Vitality

My PhD research focuses on the context of vitality. I consider the importance of vitality with its relationship with people’s health in the variety of aspects.

According to the World Health Organization Global Observatory Data Repository 2011, about one-third of adults did not reach sufficient physical activity levels (Hallal et al., 2012). Physical inactivity can cause health disorders such as non-communicable diseases (I-Min et al., 2012) and affect health-related quality of life (Kohl et al., 2012). Not only physical activity but also the meaning of vitality can be varied. Vitality is defined as a “psychological sense of aliveness, enthusiasm, or energy“ (Guérin, 2012). There are studies focused on the emotional aspects of vitality (Penninx et al., 2000). Furthermore, several scholars indicate that demographics and social contexts influence our vitality (Penninx et al., 2000; Ryan & Frederick, 1997; Van Steenbergen et al., 2016).

In line with the scholars aforementioned, my study also found the multi-dimensionality of vitality, which is perceived differently by individuals, including factors such as stamina, activeness, health, happiness, and social status (Wada et al., 2020). The research of vitality has different angles depending on the domain. As such, the different interests and usages of the term across diverse domains provide further challenges in identifying what vitality encompasses.

Data Visualization and Cartography

Visualizations offer great potential for uncovering information in data and efficiently communicating complex quantitative information (Few, 2013; Tufte, 2001). Benefiting such cognitive support for understanding a large amount of information (Few, 2013; Tory & Möller, 2004), visualizations are increasingly used to help decision-making processes (Zulkifli et al., 2015). As such, information visualization is used to solve real-world problems in a variety of different formats (Nahum Gershon & Eick, 1997). These real-world problems are often complicated due to the various data sources and people who are involved in (Nahum Gershon & Eick, 1997).

Consequently, it remains challenging to make visualization applications useful to such diverse audiences (Plaisant, 2004). Another challenge is that a majority of data available today contains geographic references such as geographic coordinates and postal addresses, which brings diverse information but also complexity (MacEachren & Kraak, 2001). Therefore, I believe the consideration of spatial data is important in visualization.

The usage of spatial data in the format of maps has a long history. Maps are used not only by researchers and experts but also by individuals for their daily lives as maps require minimum visualization literacy to understand the information on them, such as the distribution of events, phenomena, and objects (Koç, 2014). Additionally, one of the other several benefits regarding the function of maps is problem-solving (Koç, 2014). A remarkable example of it in history is a map created by Dr. John Snow in 1855, which successfully demonstrated the source of a cholera outbreak in the Broad Street area of London (Gilbert, 1958).

Modern computer-based maps are based on cartographic knowledge and techniques (Nöllenburg, 2007). My research aims to promote vitality in a city through a map-based data visualization, which is utilized by multiple parties such as government, business, and research. Therefore, I also look into studies to solve specific urban issues by using maps—in other words, maps used in certain contexts.

There are several examples of computer-based maps used in specific context aiming to solve urban issues. The San Francisco Municipal Transportation Agency developed a map-based data visualization system called TransBASE (TransBASE, n.d.) to support identifying context-specific trends and evaluating pedestrian collision events for pedestrian safety improvement (Kronenberg et al., 2015). Crime mappings are another classic example that are developed for a specific context and aim to solve a specific social problem (Fitterer et al., 2015). Psyllidis et al. developed a platform called Social Glass, which focuses on the utilization of social data – data generated through users’ social contexts such as social networks, for urban planning, and decision making (Psyllidis & Bozzon, 2015).

Challenge with the Evaluation of Visualizations

Several aforementioned studies include evaluations of their application through user studies and interviews. Yet, evaluations in visualization research are still limited compared with the research in the field of human-computer-interaction (HCI) (Elmqvist & Yi, 2015).

Additionally, case-by-case evaluations may be required as visualizations are context-specific. My research contributes to the field of visualization by providing empirical evidence how domain experts utilize web cartography application related to vitality for their decision-making process and collaboration among them by understanding their requirements on such application.

To enhance the utilization and collaboration, I hypothesize awareness (presence of data) on such application is a key factor. Therefore, my research also investigates the design factors to enhance awareness of data on the web cartography application.

Research Questions

Given those backgrounds, there are three research questions in my PhD research:

1. What data is required from domain experts who are involved in vitality projects to achieve their objectives?

2. How does a map-based visualization containing various vitality data support the visual analysis process among different domain experts?

3. What design factors on a map-based visualization raise awareness of the perspectives on vitality data among different domain experts?

To answer those questions, the iteration of designing/developing prototypes and user tests was conducted in my PhD research.

Collaborative Data Platform

My PhD research started with designing a collaborative data platform for the project, to which my research contributes, called Vitality Living Lab (VLL) project. VLL is a partnership project among businesses, public organizations, research and knowledge institutes in Eindhoven, the Netherlands. The objective of VLL is to strengthen an ecosystem for business and innovation in the domains of sports and vitality in the city and region of Eindhoven.

One of the purposes is to design and develop a data platform to manage and share sports and vitality-related data in the project of VLL. Therefore, I conducted the project document review and learned the data platform is expected to meet the component of:

1. data acquisition to collect and store variety of dataset

2. data mining to allow statistical analysis for new insights

3. data visualization as feedback of data mining, and for quick insights for diverse users, following the concept of FAIR data (Findable, Accessible, Interoperable, and Reusable).

Through this project document review, it considered that the data platform has two main aspects, the one is a collaborative platform, and the others are data analysis and visualization tools, which can be resembling with business intelligence (BI) system combining the two aspects. Therefore, I looked into existing BI systems in the market as a part of competitor analysis.

In respect of visual inspiration, I conducted media scanning (O’Grady & O’Grady, 2009) to understand the state-of-the-art technique and design of data visualizations. Websites to curate data visualization sites and/or blogs were reviewed and filed relevant sites as competitor lists. Some data portal sites were visited including the one from Eindhoven (2019), the Netherlands, where this research is taken place and listed as competitors when we found inspirational features, services, and functions. Additionally, seminal books focusing on visualizing methods are also studied to understand the basic principle and the best practice, such as the works of Tufte (Tufte, 2001).

The preliminary research helped to prepare for the interview study to understand the requirements of the project partners in the VLL project on the data platform. By using the qualitative data obtained from the interview study (discussed in more detail in the section of “user study”), I created a qualitative data-driven persona (Mulder, 2007).

I started with sketching and making paper prototypes to discuss with the internal team and confirm the design directions before moving forward to a solid digital prototype.

Finally, the digital prototypes were created by using the software, Sketch (Sketch, n.d.), as the main tool to develop user interface design. I selected this particular program because of its capability to connect other (web) applications, such as InVision (a prototyping tool that focuses on interactions and transitions) (InVision, n.d.), and various plug-ins enhancing the user interface designing process.

In parallel with the prototyping by Sketch, I also developed a web application based on the digital prototype by using Node.js and its framework, "Express."

Map-Based Data Visualization in the Context of Vitality

By using the developed collaborative data platform web application, I conducted small a small user test—asking the project partners to use the app (open-ended tasks) and evaluate the necessity feature by implementing MoSCoW (Must/Should/Could/Won't have) analysis.

Based on the finding from this study, I found the project partners were looking for the feature to analyze (visually) the combination of multiple datasets related to vitality considering the fact that vitality data is often not represented by single parameter, rather the combination of multiple variables such as physical health, mental health and social status. Therefore, my PhD research particularly focuses on the design of a map-based data visualization, which allows users combining multiple datasets visually, for supporting their insight generation and decision-making process.

Additionally, I consider the aspect of collaboration is still important to achieve the ultimate goal—promoting vitality in a city by using this type of application. It is because people who are from diverse domains work on vitality related projects and they are often interconnected. Therefore, I looked into features to enhance collaborations on the map-based data visualization application for my PhD research.

The necessity of collaborative features was also discovered through the user studies with domain experts by using the prototype. I conducted a mixed-methods study, which consists of online surveys, open-ended tasks, and semi-structured interviews with nine domain experts in the field of government, business, and research. Six out of nine participants mentioned their collaborative usage of such application for their vitality-related projects.

At this point, I hypothesized awareness (by the availability of vitality data) can be one of the factors to enhance collaborations by communicating diverse aspects of vitality data to various users from different backgrounds. The mixed-methods study also collected click events by participants and found participants’ behavior to check through the wide range of available data on the map-based data visualization application regardless of their interests and professions. This finding gave me insights into the importance and possibility of presence (of data) and its awareness by users to learn perspectives of vitality data. I believe this could be a step to collaboration with other users on the application by having interests in different aspects of vitality data.

In this proposed design of the map-based vitality data visualization application to enhance collaboration through awareness, “vitality themes” is a strong factor for awareness considering the nature of vitality data which often can’t be represented by single parameter. Vitality themes can be created by users by combining multiple datasets and shared on the application. I consider this results in the awareness of people who are involved in vitality related projects through the presented vitality themes. Therefore, how to present vitality data, which create awareness is a key topic in my PhD research.

Semi-Structured Interviews by Using Card Sorting

My first study was conducted to understand requirements on a data platform from the project partners of Vitality Living Lab (VLL) project (a partnership project among multiple organizations such as government, research and business aiming to promote vitality in a region).

First, semi-structured interviews were conducted with the eight project partners of the VLL represented the business, municipality, research/education institute, and sports organization. The interviews were audiotaped and photographed. Throughout the interviews, I, together with the other researcher, addressed the stakeholders’ “visions,” “concerns,” and “feature requirements” on the data platform.

I implemented a (closed) card sorting during the interviews to spark up the conversation by providing some key topics (potential features on a data platform here) during the interviews.

Thematic analysis (Anderson et al., 2014) was applied to analyze the data obtained from the semi-structured interviews with the eight project partners. A deductive coding approach was used to guide the analysis based on the researchers’ theoretical or analytical interests (Anderson et al., 2014).

Later, a constant comparative method (Boeje, 2002) was applied to organize key phrases within the themes (“visions” and “concerns” on the data platform).

Later, a constant comparative method (Boeje, 2002) was applied to organize key phrases within the themes (“visions” and “concerns” on the data platform).

The results of the card sorting were also analyzed to look into specific requirements for the features of the data platform. I determined to assign the following ranking scheme:

4 Core / Base / Must / Essential

3 Need to have / Main or basic features

2 Nice to have / Insights / Optional

1 Later / Ambition / Extra

0 Unnecessary

The next semi-structured interviews were conducted with citizens (N=20) to understand the perception of vitality and the kinds of vitality data they require on the data platform. In the interviews, I mainly asked the participants the following questions: (i) What is vitality?, (ii) What vitality data is required from the data platform.

Based on the results from the interviews with the project partners and citizens, I designed and developed the prototype of a data platform containing vitality data used by multiple stakeholders.

Prototype Testing and Online Survey

I conducted prototype testing with twelve project partners from the VLL project by using the developed prototype. The developed prototype has mainly two layers—the first layer faces the public which every user can access, and the second layer requires authentication, which only authorized users (the project partners) can access and use.

The first layer shows all available data on the platform with the layout of cards, where users can search and filter data. The first layer also includes separate pages for each dataset where users can look into more details as well as conduct some simple spatial analysis (layering spatial datasets).

The second layer is a user’s account page, which enables authorized users to upload data to the platform as well as to gather their favorite datasets (from the first layer) for their exploration and analysis.

This prototype testing aims to understand project partners’ general perception, usage, behavior, goals, and requirements (features and functions) on the data platform.

Although I expected that the project partners were familiar with working with data in general, there were still some barriers for them when they see the bunch of datasets upfront (even with cards’ format). They preferred a simple top page that navigates them step by step to find datasets they were looking for. As expected, the project partners’ requirements for features and functions are varied.

Generally, I found they like to use the data platform to look for datasets useful for their projects and save them as favorite. Then, they look into those datasets by filtering, comparing with other datasets, and/or analyzing statistically to gain insights for their own projects. I also found their preference to share data in public and contrary, some of them also prefer keeping some of their own data private.

They consider the first layer as a data source where they can find some public (aggregated) large scale data. And the second layer as a place they keep their own data (local and small scale, which is more confidential) and compare with the public data they find from the first layer.

MoSCoW Analysis

Based on the results from the prototype testing, I determined the features and functions of the data platform. To confirm those features and functions as well as define the priority of them for development (i.e. which features/functions have to be developed first), I conducted MoSCoW (must-have/should-have/could-have/won’t-have) Analysis with four project partners. The following are the results of the MoSCoW Analysis.

The first layer shows all available data on the platform with the layout of cards, where users can search and filter data. The first layer also includes separate pages for each dataset where users can look into more details as well as conduct some simple spatial analysis (layering spatial datasets).

The second layer is a user’s account page, which enables authorized users to upload data to the platform as well as to gather their favorite datasets (from the first layer) for their exploration and analysis.

The project partners expect that the data system is used by many different people. Therefore, they prefer a simple top page, which is easy to use for new users by providing some guides or information about the system.

For the individual dataset page (after user selects a particular dataset on the top page), the project partners require more features and functions, such as exporting, filtering, comparing, analyzing, and combining data. The requirement of the layering spatial data feature was also noticeable.

For the private page after login, they like to manage their own datasets and also public data collected from the system. It seems one of their major tasks is a making reports by using data and visualization. They also like to have some basic function to analyze data in the system. But they can analyze data by themselves outside of the system. Therefore, the feature to export data is essential. Likewise, they can also share data by themselves by using different tools. So, I didn’t see the strong demand on collaborative features from them here.

Mixed-Methods Study

Learning from the user studies of the data platform, I decided to focus on web cartography (referred to as a map-based data visualization) in my PhD research. I found the experts’ preference for using maps for their analysis and process of gaining insights regarding vitality-related data (e.g. sports participation, mental health, and social cohesion). They also prefer features and functions to enable some analysis such as layering spatial data. This can be considered as part of GIS (Geographic Information System). Therefore, I designed and developed web cartography (with vitality data) in a way that users can layer multiple spatial datasets (i.e. points, lines, and polygons).

I conducted a mixed-method study, which includes an online survey, open-ended tasks, and semi-structured interviews to answer the question: “how does a map-based visualization containing various vitality data support the visual analysis process among different domain experts?”

To answer this question, I designed this study to address domain experts’ (who work on vitality-related projects, but from different fields, such as research, government, and business) insights, goals (i.e. what they want to achieve), preferences on data, required futures, behavior (how to utilize) on the developed map-based visualization containing various vitality data. I also investigated its applicability to their projects in the real world.

The recordings of the semi-structured interviews were transcribed. The transcribed data were analyzed together with the notes the participants took during the open-ended tasks. I implemented a deductive coding approach to guide the analysis based on the researchers’ theoretical and analytical interests.

I created codes to address our research question, which are “insight,” “approach,” “data (amount),” “data (levels of detail), “data (required),” “data (irrelevant),” “impression,” “applicability,” and “features.”

While familiarizing with the data by looking through transcriptions, mentions related to collaboration stood out. Therefore, I added the code “collaboration” to the set of codes. The transcriptions were coded based on the set of predefined codes.

This study confirmed the preference for map-based data visualizations among those domain experts and several benefits of it such as the efficiency of the data presentation and the ease of spatial analysis.

However, I also found challenges when it is applied to domain experts’ projects in the real world due to in-depth and specific requirements. In terms of vitality data, the provision of themes created by the combination of several datasets (e.g. the combination of green space, sports participation, and health data) and shown together in the map-based data visualizations might help cover each specific use case among various domain experts.

I also found interesting results from the participants' click events data. Although, most of them mentioned they tend to focus on the datasets relevant to their work/project, the click events data shows the majority of them clicked through the wide range of datasets regardless.

From the results, I consider the presence (availability of diverse datasets in this case) could support people to find different perspectives (different aspects of vitality in this case). I think this could help experts’ projects by providing new ideas, and also could be an important first step for collaborative work.