A Perceptive Insight into Cities Patterns by Visualizing Urban Economies

Luca Piovano

, Alberto Andr´eu

, Iris Galloso

and Claudio Feij´oo

Virtual Reality Group, CeDInt, Universidad Pol´ıtecnica de Madrid, Campus de Montegancedo,

Pozuelo de Alarc´on, Madrid, Spain

Techno-economics Group, CeDInt, Universidad Pol´ıtecnica de Madrid, Campus de Montegancedo,

Pozuelo de Alarc´on, Madrid, Spain

Keywords:

Information Visualization, Economics, Graphs, Association Discovery Rules.

Abstract:

Urban economic activities are an essential facet in deﬁning city identity. Traditional approaches rely very

often on the most theoretical and quantitative features of the studies, excluding de-facto a direct association

between those ﬁndings and the tangible subject of the analysis. To ﬁll the gap, the Big Data era and information

visualization methodologies could help analysts, stakeholders and general audience to gain a new insight on

the ﬁeld. In this paper, we want to provide some food for thought about new opportunities arising in visual

urban economies as well as present some visual results on possible scenarios.

1 INTRODUCTION

Cities are complex systems where the global picture

of the urban dynamics is given by the sum of several

evolving and intermingled networks. As living enti-

ties, they present non-static features as well as strong

and varied interactions among their main actors: peo-

ple create social interactions, give functionalities to

the architecture, beneﬁt from services and infrastruc-

tures and connect different areas of the urban fabric.

In this context, managing to understand what a city is,

as well as how it could evolve, should be carried out

by innovative and interdisciplinary approaches, com-

prising, and not limited to, networks / graph theory,

complex system science (Miller and Page, 2009) and

agent-based computational modelling.

The development of better urban theories and sim-

ulation models rely heavily on the availability of suit-

able datasets at the city level. The incoming Big Data

era seems to promise an unprecedented amount of op-

portunities to deeply understand and describe the Big

Picture of cities. Conversely to the past, the evo-

lution of the open data movement and the increas-

ing penetration of ICT in everyday activities (e.g.

smart phones, e-transactions, Internet social networks

or smart card technologies) allow to produce, collect

and have at disposal a vast amount of such spatial

and temporal data, almost in real-time. Disciplines

such as smart cities (Hollands, 2008; Nam and Pardo,

2011; Batty et al., 2012) and city science (Laube et al.,

1998; Solecki et al., 2013) have then emerged to take

in charge the exploitation of such resources and thus

steer the whole urban planning process towards an

heavily data- and evidence-based approach.

1.1 Urban Economies and Data

One of the pillars in deﬁning urban identities is to un-

derstand its economic activities. In that sense, cities

show a high concentration of consumers, workers and

businesses in a ’restricted’ area and have the potential

to produce externalities and increasing returns (Eu-

ropean Union, 2011). The economic crisis begun in

2008 heavily stressed the traditional urban system and

created problems to all its structural components. Ac-

cordingly, new urban policies to sustain growth, em-

ployment and social progress in general have been de-

vised to tackle, and in the future to prevent or mit-

igate, such kind of problems. Policy makers have

paid their attention on agglomeration economies af-

ter some decades, especially focusing on how a num-

ber of different resources and locations with a high

density of several activities come into play on cities

dynamics and inﬂuence each other (Scott, 2009). De-

spite the current chance to have a relative easy access

to major economic data for measuring the health of

the urban economy, current researches cannot explain

thoroughly how they interplay on the underlying ur-

ban fabric. Ultimately it relapses into making difﬁ-

cult to put in practice effective policies. What it is

222

Piovano L., Andréu A., Galloso I. and Feijóo C..

A Perceptive Insight into Cities Patterns by Visualizing Urban Economies.

DOI: 10.5220/0004855102220227

In Proceedings of the 5th International Conference on Information Visualization Theory and Applications (IVAPP-2014), pages 222-227

ISBN: 978-989-758-005-5

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

still missing is the capability to produce and exploit

real-time knowledge at a ﬁne grain - in geographical

terms at the neighbourhoodscale; from a people point

of view, at the single dweller level. This is partially

justiﬁable when considering that such economic data

are usually not openly accessible, even if its digital

trace could be recorded easily. A straightforward ex-

ample could be gathering credit card transactions, for

instance both at shops and on e-commerce sites: as

they belong to either banks or other private ﬁnancial

entities, the likelihood to access to such gold mine

of information is inversely proportional to the inter-

estingness of its content. On the other hand, even in

presence of open data, urban policies suffers from the

lack of data-driven modelling and practice, thus limit-

ing its effectiveness and comprehension of urban dy-

namics.

Nevertheless, very recent works show that, by us-

ing such non-traditional sources of data, it is possible

to catch fascinating and not-trivial facets of the urban

landscape. For instance, in (Krumme et al., 2013),

the authors could access electronic records of indi-

vidual economic transactions from both North Amer-

ican and European ﬁnancial institutions to predict pat-

terns of customers’ habits in their shopping activi-

ties in the course of time. By using similar data, an

interactive visualization manages to inspect the eco-

nomic impact of the http://mwcimpact.com/Mobile

World Congress 2012 on the city of Barcelona. This

objective was achieved by visually comparing his-

torical data from the week before and during the

MWC 2012 so that differences in the purchase dy-

namics of the city along that week could emerge.

On the other hand, another visual project, namely

http://senseable.mit.edu/bbva/Spring Spree, was con-

ceived to examine spatio-temporal transactions cate-

gorized by the type of sale (e.g. fashion, restaurants)

during the week of Easter 2011 in order to detect dis-

tinctive patterns emerging across Spain.

1.2 The Role of Visualization

The last aforementioned examples introduce a couple

of important elements to reﬂect on. First of all, the

Big Data opportunities could turn into the Big Data

nightmare if policies to handle them are not imple-

mented conveniently. Indeed, current approachesusu-

ally do not scale up to face such volume and variety

of information (at a reasonable velocity), thus deter-

mining underused and wasted opportunities.

Then, another awkward point coming up when

dealing with economic studies is its (perceived?) in-

trinsic abstraction in the formulation of problems and

methodologies to work them out. Indeed, models and

formulas usually used in this ﬁeld are lacking in per-

ceptible concreteness and immediacy. In particular,

this means that their outcomes are neither easily com-

municable nor understandable outside the (few) mem-

bers of the economic ’clique’. Moreover, while talk-

ing about urban economic facts, the risk is somehow

to miss the context in which the economic analysis

is performed and therefore, to reveal the impact on

the real urban environment. We are plenty aware that

classical charts (e.g. bar, pie and scatter-plot charts)

have been extensively used in this ﬁeld, especially to

portray temporal trends of indicators and indexes. But

they usually do not associate that evidence to the geo-

graphical context they refer to. For instance, in (Mul-

brandon, 2013), a whole dissection of American in-

comes is presented through both classical and more

sophisticated visualizations. Despite that, there is a

lack of visual feedback at the geographical level (but

for some choropleth maps at US scale).

In this context, a smart approach to solve both

problems could rely on information visualization and

visual analysis disciplines. Indeed, they can provide a

strong support to economists and stakeholders in gen-

eral to highlight efﬁciently the development of urban

economies. The strength of such approaches is to ex-

ploit perceptual mechanisms of the human sight to en-

force cognitive process of learning, as well as provide

powerfultools in dynamically revealingpatterns, rela-

tionships, clusters, unknownfacts and outliers (Shnei-

derman, 1996). In recent years, the same paradigm

has been extensively and successfully applied to re-

veal cities facts and dynamics under different points

of view. So, for instance, different kinds of mobil-

ity (e.g. people and transport systems (Girardin et al.,

2008; Roth et al., 2011)) have been studied accord-

ing to a number of digital traces they leave (e.g. cell

phones, social networks activities and photographies

(Reades et al., 2009; Jankowski et al., 2010)). In this

sense, visual economies are the next facet of the urban

environment gemstone to look at.

1.3 Our Objectives

Within this context, the main contribution of this work

could be summarized with the following points: i)

to provide an introduction to both opportunities and

problems when dealing with urban data and planning

processes (mainly discussed in Sections 1.1 and 1.2);

ii) to suggest engagingabstract, economic studies also

from the perspective of the geography of the cities, in

order to visually relate economic evidences and facts

to their urban context (we will talk about it in Sections

2 and 3); iii) to show some practical examples about

our work on visualizing urban economies applied to

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223

the city of Madrid and its surroundings (discussed in

Sections 3.2 and 3.3) and provide inspiration for fur-

ther works and applications.

2 THE METHODOLOGY

The enquiry we are addressing broadly deals with

characterizing urban mobility patterns under eco-

nomic reasons. In other words, we are looking for

tell-tale insights on how dwellers-customers are ex-

ploiting urban infrastructures and, in turn, how this

will be reﬂected on the geography of the city. To this

end, we mainly rely on association discovery rules

(ADRs) or association rule learning (ARL) (Agrawal

et al., 1993). Even if they are not real economic indi-

cators, they have been extensively used in economic-

related ﬁelds (e.g. market basket analysis) for both

their simplicity and strength in searching for regulari-

ties and repetitive patterns among variables in large

databases. Furthermore, through this methodology

we can learn about spatial patterns in consumption be-

haviour even when traditional information (e.g. cus-

tomers’ preferences, habits and socio-economic de-

tails) is missing, incomplete or unreliable. Indeed,

collecting data from retail activities as in our case (see

Section 3.1) means to tackle the problem under a dif-

ferent perspective and, in this sense, ADRs are a help-

ful tool to accomplish this speciﬁc goal.

2.1 Association Discovery Rules

ADRs are techniques belonging to the data mining

domain whose goal is to ﬁnd regularities and afﬁni-

ties (rules) among entries of a dataset D. Each entry

is a collection of items: for instance, with respect to

the case studies in Sections 3.2 and 3.3, items could

be city sectors or business categories. Formally, a rule

is written as X → Y, where X (the antecedent or head)

and Y (the consequent or body) are disjoint subsets of

such items. Intuitively, the rule could be interpreted

as the trend showed by items in Y to appear with a

certain probability whenever items in X occur too. In

order to discover the most relevant rules occurring in

a dataset, we introduce some values measuring their

statistical interestingness (Tan et al., 2004) and brieﬂy

explained in the next subsections.

2.1.1 Support

The support is deﬁned as the proportion of examples

for which X and Y are simultaneously true, that is:

supp(X → Y) = |X ∩Y|/|D|,

where the |.| notation is the cardinality operator and ∩

represents the simultaneous occurrence of the events

in the two sets. In other words, this straightforward

measure is an estimation of the frequency to ﬁnd X

and Y coupled in the dataset. Trivially, the above

equation can be applied to the single set X too and in-

terpreted as the likelihood to infer relationships when

the causes are known.

2.1.2 Conﬁdence and Expected Conﬁdence

The strength of a rule is called its conﬁdence and pro-

vides the proportion of examples for which the head

X appears among those for which the body Y is true.

Mathematically speaking, this is expressed as:

conf(X → Y) =

supp(X → Y)

supp(X)

|X ∩Y|

|X|

In probabilistic terms, it can be interpreted as an esti-

mate of the conditional probability P(Y|X).

On the other hand, the expected conﬁdence es-

timates the likelihood a consequent would appear

among the dataset items independently of the an-

tecedent of the rule, that is:

conf(Y) = supp(Y) = |Y|

In other words, the last measure is used to see how

frequently an observation could be inferred regardless

the causes having originated it.

2.1.3 Lift

The lift expresses the ratio of the support of the two

observed sets to the supports of the sets considered

separately. In other words:

lift(X → Y) =

supp(X → Y)

supp(X) ∗ supp(Y)

|X ∩Y|

|X| ∗ |Y|

The lift is a comparative evaluation of the likelihood

of observed sets with respect to the expected supports

of X andY under the statistical independenceassump-

tion. Its goal is to ponder the proﬁt in applying that

rule, since it expresses how many times it is more

likely to derive a consequent from its antecedent than

without considering such statistical dependency.

To ﬁnd how much interesting could be considered

a rule, we will consider a trade-off between its sup-

port (because it means it appears several times) and

lift (because the higher this value, the stronger the rule

when compared to other ones in which only either X

or Y is present). Tables 1 and 2 show some example

in applying such rules.

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224

Table 1: First 10 results of applying ADR to a speciﬁc postal

code, namely 01. For a sake of uniformity, postal codes are

written according the colour they are depicted in Figure 1

(see the digital version).

Rule li ft supp conf conf Count

01 → 09 3.41 1% 14.74% 4.32% 28043

01 → 06 3.14 1.14% 16.73% 5.34% 31823

01 → 46 2.57 0.25% 3.68% 1.44% 7010

01 → 16 2.27 0.21% 3.08% 1.36% 5868

01 → 10 2.22 0.44% 6.44% 2.9% 12251

01 → 36 2.18 0.36% 5.27% 2.42% 10022

01 → 02 2.16 0.28% 4.05% 1.87% 7698

01 → 04 2.09 0.59% 8.59% 4.12% 16348

01 → 28 2.03 0.24% 3.59% 1.77% 6821

01 → 03 1.88 0.38% 5.61% 2.98% 10678

Table 2: ADRs applied to a speciﬁc business category

(number 3). In this case, the second and third rules (in blue)

are the best ones because they show a good trade-off be-

tween support and lift values (unlike those in purple).

Rule li ft supp conf conf Count

3 → 8 4.51 0.93% 15.77% 3.5% 25839

3 → 9 3.79 2.03% 34.57% 9.12% 56637

3 → 1 3.72 2.04% 34.7% 9.32% 56858

3 → 4 2.82 1.25% 21.27% 7.53% 34847

3 → 7 2.01 0.97% 16.5% 8.21% 27026

3 → 10 1.94 0.48% 8.22% 4.24% 13469

3 → 14 1.8 2.14% 36.5% 20.31% 59798

3 → 13 1.75 2.29% 39.03% 22.28% 63944

3 → 2 1.33 0.84% 14.2%4 10.74% 23331

3 → 15 0.95 0.49% 8.37% 8.77% 13711

3 → 5 0.78 1.93% 32.93% 42.07% 53955

2.2 Competition Index

For the scenario described in Section 3.3, we deﬁne a

further indicator - the competition index - estimating

the density of purchases performed in a given area as:

∑

{region}

∑

trans

act

where: the sums are performed over the whole sets

of geographical regions and categories of economic

activity; operators trans and act count, respectively,

the number of transactions performed and how many

businesses of type b there are in a given region.

3 VISUAL URBAN ECONOMIES

3.1 Data and Tools

The credit card transactions database used in this

work has been created by coupling open data from

statistical institutes with information obtained by pri-

vate ﬁnancial entities. Aggregated data in their whole

reﬂect the most signiﬁcant statistical trends (e.g. over-

all volume of purchases and money spent; main cus-

tomer ﬂows and shopping patterns; distribution of

transactions across city sectors) of the reference city.

While writing this paper, no access to anonymous in-

formation at single customer transactions level has

been performed. We limit our analysis to a representa-

tive subset of data of purchasing activities performed

in Madrid throughout a couple of months. However,

for the purposes of this study, this sufﬁces to show

interesting patterns and visualisation opportunities.

The dataset has been analysed by using R and SAS

scripts. Graphs have been drawn by using Gephi soft-

ware. Geographical information for the Madrid re-

gion come from http://www.madrid.org/nomecalles/

Nomecalles website and analysed using GIS tools

such as QGIS.

3.2 Scenario 1: Customers’ Flows

The ﬁrst scenario will answer to the following ques-

tion: where do people go shopping in Madrid city as

they haveto do multiple purchases along the day? The

analysis has been performed by considering postal

codes as the geographical units and by limiting at 150

minutes the maximum time between two consecutive

purchases performed by the same customer. The re-

sults of such analysis are shown in Figure 1, where

we factually visualize an origin-destination matrix in

which city sectors are disposed in a circular pattern.

The ADR analysis shown in Table 1 addresses the

same zone, namely zone 01, of the city highlighted

in the ﬁgure and just considers the ﬂows originating

from there. Arcs connecting sectors represent a bi-

directional ﬂow: the thicker the link, the bigger the

ﬂow volume between two end-points (usually the re-

lationships are asymmetric). Arcs’ colours encode the

end-point arising the greatest number of transactions.

According to this, the chord diagram puts in evidence

that the rules 01 → 09 and 01 → 06 are predominant

among all the ﬂows from and to zone 01, as also stated

in Table 1 by looking at the supp column. More-

over, it shows that direction 06 → 01 generates more

economic trafﬁc than the opposite one. A graphical

meaning could be assigned to conf and

conf indica-

tors too, being respectively, the ratio between the rule

link and its segment of origin and the length of the

segment of destination.

The same information could be mapped accord-

ing to the geographical coordinates of each zone as

in Figure 2. In this case, the graph of the city illus-

trates how connected is an urban unit with respect to

APerceptiveInsightintoCitiesPatternsbyVisualizingUrbanEconomies

225

the others given its purchases patterns. The connex-

ions are depicted proportionally to the lift index, so

that it is possible to perceive which areas of the city

are strongly connected for shopping purposes. Since

we are talking about networks, some other measures

could be used to evaluate the shopping graph. In our

case, nodes have been partitioned into clusters of sim-

ilar purchases behaviours, while their size reveals the

respective betweenness index, that is how much im-

portant a node is to connect any other couple of them.

Figure 1: Customers’ dynamics mapped by a chord dia-

gram: each segment represents a sector of the city, arranged

in clockwise order from the very centre - sector 28013 in

light-blue - till the most peripheral ones, in pink.

Figure 2: The shopping graph with Madrid postal codes.

3.3 Scenario 2: Territorial Analysis

We designed this scenario as a proof of concept to

help a businessman ﬁnding the best place(s) to set his

own activity. First of all, a territorial analysis should

be performed to classify areas according to their ’at-

tractiveness’ in hosting a business activity of the given

type, say type 3. A way to estimate such parameter is

given by the competition index introduced in Section

2.2. In absence of other constraints, this will give a

quantitative evaluation of the local turnover: the gen-

eral idea is to detect zones where the total number of

activities similar to that of type 3 is (relatively) low

and the proﬁt margin high. To visualize the results of

this analysis, we could use a choropleth map, as the

one depicted in Figure 3, where the whole region of

Madrid is shown. The lighter the colour, the less the

competition. For instance, one of the best cities found

in the given example corresponds to Fuenlabrada, in

the south of the region. A step further could be per-

formed in order to ﬁnd within that city the most suit-

able sites. On a map it is possible to place all the

businesses of type 3 to have a look at their geograph-

ical distribution. One straightforward strategy would

infer to place our activity almost far away from all the

others. Instead, a smarter approach could be to ex-

ploit ADR analysis (shown in Table 2) on our transac-

tion datasets in order to discover strong relationships

among the targeted business and activities of differ-

ent categories. The idea is about taking advantage of

typical users’ patterns to extend the range of poten-

tial customers. This way, we could create a heatmap

(see Figure 4) to have a visual insight of the candidate

sites: the best areas, shown in orange-red tones, are

those ones having a high concentration of activities

connected to our target.

4 CONCLUSIONS

In this paper, we discussed visualizing economic ev-

idence in order to understand urban patterns. Eco-

nomic activities are usually presented in abstract

forms, where all the focus is tended towards show-

ing off numbers and models. As a consequence, a di-

rect association between the theoretical ﬁndings and

the tangible subject of the analysis is often missed.

Visual economies could be an efﬁcient way to over-

come this problem because of the perceptive insight

revealing the (geographical) impact of economic ac-

tivities descriptors. Moreover, such visual approaches

have the potential to make economic datasets accessi-

ble to a broader group of potential users, including

ﬁnancial entities, policy makers and customers. In

turn this could provide advantages to improve both

business strategies and decision-making policies (es-

pecially when coupled with other mobility datasets,

such as telephone cells records) as well as increase

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226

Figure 3: Choropleth map of the region of Madrid repre-

senting the competition index for business category 3: The

darker the blue, the stronger the competition in that area

(and therefore possibly smaller the chances to succeed).

Figure 4: Heatmap on the map of Fuelanbrada (Madrid)

showing the most suitable sites where to place a business of

type 3: the more suitable areas are those coloured in yellow-

red shades.

end-users’ awareness. To illustrate our point of view,

we depicted an analysis based on ADRs techniques

and presented a couple of scenarios to show some

practical, visual examples, which could hopefully in-

spire further advances and applications, especially in

the context of city science.

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