Visual Analysis Tool for a BLE Technology based Tracking Data

Flavia A. Schneider

, Adriano Branco

, Ariane M. B. Rodrigues

, Felipe Carvalho

Simone D. J. Barbosa

, Markus Endler

and H

elio Lopes

Department of Computing, PUC-Rio, Rua Marqu

es de S

ao Vicente, 225, Rio de Janeiro, Brazil

Keywords:

Internet of Mobile Things, Data Visualization, Indoor Positioning, Data Mining, Information Systems.

Abstract:

Several systems deal with human mobility. Most of them are for outdoor environments and use mobile phones

to capture data. However, there is a growing interest of enterprises to consider indoor movement to take

employees and client classes into account. Moreover, they usually want to assign semantics to the visited lo-

cations. We propose a visual exploration tool for analyzing the dynamics of individual movements in an indoor

environment in this work. We present the use of suitable charts and animations to explore these complex data

better. Finally, we argue that one could use our solution to monitor social distancing in indoor environments,

which is a sensible thing during the current COVID-19 pandemic.

1 INTRODUCTION

Global navigation satellite systems (GNSS) enabled

the estimation of a target’s position at a given loca-

tion. This technology has numerous functionalities,

which have since been improved in outdoor environ-

ments (Kaplan and Hegarty, 2005). It allows us to

identify a position of a car to suggest a better route,

share people’s location between family groups, and

even identify iceberg displacements for environmen-

tal monitoring. In indoor environments, it is also pos-

sible to estimate a speciﬁc target location, in particu-

lar, of people (Dardari et al., 2015).

Nowadays, many enterprises want to increase the

digitization of their data and improve their opera-

tional efﬁciency. To do so, they started to collect

and analyze data about their internal workﬂows, busi-

ness processes, employees’ movement, and the re-

sources used (Curran, 2018). Employees’ higher mo-

bility within the enterprise is a clear sign of their

higher operational efﬁciency because employees be-

come more adaptive and better collaborate with col-

leagues to manage the production process and possi-

https://orcid.org/0000-0003-1176-2249

https://orcid.org/0000-0002-5200-5212

https://orcid.org/0000-0002-1614-918X

https://orcid.org/0000-0002-7540-286X

https://orcid.org/0000-0002-0044-503X

https://orcid.org/0000-0002-8007-9817

https://orcid.org/0000-0003-4584-1455

ble problems (Melamed, 2016; Marini, 2019).

Although there are systems for analyzing human

mobility in indoor environments, these systems do not

have embedded semantics. Usually they are agnostic

to a speciﬁc business ﬁeld, and they do not consider

the participants’ classes.

In this work, we propose a visual exploration tool

for analyzing the dynamics of individual movements

in an indoor environment. To do so, we ﬁrst devel-

oped a low-cost tracking system for indoor environ-

ments (Schneider et al., 2021). This system detects,

collects, processes, and stores users’ movement and

presence data. It also considers the users’ classes and

the semantics of the location. We collected these data

in two university buildings used by faculty, staff and

students.

Using this exploration tool, we report the results

comprising the data collection and visual exploration

to answer some questions about individuals’ indoor

mobility. We propose the use of suitable charts and

animations to analyze these complex data better.

We organized this paper as follows. Section 2 de-

scribes some related work about visual analysis and

visualizations for indoor mobility data visualization

systems. Section 3 presents the research questions

that guided the development of this work. Section 4

reports a visual exploration tool developed and the vi-

sual analysis of a real case study. Section 5 concludes

this work and proposes future directions.

232

Schneider, F., Branco, A., Rodrigues, A., Carvalho, F., Barbosa, S., Endler, M. and Lopes, H.

Visual Analysis Tool for a BLE Technology based Tracking Data.

DOI: 10.5220/0010454202320239

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 232-239

ISBN: 978-989-758-509-8

2 RELATED WORK

The related work presented in this section addresses

visual tools for analyzing indoor individuals’ move-

ments and the most common visualizations used for

this analysis.

2.1 Visual Analysis Tools of Individuals’

Movements

This subsection presents some related work to visual

tools for analyzing individuals movements.

According to Oppermann and Munzner (Opper-

mann and Munzner, 2020), current visual data anal-

ysis tools do not sufﬁce to support decision making

about indoor space usage over time. They designed

and implemented a visual decision support tool cen-

tered around location-based counts of the Sensible

Building Science (SBS) commercial product. Taking

advantage of WiFi networks available at some ven-

dors, SBS uses real-time location services (RTLS) to

record the number of devices per zone at regular inter-

vals. As These data are non-trajectory location-based

counts, the analysis of movement ﬂows is not sup-

ported. Their visualization system addresses multiple

levels of space and time granularity. Since our focus

is not only on space and time, our solution is not de-

pendent on RTLS WiFi capability.

We developed some interactive charts for user tra-

jectories and occupancy in time versus location. In

addition to movement ﬂows, we also want to visually

analyze the relation between individuals, locations,

and user trajectories.

Andrienko and Andrienko (Andrienko and An-

drienko, 2013) surveyed methods, tools, and proce-

dures focusing on visual analytics of outdoor move-

ment on spatial and spatial-temporal analysis, and

limited the scope of their work to movement data of

discrete objects whose spatial positions can be repre-

sented by points. They grouped their objects of anal-

ysis in four categories: trajectories, inside trajecto-

ries, birds’s-eye view on movement and investigating

movement in context. According to them, the most

usual movement visualizations for discrete entities are

static and animated maps and interactive space-time

cubes. From their example, they pointed out that

showing multiple trajectories in maps or space-time

cube (STC) may suffer from visual clutter and occlu-

sions and provides a limited representation of many

movement characteristics and their changes. They

suggest that visualizations of changes of movement

characteristics over time may be better represented

on a time graph. They considered spatial aggregation

of movement data by locations (space compartments)

and pairs of locations for presence and density analy-

ses.

Some commercial tools, such as Tableau (Tableau,

2003), allow the creation of charts and data analysis

without programming. However for indoor individu-

als’ movements, visual analysis chart bring essential

pieces of information combined with other kinds of

visualization. Tableau, like other visual data analysis

tools, does not support these types of visualizations.

2.2 Visualizations of Individuals’

Movements

This subsection presents some related work to the vi-

sualizations of individuals’ movement.

Martella et al. (Martella et al., 2017) explore the

use of data provided by low-cost mobile and ﬁxed

proximity sensors to understand behavior of museum

visitors. To show insights about behavior of museum

visitors, they proposed path visualizations. A visitors’

position heatmap in the museum layout and matrix

shows the relation between visitors and each exhibi-

tion. The qualitative results indicated the immediate

beneﬁts of the use of these analytics in practice. Their

work is interested in extracting which pieces of an ex-

hibition the visitors are interested in, the places they

visited, and how long they remained in each location.

However, the relations among locations, users, and

presence at a speciﬁc time are not relevant to their

study.

Another approach for creating ﬂows and graphi-

cal visualizations is by using process mining meth-

ods (Van Der Aalst, 2011). There are already some

studies using this process mining applied to customer

path analysis. Dogan (Dogan, 2020) showed su-

permarket customer ﬂows created with process min-

ing and compared the ﬂows of purchasing and non-

purchasing customers. Dogan et al. (Dogan et al.,

2019a) applied process mining to a data set of loca-

tions in a shopping mall to discover customer path

and to classify genders. And in another paper, Do-

gan et al. (Dogan et al., 2019b) focused on inferring a

graphical representation of human behavior by imple-

menting process mining techniques. We also adopted

this approach in our work to compare other kinds of

visualizations.

Krueger et al. (Krueger et al., 2015) presented

three interactive visualization methods to identify pat-

terns and individuals’ movements behaviors, and to

-based scarf plot. Scarf plots show gaze transitions

among areas of interest on timelines, but they are

not sufﬁcient for many areas of interest and do not

provide spatial information. Their interactive visual

analysis has three views: the ﬁrst one to identify se-

Visual Analysis Tool for a BLE Technology based Tracking Data

233

quence patterns using scarf plots, Space-Time cube

visualization to identify the room and topic transi-

tions, and the third one using venues maps for pre-

dicted and recorded occupancy of rooms comparison.

However, they were not interested in how long per-

sons remained in each place, the relations among lo-

cations, users, presence in speciﬁc time periods.

3 RESEARCH QUESTIONS

In the literature, there is no consensus on indoor in-

dividual movement visualizations. For this study,

we examined some visualizations to answer questions

about an individuals’ indoor mobility, including:

RQ1: How long does a speciﬁc individual remain in

each place?

RQ2: What is an individual’s indoor trajectory within

a certain period?

RQ3: How many individuals are at each place at a

given timestamp?

RQ4: How long do two individuals stay at the same

place?

RQ5: What is the relation between the places in

users’ trajectories?

4 VISUAL EXPLORATION TOOL

This section proposes our Visual Exploration Tool

for analyzing individual movements’ dynamics in an

indoor environment. Our purpose is to understand

movement behaviors and patterns with a dynamic vi-

sual tool to analyze such data.

Based on the analytical tasks involving individu-

als’ movements dynamics and the research questions

we deﬁned in the previous section, we deﬁned some

design goals for the tool.

G1: Provide a visual representation for length of stay

analysis;

G2: Provide a visual representation for trajectory

analysis;

G3: Provide a visual representation for space occu-

pation analysis.

In the next subsections, we explain in detail the so-

lutions proposed for each design goal. To illustrate

the explanation of these solutions, we use a dataset

acquired through the Internet of Mobile Things low

cost system (Schneider et al., 2021). This system col-

lects, processes, and stores individuals’ movements

in an indoor environment in two university buildings

used by faculty, staff, and students. It provides two

kinds of data: raw and processed. In the system, each

individual has a BLE (Bluetooth Low Energy) bea-

con device associated with them, and the places of

interest are equipped with at least one ﬁxed gateway.

The system periodically records the BLE beacons ad-

vertisements that are within the radio range of each

gateway.

Raw data is the exact data originated in the gate-

ways of the IoT system, without any processing. The

dataset comprises 456,094 movements of 12 individ-

uals, captured by 20 installed Gateways over 501

hours. We used the gateway names instead of the

places names and represented the persons by using

their beacon identiﬁcation number to maintain their

privacy in the database.

The raw data is processed in the server to clean

false positives related to the place where each indi-

vidual (Beacon) was (due to gateway coverage inter-

ference) and to avoid more than one gateway set for

each user at a given moment. This processing step re-

moves unnecessary data to be consumed in the data

analysis, also improving its performance. In this real

case, this step reduced the raw data to 56,049 data

points, 12.3% of the raw data entries, adding a delay

of 1.5 minutes, which is not an issue for our study. In

the next subsections we show a visual comparison of

the raw and processed data.

The tool is being developed using the Python

script language (version 3.7.2) (Van Rossum and

Drake, 2011) with the Plotly (Version: 4.6.0) and

Dash (Version: 1.16.3) packages. The tool provides

multiple visualizations with ﬁlters that can be applied

to many charts at the same time. It is an interactive

tool that allows users to obtain more accurate infor-

mation from a data point on the hover, and to zoom

in/out for more/less accuracy (see Figure 1 for refer-

ence). The data is load in CSV ﬁle format.

Users of the BLEVis Tool can select the following

options in the left-hand side of the dashboard:

• The database to be loaded for visualizations;

• Speciﬁc types of information, such as length of

stay, trajectory, and occupation. This selection

will only present types of charts speciﬁc to the se-

lected item.

• Filters for location, person, and time. These ﬁlters

modify all charts at the same time. The user can

select which places, which persons, and which

time range should be considered in visualizations.

In the next subsections, for each design goal we detail

the proposed visual solutions and how the research

questions presented in section 3 can be answered.

Some of those visualizations are still to be integrated

with the dynamic visualization tool.

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234

Figure 1: Visual exploration tool interface.

4.1 G1: Length of Stay Analysis

Our ﬁrst design goal is to support the visual analysis

of information related to the length of stay, to answer

the research questions RQ1 and RQ4 presented in the

section 3.

To answer RQ1 (How long does a speciﬁc indi-

vidual remain in each place?), we propose a Time

versus Place interactive chart, where each Place

is a Gateway. The name of each gateway represents a

location in the chart. The time has a granularity of 30

seconds. Each line color named as Person represents

one individual. It is possible to select each person to

show in the chart. For instance, in Figure 2b, the in-

dividual represented by U6 (red line), moved among

P01, P02, and P03 most of the time, and eventually

went to P06 and P04. If we zoom in a certain pe-

riod, it is possible to precisely get how long they re-

mained in each place. Besides, when hovering over

the cells, we can see the details of the selected data

point: name, place, and time. Figures 2a and 2b show

the same individuals within a certain period, compar-

ing raw (a) and processed (b) data in the server. The

charts also help designers adjust the sensors to obtain

better place coverage and less interference.

As the tool allows ﬁltering the individuals, the an-

alyst can select two individuals to estimate how long

(a) Raw data

(b) Preprocessed data

Figure 2: Individual’s trajectories over time versus places:

(a) using the raw data; (b) using the preprocessed data.

they remained in the same place. And this same vi-

sualization allows answering RQ4 (How long do two

individuals stay at the same place?).

This kind of visualization enables the extraction

of unexpected movement, outliers, and groups of in-

dividuals with similar trajectories over time. In the

context of this analysis, weekends, holidays, vacation

periods, and speciﬁc special days may be relevant and

helpful to understand patterns and anomalies.

Visual Analysis Tool for a BLE Technology based Tracking Data

235

(a) Raw data

(b) Preprocessed data

Figure 3: Flows between places.

Chord diagrams (Holten, 2006) represent the tran-

sitions between places and individuals. Loop lines

show whether individuals remain in a place. Having a

perception of the movement behavior between places

and total remaining time in the same place is easier in

chord diagrams. In Figure 3, each origin place is rep-

resented by one color, which identiﬁes the direction

of the movement. This ﬁgure considers all individ-

ual’s movements, and it does not identify each single

individual, but that is possible by ﬁlters.

Figures 3a and 3b show chord diagrams using the

raw and processed data, respectively. Some relations

disappeared in the second chord diagram (Figure 3b)

when compared with the ﬁrst chord diagram (Fig-

ure 3a). Gateway interference presented in raw data

were false movements represented as relations in Fig-

ure 3a. Since this paper does not analyze the indi-

vidual’s paths during the transition between places,

we consider that each user is in a place if they re-

main at least three consecutive acquisition times. The

reason for that is to eliminate false positives in the

pre-processing analysis. As in Figures 2a and 2b,

these chord diagrams reinforce the importance of pre-

processing data and removing false movements be-

tween places.

(a) All Places

(b) P03 Place

Figure 4: Heatmap: Shows the amount of time that each

pair of individuals are at the same place during a given in-

terval of time.

A heatmap represents a matrix, a relation between

two categorical variables. The color gradient can ex-

press the intensity of a third variable. It is illustrated

in Figure 4 and can also answer RQ4 (How long do

two individuals stay at the same place?). This chart

indicates the relation between individuals by showing

the amount of time (in minutes) that each pair of indi-

viduals stays at the same place during a given period.

We can observe any patterns in the change of color in-

tensity of cells across each axis. Besides, when hover-

ing the cells, we can see the frequency in which each

pair of users were together. The proposed heatmap

can help select users and evaluate their paths through

trajectory visualizations, as shown in Figure 2 (Indi-

vidual’s trajectories over time versus place).

4.2 G2: Trajectory Analysis

Our second design goal is related to getting informa-

tion regarding trajectory analysis and answering RQ2

and RQ5.

For RQ2 (What is an individual’s indoor trajec-

tory within a certain period?), we also proposed an in-

teractive chart of Time versus Place (see Figure 2)

and another interactive chart of Time versus Place

per day per person. Each line represents individ-

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Figure 5: Process view of individuals’ movement, using Fluxion Disco.

Figure 6: Animation view of individuals’ movements, using Fluxion Disco(c).

Figure 7: Place statistics.

uals’ trajectories through places and the time spent

at each one. Each graph of the Time versus Place

per day per person chart represents each day of a

person in one different color. The daily charts sup-

port the visualization of path patterns and behavior

identiﬁcation between days and persons. As previ-

ously mentioned, the data on a certain person can be

selected from the chart according to the period of in-

terest. From one to all persons can be selected.

We may also analyze individuals’ movements

through speciﬁc process mining visualizations, like

the ones provided on the Disco process mining com-

mercial software

. This tool allows analyzing the

performance metrics directly and intuitively and an-

imates the movement history on the model. We con-

verted the data into an event log, where individuals’

ids became cases, and places became the activities in

a process mining context (Van Der Aalst, 2011). Fig-

ure 6 shows the frequency of different individuals in

https://ﬂuxicon.com/disco – last visited in September

2020.

a speciﬁc path (from one place to another). Here, the

boxes and lines represent the places and the trajec-

tory from one place to another. A similar view with

absolute frequency or the maximum number of repe-

titions is also available, instead of frequency of cases.

Another useful Disco view is the animation, which

dynamically presents the history of user movement

within a speciﬁc period, and shows, for instance, an

evaluation of bottlenecks, illustrated in Figure 6. Fig-

ure 7 presents statistics on places, which show that

29% of individuals appeared in G02 and presented a

mean duration of two hours for ﬁve days and 18 hours.

Figure 8 shows two different snapshots of an anima-

tion that represents individuals’ movements through

places conﬁgured with gateways in a dynamic visual-

ization. Each colored point identiﬁes one individual.

Each place is illustrated in the ﬁgure by a gateway

number. For instance, place 01 is G01. This visualiza-

tion enables the analysis of dynamics of individuals’

movements in an enterprise within a certain period or

in real-time. This dynamic visualization was inspired

Visual Analysis Tool for a BLE Technology based Tracking Data

237

(a) Timestamp 1

(b) Timestamp 2

Figure 8: Images of an animation of individ-

uals’ movements through places at two differ-

ent time stamps. Figure based on code from

https://ﬂowingdata.com/projects/2015/timeuse-simulation/.

by one at Flowingdata.

We propose some visualizations and animations to

address RQ5 (What is the relation between the places

in individuals’ trajectories?). The ﬁrst one is the use

of a directed graph visualization of individuals’ move-

ments. It shows relations among places, directions,

and frequency of individuals’ movements between

places in a certain period. However, it does not ev-

idence how long each person remains in a place. Fig-

ure 9 illustrates this idea with the raw and processed

data. The second one is chord diagram (Figure3)

which provides the ﬂows between places and also evi-

dences the time remained in each place. However, the

exact number of interactions or movements between

places is not as straightforward as in directed graphs.

https://ﬂowingdata.com/projects/2015/timeuse-

simulation - last visited on December 2020.

(a) Raw data

(b) Preprocessed data

Figure 9: Directed graph of place transitions during a period

of time.

4.3 G3: Occupancy Analysis

Our third goal is to support the visual of occupancy

analysis. To reach this goal and answer RQ3 (How

many individuals are at each place at a given times-

tamp?), we selected a dynamic heatmap chart, as il-

lustrated in Figure 10. This chart shows the number of

people in each location over time. The time granular-

ity is 30 seconds. The period selection directly in the

heatmap is also available. One can use this informa-

tion to manage and optimize the usage of places and to

help with the automation of environmental conditions

in ofﬁces, such as controlling lighting and air condi-

tioning systems. In the epidemic situation that we are

facing, we could use this system to monitor each place

occupancy to keep social distancing requirements, be-

ing aware of the maximum number of individuals in

each place. This chart is interactive: which places to

see and the time range selection are available.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

238

Figure 10: Occupancy of individuals in places over time.

5 CONCLUSIONS

We proposed a visual tool prototype and demon-

strated it with some charts and animations to visualize

a real-case scenario of an indoor movement dataset

generated in two university buildings used by faculty,

staff, and students. In this set, we explored the data

visually to answer some questions about individuals’

movements. We visualized the relations between in-

dividuals and places and the inter-relations with in-

dividuals × individuals and places × places. The

contribution is how we adapted the charts and ani-

mations to answer questions in this context and to

enhance analysts’ experience by reducing the time

spent on the analysis. Our future work aimed to in-

corporate more chart integration, ﬁlters, and function-

alities such as event thresholds in the dash tool to

(a) to facilitate environmental automation or monitor-

ing for social-distance control for COVID-19, to ad-

just the air-conditioning temperature, and to estimate

resources available; (b) to identify outliers and predict

movement online for indoor environments; and (c) to

make it more usable for human operators to derive in-

sights about the indoor movements. Finally, we plan

to test the tool by using another dataset and to conduct

an empirical study to evaluate its efﬁciency and ease

of use.

ACKNOWLEDGEMENTS

This work was partially supported by CNPq - Con-

selho Nacional de Desenvolvimento Cient

ıﬁco e Tec-

nol

ogico.

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