Dashboard Design: Interactive and Visual Exploration of Spotify Songs

Sarah Clavadetscher, Michael Schlotter, Nadine Christen, Juliane Streitberg

and Michael Burch

University of Applied Sciences, Chur, Switzerland

Keywords:

Dashboard Design, Visualization Tool, Interactive Information Visualization, Spotify.

Abstract:

In this paper we describe an approach to create interactive visualization tools for simple datasets that exist

in various application domains which many people are familiar with and interested in, like sports, entertain-

ment, trafﬁc, or health care. Such data problems require a simple but elegant visual solution to support the

non-experts in information visualization at their tasks at hand, supported by easy-to-understand interaction

techniques. We start our approach with the design phase in which a hand-drawn mockup is created and based

on this, an interactive dashboard in Dash, Plotly, and Python is built. The design of the tool is guided by user

feedback of 23 participants in qualitative interviews taking into account eight relevant criteria before starting

the design of a visualization tool. We illustrate the usefulness of the tool by applying it to a dataset focusing

on songs from the music streaming platform Spotify while we integrate several diagrams in a multiple and

coordinated views manner to visually explore a given dataset based on several visual perspectives. With the

combination of the many diagrams we can ﬁnd insights in the mood categories of the songs and several other

attributes, hence allowing visual analyses and explorations. Finally, we discuss limitations and scalability is-

sues of the approach.

1 INTRODUCTION

Designing dashboards for a given dataset scenario is

a challenging task, in particular if the dashboard has

to include various user interface components such as

sliders, menus, buttons, and so on, as well as visual

components that are based on a mixture of visual

variables to build interactive diagrams with which

users can explore their datasets (Ware, 2004; Ware,

2008). The design phase is typically guided by hy-

potheses about unknown data and involved tasks that

have to be answered by means of interactive visual-

izations (Burch, 2022) in a multiple and coordinated

views manner (Roberts, 2003).

In this paper we focus on the creation of such an

interactive tool in form of a dashboard implemented

in Dash, Plotly, and Python, demonstrating how easy

it is to come up with an appropriate solution based

on a hand-drawn mockup (see Figure 1). We start by

understanding the data structure and format, which in-

sights it might contain, and a possible design solution

based on the information pieces we have in the begin-

ning. Since there are various parameters in the begin-

ning to adapt we start by designing a dashboard on

high-level design decisions and are still able to adapt

the design after further iterations.

To get a better understanding for the most relevant

points before starting the design and implementation

phase we conducted user interviews to collect qual-

itative feedback about the relevance of the points to

be included. This evaluation is important since we

cannot include all of the points equally in the design

since they stand in some kind of trade-off behavior.

Hence, some kind of priority list would be beneﬁcial

based on the feedback of several users. However, in-

dependent from the users, we have to walk through a

series of ideas, concepts, and technologies in the de-

sign process, always keeping in mind that the created

and implemented tool has to be used by non-expert

users in general which was also the major point on

the participants’ priority list.

We illustrate the usefulness of the designed and

implemented tool by applying it to data from the mu-

sic streaming platform Spotify and the stored meta

data. Our goal was to allow users to easily explore

the data for correlations and dependencies (Heinrich

et al., 2011) in the data attributes, possibly letting

them ﬁnd their desired songs, based on a variety of at-

tributes. Moreover, we discuss scalability issues and

limitations of the visualization approach.

Clavadetscher, S., Schlotter, M., Christen, N., Streitberg, J. and Burch, M.

Dashboard Design: Interactive and Visual Exploration of Spotify Songs.

DOI: 10.5220/0012359100003660

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) - Volume 1: GRAPP, HUCAPP

and IVAPP, pages 653-660

ISBN: 978-989-758-679-8; ISSN: 2184-4321

653

Figure 1: A hand-drawn mockup of the dashboard: The left part of the user interface contains the parameter panel while the

rest of the user interface is used for the multiple and coordinated views on the data (here Spotify data). The currently visible

diagrams are a parallel coordinates plot (PCP), a scatterplot, and two bar charts (the upper one only using the visual variable

length and the bottom one using a stacked variant of the standard bar chart).

2 RELATED WORK

Dashboards (Bach et al., 2023) get more and more in

focus in the ﬁeld of information visualization (Burch

and Schmid, 2024) since they provide easy-to-create

and easy-to-implement solutions to data problems at

hand. However, the most important aspect in dash-

board design is the phase before starting the im-

plementation in the form of producing source code.

We need some knowledge about the data format and

structure, the hypotheses and research questions as

well as the users of the ﬁnal tool with their tasks-at-

hand (van Wijk, 2005) to create a successful solution

for data analysis and exploration.

Designing a dashboard combines at least two ma-

jor design problems which come in the form of the

user interface design as well as the design of the dia-

grams (Rosenholtz et al., 2005; Tufte, 1992) with all

of the included visual variables that build a mixture of

parameters with options for changes and adaptations.

But also the interaction techniques (Yi et al., 2007)

and the algorithmic concepts that we typically do not

see in the user interface are of importance to get the

best out of it. To guarantee a suitable outcome for

data analysis and exploration tasks we have to follow

design rules, also focusing on aesthetic criteria (Bar

and Neta, 2006).

We can ﬁnd lots of dashboard examples, in par-

ticular for applications in information visualization,

however, not many of the designs try to integrate de-

sign rules following interface, visualization, interac-

tion, and algorithmic concepts at the same time. For

example, FitYou (Zacheo et al., 2023) focuses on

health data but it is unclear which of the four afore-

mentioned components the tool focuses on the most.

Another one (Shan, 2023) focuses on health security

attacks without explicitly discussing the data in use

and which algorithms are linked to which interaction

techniques. Soccer athlete data is visually represented

in a dashboard (Boeker and Midoglu, 2023) but the

design and linking of the visualizations and the inter-

actions is not described in much detail.

Apart from dashboards we can ﬁnd a really long

list of visualization tools, most of them equipped with

complex visualization techniques only understand-

able by expert users, for example in the domain of

IVAPP 2024 - 15th International Conference on Information Visualization Theory and Applications

654

eye tracking (Kurzhals et al., 2017), software visual-

ization (Burch et al., 2017b), or graph and network

visualization (Burch et al., 2017a). There are various

examples in recent years with more and more follow-

ing in the future, but still the design phase is under-

represented, in particular if the focus is on easy-to-

understand visualizations.

3 QUALITATIVE USER

FEEDBACK

We recruited participants working in the ﬁeld of vi-

sualization to provide qualitative feedback based on

a list of criteria that we took into account when de-

signing a dashboard for data visualization. Since each

user might have a different opinion on the criteria we

averaged the results to get a general impression about

the most relevant criteria before starting a dashboard

design for a visualization tool.

3.1 Hypotheses and Research Questions

The research question in this work focuses on a list of

aspects when developing a visualization tool in form

of a dashboard and asks whether there is a clear or-

der among eight important criteria for such a visual-

ization tool: Easy to understand, application indepen-

dency, interaction techniques, easy to install, comfort-

able data upload, several data perspectives, low costs,

and easy to extend.

Before inviting people to give qualitative feedback

we discussed about hypotheses that describe the out-

come of the summarized qualitative feedback. Based

on that we developed a list of three hypotheses:

• Hypothesis H1. The feedback of the recruited

participants will clearly state that the major point

when developing a visualization tool for their data

is the easy-to-understand character.

• Hypothesis H2. The costs of the developed data

visualization tool will only play a minor role dur-

ing the development and when the tool is ﬁnally

used.

• Hypothesis H3. There is a clear order among the

points in the criteria list, that is, not all of the cri-

teria are more or less equally ranked.

3.2 Participants

23 participants took part in the qualitative feedback

experiment. We recruited them by sending emails

containing clear instructions and asking for an order

of the aforementioned criteria. The participants sent

back their impressions as textual feedback that we had

to summarize in numbers and facts. All of the partic-

ipants were aged between 23 and 46 years at an aver-

age of 31.3 years while 9 were female and 14 male.

3.3 Questionnaire, Tasks, and Feedback

We asked the participants to state their age, gender,

and experience in their favorite research ﬁeld.

The task for them was to return the criteria in de-

creasing order starting with the most relevant one for

a visualization tool focusing an data aspects. More-

over, they were asked to mention additional criteria

that they also identify as relevant and that were not in

the list.

3.4 Results

When evaluating the feedback we mostly focus on the

hypotheses and the research question. We compute a

priority list from the returned order given by the par-

ticipants. To reach this goal we compute the average

place on which a criterion is set in the priority list.

This means the lower the average place of a criterion

is the more it was suggested as being important, that

means it was rated with a higher priority (see Table 1).

Table 1: From the qualitative feedback we created a priority

list for all the eight criteria. The priorities were based on

the average ratings of the participants, hence no individual

opinions are taken into account in this summary.

Criterion Average rating

Easy to understand 2.1

Application independency 3.9

Interaction techniques 4.6

Easy to install 4.9

Comfortable data upload 4.9

Several data perspectives 5.0

Low costs 5.2

Easy to extend 5.3

We see a clear tendency to the criterion that the

tool should be easy to understand which is inline with

hypothesis H1. Also hypothesis H2 can be conﬁrmed

stating that the low costs of the tool are not playing

the biggest role. However, hypothesis H3 must be

rejected. There is no clear order among the criteria.

Only the easy to understand criterion stands out fol-

lowed by the application independency.

The participants also mention that it might be im-

portant in which context the tool is used. For exam-

ple, in an industry context, money does not matter

for the user because the tool’s costs are covered by

the company, however, in a student’s context it might

Dashboard Design: Interactive and Visual Exploration of Spotify Songs

655

be important to get cheap tools since the university is

typically not offering any desired tool for free.

4 DATA AND

TRANSFORMATIONS

The data under investigation in our application exam-

ple contains a list of songs from the platform kag-

gle.com (Musicblogger, 2020) which is freely acces-

sible. The data consists of a mixture of data attributes

in various scale levels with nominal attributes, e.g.

song titles, artists, ids, and mood categories which can

be happy, sad, energetic, and calm.

The major part of the attributes is based on

metrically scalable attributes like popularity, length,

danceability, acousticness, energy, instrumentalness,

liveness, valence, loudness, speechiness, and tempo.

Those describe the measurable musicalic properties

of the songs. Two more ordinal attributes can be

found like key and time signature, as well as the re-

lease date.

In total, the dataset contains 686 lines, i.e. dif-

ferent songs. This number is only a small portion of

the actually existing songs on Spotify since we used

it only for testing purposes. The songs stem from 540

different artists and have been produced in the time

period from 1963 to 2020.

To get the data in the required format for the inter-

active tool we applied some simple algorithmic trans-

formations to it like sorting, data splitting, and catego-

rization, just to mention a few from a long list worth

integrating. Since the focus is on easy to understand

concepts integrated in a dashboard we also take into

account algorithms that are powerful but still useful

by the non-expert users.

5 USER-CENTERED DESIGN

As already described earlier we focus the implemen-

tation on the result of a design phase that ends up in

a hand-drawn mockup of the user interface including

interactive diagrams. Moreover, tasks and hypotheses

play a crucial role when developing the interactive vi-

sualization tool.

5.1 Tasks

We integrate some functionality for at least four major

tasks.

• Mood correlations: We search for a list of songs

with high values for the attributes danceability

and popularity.

• Song lengths: We search for implications that the

average song length decreases during the stream-

ing age (Kopf, 2019).

• Attributes: We search for songs that follow a cer-

tain attribute correlation pattern (tempo, energy

etc.) as well as the different mood categories they

belong to.

• Mood distribution: We are interested in the num-

ber of songs per mood category and what a com-

parison between them will tell us.

5.2 Data Hypotheses

For the analysis of the data we come up with several

hypotheses. Firstly, those are used to guide the dash-

board design. Secondly, they are used to test whether

the tool can be applied to the Spotify data in order to

conﬁrm or reject hypotheses (Keim, 2012).

• Hypothesis 1: Songs in the mood category calm

have been mostly created in the second half of the

time period.

• Hypothesis 2: The analyzed songs get shorter and

shorter in length during the explored time period.

• Hypothesis 3: Songs in the mood category calm

have a large value for the attribute instrumental-

ness compared to songs in the mood category en-

ergetic.

6 DASHBOARD CREATION

To test the earlier mentioned hypotheses, an interac-

tive dashboard was designed (see Figure 1). This con-

tains different diagrams as visualization elements.

The created visualization elements (see Figure 2)

are summarized in a dashboard equipped with several

ﬁlter functions. At the top left, moods that can be

selected will be displayed. There is also a button that

can be used to cancel this selection. Directly below

is a date picker with which the displayed songs can

be narrowed down by release date. Below that, basic

information about the data set can be found, such as

the number of songs and artists.

At the bottom of the left hand side is a simple

treemap (Shneiderman, 1996) visualization that per-

forms two tasks in one. On the one hand, the total

number of songs per mood is displayed through four

colored ﬁelds. The size of the ﬁeld indicates the distri-

bution of songs per mood. The exact number of songs

corresponding to this category is displayed by hover-

ing the mouse over a category. On the other hand,

IVAPP 2024 - 15th International Conference on Information Visualization Theory and Applications

656

Figure 2: The implemented dashboard based on the design in form of a hand-drawn mockup: We can see that the standard bar

chart as designed in the mockup phase in Figure 1 has been removed and only the stacked bar chart variant is displayed.

the visualization also serves as a legend so that the

mood categories can be identiﬁed in the other visual-

izations. This is because the color assignment of the

moods runs through the entire dashboard. The col-

ors of the moods were selected based on color the-

ory (Lundberg, 2022). Accordingly, the color assign-

ment is as follows: happy: yellow, sad: purple, en-

ergetic: red, and calm: green. Shaded colors were

used rather than primary colors for a visually appeal-

ing look.

The core of the dashboard consists of the follow-

ing charts:

• Attribute Overview Songs. This shows which

songs have which attributes and which strengths.

The attributes are lined up one after the other on

several y-axes as parallel coordinates. The advan-

tage of this is that attributes with different value

ranges can be mapped simultaneously. Each song

gets its own polyline. In order to be able to rec-

ognize commonalities of the songs based on their

mood, the songs are color coded, as mentioned

above (Hypothesis 3).

• Correlation of Attributes. At the bottom left,

songs can be displayed and compared in a scat-

ter plot by selecting two categories. On the one

hand, correlations between the two selected at-

tributes can be identiﬁed. On the other hand, the

tough color assignment also makes any clusters of

the moods visible.

• Song Distribution. The stacked bar chart shows

how many songs per year of a mood are in the

dataset. This indicates how the moods are dis-

tributed over the individual years. Moreover, it

becomes easy to see if certain moods were more

prevalent than others at a particular time point

(Hypothesis 1).

• Song Length. The bar chart shows the average

song length (y-axis) by release year (x-axis). This

allows us to illustrate the evolution of song lengths

per mood or across all moods over the years (Hy-

pothesis 2).

Switching between the song length diagram and

the song distribution diagram is possible using a but-

ton. This ensured that all visualizations were large

enough and remained legible. The dashboard is based

on Spotify’s corporate design and was therefore de-

signed in the so-called dark mode. However, switch-

ing between dark mode and scientiﬁc mode in the

header at the top right is possible if this is appropriate

and user-desired. The latter contains the same func-

tions and color schemes as the dark mode but on a

white background (see Figure 3).

7 INTERACTION TECHNIQUES

The planned functions mentioned above could be im-

plemented in an operational dashboard. In addition to

the functionality of the visualizations, emphasis was

also given to interaction. The color coding of the

moods mentioned above ensures that they can be as-

signed to each other throughout the entire dashboard

and that a uniform image is created.

Parts of the dashboard are designed to be interac-

Dashboard Design: Interactive and Visual Exploration of Spotify Songs

657

Figure 3: The dashboard in scientiﬁc mode, for example by using a different background color

tive to enable individual exploration of the data in the

dataset.

• Mood Filter. At the top left of the diagram, as

already mentioned, individual moods can be se-

lected or deselected. The dashboard will then only

show the songs of the selected moods. At least

one mood must be selected.

• Year Filter. The period to be displayed can be

speciﬁed below. This ﬁlter also applies to the en-

tire dashboard.

• Manual Adjustments Attributes Overview

Songs. Using the mouse, a range of values of a

desired attribute can be selected. The songs which

are in this range will then be highlighted. Further,

the attributes can be arranged in the desired order

using drag and drop (see Figure 4).

• Further Information on Song Length. Hover-

ing over individual bars displays the year with the

average song length in minutes (see Figure 5 (a)).

• Display Correlation of Attributes. The moods

and periods displayed here can be adjusted using

the ﬁlter functions on the left side of the dash-

board. Above the chart, two drop-down menus

can also be used to select the song attributes to be

compared. If a speciﬁc value space is to be dis-

played, it can be selected manually by moving the

horizontal or vertical lines. By mouseover over

a displayed point, the values of the attributes, the

song title, and the artist are indicated (see Figure 5

(b)). The user gets a helpful additional feature if

the dashboard is started locally via a Python de-

velopment environment. The corresponding song

is opened in Spotify’s web application by clicking

on a point in the scatter plot. This allows the user

to compare the songs visually and acoustically.

8 ANALYSIS OF PERFORMANCE

Since the data basis for the visualizations is relatively

tiny and the algorithms used belong to the simpler

ones, the response time of the dashboard shows an

expected fast value of 271ms at a maximum. Many

users abort the loading process after waiting for about

two seconds (Guelle, 2022). Therefore, long load-

ing times should be avoided at all costs, suppose too

long loading times still occur when expanding the

database. In that case, they can be reduced by per-

forming the calculations already at the data set level

instead of loading the visualizations, as constructed

for this dashboard.

The dashboard is ideally used on a screen that

measures 15 inches or more. Since there is no re-

sponsive design, not all representations are displayed

as intended on smaller screens. Viewing with mobile

devices such as smartphones is, therefore, only some-

times possible.

9 CONCLUSION AND FUTURE

WORK

In this paper we have shown an approach to create in-

teractive visualization tools in form of dashboards by

using Dash, Plotly, and Python. We start with a de-

sign phase, taking into account tasks and hypotheses,

IVAPP 2024 - 15th International Conference on Information Visualization Theory and Applications

658

(a)

(b)

Figure 4: Manually setting ﬁlters in combination with selected moods.

(a) (b)

Figure 5: Two visual insights into the dataset: (a) The average song length when hovering over. (b) Mouseover in a manually

adjusted scatter plot.

and based on that design we start implementing an

interactive visualization tool. The designed and im-

plemented dashboard provides some opportunities to

explore an existing dataset interactively, however, the

visualizations are by no means complete and could

be further expanded. Since the dashboard was devel-

oped for a speciﬁc dataset, care should be taken when

extending it to use the same attributes for additional

songs, or even more, for other datasets in the same

data format.

The search for a song is not included in the dash-

board by means of a search window, which is recog-

nizable in the draft. This was omitted because the

dashboard is more about the attributes and moods of

the songs and not necessarily about the ability to ﬁnd

a speciﬁc song.

The selection at the date picker needs to be more

practical due to the default functionalities of Plotly.

The goal would be to set the period via the displayed

windows. However, if data from 2000 to 2010 is to

be displayed, all previous years and months must ﬁrst

be clicked through. The alternative is to edit the data

directly by typing in the desired span. In this case,

however, the displayed windows are again disturbing.

If there is a possibility to improve this with reason-

able effort in the future, this should be implemented

to increase the usability of the dashboard.

As mentioned, the presented dashboard is only us-

able well over speciﬁc screens. Therefore, the dash-

board still needs to be extended with a responsive de-

sign so that the application is possible without prob-

lems with smaller screens and mobile devices.

Dashboard Design: Interactive and Visual Exploration of Spotify Songs

659

To further improve the user experience, imple-

menting an additional feature to listen to songs on

Spotify would be recommended. Moreover, we plan

to evaluate the created dashboard in a user study,

also with eye tracking (Duchowski, 2003; Holmqvist

et al., 2011). Finally, the dashboard should be ex-

tended in a way to make it easy to extend to other

dataset scenarios, i.e. the dashboard might be able to

detect the data types in the dataset and the data for-

mat and based on this, can start with a desired user

interface and visual components.

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