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Abstract

This study will compare the results of measuring Urban Complexity using Shannon-Wiener index in two different methods. Using a joint dataset retrieved from Foursquare API, we will measure the degree of urban complexity of every street 1. relating every amenity to the closest street segment in a computational way and then applying the calculation to the segments, and 2. applying the calculation to every cell of a grid that will be combined with the street network afterwards. The selected case study is the city of London and the dataset employed will be retrieved from Foursquare. Over 79,000 venues were collected and classified in over 660 categories. In order to proceed to the analysis, these 660 categories will be reduced to 10 based on the classification of activities observed in the public space from the traditional urban discipline. Then the urban complexity index of each Street segment of London will be measured as a simultaneous calculation of the density and diversity of collected and classified economic activities.
CAI  S  A CI
CACAI SI  SAI I
SS  AA,, AIA C, ISA I
niversity of Alicante, Spain
ampere niversity of echnoloy, inland
SI nit, stonia
ASAC
his study will compare the results of measurin rban Compleity usin the Shannoniener inde
in two different methods. sin a oint dataset retrieved from oursuare AI, we will measure the
deree of urban compleity of every street  relatin every amenity to the closest street sement in a
computational way and then applyin the calculation to the sements and  applyin the calculation
to every cell of a rid that will be combined with the street networ afterwards. he selected case study
is the city of ondon, and the dataset employed will be retrieved from oursuare. ver , venues
were collected and classified in over  cateories. In order to proceed to the analysis, these 
cateories will be reduced to  based on the classification of activities observed in the public space
from the traditional urban discipline. hen the urban compleity inde of each street sement of ondon
will be measured as a simultaneous calculation of the density and diversity of collected and classified
economic activities.
Keywords: urban complexity, Shannon-Wiener, social media, London.
 ICI
he use of social media data when analysin the city is nowadays becomin popular in the
urban plannin discipline . IS technoloies allow us not only to visualie but also operate
with data retrieved from social networs and online servers. urthermore, due to the
populariation of social media amon smartphone users, data samples are reater in surface
and in number of points than any other data sources so far ,  and they have a hiher
spatial and temporal resolution .
In this paper, we aim to study the intanible relations between city form and the
distribution of activities hosted by amenities. e speculate that compact urban forms foster
a hiher variety of amenities capable to host a mi of different activities, which are beneficial
to life in the city. erceivably dense urban areas with small narrow streets such as uropean
old towns enerally cluster a hih amount of venues with a hih level of compleity.
Scholars have attempted to measure the urban compleity inde throuh the application
of standardied indees. Specifically, this article uses the Shannoniener inde tain
points relative to venues reistered in oursuare for the calculation. his data source has
been utilied throuh the socalled Grid calculation method, capable of determinin an inde
of compleity for a iven area divided in eual cells . otwithstandin, we aim to measure
the compleity within the street. hus, in this paper we will obtain a value of urban
compleity attached to every street sement of the street networ of ondon city refinin the
calculation for a iven surface two dimensional into a unit of lenth one dimension. o
do so, we will test two different methods Closest oint C and rid method.
ORCID: http://orcid.org/0000-0002-4092-1782
ORCID: http://orcid.org/0000-0002-4853-9887
ORCID: http://orcid.org/0000-0002-6592-9588
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Sustainable Development and Planning IX 369
doi:10.2495/SDP170321
e epect to obtain similar results. owever, when measurin with Closest oint C
calculation these venues would be divided in smaller roups relatin every one of them to
the closest street and the compleity value would be lower. n the contrary, sprawl fabric
with a low density of amenities miht obtain a low value of compleity when measured
throuh rid calculation, but a hiher value when measured throuh C calculation, since
points would be lined to the closest street without tain a maimum distance into account.
. lacebased social media
e can define placebased social media as those social networs that record and store the
contents shared on their platforms and the eoraphic location they were shared from. his
data can often be accessed by third parties from AI services. Since our research focuses on
the metadata reardin the contents location rather than the personal information of users,
our methodoloy is about which place people are postin from rather than who is postin.
he use of placebased applications in spatial plannin research is enerally lined to the
study of landscape or in particular, to observe a particular area in order to uantify the impact
of certain factors  to describe intanible urban phenomena.
lacebased social media such as oole laces has been used by urban studies scholars
as a tool to measure urban compleity , ,  due to its ability to represent the totality
of businesses reistered in oole with eoraphic coordinates.
In this way, placebased social media offer a virtual representation of the heteroeneity of
the economic activities of a certain area, constantly updated and revised by the users
themselves. In addition to location, they offer other information such as name, address, and
business cateory , allowin the classification of activities accordin to type. Althouh
this data can be easily accessed and it is storin the eact eoraphic coordinates of
businesses, the reliability on the content itself it may be hard to verify.
. oursuare
In this study, we are oin to focus on oursuare, a social media platform that allows users
to add enues worth visitin and epressin preferences. he app also allows checins
for users who intend to state their presence in a specific venue. riinally oursuare was
desined to be used as a ame, where users with most checins in one venue could earn
bades and popularity . hile the oursuare mobile app still allows users to checin
and review venues, the ludic component has been removed from the service. In the past years,
venues were mostly trendy places such as bars, restaurants and clubs, but only recently
oursuares coverae because broader and more detailed. oday the list of venues is no
loner limited to few urban amenities but to the overall set of locations, places and spaces
present in one city  includin open areas, infrastructures and buildins hostin real estate or
small businesses.
oursuare is understood by omninos et al.  as a collective nowlede tool throuh
which users use information from third parties when main decisions about areas of interest
in the urban contet  such as how to spend their free time ., what venues its worth
visitin ., and when to o to visit the selected venues  . here are several
studies that discuss oursuare users location choice and their relation to the amelie
interface capable of influencin individuals decision main in the city . In
addition to the proress that users can mae by checinin venues and leavin reviews,
users behaviour is an aspect of the individual spatial self, which can be interpreted as a
representation of lifestyle in order to create or maintain social relationships .
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he base of this platform is a series of public or private places listed by users themselves,
in which anyone can reister a visit by checinin . hese places are classified throuh
cateories that are predetermined by the application. hus, data obtained from the searches
throuh this app are liely to be filtered .
he main implementation of oursuare in the field of urbanism is framed in the ability
to  list places and reister them with a diital identification and coordinates, and  addin
metadata to every reistered place. Specifically, when measurin user preferences for both
oursuare application and the physical space of the city, we will place particular emphasis
on data about the number of visitors and checins. In other words, oursuare, with its
particular amelie interface allows the user to reveal where heshe has been and how many
times. In this way, the information obtained from this app allows to understand human
mobility in its spatial, temporal, social, and content aspects , p. .
here are several articles that relate the use of the oursuare to the observation of the
diversity of economic activities in a iven area  listin reistered places and observin
distribution patterns.
he dataset retrieved from the oursuare AI corresponds to every point obtained in the
selected uery area would correspond to a physical place facility, venue, amenity in the
urban environment reistered by the app. In this way, for each place we obtain the followin
data
. Spatial coordinates atitude and onitude
. usiness ame Identification
. Cateory and Subcateories
. Address, street and number
. Averae ratin
. umber of visits or different users who have visited the site one visit per user
. umber of checins or sum of all visits that all users have made onsite.
 CAIS  ACS
he eoraphic distribution of amenities is topical to the definition of spatiotemporal
patterns of urban and rural settlements. umerous studies have revealed the spatial relations
that overn the location choice of these establishments althouh it is primal to map and
understand these relations in their sociocultural contet , . In particular we are oin
to focus on clusterin and dispersal phenomena to learn how the spatial relations between
socioeconomic establishments are typical to the urban and reional contets of one city.
ehl et al.  maes a classification of observed activities performed by people within
the urban space. hese activities were classified by nature in ptional, ecessary and
Social bein ptional those performed when one has free time ym, museum, spa, etc.,
ecessary those interated in the daily routine school, office, maret, etc. and Social
would include those activities that involve interaction with others bar, caf, club, etc. .
In this contet, we may understand Social activities as a part of those performed when one
has free time, so we included them into the cateory ptional. hus, we firstly divide all
activities in these two maor roups, ecessary and ptional.
In order to be able to operate with cateories in a more detailed way and define uses, it is
needed to define activity types more accurately, but at the same time to let these new
cateories be fleible enouh to include all ind of oursuare venues in them. o do so we
use the SI nit rban activity heel .
he novelty of this approach is the classification of amenities not by type or function but
accordin to the spectrum of human activities they can host. In this way, we are oin to
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perform an indirect observation of activity patterns that can be scaled from local to reional
scale  somethin that is traditionally performed with lon terms and microscale only
surveys a field observation.
ollowin this discourse, we created cateories accordin to the SI nit rban activity
heel i. .
his classification method was created from empirical observations of people performin
activities in public and private spaces to study the difference between what the city offers
Supply throuh its amenities and what people do in the city emand. his method is
desined to cateorie oursuare venues to map and measure the Supply and other datasets
such as Instaram and witter to measure human activity patterns in the city emand. In
this paper, we reclassified oursuare data only to measure the compleity of activity
patterns offered by the city and study if their distribution has a clear relation to the perceived
form of the city.
In this paper ver , venues were collected and classified in over  cateories. In
order to proceed to the analysis, these  cateories were reduced to  based on the SPIN
Unit Urban Activity Wheel i. .
 A CI
he concept of Compleity in urban studies starts to be related to the economic activities in
urban spaces reistered in online applications and web services in the mids.
owever, a standardied methodoloy to measure it has not been developed yet. o do so,
first of all, it will be necessary to define this concept and identify methods throuh which it
can be observed and measured.
ther previous authors such as ui Snche , p.  have defined the city as a complex
self-regulating system in which all the elements that shape it in their multiple scales are
related to each other in a comple networ, in which the minimum modification in one of
its elements will be a readustment on a lobal scale. lanco and Subitrats  associate
this heteroeneous compleity with a real and potential conflictan effect of increasin
sie and conseuent compleity of cities, main them unable to be manaed , or
becomin a challene per se for policy maers .
iure  SI nit rban activity heel describin activities in urban space .
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iure  Cateories in oursuare reclassified into Urban Activity Wheel of activities in
urban space. (Self-elaborated.)
rom a pramatic point of view, ueda  refers to compleity as the measure of the
degree of organization of the urban system in terms of the diversity of the mix of uses and
services , p. . Compleity defined as the set of relations between all elements that
constitute the city, the comple selfreulatin system shows the ecosystemic nature of the
same. or this reason, ueda  states that this deree of compleity can be obtained
throuh the application of the euation correspondin to the Shannoniener compleity
inde developed to measure the deree of entropy of an ecosystem .
lorida  will later relate this urban compleity of cities to economic rowth throuh
what he calls the creative class, which he places in specific spaces characteried by
tolerance, openness, and creativity of business . o this end, he elaborates a series of
indicators capable of measurin particular aspects that directly influence what he calls social
compleity.
ithin the field of urban studies and in relation to social networs and web services,
olascoCirueda and arcaayor  use data collected from oole laces to evaluate
the functional urban compleity of economic activities that directly affect urban space
, p. . hey concluded that the functional appeal lined to the tourism eperience and
indicators of sustainability in its case study  enidorm, Spain  is proportional to the deree
of compleity throuh the application of the Shannoniener inde. herefore, they also
corroborate the possibility of applyin this inde as a truthful indicator of urban compleity.
ustos ernndes  also deals with compleity within the field of urbanism, drawin
relationships between it and concepts such as density, diversity or specialiation with
specific cases of urban reeneration. ie olascoCirueda andarcaayor, he
relates the improvement of the space eperience with the increase of activities and relations
resultin from the increase of urban compleity .
herefore, we could define of urban compleity as a simultaneous measure of both the
density and diversity of economic activities in a iven urban space, which is directly related
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Sustainable Development and Planning IX 373
to spatial eperience and can be measured uantitatively throuh the application of the
Shannoniener diversity inde.
 
he followin section will eplain the two methods used for calculatin urban compleity
by applyin the Shannoniener inde usin location data retrieved from oursuare. he
first method rid method divides the land into a rid of cells and calculates for each cell
an inde of compleity that will later be transferred into every street sement contained in
the cell. he second method Closestoint method relates every eolocation to the nearest
street sement and calculates the compleity inde for the street sement per unitary lenth
value. he results obtained from both methods will be interpreted and visualied throuh a
IS platform from which maps will be plotted.
he formula employed for calculation is iven below, where rban compleity
inde S  number of cateories pi  number of amenities with cateoryi divided by
number of amenities. 
 . 
. rid method
After every point is recateoried usin the Urban Activity Wheel of cateories as a
reference, points are laid out on a map usin the location data stored as metadata in form of
eoraphic coordinates. Subseuently, the area the city of ondon in our case is divided
into a rid where each cell has dimensions of  meters. he formula used does not
refer directly to any unit of area or lonitude. his rid is the basis for comparin cells with
others with one same dimension. In this paper, it has been taen the value of  meters
as unitary tain as a precedent the wor of olascoCirueda and arcia ayor . ence,
oursuare points are bounded inside every rid cell boundary with their cateory values. If
no points were found in a cell, it would be removed from further calculation and assined a
null value. As noted previously, the formula taes into account the number of cateories and
the number of items for each cateory inside every cell. After runnin the Shannoniener
inde calculation, the values obtained are transferred from every cell to every street sement
comprised within that cell. he results obtained will mirror the value of urban compleity in
every street sement per unitary measure of area.
. Closest oint C method
he closest point has accessibility information involved, althouh not precise all the time.
he closest street to any venue is not always the one from which one can access. owever,
it is true in most of the cases. herefore, urban compleity inde calculated throuh C
method in an ideal scenario, would measure the level of compleity of one street sement
reardin all the venues to which one can access from that sement.
Similarly, as described above, we firstly need to reclassify the cateories and plot de
dataset in the map throuh the eoraphical coordinates attached to every oursuare point
as metadata. he street networ with which we will operate should be larer than the area
covered by oursuare points to avoid edge effect in which points located on the ede or near
the edes are findin their closest street only from inside of the clippin boundary whereas
the actual closest street could be located outside the operation area. irstly, we calculate the
closest street sement for every oursuare point in the dataset.
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374 Sustainable Development and Planning IX
he results of this calculation are displayed for every street sement containin the
information about the amount of oursuare points related to it. In similarity with the
previous method described, all streets unrelated to any point are removed from the calculation
and assined a null compleity value. ue to the variety of lenth between sements, the
compleity value should be divided by the lenth of the sement in order to obtain a unitary
value. hus, the Shannoniener formula is applied for every street sement and the results
obtained will represent the value of compleity per unitary measure of lenth.
 SS
After the application of both calculation methods, the results obtained have been represented
in two comparative maps. he case study represented is the city of ondon, .
he center of the city confined between eents par, yde ar and ictoria par,
toether with the south ban of the hames presents a hiher urban compleity inde than
the rest of the study area in both cases. In addition, a series of commercial aes arraned
radially present the hih presence of establishments of commercial activity. his ais is
common to both results and they conform a networ in which the nodes i.e. lephant
Castle station, enninton par, Cramberwell reen par have an eceptionally hih rate
of urban compleity.
uantitatively, we observe how the results obtained from the rid method represent more
uniform values with a variation between . and ., whereas values obtained from C
method vary between . and .. owever, the unconventionally hih values happen in
specific locations, which will be later discussed in the iscussion section.
ualitatively, the I method provides uniform values by proimity to the location,
while the C method presents the value of each sement independently of the nearest ones.
In this way, the I method calculation colored spots with the same compleity value are
observed due to the superficial nature of the calculation at oriin, while in the C method
larer discontinuities and differences between neihborin streets are observed since each
sement is analyed independently.
 ISCSSI
oth calculation methods display similar overall results. owever, due to the different nature
of them onedimension vs twodimensions each of them contains certain bias, which are
described below
. rid ias data loss
C method avoids all data loss from calculation. here are no venue points lost from the case
study area, since every point is related to the closest street without tain this distance into
account. n the contrary, rid calculation method misses all points located in areas without
any street sement capable of representin the value of the rid. An eample would be a par
with an area over  meters surrounded by streets. ven if there were any venues inside
the par, it would not be measured by any rid that at the same time includes a street sement,
so the par would appear as empty and the streets would represent a lower compleity value.
. C ias edes and culdesacs
C method produces a bias in which some small sements collect a lare amount of venues.
hose sements have almost no lenth and they are enerally plotted as edes or cul-de-sacs.
In these cases, these small sements would collect an eceptionally hih amount of venues
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Sustainable Development and Planning IX 375
iure  Calculation of Shannoniener inde per street sement usin rid ethod.
(Self-elaborated.)
iure  Calculation of Shannoniener inde per street sement usin C ethod. (Self-
elaborated.)
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376 Sustainable Development and Planning IX
because of the way in which the eometry of the networ is plotted. ue to their petite lenth,
these sements would maintain an eceptionally hih level of compleity after unifyin the
value.
here is no such problem with rid method, since it does not tae into account the lenth
of the street but it is essentially independent of any eometry.
 CCSIS
his paper proved that compleity of urban space can be measured throuh Shannoniener
inde and represented alon every linear street sement within the street networ of a city.
oth methods tested C and I method seem relevant and offer a uniue
representation of the city. ein these two methodoloies uniue, each of them present a
different bias that the other could solve. owever, the overall story told by both results is
consistent and coincidin.
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While commerce is one of the key activities in cities, its spatial description still requires further attention, especially by considering the different dimensions of commercial space: physical, economic and socio-symbolic. The latter is becoming more and more important in an era where consumption is at the centre of social relations. Further, although data availability has been an enduring obstacle in commercial research, we are witnessing the advent of new data sources, and social-network big data is an opportunity to unveil the places to which consumers attribute prestige or symbolic capital, at the extent of entire metropolitan areas. This paper compares the physical, economic and socio-symbolic dimensions of commercial spaces through the analysis of three different commercial data sources: cadastral micro-data, business register and social-network big data. For the case of Madrid Metropolitan Area, the three databases are compared with correlation analysis and density maps, coming out as partly redundant and partly complementary. Getis-Ord's hotspot statistics integrated into a cluster analysis enable a comprehensive understanding of commercial environments, enriching previous spatial hierarchies. The spatial distribution of symbolic capital unveils a relation with socio-spatial segregation and paves the way to new reflections on the spatiality of consumption as a social practice.
... Abarcan revelar determinados patrones espaciotemporales de conducta de la población y sus preferencias (Aliandu, 2015;Cerrone et al., 2018;A. Chen, 2005;Martí, Serrano-Estrada, & Nolasco-Cirugeda, 2017;Scellato, Noulas, Lambiotte, & Mascolo, 2011;Xia, Schwartz, Xie, & Krebs, 2014;Zhong, Arisona, Huang, Batty, & Schmitt, 2014); su percepción del entorno físico (Aiello, Schifanella, Quercia, & Aletta, 2016;Hess, Iacobucci, & Väiko, 2017;Pelechrinis & Quercia, 2015;Quercia, Schifanella, Aiello, & McLean, 2015), o la identificación de variedad de usos y actividades en la ciudad para conocer su complejidad (López Baeza, Cerrone, & Männigo, 2017;Shelton, Poorthuis, & Zook, 2015). El valor fundamental de estas fuentes es precisamente la granularidad de la información y los matices que ésta puede aportar por el hecho de combinar datos estructurados y no estructurados 3 en una única fuente global. ...
Article
ResumenLa popularización del ‘smartphone’ como dispositivo capaz de producir y tener acceso a información geolocalizada en el entorno físico del usuario, ha popularizado las plataformas de recomendación de lugares de interés. Nutriéndose de información proporcionada por sus usuarios, las actividades económicas en el espacio físico pueden o no contar con una representación digital accesible desde las plataformas —constituyendo la base de sus modelos de negocio. Dado que esta información es generada por los usuarios de la plataforma, puede suceder que algunas ubicaciones no queden representadas, o algunas actividades prevalezcan sobre otras. Considerando fiables las fuentes oficiales de datos abiertos, y por tanto aprovechables para verificar los datos colaborativos de las plataformas digitales, se ha elaborado una comparativa de éstas en el ámbito geográfico de Madrid, con el fin de evaluar los posibles decalajes entre la ciudad construida, y su representación digital —identificando los ámbitos urbanos sobrerrepresentados digitalmente, y aquellos en contraste segregados.AbstractThe popularization of the ‘smartphone’ as a user and producer of geolocated information has leveraged the consolidation of place-recommendation digital platforms. Based on user-generated data, the economic activities in the physical realm may have a digital representation through the platforms or not —constituting the backbone of their business models. Given that platforms rely on volunteered geographic information, some locations could be unrepresented, and some categories of activities might prevail. Considering institutional Open Data a reliable source to verify the collaborative data in the platforms, this work provides a comparison of both sources in the City of Madrid (Spain) to evaluate the potential disparities between the physical city and its digital representation —identifying those ‘digitally overrepresented’ or digitally segregated areas.
... Abarcan revelar determinados patrones espaciotemporales de conducta de la población y sus preferencias (Aliandu, 2015;Cerrone et al., 2018;A. Chen, 2005;Martí, Serrano-Estrada, & Nolasco-Cirugeda, 2017;Scellato, Noulas, Lambiotte, & Mascolo, 2011;Xia, Schwartz, Xie, & Krebs, 2014;Zhong, Arisona, Huang, Batty, & Schmitt, 2014); su percepción del entorno físico (Aiello, Schifanella, Quercia, & Aletta, 2016;Hess, Iacobucci, & Väiko, 2017;Pelechrinis & Quercia, 2015;Quercia, Schifanella, Aiello, & McLean, 2015), o la identificación de variedad de usos y actividades en la ciudad para conocer su complejidad (López Baeza, Cerrone, & Männigo, 2017;Shelton, Poorthuis, & Zook, 2015). El valor fundamental de estas fuentes es precisamente la granularidad de la información y los matices que ésta puede aportar por el hecho de combinar datos estructurados y no estructurados 3 en una única fuente global. ...
Article
Full-text available
Resumen La popularización del ‘smartphone’ como dispositivo capaz de producir y tener acceso a información geolocalizada en el entorno físico del usuario, ha popularizado las plataformas de recomendación de lugares de interés. Nutriéndose de información proporcionada por sus usuarios, las actividades económicas en el espacio físico pueden o no contar con una representación digital accesible desde las plataformas —constituyendo la base de sus modelos de negocio. Dado que esta información es generada por los usuarios de la plataforma, puede suceder que algunas ubicaciones no queden representadas, o algunas actividades prevalezcan sobre otras. Considerando fiables las fuentes oficiales de datos abiertos, y por tanto aprovechables para verificar los datos colaborativos de las plataformas digitales, se ha elaborado una comparativa de éstas en el ámbito geográfico de Madrid, con el fin de evaluar los posibles decalajes entre la ciudad construida, y su representación digital —identificando los ámbitos urbanos sobrerrepresentados digitalmente, y aquellos en contraste segregados. Abstract The popularization of the ‘smartphone’ as a user and producer of geolocated information has leveraged the consolidation of place-recommendation digital platforms. Based on user-generated data, the economic activities in the physical realm may have a digital representation through the platforms or not —constituting the backbone of their business models. Given that platforms rely on volunteered geographic information, some locations could be unrepresented, and some categories of activities might prevail. Considering institutional Open Data a reliable source to verify the collaborative data in the platforms, this work provides a comparison of both sources in the City of Madrid (Spain) to evaluate the potential disparities between the physical city and its digital representation —identifying those ‘digitally overrepresented’ or digitally segregated areas.
... By drawing from Four Corners, Instagram, Twitter, a rich set of maps and graphics have been elaborated by the SPIN unit. As an example, Fig. 3 shows the density and frequency of use of public places and services, determined through a previously tested methodology based on indicators drawn from social platforms open access data [26]. ...
Chapter
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Chapter
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