Interactive Exploration of Complex Heterogeneous Data:

A Use Case on Understanding City Economics

Rainer Splechtna

, Thomas Hulka

, Disha Sardana

Nikitha Donekal Chandrashekar

, Denis Gra

canin

2 a

and Kre

simir Matkovi

1 b

VRVis Research Center, Vienna, Austria

Virginia Tech, Blacksburg, Virginia, U.S.A.

Keywords:

Visualization, Visual Analytics, Complex-data Exploration, and Analysis.

Abstract:

The analysis of complex, heterogeneous data containing spatial and temporal components is a non-trivial task.

Besides data heterogeneity and data quantity, the exploratory nature of data analysis tasks, which are only

roughly speciﬁed when analysis starts and are reﬁned during the analysis, poses main challenges. In this paper,

we describe a holistic approach to interactive visual analysis of such data. We use the IEEE VAST Challenge

2022 data set for this purpose. To support the exploratory tasks dealing with the economic health of a city,

we apply different data processing, introduce new views, and employ complex interactions. All these steps

are necessary for an efﬁcient workﬂow. We rely on the well-known paradigm of coordinated multiple views.

In addition to the standard views, we introduce the interactive map view, which supports the visualization of

different statistical values on the map itself. All views are interactive and support multiple composite brushing.

Our results illustrate the effectiveness of our approach and show its applicability to similar data and tasks.

1 INTRODUCTION

The interactive visual analysis represents an impor-

tant counterpart to purely automatic data analysis, es-

pecially for exploratory tasks problems that are not

clearly speciﬁed. In such cases, a data analyst is in-

terested in understanding data, getting an overview

and insight about trends and patterns, and, eventu-

ally, getting an insight into data and underlying phe-

nomena or systems. Advancements in sensor, com-

puting, and storage technology result in ever-growing

data. A large variety of data collection methods leads,

at the same time, to data complexity. The resulting

data assembled from various sources is then often un-

structured, incomplete, or erroneous. An excellent

example of such data is the IEEE VAST Challenge

2022 data set (Crouser and Cook, 2022). It is a large

data set consisting of many ﬁles with a total size of

16.7 GBytes.

There are many possibilities for how to approach

such large and complex data. He et al. recently de-

scribed how hard it is for students to approach the

analysis of complex data such as VAST Challenge

data (He et al., 2022). Due to the exploratory nature of

https://orcid.org/0000-0001-6831-2818

https://orcid.org/0000-0001-9406-8943

the analysis tasks, an interactive solution coupled with

automatic analysis often represents the ﬁrst choice for

data analysis.

The IEEE VAST Challenge 2022 deals with the

ﬁctitious city of Engagement in Ohio. The analaysis

is divided into three challenges, each covering a dif-

ferent aspect. A large number of challenge entries and

the variety of the proposed solutions proves the rele-

vance of the problem and the need for novel solutions,

which advance the current state-of-the-art.

In this paper, we describe how we approach the

2022 IEEE VAST Challenge Three, Economic, which

deals with the ﬁnancial health of the city. Since the

tasks are of exploratory nature, we rely on an interac-

tive solution. We present our comprehensive solution

to the the problem of analysis of complex data sets,

including spatial and temporal components, among

other attributes, that describe citizens and businesses

in a city. We describe the whole workﬂow, including

data processing, which results in structured data sets,

visualization, and interaction design that goes beyond

standard views. We also provide a brief evaluation

and a description of our ﬁndings.

Although we do provide a novel map view, in

this paper, we are not primarily concerned with in-

troducing novel visualization techniques. Our focus

214

Splechtna, R., Hulka, T., Sardana, D., Chandrashekar, N., Gra

canin, D. and Matkovi

c, K.

Interactive Exploration of Complex Heterogeneous Data: A Use Case on Understanding City Economics.

DOI: 10.5220/0011787500003417

In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP, pages

214-221

ISBN: 978-989-758-634-7; ISSN: 2184-4321

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

is on leveraging the capabilities of coordinated multi-

ple views in a complex analysis setting. Our contri-

butions can be summarized as follows: (1) An inte-

grated visual analytics framework that supports inter-

active analysis of complex, unstructured data; and (2)

A novel map view that integrates descriptive statistics

visualization with the conventional map. Interaction

and on-the-ﬂy data aggregation are supported in order

to cope with data size and complexity.

2 RELATED WORK

“Visual analytics is the science of analytical

reasoning facilitated by interactive visual inter-

faces.” (Thomas and Cook, 2005). One of the main

challenges of visual analytics is dealing with a huge

amount of heterogeneous data since the analysts of-

ten fail to fully grasp the presented data due to the

cognitive overload (Thomas and Cook, 2005). There-

fore, it is critical that analysts can interactively ﬁlter,

visualize, and navigate data to reduce the cognitive

overload (Card et al., 1999).

Interactive exploration of data can be facilitated

by providing customized views and by simultane-

ously providing multiple perspective of the data set.

Coordinated Multiple Views (CMVs) provide such

multiple simultaneous perspectives of the data and al-

low integration of customized views (Roberts, 2007),

thus providing a deeper understanding of data. The

user can interactively select some of the data items in

one view (brushing), and all items that belong to the

same records will be highlighted in all other views

(linking) (Buja et al., 1991; Roberts, 2007). Brush-

ing and linking are two essential aspects of interacting

with high-dimensional data.

Visual analysis of spatio-temporal data, including

movement and mobility data (Andrienko et al., 2013;

Pelekis and Theodoridis, 2014), provides many op-

portunities for data-driven analysis, especially related

to the urban computing issues and the related big data

(e.g., human mobility and trafﬁc) challenges (Zheng

et al., 2014). Such data includes a large number of

time-series data that have geo-located attributes. Cus-

tomized views that are a part of a CMV system sup-

port simultaneous interactive visualization and analy-

sis of such time series ensembles.

Visual analytics is very important for urban com-

puting (Fortini and Davis, 2018) and urban informat-

ics, a discipline that integrates urban science, geo-

matics, and informatics (Shi et al., 2021). Urban vi-

sual analytics can be combined with automatic analyt-

ical approaches to support data exploration and visual

learning (Zheng et al., 2016). Clarinval and Dumas

provide a review of urban data visualization (Clarin-

val and Dumas, 2022).

3 DATA AND TASKS

The VAST Challenge simulates a long-term exper-

iment in the ﬁctitious city of Engagement, Ohio.

The idea is that the city conducts a participatory ur-

ban planning experiment. About a thousand citizens

agreed to provide data using a city’s planning app.

The app records the places they visit, their spending,

their purchases, and many other things. The city will

use the data to assist its city revitalization plans. Vi-

sual analytics is identiﬁed as a promising way of mak-

ing insights into collected data.

The data itself is divided into several ﬁles having

different structures. The following ﬁles are available:

• Journals. The journals include ﬁnancial, travel,

social, and check-in data that consists of individ-

ual events, such as banking account activity (ﬁ-

nancial) or visits to various places (check-in).

• Attributes. There are eight attributes ﬁles.

They describe, among others, schools (loca-

tion, capacity, . . . ), apartments (location, bed-

room count,. . . ), or citizens themselves (educa-

tion level, interest group, apartment ID, . . . ).

• Activity Logs. The activity logs record the status

of each participant with a time step of ﬁve min-

utes. For each time step and participating citizen,

we have position, bank balance, sleep or hunger

status, etc.

Along with the data, the analysis tasks are also de-

ﬁned. The main task is to consider the ﬁnancial health

of the city (Crouser and Cook, 2022). Analysis of this

data shall answer questions like which businesses are

growing or shrinking over time, how people change

jobs, or whether standards of living improve or de-

cline over time. Three speciﬁc tasks are deﬁned:

1. Over the period covered by the data set, which

businesses appear to be more prosperous? Which

appears to be struggling?

2. How does the ﬁnancial health of the residents

change over the period covered by the data set?

How do wages compare to the overall cost of liv-

ing in Engagement? Are there groups that appear

to exhibit similar patterns?

3. Describe the health of the various employers

within the city limits. What employment patterns

do you observe? Do you notice any areas of par-

ticularly high or low turnover?

Interactive Exploration of Complex Heterogeneous Data: A Use Case on Understanding City Economics

215

Figure 1: Data pre-processing for one of the business-centric data sets, the weekly pub visit seasonality. Similar steps are

needed to create other data sets as well.

Figure 2: The curve view showing the check-in counts for

each pub and for each week as a time series. Patterns are

different for weekdays and weekends.

Figure 3: The newly developed map view shows various

businesses as colored stars, and, for the selected businesses

(pubs here), distribution of additional statistics.

Notably, no metrics are deﬁned that strictly deﬁne

what it means that businesses are doing well. In or-

der to answer such questions, an interactive approach

with human in the loop is needed. It is in the course of

analysis that we reﬁne our understanding of the task

and how we can tackle it in view of the available data.

Depending on the task and, in particular, on the

subject of interest, we create a different data set that

allows us to interactively reﬁne our focus. We use

different data sets to analyze citizens and businesses.

Data processing was carried out by several F# and

Python scripts that read and write ﬂat ﬁles.

Most of the analysis methods require tabular data

where each row represents a record and each col-

umn an attribute. In addition to common table data,

where attributes are numeric or categorical values, we

also allow curves, i.e., a sequence of (x, y) pairs, to

be a record’s attribute, as described by Konyha et

al. (Konyha et al., 2006). In our example, we create a

citizen-centric data set, where each record represents

a citizen, a business-centric data set, where each row

corresponds to a business in the city; and several sub-

sets of the business data set, where we focus only on

a particular business type, e.g. pubs or restaurants.

The citizen-centric data set contains attributes

such as apartment location, household size, educa-

tion level, and interest group. In addition to these

scalar attributes, which are easily extracted from the

provided data ﬁles, we compute time series attributes,

such as spending for education, recreation, or account

balance. This special attribute has a sequence of pairs

of time and amount spent on education.

For the business-centric tasks, we create a data set

where each record corresponds to a business and pro-

vides scalar attributes (e.g., the number of guests), de-

scriptive statistics such as total, maximum, and aver-

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

216

Figure 4: A snapshot from an analysis session. The citizen-centric data is used, and scalar attributes are shown on the left, the

map view is in the center, and the time series attributes are shown on the right. Details for the brushed data are shown in the

data table at the bottom. The views can be freely conﬁgured. One possible conﬁguration is shown here.

age spending of customers, and time series attributes

that describe how income, number of visits, or other

values change over time. Figure 1 shows the structure

of the business-centric table.

Figure 2 shows the number of visitors that entered

pubs on a weekly basis. For each pub and for each

week during the time interval captured in the data, the

number of visitors in each hour is shown as a curve.

We can clearly see different patterns for weekends and

for weekdays. Interestingly, when we aggregated the

data to show aggregated numbers per weekday, we re-

alized that Monday had some irregularity. The guests

that came to a pub after midnight on Sunday nights

are technically Monday guests. For the analysis of

the weekly seasonality pattern, however, they should

be counted as Sunday guests. We, shifted seven bins

for each day of the week such that they align with the

citizen’s sleep and awake cycles so that a day refers

to the time from 3 AM until 3 AM of the next day.

From our experience, no matter how well-planned

the data processing is, the analysts often need some

additional data aggregates during the analysis. If sys-

tems and tools do not support on-the-ﬂy data aggre-

gation and derivation (Konyha et al., 2012), the anal-

ysis has to be stopped, data has to be processed again,

and the analysis continues. On-the-ﬂy data derivation

makes the workﬂow much more ﬂuent. We support

data derivation and aggregation so that the users can,

e.g., compute scalar curve aggregates or curve aggre-

gates (e.g., the ﬁrst derivative).

4 VISUAL MAPPING AND

INTERACTION DESIGN

Due to the exploratory nature of the analysis tasks,

we decided to rely on the well-known coordinated

multiple-views paradigm. The standard views, e.g.

histograms, parallel coordinates, or scatter plots, are

not sufﬁcient to analyze our data. The spatial and tem-

poral components require additional views.

As the data is provided in a context of a city, a

map view becomes our central view. We have de-

signed a new, freely conﬁgurable map view to sup-

port analysis tasks. The newly developed map view

loads coordinates of buildings, apartments, and busi-

nesses from several ﬁles and depicts the spatial con-

text. Each business type can be represented with its

own color and symbol. The user can conﬁgure vari-

ous parameters, such as what is shown, which colors

and glyph sizes are used, etc. In addition to the loaded

data, various aggregated statistic values for businesses

can be shown. We provide histograms associated with

the individual businesses as an overlay. Each his-

togram is additionally connected to its related busi-

ness in order to reduce mental load when associating

histograms with buildings. Figure 3 shows the map

view and its control. The histograms in Figure 3 show

selected statistics for each pub. The histograms show

the distribution of distances which customers travel

on their way towards a pub. Most of the customers

Interactive Exploration of Complex Heterogeneous Data: A Use Case on Understanding City Economics

217

do not travel far in order to reach the pub. There are

just a few pubs with customers from far away. Such

overview statistics help in identifying widely popular

pubs.

The map view is integrated into the coordinated

multiple views system alongside standard views. Fig-

ure 4 shows a snapshot from an analysis of a citizen-

centric data set. The map view is in the central posi-

tion and shows apartments where citizens live as gray

and orange dots, as well as schools, pubs, and restau-

rants as stars. The views on the left show the scalar at-

tribute available for each citizen, and the curve views

on the right show the time-dependent attributes. The

details for the brushed data are shown at the bottom.

All views are linked, and composite brushing is

supported. The user can interactively select a sub-

set of data in any view, and the corresponding items

in all views will be highlighted. Several brushes can

be combined using Boolean operations to form com-

posite brushes. Multiple composite brushes are sup-

ported to ease comparisons. Depending on the view

type, different brushing interactions are supported. In

a histogram, the user can select one or more bins. In

a scatter plot, a rectangular area can be selected. The

curve view supports the line brush, i.e., the user draws

a line, and all curves that cross the line are selected.

Finally, in the map view, the user can zoom and pan,

move the overlaid histograms, select what is shown,

deﬁne dots and glyph sizes, etc.

5 INTERACTIVE EXPLORATION

AND ANALYSIS

We have used the above-presented system to analyze

the Challenge data. As stated above, depending on the

tasks, we use either the citizen-centric or the business-

centric data set. In the following, we brieﬂy describe

some of the interesting ﬁndings for both data sets.

5.1 Business-centric Analysis

For the business-centric tasks described in Section 3

we utilize the data speciﬁcally tailored for these tasks.

Business Prosperity Analysis. It should answer

these questions: Which businesses appear to be more

prosperous? Which appears to be struggling?

For the ﬁrst step of the analysis, we look at an

overview of the provided data. In particular, we look

at the turnover (for pubs and restaurants) or wages

(for workplaces) and the number of customers (for

pubs and restaurants) or the number of employees (for

Figure 5: The ﬁrst derivative of the number of check-ins at

workplaces (top), restaurants (middle), and pubs (bottom).

Figure 6: The number of check-ins for workplaces, the top-

most curves (top) and the bottom-most curve (bottom).

workplaces). For workplaces, we infer the number of

employees from the number of check-ins. The stabil-

ity of a business is inferred from the ﬁrst derivatives

of the aforementioned numbers.

Figure 5-top shows the ﬁrst derivative of the num-

ber of check-ins at workplaces per week. The situ-

ation is rather stable after the ﬁrst couple of weeks,

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

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Figure 7: Top-most and bottom-most curves of turnover for

pubs (top). The check-in counts correlate with the selected

turnovers (bottom).

Figure 8: Top-most and bottom-most curves of turnover for

restaurants (top). The check-in counts correlate with the

selected turnovers (bottom).

Figure 9: Top: Using multiple brushes to limit the ﬁrst

derivative of check-ins at workplaces to zero within the

“stable” weeks. Bottom: The number of “stable” check-

ins/employees ranges from small to large businesses.

i.e., the change in the number of employees is rather

low. The variation in wages per workplace over time

is also quite low and correlates with the stable number

of employees. Figure 5-middle shows the ﬁrst deriva-

tive of the number of check-ins at restaurants. This

Figure 10: Using multiple brushes to limit the ﬁrst deriva-

tive of check-ins at workplaces to plus/minus three.

Figure 11: Using multiple brushes to ﬁnd workplaces that

have a large raise of the number of employees but no decline

using the ﬁrst derivative of check-ins at workplaces.

also shows a rather stable behavior after the ﬁrst cou-

ple of weeks. Figure 5-bottom shows the ﬁrst deriva-

tive of the number of check-ins at pubs. This shows

the most ﬂuctuation and can probably be attributed

to the nature of the business with special events like

bands playing at the pub or public viewing events.

All three derivatives show a common pattern of

disarray, mostly decline, in the ﬁrst three weeks of

the time span covered by the data before reaching the

rather stable behavior mentioned above. The decline

at the end of the observed time period stems from the

fact that we aggregate the data per calendar week, but

the last four days of the last week are not provided.

The prosperity of a business is inferred from the

number of employees (workplaces) or the turnover

(pubs and restaurants). We brush the top-most curves

of the number of check-ins to select prosperous work-

places (Figure 6-top). The data table, shown in Fig-

ure 4 at the bottom, can then be used to retrieve the

details for the brushed data, the top ﬁve workplaces

in this case.

Likewise, we brush the bottom-most curves of the

number of check-ins to select struggling/small work-

Interactive Exploration of Complex Heterogeneous Data: A Use Case on Understanding City Economics

219

Figure 12: The spending on food and the money balance

curves for all citizens. There are four main groups of food

spending. The money balance of citizens increases at dif-

ferent slopes.

places (Figure 6-bottom). This gives us three work-

places that went out of business during the tumultuous

ﬁrst weeks of the observed time period.

In a similar fashion, we brushed the top-most

turnovers for pubs (Figure 7) and restaurants (Fig-

ure 8) and bottom-most turnovers to determine pros-

perous (purple) and struggling (orange) businesses.

Business Health Analysis. It should answer this

question: What employment patterns do you observe?

To describe the health of a business, we opt for evalu-

ating the dynamics of the employment situation.

The ﬁrst pattern we observe is one of stability,

shown in Figure 9. When omitting the ﬁrst three

weeks and the last (due to data artifacts), we see that

at 162 of 251 ( 65%) workplaces, ranging from small

to large businesses, the number of employees does not

change at all.

We can reveal additional patterns for the number

of employees by changing the brush to include ever

higher values of the ﬁrst derivative of check-ins in

workplaces, i.e., the change in the number of employ-

ees. For 34 of 251 ( 14%) workplaces, the change

in the number of employees is minimal (plus/minus

one); for 19 of 251 ( 8%) workplaces, the change

in the number of employees is just plus/minus two

employees, and for 9 of 251 ( 3.5%) workplaces the

change in the number of employees is plus/minus

three employees (Figure 10).

We also detect two workplaces that raise their

number of employees and stay at this new level (Fig-

ure 11) and one workplace that goes bankrupt and one

that loses half of its employees but stays in business.

5.2 Citizen-centric Analysis

Food, Money, and Joviality: Let us now take a

closer look at two curve views from Figure 4. Fig-

ure 12 shows the spending on food curves and the

money balance curves. We can discern four groups

of curves for food spending. There are participants

who left the experiments, so their spending on food is

zero. Then, there is a large cluster of citizens who do

not spend a lot, and there are many citizens who have

a medium spending on food. Finally, just a few of the

citizens spend a lot on food.

If we brush the citizens with high spending on

food (orange brush in Figure 13), we see that all of

them have a relatively low account balance and a rel-

atively high joviality value at the same time. If we

create another brush now and select citizens with high

account balance (purple brush in Figure 13), we see

that they do not spend much on food and have rela-

tively low joviality values. The map view shows us

where the two brushed groups live. We can see that

the two groups live next to each other in some parts

of the city and that they are separated in other parts.

6 CONCLUSION

A holistic analysis of complex data can be success-

fully performed with the help of visual analytics.

Especially for exploratory analysis tasks, interactive

methods are the ﬁrst choice.

Due to the complexity and heterogeneity of data

and tasks, extensive data processing is often required,

resulting in multiple data sets. It is important to cor-

rectly identify the object of interest and decide what

constitutes an atomic data item. We have highlighted

two possibilities (out of many) in this paper, one with

the citizen as the primary focus and one with busi-

nesses as the main focus.

Custom views are also often needed when analyz-

ing such complex data. Our newly introduced map

view was often used as the central view in the analy-

sis. Its multiple customization options may seem too

complicated at ﬁrst glance, but once the view is con-

ﬁgured and saved, the analysis is very efﬁcient. The

ability to let the user choose what to display is much

appreciated by advanced users. Reasonable default

values are very important and reduce the steepness of

the learning curve. A single view is by no means suf-

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

220

Figure 13: Only relevant views from Figure 4 are shown here. We select the citizens who spend a lot on food (orange brush)

and see that they have a low bank account balance and high joviality values. If we select the citizens who have a lot of money

(purple brush), we see that they do not spend a lot on food and are not happy! The map shows where they live.

ﬁcient for a thorough analysis. CMVs with support

for complex brushing allow for efﬁcient exploration

of different hypotheses and understanding of the data.

We plan to further improve the newly proposed

map view, introduce new views as needed, and com-

bine the interactive approach with automated analy-

sis methods in the future. Two additional challenges

that are deﬁned for the same data as part of the 2022

VAST Challenge will be used for evaluation. In addi-

tion, we plan to apply the approach described here to

real-world data.

ACKNOWLEDGEMENTS

VRVis is funded by BMK, BMDW, Styria, SFG, Ty-

rol, and Vienna Business Agency in the scope of

COMET – Competence Centers for Excellent Tech-

nologies (879730), which is managed by FFG. Parts

of this work have been supported by Virginia Tech In-

stitute for Creativity, Arts, and Technology.

REFERENCES

Andrienko, G., Andrienko, N., Bak, P., Keim, D., and

Wrobel, S. (2013). Visual Analytics of Movement.

Springer, Berlin.

Buja, A., McDonald, J. A., Michalak, J., and Stuetzle, W.

(1991). Interactive data visualization using focusing

and linking. In Proceeding of the IEEE Conference

on Visualization ’91, pages 156–163.

Card, S. K., Mackinlay, J., and Shneiderman, B., editors

(1999). Readings in Information Visualization: Us-

ing Vision to Think. Interactive Technologies. Morgan

Kaufmann, San Francisco.

Clarinval, A. and Dumas, B. (2022). Intra-city trafﬁc data

visualization: A systematic literature review. IEEE

Transactions on Intelligent Transportation Systems,

23(7):6298–6315.

Crouser, R. J. and Cook, K. (2022). VAST Chal-

lenge 2022. https://vast-challenge.github.io/2022/

description.html. [Online; accessed 15-November-

2022].

Fortini, P. M. and Davis, C. A. (2018). Analysis, integra-

tion and visualization of urban data from multiple het-

erogeneous sources. In Proceedings of the 1st ACM

SIGSPATIAL Workshop on Advances on Resilient and

Intelligent Cities, pages 17–26, New York. Associa-

tion for Computing Machinery.

He, E. W., Tolessa, D., Suh, A., and Chang, R. (2022).

Analysis without data: Teaching students to tackle the

vast challenge. arXiv.

Konyha, Z., Le

z, A., Matkovi

c, K., Jelovi

c, M., and Hauser,

H. (2012). Interactive visual analysis of families

of curves using data aggregation and derivation. In

Proceedings of the 12th International Conference on

Knowledge Management and Knowledge Technolo-

gies, i-KNOW ’12, New York. Association for Com-

puting Machinery.

Konyha, Z., Matkovic, K., Gracanin, D., Jelovic, M., and

Hauser, H. (2006). Interactive visual analysis of fami-

lies of function graphs. IEEE Transactions on Visual-

ization and Computer Graphics, 12(6):1373–1385.

Pelekis, N. and Theodoridis, Y., editors (2014). Mobility

Data Management and Exploration. Springer, New

York.

Roberts, J. C. (2007). State of the art: Coordinated mul-

tiple views in exploratory visualization. In Proceed-

ings of the Fifth International Conference on Coordi-

nated and Multiple Views in Exploratory Visualization

(CMV ’07), pages 61–71. IEEE.

Shi, W., Goodchild, M. F., Batty, M., Kwan, M.-P., and

Zhang, A., editors (2021). Urban Informatics. The

Urban Book Series. Springer, Cham.

Thomas, J. J. and Cook, K. A., editors (2005). Illuminating

the path: The Research and Development Agenda for

Visual Analytics. IEEE Computer Society.

Zheng, Y., Capra, L., Wolfson, O., and Yang, H. (2014).

Urban computing: Concepts, methodologies, and ap-

plications. ACM Transactions on Intelligent Systems

and Technology, 5(3).

Zheng, Y., Wu, W., Chen, Y., Qu, H., and Ni, L. M. (2016).

Visual analytics in urban computing: An overview.

IEEE Transactions on Big Data, 2(3):276–296.

Interactive Exploration of Complex Heterogeneous Data: A Use Case on Understanding City Economics

221