though not post in their personal social media feeds). Datacat could fulfill this vocalized need. This enthusiasm and interest positively reinforced our service concept idea, and the results of this session could be considered proof of concept.

We also managed to talk with the teacher, who was present during the whole session, and asked her opinion on what she had seen in terms of content and scope. The teacher was very positive, and asked us to share our project upon completion, so she could discuss it with her colleagues in the regional social science teacher board. She recognised the fact that data was not on the educational agenda per se, but only surfaced as a subtext in larger, more unstructured themes such as creation of identity. Datacat could provide a solution to this with a focused, structured module on data and the smart city.

Figure 34: The high schoolers working through the prototype

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Youth Café 3

Having validated our concept at the high school, we wanted to discuss our ideas with Oliver, Francesca, Klara and Louise again for a last time before finalising the project. This was in order to take them through our concept, and see if they could understand and stand behind the idea and its implications. We also wanted to evaluate our inclusion process, because them having a good experience of the process became a key learning objective for us.

The workshop was planned and executed in the following way.

Tomorrow Headlines We went through a tomorrow headlines-exercise in pairs for 15 minutes. (Service Design Tools, 2015) The youth filled in a front-page ‘newspaper’ we had constructed with guiding elements such as pictures and headlines. We did this exercise to gauge how they would think, when faced with the foundational elements of our concept. Namely data-supported, citizen-oriented, municipal innovation.

The exercise produced good results in the sense that the youth quickly identified key aspects such as bridging the gap between the physical and the digital in the city space and a focus on ease of use that fits into the everyday life of users.

Figure 35: The youth team begins the Tomorrow Headlines exercise

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Service Walk-ThroughWe then took them through the service concept by playing out a scenario in a board game-like setting (Stickdorn & Schneider, 2011, p.11). We decided on this rather than using a slideshow or simply a piece of text because having the elements of the service as tangible artefacts make them more approachable and easy to talk about. The aim of the exercise was to present the concept in a manner that would invite the youth to critique it, and perhaps be able to relate it to their own participation in the design process.

Figure 36: The youth team experience the service walk-through

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Discussing the Service ConceptThe session was a success in that the youth fully grasped the service and the implications it would have for the different stakeholders. Noting, for example, the need for the module in schools to be rounded off with an assignment to ensure that students would actually participate. They also pointed out that the feedback element of the app was crucial, as they had all had bad experiences with poor feedback after completing surveys.

They did also contribute with reflections on further refinement. For example, users should have the option to select what kind of notifications they would allow, in order not to get the same notification every time they passed a given spot in town. Furthermore they envisioned the service scale beyond schools, to give more citizens access to the transparency of Datacat, but agreed that starting in schools was a good approach to get the service off the ground.

Discussing the ProcessFinally we engaged the students in a casual conversation about their experience of the design process. They mentioned that presenting in front of us in the very beginning had felt a bit intimidating, when we went to CIEC to comment on their projects, but they were happy that we had chosen the places we did and the cozy atmosphere we had created at the sessions.

The youth were positive about our various workshops, and they liked how we had organized the process through Facebook. They admitted to being nervous at the beginning of the process about their own abilities to contribute to the project.

Overall, however, the feedback was extremely positive. The following quote from Klara sums up their feedback:

“I felt like a real part of the project… because of the feedback from you and the continuous inclusion” [own translation from Danish]

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Figure 37: The full design team

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Process Reflections

Taking a step back and looking at our vision of and expectations for the co-creation process that resulted in our solution, it has been both challenging and very rewarding to facilitate.

We went into the process with a clear vision of the value that an open and accommodating design process could bring to the project. The process, however, cannot be described strictly as a co-design process, as we have taken steps as designers without users involved, and as such the youth have not had the needed position of influence, or been involved across the whole span of the design process.

We have applied instances of co-creation throughout, convinced of the value this could provide. This value can be summed up to the fact that we can not fully equate our own high school experiences with that of contemporary youth. In this sense, involving our target group in key stages of our design process allowed us to meaningfully develop our service concept in tune with the youth’s lived experience. This also applies to our relationship with Ordrup High School where we engaged with a whole class of students in different ways on two different occasions.

Overall we have striven for an iterative design process which was adaptive to outside inputs, allowing for new insights to influence the process. This has been true not only of how we tried to be as open as possible to the contributions of our youth design partners, but also in the way research informed our process. Exploring the notion of smart cities has alternately challenged and reinforced the direction of our concept.

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Final Concept: Datacat

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Final Service Description

Datacat is an educational service aimed at youth. Facilitated by a mobile application and introduced in a high-school classroom setting, Datacat enables youth to view the digital data they create, and voluntarily contribute this data to municipal urban development projects.

Datacat is developed by the municipality. The municipality spreads word of the service through, for example, unions, and individual teachers can choose to implement the short course. When a teacher decides to use Datacat, they contact the municipality and receive the materials necessary to teach the course.

For an overview of the service concept in video, please see: https://vimeo.com/125918990

Students are introduced to data literacy through a short lecture. They then download an app and link their social media accounts. This allows them to see an overview of the data they create online in a secure location named the Databox.

Students can also view various municipal projects requesting data near them. These are named Challenges, and are created by municipal workers. Through Datacat, the students can contribute portions of their private data or follow these projects. Data contribution is entirely voluntary. They then gain access to a Challenge Wall within the app, where they can view updates on project progress and leave comments. These updates provide transparency, and it is required that all Challenges give regular feedback on project progress.

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Blueprinting

The following service blueprint explains the service concept incorporating the perspectives of the different actors and the flow of interaction between them. The process of creating the blueprint worked as a tool to explore the user experience and refine aspects of the service (Stickdorn & Schneider, 2011, p.204).

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STUDENT

PHYSICALEVIDENCE

Receive request

Send material

Challengecreated

Receivematerial

Face-to-faceCourse material Datacat AppSmartphone Databox Challenge Challenge

Learnmaterial

Give introduction to students

Request course

material

LearnDownload

appCreateaccount

Create Databox &Connect accounts

Find Challenge

Respond to Challenge

Receivedata

Connect Cloud

Store newuser

Pre-Service Part 1: Data literacy Part 2: Participation in city challenges

Level 1Smart Citizens

TEACHER

MUNICIPAL WORKER

MUNICIPALITY

CLOUD

Create challenge

Line of secure virtual interaction

Line of visibility

Municipality actors

School actors Sequencing events Event not fixed in time

Non integral event

Gray background: Invisible actors

White background: Visible actors Variable durationCO

LOR

S

SY

MBO

LS

LEGEND

61Databox DataboxChallenge Wall

Challengeprogression

Challenge Wall+ Face-to-face

Get pointsGet access to

Challenge WallGet

notification

Class discussion

Class discussion

Give moredata

Requestdetailed data

Receive moredata

Find requested data

Provide archived data

Search data from older Challenges

Get data

Give updates on Challenge

Put data on Challenge Wall

Get feedback

Get feedback

Challenge

After service

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Stakeholder Analysis

Stakeholder Map

This section gives an overview of the stakeholders identified in relation to the service concept. As a foundation for this, we used Edward Freeman’s view on what constitutes a stakeholder:

“any group or individual who can affect or is affected by the achievement of the organization’s objectives” (1984, p.46)

This stakeholder map (Figure 38) provides a visual representation of the people and groups involved (Stickdorn & Schneider, 2011, p.150). Mapping stakeholders visualizes the complexity and relations of the actors surrounding the service, and may thus be used to explore various service implications.

The stakeholder map was created by first brainstorming a list of stakeholders. Then, the list was visualized and the stakeholders were placed in three main categories; Primary stakeholders, Secondary stakeholders, and Tertiary stakeholders, to show their immediate relationship with the service.

Figure 38: Stakeholder map

Secondary

Tertiary

Primary

Municipality

Government Consultants

Citizens

Local businessesService providers(Facebook, Google, etc.)

The city

Schools

Teachers

Students

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Power-Interest Matrix

To identify the power and interest of the different stakeholders towards the service, we mapped the stakeholders across the axes of power and interest, ranging from high to low, thus creating four quadrants (Figure 39). Here it became apparent that the stakeholders in closest proximity to the service are also placed in the top two quadrants, thus indicating a large influence in the service. What is worth noting is the placement of the students in the high power/low interest quadrant. The goal is to slowly raise their interest towards the service, and thus towards data awareness and co-creating city improvements, through Datacat.

Together with the motivation matrix, the stakeholder map and power-interest matrix provides a solid overview of who can affect or is affected by the service, and what kind of value stakeholders can gain and provide.

Figure 39: Power-Interest Matrix

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Motivation Matrix

The motivations of the different stakeholders are shown in this matrix (Figure 40).

Several decisions regarding Datacat are grounded in motivational considerations.One key decision here is the impact of choosing to introduce the service in high school curricula. This is not done via the political channel of the ministry of education, rather Datacat is introduced as a module for social science teachers to easily access and implement in their course flows. This has the potential to be an impetus for bottom up organisational change in the sense that data literacy can become better rooted in Danish education without political decision-making.

Similarly, the decision to focus on schools provides a solid foundation for the service to be tested and grow within. The motivation on the side of schools has been confirmed through the workshops and prototype lecture.

As for the municipality, a key motivation is the opportunity to engage youth in a way that supports the vision towards a smarter, more inclusive city. The municipality’s motivation for deploying the service is considered in light of current undertakings.

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Figure 40: Motivation Matrix (Service Design Tools, 2015).

Youth

Teachers

EducationalSystem

MunicipalProjects

CPH Citizens

Youth Teachers Educational System Municipal Projects CPH Citizens

Encouraged in classBetter cityBeing heard

Moral obligation.Provides the teacherinfo on how the youthuse data

Insights through dataProject ValidationFresh perspectivesThe motivation to use data better

Foundation for a more liveable cityA unique insight towhat youth thinks onvarious topics

Facilitate educationon data literacy

Primary access to Datacat service

Their mission is toteach and havepersonal interest in and awareness of data

Early adopters of dataliteracy courseBottom up organisational change

Funnel youth and their insights intoDatacat

Smarter citizens/ youth into the city

The best possibleeducation for the youth

A more liveable cityA voice in Urban PlanningEntertainment activityTransparency aboutplanning

Creating a case for thecourse and providestraining

A driver of changetowards data literacy

To be more inclusiveBetter city

A physical space for youth to become engaged with urban development

The opportunity to create projectsthat then can becomedatacat contents

Prov

ides

Thro

ugh

Data

cat

Achieve higher quality of educational programs

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Datacat Datatypes

The Datacat service could provide access to a wide variety of digital data. By connecting to any relevant online service providers such as Facebook and Google through their APIs, a summary of the user’s data can be viewed. For example, Datacat could create awareness of Facebook’s stored 3D facial-data (Taigman et al., 2014) or what kind of profiling the user is subjected to in Google’s ad-ecosystem. The Databox would also display location-data that is created through a user’s mobile device.

Furthermore, Datacat could be set up to work with any IoT-devices in the user’s life. Connecting a smart-fridge could thus allow for a run-down of what kind of data such a device generates, how it may be used and how it may be controlled.

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Municipal Projects

DatacatService

Citizens

Data Literacy

Service Outcomes

Strategic Goals

Data literacy is the long-term strategic goal of this service. This goal is supported by a secondary benefit which is a higher citizen involvement in municipal projects (Figure 41).

Increasing data literacy is addressed in a manner that the design strategist Dan Hill likens to the diegetic effect known as a MacGuffin, popularised by Alfred Hitchcock. A MacGuffin is a device to propel the plot forward. Subsequently, Hill uses the MacGuffin-concept as a metaphor to help iterate an approach to solving long-term, strategic problems, in that what might be seen as the real project at first glance, is actually situated in a grander scheme - the strategy or plot - which otherwise would be hard to change meaningfully by approaching it head on. (Hill, 2014)

In this sense Datacat becomes a mean to introduce the issue of data literacy in the Danish high school system. Had we simply approached the ministry of education with the suggestion to introduce data literacy in Danish high schools, it would be quite easy to dismiss us.

By involving Copenhagen municipality, the short-term need for citizen data can be used as motivation to develop this service. This motivation, combined with a structured module for teachers to take up on their own, is perhaps more likely to incur long term change in an otherwise impenetrable institution. This can be described as a servo-mechanism, where the impact of small initiatives is multiplied. (Hill, 2014)

Figure 41: This illustration shows how reaching the long term strategic goal (data literacy) is facilitated by the short-term service benefits (im-proved citizen involvement in municipal projects)

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Technical Analysis

This service would be technically configured as a distributed system. A distributed system can be defined as one “in which components located at networked computers communicate and coordinate their actions only by passing messages” (Coulouris et al., 2012). The service relies on cloud computing to provide ubiquitous access (Mell & Grance, 2011). As the cloud would be implemented on cluster computers it is by definition a distributed system (Coulouris et al., 2012). Cluster computers are well equipped to implement cloud computing as they consist of “a set of interconnected computers that cooperate closely to provide a single, integrated, high-performance computing” (Coulouris et al., 2012, p. 29).

We chose to use cloud computing as this would provide several benefits. First, as Challenges are often being added and removed, and comments and posts are continuously being created, the service must have the capability to rapidly update and distribute content. As, according to Mell & Grance (2011) cloud computing offers rapid elasticity, broad network access and on-demand service, it seems like an appropriate fit. Cloud computing also “reduces requirements on user’s devices” (Coulouris et al., 2012, p. 29), enabling widespread Datacat access through a mobile application. The cloud would not be hosted by the Municipality. Instead, they would subscribe to an IaaS (infrastructure as a service) cloud host (Mell & Grance, 2011), and receive some cloud space. Amazon AWS, for example, could provide this service (http://aws.amazon.com/ , 2015) Datacat itself provides software as a service, Saas (Mell & Grance, 2011). The Datacat frontend and backend software would be placed over this cloud infrastructure.

The municipality would have the ability to create Challenges, use the Challenge Wall, and manage data through the secure backend software layer. Students, upon creation of a Datacat account, would gain access to a personal, secure area within the cloud (the Databox). The municipality cannot view or access student Databoxes. Because of the necessary security structure, the cloud would be hosted by the previously mentioned third-party cloud service provider to eliminate conflict of interest. See Figure 42 for a visual explanation of this system.

We define the front end to be the part of the service that students interact with. The back end is the part of the service into which municipal workers can input Challenges (becoming ‘Challenge owners’), communicate with students and interact with data.

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Figure 42: Data flow diagram

Student’s Mobile Device

Student

Datacat CloudStudent Databox

Cluster Computer

Internet of Things Data

Other

Municipal LAN

Software Layer

Internet

Cloud hosting

Informationtransfer

Permission enabled informationtransfer

Internetspace

Software

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Users Response

userID int

email string

password varchar

name string

age int

gender char

school string

responseID int

userID int

Tags

tagID int

tag_name string

_Accepted Datatypes

datatypeID int

datatype_name string

Tag Search

tagID int

challengeID int

Datatypes Requested

datatypeID int

challengeID int

challengeID int

date datetime

response_data_url string

Challenge

challengeID int

chal_ownerID int

description string

name string

date datetime

Post

postID int

challengeID int

date datetime

description string

content_url string

Comment

commentID int

postID int

userID int

text string

date datetime

Challenge owner

chal_ownerID int

name string

department string

position string

email string

password varchar

1 2

4

5

6

7

8

9

10

3

media_url string

Figure 43: Datacat database

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In the front end, users create an account (1, figure 43). They can then access their own Databox. The user connects their social media network to Datacat through their Databox. To securely access a user’s social media network, we suggest that connections are established using a protocol called OAuth (http://oauth.net/, 2015). This protocol is also used by Datacoup, an online service which also provides a data overview (http://datacoup.com/, 2015). According to them, OAuth enforces tight security standards to keep any harm from happening to its users (http://datacoup.com/, 2015). OAuth prevents the service provider from accessing any passwords or usernames.

Data is not stored within the Databox, users can merely get an overview. We have, therefore, not designed a database for the Databox part of the service. Not all the data visible in a student’s Databox will be relevant for challenges, but providing an overview of all the data a student creates is integral to raising data awareness. If a user decides to respond to a Challenge, the data is copied through their mobile device, into the Datacat cloud, and the URL describing the data’s location is recorded in a database. See figures 44 and 45 for a visual explanation. When the user responds to a challenge, they create an entry in the response table (2). Each response is linked to a challenge and contains one datatype. If a respondent wishes to submit more than one datatype, more than one response is created.

All the details in regard to the technological infrastructure, such as specific server type and network hardware, such as firewalls, are out of our scope. However, we would recommend that the Datacat database security system is modeled on the Datacoup security information published online (http://datacoup.com/, 2015). Personal student details such as name, age, and gender, should be encrypted within the database to protect student privacy.

When a user responds to a Challenge they become authorized to access the Challenge Wall. In this secure digital forum, they can communicate with the Challenge owner by viewing posts (3) and replying with comments (4).

The back end of this service is accessed by the municipality. Municipal workers must create a Challenge owner account (5) to post a Challenge. They can then upload Challenges using a form (6). Challenges consist of a description, tags (selected from a bank of pre-defined tags, for example ‘park’ or ‘transport’) (7) and the permitted response datatypes (for example, location or text) (8).

After a Challenge has been submitted, the Challenge Wall is created. Through the back end, the municipal worker can use the Challenge wall to update respondents with project progress by creating posts (3), and replying to their comments (4).

The data collected through individual Challenges can later be used by other municipal workers for other projects. This data transfer occurs outside the Datacat service, however all data collected by Datacat is organized to allow for easy access. The response table contains a direct link to the cloud-hosted data (2). The associated tables allow the data to be sorted and accessed by tag (9) and datatype (10).

Database

Updates to challenges,posts, and comments

User can comment on posts

Read-only connectionthat allows user to

view data created

User comments arerecorded in database

Online Services Datacat Cloud

Mobile Device

Database

Mobile Device

Online Services Datacat Cloud

Data is uploadedto the Datacat cloud, where it is stored

Selected data is downloaded onto

user’s mobile device

URL of stored

data is recorded in database

Figure 44: Data flow while viewing challenges and Databox

Figure 45: Data flow while responding to Challenges

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Datacat App Design

We considered the app flow from the student’s perspective, and created the following mock-ups (Figure 46) and a flow diagram (Figure 47, over) as an early visualization of how the service could be presented.

Figure 46: Datacat app mockups

Databox Visualization of social network contacts and connections

Overview of location data

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Challenges posted by the municipality

Description of a Challenge Overview of Challenges completed by the user

Challenge Wall

74Login

Databox Home

Add Data Source

Personal Info

Account Settings

Progress Feedback

All Data Recieved

City Challenges

Challenge Walls

My Challenges

Challenge Wall

Provide Response

Challenge

Account

Menu Options

Challenges

My Challenges

Challenge Overview

Data Overview

{Response Prompts

Figure 47: App flow diagram

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Scenarios

Figure 48: We put our previously defined personas into two scenarios highlighting the difference between two possible use cases of this service.

Jimmi has the data literacyclass, where he downloadsthe Datacat app and learnsabout how to participate.

Later,

Jimmi decides to goskateboarding.

He passes some construction onthe way and becomes annoyedat the inconvenience.

Suddenly he recievesa notification on his phone

It says: “What would youlike to see here?”

A week later, he recievesanother notification.

Jimmi responds.

A month later, Jimmi receivesaccess to the finalized plansfor the project.

He is pleased to see some skateequipment has been included.

This time, on the challenge wall,Julie from the municipality isasking specifically which typeof skate equipment should beplaced in the area.

Jimmi considers this, and realizes that the area would be much nicer for him if therewas some skateboarding equipment.

He took some picturesof a cool skate ramp and posted them on instagram last week, so he submits those photos.

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Mette has the data literacy

class, where she downloadsthe Datacat app and learnsabout how to participate.

During her spare time, shebrowses the municipal Challengeboard.

She notices a challenge relatedto improving a small square, and realizes she was there yesterday.

She decides to participate.

She considers what would improvethat area and realizes that some

peaceful green space would help.

She remembers an urbangardening project she was involved

in last summer, and realizes that the knowledge from this project could help improve the square.

She donates access to a facebook album containingpictures of the urban gardenproject

A month later, Mette receives access to the finalized plans

for the project.

She is pleased to see some relaxinggreen space is included, along withsome of the plant species from theurban garden!

She also donates limited accessto some plant experts in her network

Mette follows the project progresson the Challenge wall.

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Similar Services

Several services exist with similarities to Datacat, although none provide the full combination of functions that makes Datacat relevant to the smart city of the future. As mentioned, our research uncovered several initiatives within Denmark aimed at increasing youth data literacy. The additional services described here relate to buying and selling data, and several relate to municipal improvement but none include a focus on increasing data literacy.

Data Marketplaces

Data ExchangeDataexchange (http://new.thedataexchange.com/, 2015) is a British company that provides buyers with access to over 500 suppliers of data. They offer a marketplace for data, establishing it as something valuable. The data consists of datasets of various kinds assembled by individuals who put it up for sale on the exchange.

DatacoupMuch in the vein of DataExchange, Datacoup is a market for data (http://datacoup.com/, 2015). Their approach to the material is different in that Datacoup encourages consumers to provide their own personal data from various sources both online and offline, for them to make available to paying companies in need of a specific type of data on a given segment.

Municipal Improvement

blivhørt.dkBlivhørt (blivhoert.kk.dk, 2015) is a portal from the municipality of Copenhagen that provides citizens with information about projects that the city is making decisions about. Citizens can reply with comments to the information presented.

skabdinbySkabDinBy (http://skabdinby.dk/, 2012) was a 2012 urban planning experiment in Copenhagen. The experiment designated 15 sites in Copenhagen where citizens were invited to participate in a co-creation process, contributing ideas for features and maintenance. In addition to the physical challenges, the visitors to the website were also invited to respond to “challenges” with text about different aspects of the city such as “where is your favourite, underused quiet spot in Copenhagen?”. An important point in this project was that challenges were accessed from a map of the city, so people related specifically to the geography of the city, and not in an abstract manner.

Improve Your Cityimproveyourcity.org is a website that encourages citizens to participate in creating a smarter city through three principles of open government. Namely participation, collaboration and transparency.

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Future Steps

In this section we will briefly present some considerations on future steps that would further define the Datacat concept.

First, we realise that the development of our concept has been centered around input from schools and youth. This decision has been grounded in issues relating to accessibility, as we did not manage to establish working-relations with the municipality of Copenhagen despite efforts on our part to contact Copenhagen Solutions Lab (http://cc.cphsolutionslab.dk, 2015), the laboratory for inclusion of youth in the city, By-X (http://by-x.dk/, 2015) as well as smart city special consultants in the technical and environmental administration.

Had the opportunity presented itself, we would have liked to present representatives of the municipality with our concept, in order to incorporate their experience and feedback into our development. A workshop with several municipal employees could have aided us in our efforts. Examining how the municipal employees would approach and dissect a collection of data could provide us with valuable insights into how this aspect of Datacat could function.

Second, a natural next step would be to prototype the experience of the teacher as well. Questions pertaining to how the educational material might be formatted, as well as how the Datacat application might support classroom discussion would be valuable to have answered by the teachers.

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Scalability

When discussing matters of scalability it is first and foremost important to understand what defines scalability. There are several ways to measure whether a concept, product or service is scalable, but a broad definition is put forward by Bondi (2000), namely that scalability refers to a system’s ability to be enlarged to accommodate growth.

As Datacat is a distributed system, an increasing number of City Challenges can be added, by an increasing number of municipal workers. Furthermore, the system is able to accommodate a continuous increase in users and content, only at an increased cost at the cloud hosting company. Therefore, as Laudon et al. (2008) suggests, a system that is able to increase in size as demand warrants is a scalable system — which Datacat can then be considered to be.

To where and whom could Datacat scale?The government, and the municipality is funded with tax money, thus the financial part of scaling is in place if Datacat is valued in the budgetary negotiations.

Datacat could scale upwards to a regional, national or even continental level with challenges for thousands or millions of people at a large scale, albeit keeping in mind that larger scale requires additional analytical capabilities to make sense of the data. Or, it could scale downwards, to e.g. schools who put forward challenges for their students much akin to the kind of question we posed at Ordrup high school in the final stages of our process. This way, Datacat would still operate within the public sphere.

Datacat could, however, also scale to other stakeholders including businesses or citizens, as Challenge owners. This could potentially spark social innovation in communities and neighborhoods, or provide unique insight to businesses from customers, or potential customers.

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Reflections

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Validity and Reliability

“Without rigor, research is worthless, becomes fiction, and loses its utility.” (Morse et al., 2008)

Reliability and validity in relation to qualitative research is much explored in literature, and new criteria have been posed in order to determine reliability and validity, and thus to ensure rigor, in qualitative inquiry (Altheide & Johnson, 1998; Leininger, 1994; Lincoln & Guba, 1985; Rubin & Rubin, 1995, in Morse, 2008).

We have been working with the notion of verification, which Morse (2008) defines as “… the process of checking, confirming, making sure, and being certain. In qualitative research, verification refers to the mechanisms used during the process of research to incrementally contribute to ensuring reliability and validity and, thus, the rigor of a study.”

This essentially means that we have made efforts towards catching mistakes and errors in the process, in order to avoid having them built into the final service. Furthermore, Morse (2008) goes on to say, that if the principles of qualitative inquiry are followed, the analysis afterwards will then be self-correcting.

As our design process has been iterative, we have gone back and forth between research and user involvement, reframing the design challenge, discovering and reading new literature, and using various methods for data collection and co-creation. This relates to reliability and validity, as Morse concludes “… qualitative research is iterative rather than linear, so that a good qualitative researcher moves back and forth between design and implementation to ensure congruence among question formulation, literature, recruitment, data collection strategies, and analysis.” (2008). This makes it possible for the researcher to identify when to change path or modify the process in order to achieve reliability and validity and to ensure rigor (ibid.), and as such we argue that our process, final service and project as a whole both achieves said reliability and validity, and ensures the essential rigor.

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Did We Meet the Learning Goals?

In our assessment, we did meet the learning goals outlined at the beginning of this report. This project has helped propel our understanding of the service design field in several key areas, while pointing towards domains worth exploring further in future research.

Developing our concept with potential users has been a great experience. While challenging to plan and facilitate, we feel that we succeeded in creating a space of mutual learning, essentially exploring the designer’s role as facilitator rather than expert.

With the kind contributions of several collaborators we feel confident that we succeeded in creating a viable, scalable and value-creating system of collaboration between diverse stakeholders to reach a strategic goal.

We recognise that our process has been limited in terms of time, and so it has regrettably not been possible to establish a practical overview of the organisational implications on the municipal-side of the service. However, we have been able to internalise and deploy communication know-how to present our concept in a relatable way for all involved.

Overall our process has supported our learning of the manifold considerations to be dealt with in relation to innovation around a public institution, and how we might achieve strategic change through a service concept.

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Group Work Reflections

This section will discuss the group work of our interdisciplinary team, and how we as a team collaborated towards a shared goal.

The groupOur group consists of people with different backgrounds and nationalities contributing with a range of different world-views and competences. If utilized properly this can be a strength, if not, it can be a barrier to a healthy group dynamic. In our process the group work has been a pleasant experience, with each member contributing evenly throughout. The different world-views provided relevant perspectives in discussions, both on a personal as well as professional level.

Meetings and presentationsIn group meetings everyone always had a say, and all viewpoints were equally legitimate. Matters of concern were discussed logically as well as creatively which always elicited a positive output.

Group presentations were consistently planned ahead, and divided equally between us, for everyone to be a part of presentations and practice their presentation skills.

NetworkFurthermore, everyone voluntarily provided access to their personal as well as professional networks in order to open up doors for the project to move forward. First, this made it possible to attend the Service Design event at CIEC, which then provided an opportunity to recruit the enthusiastic young people who partnered with us. Second, it made it possible to both observe and later test a small-scale prototype at Ordrup High School. Simultaneously, it provided an insight to the working mechanisms of Copenhagen municipality in regard to citizen inclusion and urban planning.

Writing the reportsLastly, the process of compiling the reports truly showed the collaborative spirit. The process of writing the report was structured and planned, and as such we had a great deal of writing in drafts.

Final remarksAltogether the process has been a learning experience for everyone, both in service design as well as in collaborative team work. We will all walk away from this experience with more knowledge, a stronger service design skill set, and more practical experience with designing services with external stakeholders.

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Appendices

89

Appendix A

Video Sketching Workshop

This appendix presents videos from the one-week workshop titled “Skething in Video”. The videos here show sketches of various concepts we came up with, as we iterated on ideas related to data, location and privacy.

‘HangOutMap’.https://www.youtube.com/watch?v=jS2HRVacQks.This video is about a service concept where plac-es to hang out are accessible via a digital map.

‘DangerMapsFinal’.https://www.youtube.com/watch?v=149EIVjO3aw.This video shows a concept where users can report incidents of violence, which accumulates to a map of dangerous areas.

‘McForceFeed’. https://www.youtube.com/watch?v=JD_GtBX31ec.This video presents a concept of how privacyviolations can be capitalized.

‘NoizzeMap’. https://www.youtube.com/watch?v=by5hav_OcxY.This video presents a concept wherein users can interact with a map and hear the music played at different establishments, before deciding whether to go there or not.

Survey Responses

https://goo.gl/XrEqA7

Appendix B

‘BlueSkyJim’. https://www.youtube.com/watch?v=ChkeOJtcEg4.This video presents an idea of how people might locate outdoor gym facilities with their friends.

‘Final Sketch’https://vimeo.com/127357232.This video presents the final video of the work-shop titled “Sketching in Hardware”. The video is a presentation of a service based on blue-tooth-devices, in a kickstarter-esque style..

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Semi-StructuredInterview Guide

What do teens use online Which services and why? Why not the others?

What games do teens playWhat do teens collect

What are the last 20 pictures you’ve taken? (Show & describe) What about location?

What do teens do? Using the city After school activities

What do you care about?Which is public and which is private?

How do you find new places or share your good experiences?

Do they consider the value of people getting the same kind of experience

What do you share online? Why? For who?

Appendix C