# CrowdWeb: A Visualization Tool for Mobility Patterns in Smart Cities

Yisheng Alison Zheng, Abdallah Lakhdari, Amani Abusafia, Shing Tai Tony Lui
and Athman Bouguettaya  School of Computer Science
The University of Sydney, Australia
{yzhe5242, abdallah.lakhdari, amani.abusafia, slui2950,
<EMAIL_ADDRESS>
###### Abstract

Human mobility patterns refer to the regularities and trends in the way people
move, travel, or navigate through different geographical locations over time.
Detecting human mobility patterns is essential for a variety of applications,
including smart cities, transportation management, and disaster response. The
accuracy of current mobility prediction models is less than 25%. The low
accuracy is mainly due to the fluid nature of human movement. Typically,
humans do not adhere to rigid patterns in their daily activities, making it
difficult to identify hidden regularities in their data. To address this
issue, we proposed a web platform to visualize human mobility patterns by
abstracting the locations into a set of places to detect more realistic
patterns. However, the platform was initially designed to detect individual
mobility patterns, making it unsuitable for representing the crowd in a smart
city scale. Therefore, we extend the platform to visualize the mobility of
multiple users from a city-scale perspective. Our platform allows users to
visualize a graph of visited places based on their historical records using a
modified PrefixSpan approach. Additionally, the platform synchronizes,
aggregates, and displays crowd mobility patterns across various time intervals
within a smart city. We showcase our platform using a real dataset.

###### Index Terms:

Human Mobility, Mobility Pattern, Crowd Mobility, Social Networks, Flexible
Pattern

Human Mobility Patterns are the series of places frequently visited by an
individual [1]. Detecting these mobility patterns is crucial in various
domains such as pandemic prevention [2], urban planning [3], crowd management
[4, 5], and location-based services [6, 7]. Several studies have demonstrated
that human mobility is highly predictable due to the regularity of daily
routines [1, 8]. The acquisition of human mobility patterns involves examining
spatio-temporal attributes and uncovering potential regularities in individual
and population movement trajectories [2, 9]. Several models have been proposed
to represent and predict human mobility patterns [10]. The availability of
location-based data through social networks offers a unique opportunity to
comprehensively investigate these patterns from both quantitative and detailed
perspectives [2].

The detection of individual mobility patterns necessitates analyzing the
historical data of the places visited by individuals. Deep learning approaches
have been suggested for forecasting a user’s next point of interest [11, 10];
however, these methods have limited accuracy, ranging from 8% to 25%.
Detecting an exact mobility pattern for a user is challenging because of the
inherent flexibility of human movement [1, 2]. For example, a user who
regularly eats Thai food for lunch between 12:00 and 13:00 may visit a
different Thai eatery each day, e.g., Thai Express on the first day, Seasoning
Thai on the second day, and Thai Pothong on the third day. Despite the user’s
consistent dining habits, it is difficult to recognize this pattern due to the
varying locations of the restaurants. Consequently, we proposed a platform for
visualizing the mobility patterns of labeled locations to more accurately
define users’ mobility patterns [12].The platform displays a set of frequent
mobility patterns by using a modified PrefixSpan algorithm [13]. However, the
platform was designed to detect individual mobility patterns, making it
unsuitable for representing a group of users (i.e., a crowd) on a smart-city
scale.

Detecting crowd mobility in a smart city is crucial in applications such as
crowd management [4, 14, 15], IoT services [16, 17] and pandemic control [2].
Detecting crowd mobility is challenging because people have different spatio-
temporal patterns [12, 18]. In this paper, we extend the aforementioned
platform to compute and visualize the patterns of a crowd in a smart city over
various time periods (See Fig.1). The platform utilizes users’ records after
labeling the locations and their computed mobility patterns to compute the
crowd mobility patterns and distribution (See Fig.3). The mobility patterns of
each user are detected using a modified PrefixSpan algorithm [13]. Our
platform synchronizes the mobility patterns of crowds by identifying a map of
their visited places (see Fig.3. Moreover, we align the crowd’s patterns using
different time windows. Our platform facilitates the analysis and
comprehension of a crowd’s movement within a city.

##  I Crowd Mobility Patterns Detection Framework

The crowd mobility patterns detection framework aims to synchronize,
aggregate, and display crowd distributions and mobility patterns across
various time intervals within a smart city. The framework comprises three
phases: data acquisition and pre-processing, individual mobility patterns
detection, and crowd distribution and mobility patterns synchronization and
aggregation (See Fig. 2). In what follows, we discuss each phase in details:

Figure 1: System overview

###  I-1 Data Acquisition and Pre-processing

During this phase, users’ information and daily visited places data are
acquired from a geo-location dataset. In this demonstration, we used a public
Foursquare dataset as our default dataset [12]. The Foursquare dataset is a
geo-tagged social media (GTSM) dataset, where users check in at the venues
they visit. We used the New York dataset, which comprises 227,428 check-in
records. The dataset was collected over an 11-month period (April 2012 to
February 2013). As the GTSM dataset is collected by allowing users to check in
voluntarily, it is possible that users are not checking in regularly. This
leads to a sparse dataset. To confirm this, the average number of records for
each user is examined. The average is approximately 210, and the median is
153. As there are approximately 330 days in the data collection period, there
would be less than one record per day. Hence, the dataset is sparse. In
addition, there are 1083 users in the dataset. Therefore, to address the data
sparsity, we aimed to extract data from months with rich check-in records.
After investigating, we found that the best month is the period of time from
April to June. Therefore, for the experiment, we employed data from these
three months. Moreover, we discovered that users were not recording their
movement patterns on a daily basis. However, in order to extract a descriptive
human mobility pattern for the users, we would need to ensure the user records
are rich. Hence, we selected users with less than 2 hours check-in records for
more than 50 days within the 3-month period.

###  I-2 Individual Mobility Patterns Detection

As presented in [12], a modified PrefixSpan algorithm is used to detect the
mobility patterns of each user [13].

###  I-3 Crowd Mobility Patterns Synchronization and Aggregation

In this step, we synchronize and aggregate all the users’ mobility patterns
based on time. Users who frequently visit a specific location at a particular
time are categorized together as a group (See Fig.3). For example, any user
with a pattern of visiting a certain microcell (e.g., shops) at a certain
selected time (e.g., 8:00 am) will appear in the smart city at the selected
time (See Fig.3). Moreover, if we change the time, the crowd locations may
change to other microcells, depending on their patterns (See Fig.4). Our
platform uses the aggregated patterns and distributions to visualize the crowd
movement in a smart city.

Figure 2: Crowd Mobility Patterns Detection Framework Figure 3: The crowd in a
smart city from 9-10 am

##  II Demo Setup

Our interactive web application demonstrates crowd mobility in a smart city.
We will also present a recorded video that presents the entire process of
using the platform to display and interact with the default users’ patterns in
real-time. The video can be found at this link: . Booth visitors can select
from a list of available users to visualize their networks and mobility
patterns. Additionally, they can choose the city visualization and observe
crowd movements across various time frames. If any audience member is willing
to share their check-in history, we can upload it to the platform and
visualize their patterns.

Figure 4: The crowd in a smart city from 9-10 am

##  III Mobility Patterns Exploration

To investigate the detected mobility patterns using the Foursquare dataset, we
evaluated the effect of the Minimum Support Threshold $min\\_support$ on the
patterns detected by the modified PrefixSpan [12]. Firstly, we examined the
effect of changing $min\\_support$ on the number of sequences extracted per
user. Secondly, we assessed the effect of changing $min\\_support$ on the
average length of the sequences extracted per user.

The first experiment examined the effects of changing $min\\_support$ on the
number of sequences extracted per user. Figure 5 illustrates the correlation
between the number of sequences per user and minimum support threshold. In
general, the number of sequences per user decreased as the minimum support
threshold increased. This trend occurs because a higher minimum support
threshold value makes it more difficult for a pattern to be recognized as a
sequential pattern. It is also notable that, as the minimum support threshold
increases from 0.25 to 0.5, there is a significant decrease in the number of
sequences per user. Conversely, when the minimum support threshold rises from
0.5 to 0.75, the decline in the number of sequences per user is less
pronounced. To ascertain the previous evaluation, we present the distribution
of the number of sequences with $min\\_support$ = 0.5 in figure 8.

Figure 5: Average number of sequences per user vs. minimum support threshold
Figure 6: Distribution plot of the number of sequences with $min\\_support$ =
0.5

The second experiment examined the impact of modifying $min\\_support$ on the
average length of the sequences extracted per user. Figure 7 illustrates the
relationship between the average length of sequences per user and the minimum
support threshold. Generally, the average length of sequences per user
decreases as the support threshold rises. Moreover, when the minimum support
threshold increased, the likelihood of a longer pattern being recognized as a
sequential pattern was considerably lower than that of a shorter pattern. For
instance, it is reasonable to expect that the pattern ’Eatery’ would appear
more frequently than the pattern ’Eatery, Shops’ in a sequence database,
leading to a higher probability of ’Eatery’ being certified as a sequential
pattern compared to ’Eatery, Shops’. To ascertain the previous evaluation, we
present the distribution of the average length across the levels with
$min\\_support$ = 0.5 in figure 6.

Figure 7: Average length of sequences per user vs. minimum support threshold
Figure 8: Distribution plot of average length with $min\\_support$ = 0.5

##  IV Conclusion

This paper presents a web platform that visualizes human mobility patterns at
both the individual and city-scale levels, making it more suitable for
applications within a smart city scale.In addition to individual mobility
patterns, the platform enables the visualization of multiple users’ mobility
patterns, offering a city-scale perspective. In the future, we plan to allow
users to scale the time frames for the crowd movement and automate the crowd
movement animation.

## Acknowledgment

This research was partly made possible by LE220100078 and DP220101823 grants
from the Australian Research Council. The statements made herein are solely
the responsibility of the authors.

## References

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# Incremental Cost-Effectiveness Statistical Inference: Calculations and
Communications.

Robert L. Obenchain
Risk Benefit Statistics, Clayton, CA 94517

(December 2023)

###### Abstract

We illustrate use of nonparametric statistical methods to compare alternative
treatments for a particular disease or condition on both their relative
effectiveness and their relative cost. These Incremental Cost Effectiveness
(ICE) methods are based upon Bootstrapping (i.e. Sampling With-Replacement)
using observational or clinical-trial data on individual patients. We first
show how a reasonable numerical value for $\lambda$, the Shadow Price of
Health, can be chosen using functions within the ICEinfer R-package when
effectiveness is not measured in “QALY”s. We also argue that simple histograms
are ideal for communicating key ICE findings to regulators, while our more
detailed graphics may well be more informative and compelling for some health-
care stakeholders.

##  1 Introduction

We outline and update statistical methodology needed by Health-Outcomes
researchers to make powerful and robust inferences using either clinical trial
or real-world (observational) data to make realistic Head-to-Head comparisons
of cost and effectiveness differences between a pair of alternative treatments
for a given disease or health-care condition. We also review and illustrate
use of Incremental Cost-Effectiveness “ICE Preference Maps” that can be
distinctly non-linear to incorporate realistic consumer preferences from
empirical health care research.

###  1.1 The Cost-Effectiveness Plane

As proposed in Black (1990), the Cost-Effectiveness plane displays measures of
treatment “effectiveness” along its horizontal $x-$axis and measures of
treatment “cost” along its vertical $y-$axis. This display is “Incremental” in
the sense that differences in treatment outcomes : [“New” treatment minus
“Standard” treatment] are plotted. In other words, the “ICE Origin” at $(0,0)$
represents the relative position of the current Standard treatment. The New
treatment is then relatively More Effective at points with $x-$values that are
strictly positive and Less Costly at points with $y-$values that are strictly
negative; see Figure $1$ on page $3$.

Figure 1:  This graphic from Obenchain (2008) illustrates some important
characteristics of coherent ICE preferences on the (Euclidean) Cost-
Effectiveness plane of Black (1990). In particular, we note that preferences
are symmetric relative to the upper-left to lower-right diagonal line, $x=-y$.
This symmetry was first depicted graphically in Laupacis et al.(1992) and is
dictated by the Fourth Axiom of Table $1$.

Four key axiomatic properties of ICE preference maps are listed below in Table
$1$. When first examining this table, it may be helpful to note that the
linear “Net Benefit” preference map, $NB(x,y)=x-y$ of Stinnett and Mullahy
(1998) satisfies these axioms. Subsections $\S 3.1$ through $\S 3.5$ of
Obenchain (2008) provide highly detailed motivations for all four of these
axioms.

TABLE 1 – Four Axioms of Coherent ICE Preferences
---
Indifference and |  $P(x,y)=0$ when $x=y$,
Direction of Preference |  $P(x,y)>0$ when $x>y$, and $P(x,y)<0$ when $x<y$.
Monotonicity |  $P(x,y)\geq P(x_{o},y_{o})$ for all $x\geq x_{o}$ and $y\leq y_{o}$.
Re-labeling |  $P(x,y)=-P(-x,-y)$.
Symmetry and Anti-symmetry |  $P(x,y)=P(-y,-x)=-P(y,x)$.

###  1.2 ICE Statistical Inference: Basics

In Incremental Cost-Effectiveness inferences, Costs are always expressed in
monetary units while Effectiveness is measured in units considered fully
appropriate for the condition or disease being treated. The ICEscale()
function within the ICEinfer R-package, Obenchain (2007-2020), facilitates
choice of a numerical value for $\lambda$ by calculating a purely
“statistical” value. This value is the ratio of the Standard Deviations of the
observed Cost and Effectiveness Differences. In the numerical example
introduced below in $\S 1.3$, this ratio is $4.556/0.21309=21.381$.

The $\lambda-$values that I personally recommend for general use are Integer
Powers of 10. In these cases, only the location of the decimal point on axis
tick-mark labels change due to choice of $\lambda$. Thus the shadow price
actually used in our numerical example will be simply $10$ …the Integer Power
of 10 closest to $21.381$.

When cost-measurements are re-expressed using a different currency and/or
alternative effe-measures are reported, the ICEscale() function typically
proposes using a different “Integer Power of 10” shadow price. Plots generated
by functions within the ICEinfer R-package may also adjust the aspect ratio of
a plot to make it appear more nearly “Square”.

Some regulatory authorities and “Health Outcomes” researchers prefer
expressing treatment Effectiveness in Quality Adjusted Life Years (QALYs)
preserved; see: Weinstein, Torrance and McGuire (2009) or Neumann, Cohen and
Weinstein (2014). Unfortunately, re-expressing effectiveness estimates in
QALYs can be both difficult and highly subjective …especially when the
condition being treated is not life-threatening.

###  1.3 ICE Preferences

The ICE Preference Maps (functions) introduced in Obenchain (2008) are of the
general form:

$P(x,y)\propto(x^{2}+y^{2})^{(\beta-\gamma)/2}[x-y]^{\gamma},$ (1)

where $\propto$ means “is proportional to”, $\beta$ and $\gamma$ are strictly
positive “power” parameters, and the special notation $[z]^{c}$ denotes a
“signed-power.” Specifically, $[z]^{c}$ denotes the product of sign(z), which
is +1, 0 or -1, times the absolute value of z raised to the power c. Special
care is taken in expressing equation (1) because non-integer powers of
negative real numbers are generally imaginary. ICE preferences need to be
expressed as real values even though they may provide only ordinal measures of
preference strength.

It is straight-forward to verify that all ICE maps of form (1) satisfy axioms
1, 3 and 4 of Table 1. The linear preference map, $NB(x,y)=x-y$ of Stinnett
and Mullahy (1998) is the special case where $\beta=\gamma=1$.

To satisfy axiom 2, the following restriction on the ratio of the $\gamma$ and
$\beta$ power parameters is required:

$0.1715729...=1/\Omega\leq{\gamma}/\beta\leq\Omega=(1+\sqrt{2})^{2}=5.828427...$
(2)

For a given numerical value of $\lambda$, the difference in Effectiveness
between treatments, $\Delta E$, can be re-expressed in cost units via
multiplication: $\lambda\times\Delta E$. Similarly, a difference in Cost
between treatments, $\Delta C$, would be converted into Effectiveness units by
division: $\Delta C/\lambda$.

###  1.4 Returns-to-Scale

Suppose now that the observed treatment differences in cost, $y$, and
effectiveness, $x$, are somehow both multiplied by a strictly positive and
finite real valued factor, $f$. In other words, the observed effectiveness
difference of $x$ becomes $f*x$, while the observed cost difference of $y$
becomes $f*y$. The resulting new value of preference in Equation $1$ is then:

$P(f*x,f*y)\propto
f^{(\beta-\gamma)+\gamma}*(x^{2}+y^{2})^{(\beta-\gamma)/2}*[x-y]^{\gamma}\propto
f^{\beta}*P(x,y).$ (3)

In other words, for every map in our 2-parameter family, returns-to-scale
depend solely upon the $\beta$ power parameter associated with only the ICE
radius factor. Specifically, returns-to-scale will be:

Decreasing when $0<\beta<1$;

Constant [linear] when $\beta=1$; or

Increasing when $\beta>1$ and finite.

###  1.5 A Numerical Example using simulated Effectiveness and Cost Data

Table [$2$] provides Summary Statistics for the patient-level data that we
will analyze to illustrate basic concepts.

TABLE 2 – Summary Statistics for the Effe-Cost Example
---
Variable | Min. | 1st.Qt | Median | Mean | 3rd.Qt | Max.
99 Std. Treatment Patients |  |  |  |  |  |
effe | 0.076 | 2.436 | 3.615 | 3.653 | 4.624 | 8.373
cost | 0.288 | 53.857 | 78.073 | 76.497 | 98.436 | 172.0
101 New Treatment Patients |  |  |  |  |  |
effe | 0.816 | 3.210 | 3.890 | 4.000 | 5.079 | 8.373
cost | 11.99 | 48.33 | 63.39 | 68.82 | 90.68 | 150.81
Figure 2:  Simulated data on $200$ patients illustrate the case where the
Shadow Price of Health is taken to be $\lambda=10$. The dataset contains $99$
patients (colored Red) who received the “Standard Treatment” and $101$
patients (colored Blue) who received the “New Treatment.” The solid Red and
Blue points correspond to the two sample Means of $x=Effe$ and $y=Cost$
coordinates. While these two mean-values suggest that “New” could be both less
costly and more effective than the “Std” treatment, our objective will be to
estimate the statistical confidence that is associated with this possibility.

###  1.6 Wedge Shaped ICE Confidence Regions

The ICE Statistical Inferences of interest concern Two Distinct Dimensions:
Cost and Effectiveness. Furthermore, two (or more) treatments are to be
compared within both of these dimensions; see Laupacis, Feeny, Detsky and
Tugwell (1992). Early proponents of using polar coordinates in ICE
calculations were Obenchain (1997) as well as Cook and Heyse (2000). To this
day, most “Health Economists” talk only about Ratios and Cartesian
(rectangular) coordinates.

The first “Wedge Shaped” Confidence Regions were strictly parametric regions
that assumed observed data followed a Bivariate Normal distribution and used
approximations to Fieller’s Theorem (1954). The accuracy of this approach was
greatly improved by Chaudhary and Stearns (1996), and it was subsequently
found that these regions are frequently highly similar to those produced by
Bootstrapping.

Typical analyses examine treatment differences of the form $(New-Std)$, which
can be viewed as placing the $Std-$treatment at the “ICE Origin”, $(0,0)$,
shown as the Solid Red Dot in each panel of Figure $3$. The $(New-Std)$
difference is then represented by the Solid Blue points in each panel of
Figure $3$. Several properties of the “Wedge Shaped” (Bootstrap) ICE
Confidence Regions are pointed out in the detailed captions of Figures $3$ and
$4$.

Figure 3:  Although the two alternative ICE perspectives (alias and alibi)
depicted here typically scale the Effe and Cost axes differently, these
perspectives still have much in common. Note that the Solid Red point in
Figure $2$ appears at the ICE origin in each of these plots, while the Solid
Blue “New” treatment means appear near the numerical Centroid of each
Bootstrap scatter. Close examination of the points near the edges of these
somewhat “elliptical” scatters confirms that both depict the same bootstrap
scatter containing $R=25000$ replications. Since $688$ ICE Angle order-
statistics (Black Dots) are (clockwise) below the “lower limit” of the Central
Wedge and $562$ are (counter-clockwise) above the Wedge “upper limit”, each
wedge contains $25000-688-562=23750$ replications. The Confidence Level of
both of these wedges is thus $95\%$. Figure 4:  The $95\%$ ICE Alias
Confidence Wedge of Figure $3$ is “Re-Colored” here using the ICE Omega
Preference Map that is as “nonlinear” as is possible and yet still satisfy
Axiom Two: Monotonicity. The ICE Alias perspective in this (large) plot is
identical to that of the left-hand sub-plot in Figure $3$. Note that there are
many more numerically small preferences (colored Yellow) within this $95\%$
Confidence Wedge than within the same wedge on Figure $3$.

###  1.7 The Cost-Effectiveness Frontier

Our example a “Cost-Effectiveness Frontier”, depicted below in Figure $5$, is
based upon one published by the Health Economics Resource Center, U.S.
Veterans Affairs.gov. I have added a point labeled “WW” with effe $=0$ and
cost $=0$ that represents a hypothetical treatment strategy called Watchful
Waiting.

Figure 5:  Treatment option A on this graphic is (strictly) dominated by B,
which is both more effective and less costly than A. Next, note that while
treatment C is more effective than B and less costly than D, treatment C is
strictly above the straight line segment connecting treatment options B and D.
Thus, treatment C is said to be Extendedly Dominated by some mixture of
treatment options B and D. The $5$ treatment options actually on the “Cost-
Effectiveness Frontier” are thus: WW, STD, B, D and E.

If data on measures of Effectiveness and Cost are available on patients
receiving treatments B, C or D, functions within the ICEinfer R-package could
be used to compare outcomes on C with any “Mixture” of patients receiving
treatments B or D. Many such “Mixtures” may well prove to be more Cost-
Effective than treatment with option C.

##  2 Recommendations

The right-hand sides of Figures $6$ and $7$ on pages $12$ and $13$ illustrate
the ICE Statistical Inference perspectives championed here. Numerous consumer
research studies have reported that preferences typically are distinctly
nonlinear …as depicted in the right-hand “map” of Figure $6$. When
communicating study findings, we recommend following the “KISS Principle” of
Zellner (1991, 2001): Keep It Sophisticatedly Simple!

Figure 6:  When $\lambda=1$ and $\beta=1$ (linear returns), note that there
are numerous clear differences between the ICE Preference Maps for traditional
(linear)“Net Benefit” ($\gamma=\eta=1$) on the Left and the (highly nonlinear)
“ICE Omega” map on the Right ($\gamma=\eta=5.828$). Note that both Maps are
colored Yellow towards Green (numerically positive values) below their lower-
left to upper-right diagonal, and Tan towards Red (numerically negative
values) above that same diagonal. In fact, note also that the “Net Benefit”
map is essentially “One - Dimensional” (i.e. color depends only on the scalar
Effe-Cost difference) while the nonlinear map is fully “Two - Dimensional”.

Both histograms illustrated in Figure $7$ are highly favorable to use of the
“New” treatment over the “Std” …i.e. both contain only strictly positive
estimates of preference for “New”. While the left-hand histogram suggests
stronger (more positive) preferences, this less-sophisticated (linear)
analysis could easily be “badly biased”. While I recommend presenting both
histograms to all interested parties, the more-sophisticated (nonlinear)
analysis is both more conservative and potentially more realistic. In fact,
this sort of non-linearity strikes me as being more likely to be confirmed by
evidence from actual practice.

Figure 7:  These two simple Histograms illustrate both similarities and
differences between the potentially over-simplistic “Net Benefit” evaluation
on the Left and the potentially more realistic (and distinctly nonlinear) ICE
Omega Preference Map (shown in Figure $4$) on the Right. The pure simplicity
of these graphics may make them ideal for convincing Health-Care
administrators and regulators that the “New” Treatment delivers outcomes with
superior Cost-Effectiveness relative to the “Std” Treatment. Since the Right-
Hand Histogram quantifies Non-Linear preferences, it probably portrays a
slightly more realistic (less optimistic) evaluation.

##  3 Summary

This paper has outlined and updated the statistical methodology needed by
Health-Outcomes researchers to make powerful and robust inferences using
either clinical or real-world (observational) data to make realistic Head-to-
Head comparisons of cost and effectiveness differences between a pair of
alternative treatments for a given disease or health-care condition. The Cost-
Effectiveness “ICE Preference Map” used in this approach can be distinctly
non-linear to incorporate truly realistic consumer preferences from empirical
health care research.

Over the last 50 years, computers have helped shape statistical theory as well
as its practice. Freely available software can provide computational and
visual fast-tracks into the strengths and weaknesses of alternative
statistical methods. Why not analyze hundreds or thousands of numbers using
millions or billions of calculations, Efron (1979). In reality, R functions,
R-Core Team (2023), are almost indispensable tools for today’s students,
teachers, applied researchers and data-scientists who use and/or extend
existing computational and visualization methodologies.

## References

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