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Purpose – In the context of digital spatial analysis and modeling urban space and processes, this article presents a methodology to update and operationalize Jan Gehl’s traditional observations on activities people engage in public urban space. We aim to show how shared (big) data can help to understand contemporary urban processes and retool urban planning and management for the common good. Design/methodology/approach – The article details how newly computed analyses, such as Shannon-Wiener Index of complexity of activities as well as gravity and centrality indexes, can be implemented to study the experiential qualities of public spaces and development opportunities of urban spaces and neighborhoods. The proposed method is tested in the city of Turku in Finland, where an interactive interface called Turku Open Platform is used by developers and stakeholders, integrating these analytics to decision- making and public discussions. Originality/value – The so-called human behavior or city social dynamics or practices are not exclusively determined by the morphology of the place or its function, but they have an anthropological basis. Social needs (need for security, for openness, of play, for isolation and encounter, etc.) are anthropological requirements generated and developed socially. In this context, structure, function and form are not sufficient for the generation of social relations, but they can only favor it. By measuring these social needs stored in online social media servers, a new layer of the city is defined and thus, it is available for analysis and eventually intervention. This whole process constitutes the city as a hybrid space that can only be fully comprehended by analyzing the layers of information beyond the spatial form. A great part of this information is registered in online servers, and it is rated and reviewed by apps and social media users. This could be understood as a sample of human behavior or social dynamics and practices to which one can access by mining API data. Practical implications – Re-organising both location-based social media data, statistical sources and configurational spatial analysis, the presented method unearths emerging activity patterns across scales from local to regional, shifting focus from the traditional functional analysis of urban space towards understanding activities and, thus, the human perspective of use, practices and new agencies.
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Integrative Urbanism: Using Social Media to Map
Activity Patterns for Decision-Making Assessment
Damiano Cerrone *
School of Architecture
Tampere University of Technology
Korkeakoulunkatu 10, 33720 Tampere, Finland
Jesús López Baeza
Department of Building Science and Urban Planning
University of Alicante
c/ San Vicente del Raspeig s/n 03690 San Vicente del Raspeig,
Alicante
Panu Lehtovouri
School of Architecture
Tampere University of Technology
Korkeakoulunkatu 10, 33720 Tampere, Finland
* Corresponding author
Structured Abstract
Purpose In the context of digital spatial analysis and modeling urban space and
processes, this article presents a methodology to update and operationalize Jan Gehl’s
traditional observations on activities people engage in public urban space. We aim to
show how shared (big) data can help to understand contemporary urban processes and
retool urban planning and management for the common good.
Design/methodology/approachThe article details how newly computed analyses, such
as Shannon-Wiener Index of complexity of activities as well as gravity and centrality
indexes, can be implemented to study the experiential qualities of public spaces and
development opportunities of urban spaces and neighborhoods. The proposed method is
tested in the city of Turku in Finland, where an interactive interface called Turku Open
Platform is used by developers and stakeholders, integrating these analytics to decision-
making and public discussions.
Originality/value The so-called human behavior or city social dynamics or practices
are not exclusively determined by the morphology of the place or its function, but they
have an anthropological basis. Social needs (need for security, for openness, of play, for
isolation and encounter, etc.) are anthropological requirements generated and developed
socially. In this context, structure, function and form are not sufficient for the generation
of social relations, but they can only favor it. By measuring these social needs stored in
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Proceedings IFKAD 2018
Delft, Netherlands, 4-6 July 2018
ISBN 978-88-96687-11-6
ISSN 2280787X
online social media servers, a new layer of the city is defined and thus, it is available for
analysis and eventually intervention. This whole process constitutes the city as a hybrid
space that can only be fully comprehended by analyzing the layers of information beyond
the spatial form. A great part of this information is registered in online servers, and it is
rated and reviewed by apps and social media users. This could be understood as a sample
of human behavior or social dynamics and practices to which one can access by mining
API data.
Practical implications Re-organising both location-based social media data, statistical
sources and configurational spatial analysis, the presented method unearths emerging
activity patterns across scales from local to regional, shifting focus from the traditional
functional analysis of urban space towards understanding activities and, thus, the human
perspective of use, practices and new agencies.
Keywords Activities, Social Media, Urban Analytics, GIS, Urban Network Analysis
Paper typeAcademic Research Paper
1 Introduction
Sifting through aggregate social media data, anthropologists can reveal cultural
expressions and social norms. Within the same data, sociologists find social hierarchies
and the emerging roles of individuals in their communities. What happens when urbanists
look at the city through the eyes of social media data that is also geolocated? Datasets like
Instagram pictures and Foursquare venues provide us with new perspectives into
mediated urban spaces, where the physical and digital self affect the built environment,
leaving a digital trace of a physical presence in the form of data point.
Urban metaMorphology (Cerrone et al., 2015) explores this abundance of digital
traces to reveal the collective image of the city, bringing the point of view of dwellers and
users of public space closer to the processes of urban planning and management. While
the call for participation in planning is not new, digital tools and mobile Internet are
creating a new situation worth a careful study and inspirational action. As one element of
the current situation, we assume that shared (big) data on the locational patterns and
temporary rhythms of city life can help to retool urban planning and management for the
common good, deepening into the so-called socio-spatial realm; the dual relation between
people and space.
In this context, the categorisation of activities people engage in becomes an important
methodical issue. In urban planning, we are used to functional typologies that separate for
example housing, leisure and work, as well as organisational typologies that distinguish
public and private spaces and providers. National statistics, marketing and other fields
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have their own systems to make sense of the multiplicity of human behaviours and their
spatial underpinning.
In what follows, we argue for a novel approach, the “Urban Activity Wheel”, that re-
posits some classic readings on public space and human behaviour to the current moment
of mediated urban space.
2 Jan Gehl’s Categorisation of Urban Activities
In Life Between Buildings, originally published in Danish in 1971, Jan Gehl
distinguishes three main types of activity people engage in public urban space: necessary,
optional and social (Gehl 2006: 9-14). Gehl’s aim was to re-value the social significance
of urban spaces and improve their design in the context of modernistic post-war planning.
In his view planning ideology had led to a situation where “streets and squares
disappeared from the new building projects and the new cities” (ibid.: 45). Gehl notes that
the social consequences of modernistic planning were not discussed, because “it was not
recognized that buildings also had great influence on outdoor activities and consequently
on a number of social possibilities.” (ibid.:46) Thus, the evaluation of the modernistic
projects came relatively late. In 1970s and 80s, a widespread criticism led to alternative
design approaches that took inspiration from history, everyday practices, structuralism,
critical theory or a combination of these (eg. Rossi, Siza, Plater-Zyberk, Habraken,
Tschumi).
Interesting for the present research on categorizing activities in contemporary cities is
the way how Gehl and his contemporaries understood the linkages between space and its
use, on one hand, and between the different uses, on the other hand. Gehl’s basic
assumption is that the necessary activities are rather insensitive to the quality of physical
environment, while the optional activities are greatly influenced. This makes sense: one
goes to grocery store despite bad conditions but takes a sunbath only in good weather and
in nice location. The third category, social activities, are for Gehl “resultant”, because “in
nearly all instances they evolve from activities linked to the other two activity categories”
(ibid.: 12). Gehl puts high value on spontaneous “low intensity” social activities chance
encounters on streets and watching the crowd in line with other commentators such as
Jacobs 1969, Whyte 1988 and Sennett 2000. Passive and change contacts are valuable in
themselves as a form of enjoyable city life, but they also form a possible starting point for
contact at other levels (acquaintancy, friendship, partnership), give easy possibility to
maintain already established contacts, are source of information (especially for children
and newcomers) and may provide inspiration and stimulation (ibid.: 15). – In a nutshell,
for Gehl actively used public urban space is a key social good and physical urban design
has big role in facilitating that social good.
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3 Challenge to study the contemporary, mediated urban spaces
Fifty years after their original formulation, many of Gehl’s ideas still hold. Liveable
city and regional centres, densification, pedestrianisation, high-quality public space,
active street-frontage and programmed events have become standard practices, with
Gehl’s own architectural company as a visible player today. But many things have also
changed since 1970s. The influence of ICT on cities and the urban system has been
debated since 1980s (Castells, Graham & Marvin, Brenner & Schmid). For public urban
space, the recent popularization of the mobile Internet and the linked introduction of near-
ubiquitous smart devices is be a crucial issue, still to be conceptualized and probed. Our
agency as humans, as users of space and as activators of social phenomena is changing.
The role of physical space and age-old locational logics are mutating. To study the on-
going change, a relevant categorization of activities is a stepping-stone. In what follows,
we discuss the multiplicity of urban activities using Jan Gehl as a main inspiration, but
moving decidedly towards the digital realms.
Urban form is a classic object of enquiry in architecture and planning. Currently,
digital connectivity challenges the power of physical space in explaining how we use and
experience urban places. Whole new patterns of local, citywide, regional and international
uses are emerging. In architecture and urban planning, a rich tradition of analysis and
practice foregrounds physical space and spatial configuration as the set of constraints that
shape flows of people and direct the evolution of urban activity patterns.
We argue for a fresh view on urban dynamics, a view that puts the activities people
engage in buildings and public urban space on par with space. We claim that architects,
planners and developers can influence the evolution of cities as well through fostering
new activities as through intervening in space.
By collecting and analysing publicly shared social digital traces, Urban
metaMorphology breaks down the wealth of data at our disposal into singular choices. It
maps behavioural patterns to understand what type of environment social media users
prefer and how they choose to represent themselves in those spaces.
In the current study, we prioritise the use of Instagram and Foursquare, which remain
significant across countries and social strata. Harvesting data from those sources allows
us to abandon traditional survey methods, and rely almost exclusively on new
technologies that are undeniably widespread and capable to reach way more user than any
possible survey or users’ group analysis could. So we can perform partially automated
digital surveys that provide new insight into social patterns, as well as into the way the
city is lived and perceived. This way we can understand not only the relationship between
different groups and the city, but also the potential of certain locations and public spaces.
To do this, no personal data is harvested nor analysed, ensuring high ethical standards
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throughout the research. Nevertheless, we can highlight problems and find potentials for
urban development that were not ‘seen’ before.
Social dynamics are not exclusively determined by the morphology of a place but
they have an anthropological basis. Social needs (need for security, for openness, of play,
for isolation and encounter, etc.) are human requirements generated and developed in
accordance to cultural practices developed in physical place. In this context, planning for
structure, function and form are not sufficient for the generation of social relations; they
can only favour it. In the context where social platforms are ubiquitous, socio-cultural
expression and economic transactions are no longer space-bound, dramatically
influencing the determination of social needs, which can be influenced by global trends.
The spatial arrangement of places to satisfy those needs -- understood as accessibility,
location and values of urban amenities capable to host human activities -- is directly
related to the characterisation of spatial and temporal patterns of settlements. Spatial
relations that conduct the choice of location of these places have been studied by several
scholars. However, it is crucial to map and comprehend these relations within their social
and cultural frames of reference. More specifically, we canter on dispersal and clustering
phenomena to gain information about how the spatial connections between social and
economic amenities or vicinities are characteristic to the urban and regional contexts. This
approach is widely spread in academic research and urban consulting but it considers
visibility, vicinity and accessibility as tangible elements of the equation.
4 Dataset
Two social networks are used as the base of this study: Foursquare and Instagram.
Foursquare is based on a series of spaces listed by the users and categorized according to
the nature of the activity that can be performed in them. This social network has the
particularity of being used to register real time visits to places and award them with coins.
In consequence, further from the description of places, several studies talk about user's
location choice in the social network Foursquare and the relation to its game-like interface
capable of affecting individual's decision making within the urban context (Frith, 2014;
Jin et al., 2016; López Baeza et al., 2017; Schwartz and Halegoua, 2014), and being a
feature of the individual spatial self, which is described as an intentional representation of
lifestyle in order to perform social relationships (Barkhuus et al., 2008).
Instagram is based on an interactive platform in which users can upload photos and
videos and use them as nodes for interaction with other users through comments, likes
and messages. Content, motives, intention and styles of Instagram pictures have been
studied in several disciplines, since they constitute the birth of a new contemporary
graphic style consequence of new contemporary social behaviours and practices
(Manovich 2008). Acknowledging the relevance of Instagram from sociological and
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anthropological perspectives, this study centres mainly on spaces of representation and
activity depicted in order to extract socio-spatial information.
5 Operationalising Gehl’s ideas
Users’ performativity in location-based social networks centred on urban spaces is
contingent on the places where the actions take place. In Foursquare, registered places are
sorted into 'categories' attending to a main function or an action that it hosts. This
cataloguing suffers variations according to the social network the same space can be
classified as 'food' in Google Places, or as 'restaurant' in Foursquare. In this context,
when trying to operate with data from different social networks Foursquare and
Instagramthere is a need to equate a categorization that is common in terms of places
and in terms of actions.
Since our aim is study activity patterns across cities while maintaining the human
scale, a new categorization must be implemented, being broad enough to cover the entire
spectrum of human activities able to be performed within urban space, broadening Gehl’s
sphere of action -open pedestrian public spaces- up to the entire city.
5.1 Optional VS Social
Mapping activities begins with retrieving the Foursquare dataset of all urban
amenities and categorising them to the nature of the activity that can be performed in
them. Traditionally, Jan Gehl prosed and actuated a reliable classification system to study
public space, to study the links between design and public life. He first introduces the
concept in 1971 and sets it as the ground for further studies. The classification of
activities is defined as “the three types of pedestrian activities” suggesting that good
quality spaces need to contain a mix of all of them. These activities were classified in
Necessary and Optional. Necessary activities would be those, which are integrated, in the
daily routine and have to be done (going to school, buying the groceries, etc.). Optional
activities are those linked to leisure, social life and personal care (doing sports, being
together, walking the dog). Even though Gehl’s focus in small public spaces such as
streets, squares and parks, his approach can be used to survey urban space in general
thanks to its elemental yet powerful classification. Operationalising and scaling up this
approach can allow us to estimate and study activity patterns for one or many cities while
keeping the resolution of our analysis at the human scale. To do so we gather Foursquare
data and reclassify urban amenities according to a more discrete categorisation based on
the Gehl’s original definition of Optional and Necessary.
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5.2 Sub-categorisation
To be capable of working with these categories in a precise manner and define more
specific city patterns, it is necessary to have a finer grain of understanding of activity
patterns and define activity types more particularly.
Combining previous categorizations (Bowman 1998, Jiang et al. 2012, Axhausen et
al. 2000, Zhong et al. 2015 among others) with empirical observations and data retrieved
from content-centred social networks Instagram, Facebook, VK, Twitter and place-
centred platforms Foursquare, Google Places, OSM, we developed a categorization
including all activities capable of being performed in the city with the purpose of
recognizing specific spatial patterns. This observation is performed habitually with long-
term and micro-scale only surveys and field studies (López Baeza et al., 2017). The
following categorization of activities is constructed from the perspective of dwellers in
the city, considering what each urban amenities could offer them in their everyday life.
Taking care: to maintain or improve the condition of health (i.e. hospital, doctor,
dentist, health centre)
Income: to receive a payment for a good or service (i.e. office spaces, or any other
working activity)
Nutrition: to consume food with no other purpose (i.e. food that is ready to be eaten)
Mobility: horizontal physical displacement by a mean of transport (i.e.
transportation nodes or stations, commuting, travelling)
Education: to learn by being taught (i.e. schools, kindergarten, university, people
studying)
Civic: to pertain to citizenship (i.e. rallies, parades, public events and
commemorations, volunteering, going to church, all activities that are part of the
civic life of one community)
Leisure: to recreate by making use of the freedom from demands of work or duty
(i.e. picnic, playing, reading, exhibition, sun bathing, walking the dog)
Social: to be in companionship with others (i.e. bar, nightclub, café, home party,
dinner)
Consumption: to make use of a good or service (i.e. stores, boutiques, retail,
supermarket)
Personal care: to maintain or improve the condition of comfort, wellbeing, fitness
(i.e. sports, spa, nail care, make up)
This categorisation can also be used to study the differences between what the city
offers (supply) and what people do in the city (demand). By categorising places not by
function but in accordance with the spectrum of human activities can be performed in
them, we complete an indirect observation of activity patterns able to be compared from
local to regional scale.
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6 Case study: Turku
The test ground for our method is the city of Turku, located in the south-western coast
of Finland. Turku has about 190,000 inhabitants (region 300,000) and harbours some of
the oldest historical assets of the country. Because of fresh investments in culture and
industry, Turku is developing relatively rapidly, requiring changes in urban structure and
infrastructure. In 2017, City of Turku decided to finance through its Urban Studies
Programme a pilot study for a decision-making support system that would combine and
visualise different datasets, helping to recognise urban development potentials and discuss
about them both amongst planning professionals and with citizens and stakeholders.
The pilot, titled Turku Open Platform (TOP), evolved to an interactive web interface.
TOP website is currently in test-phase. It is used by urban planners, developers and other
stakeholders to learn about the city space with fresh eyes, assess potentials and support
decision-making and negotiations. A key feature is real-time visualization of users’ own
and context tailored indicators.
Figure 1: Image of the interactive web interface.
Indicators are grouped by the nature of their source, being: SPACE those related to
the mathematical computation of the street network from Space Syntax perspective,
ACTIVITIES those related to locations -establishments and places- and POPULARITY
those related to people’s preferences and activeness. Those indexes obtained from
Instagram and Foursquare are shown in the following table:
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Table 1. Indexes obtained from Foursquare and Instagram related to activities.
Source
Data
Calculation
ACTIVITIES
Diversity
Foursquare
All activities and categories
Shannon-Wiener
entropy
Optional Activities
Foursquare
Activities classified as optional
Gravity index
Necessary Activities
Foursquare
Activities classified as
necessary
Gravity index
Consumption
Foursquare
Activities classified as
consumption
Gravity index
Mobility
Foursquare
Activities classified as
mobility
Gravity index
POPULARITY
Venues Popularity
Foursquare
All activities weighed by
number of visits
Gravity index
Preference for indoor
spaces
Instagram
Pictures taken indoors
Gravity index
Preference for public
spaces
Instagram
Pictures taken outdoors
Gravity index
Lively Places
Instagram
Activities classified as optional
Gravity index
The tool is designed according to two different ways of space evaluation: ranking and
filtering. Ranking mode offers two simultaneous measurements of all indexes in two
selected urban spaces, allowing comparison between them. Filtering mode is
implemented to one single area and has the capability of allowing the user to modify the
visualization parameters (i.e. visualize areas with a high level of Consumption and
Leisure activity). Each parameter can be turned on/off and modified quantitatively to
match all possible visualization demands to facilitate negotiation.
Data from different sources have been post-produced through the application of
mathematical indexes described in this part. Not only we see the distribution of places but
also their affection to the spaces surrounding them. To do so, all computations are
measured from every point in each working dataset to every place in the city through a
regular grid of points. The output of this process is a grid of points, which covers the
entire city, where every point of the grid is weighted by the resulting value of the
application of each index.
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Figure 2. Fragment of the regular grid of points used for the calculation
6.1 UNA Gravity calculation
A measurement of distance along the network, computing the number of destinations
reached from each origin within a given radius, outputting a ratio of attractiveness divided
by the travel cost; the resistance of the formal street network itself as perceived sensitivity
to distance.
where Gravity[i]r is the resulting value in every point i of the grid within a r radius set
as 400 meters acknowledged as a walkable distance to every point j with a weight W
(Sevtsuk et al. 2013). When measuring ‘preference for public spaces’, the gravity index
would be implemented to all public spaces registered in Foursquare weighed by the
number of check-ins registered, measuring the distance from every point in the city grid
to every public space found in a 400 meter radius taking into account the street network
resistance.
6.2 Shannon-Wiener entropy index
An index based on entropy as a measure of diversity is employed to assess urban
complexity. Local amenities are classified within n common categories. This index, H(n),
is defined as follows:
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where pi is the probability that a venue belongs to a certain category, given by the
normalised number of venues (number of venues within the category divided by the total
number of venues in the city). Upper and lower bounds on H(n) are defined by,
respectively, a uniform distribution of venues across all n categories and a distribution in
which venues are concentrated within just one category. In other words, this computation
gives a measure of the amount of places found in an area, and the amount to different
types of places simultaneously (López Baeza et al. 2017).
7 Discussion and Conclusions
7.1 Experiences and results in Turku
As mentioned above, the Turku Open Platform is in test-phase. Regarding the general
question of IFKAD on how knowledge might influence decision-making and design, we
can thus offer some early observations, only.
Clearly, the new datasets did provide for new insights and new lines of thinking
amongst the planning and urban studies community of Turku. Even in a rather small city,
there are both ‘known un-knowns’ and completely hidden issues that the TOP tool has to
an extent brought to surface. The new knowledge and the new way to package and
present knowledge has a clear potential to influence decision-making and public
discussions on the development priorities and content of urban projects.
On the other hand, it seems that the very open approach regarding possibilities to
‘play’ with vary many possible combinations of data has caused confusion amongst test
users. There seems to be need to provide for a limited number of theory-based and expert-
proofed data and analysis options, with some threshold values in each dataset. This work
is not, yet, done.
Furthermore, questions that at first seem to be ‘technical’ in their nature, such as
computer power, transmission speed, and language of the application, may be very
important in making the application broadly used and liked by users. These issues will be
even more important in the future because after testing, City of Turku has expressed its
will to open TOP to fully public use. The way to realise the opening and find ways to
support multiple users remains to be decided.
7.2 Limitations
Since the majority of data gathered about the popularity of places is produced by
Instagram and Foursquare users and therefore, limited by the socioeconomic and
demographic segmentation of such. However, data centred in the description of
characteristics of places -instead of active human behaviour- have no such bias.
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It is crucial to perform a re-classification of the data prior to the implementation of
calculations and analytics. As discussed above, categories of activities in space must
cover the entire spectrum of what can be performed in the city, and be particular enough
to determine specific spatial activity patterns.
7.3 Methodological and theoretical implications
Since activities are the drivers of what makes a place urban, the methodology
proposed in this paper studies urbanity understood not only as structure, form and
function of the physical space but also as presence, diversity, and complexity of activities.
This grid-based approximation aims to reveal qualitative and quantitative characteristics
of the socio-spatial dialectic even when the physical morphology does not portray them.
Following the discourse of Bingham, Nabatchi and O’Leary (2005) regarding
management and the making of places, the current shift towards participation and active
bottom-up procedures portrays the exhaustion of the top-down-only tradition described by
Healey (1995), since local dynamics must be considered when acknowledging the city as
a procedural space. These small-scale dynamics are not always formal, built and tangible;
digital tools can help unveil them and make them measurable.
7.4 Potentials for Urban Planning
The method presented in this paper is placed in between the sphere of public
participation -in the sense that takes society into account by mining their behavioural
patterns- and elective democracy in the senses that a limited number of actors and
stakeholders take active role in decision-making and management processes.
These kind of hybrid approaches have been tested in the field of interface design for
integrative negotiation and put into practice with great success (Noyman et al. 2017).
Since “data driven technology and user-centric, co-creation approaches can eminently
support urban development and place-making activities” (López Baeza et al. 2018), a
new field of research is emerging and the applicability and operationalization of means
and methods is crucial at this stage. To do so, not only should we look into scenarios for
ways in which data collection and analysis can be deployed, but rather we should have a
wider awareness of how these techniques can be used to tackle new problems
complementing existing/traditional methodologies.
Acknowledgements
This work is co-financed by Tampere University of Technology’s URMI project
(Urbanization, Mobilities, Immigration SA 303618) and City of Turku’s Urban Studies
Programme.
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Delft, Netherlands, 4-6 July 2018
ISBN 978-88-96687-11-6
ISSN 2280787X
... El estudio de las tipologías de actividades dentro de la disciplina urbanística ha sido abordado previamente centrándose en la función primaria de los lugares, la correspondencia con las categorías normativas de usos del suelo, la importancia de la actividad en la rutina diaria o su grado de necesidad (Ben-akiva & Bowman, 1998;Buttimer & Seamon, 1980;Gehl, 1987;Jiang, Ferreira, & González, 2012;Seamon, 1979). Estas referencias son tomadas por Cerrone et al. (2018) en el desarrollo de una clasificación de actividades en el espacio urbano basada de manera primaria en la necesidad de acción -con origen en la primera clasificación de Gehl (1987) en actividades necesarias y opcionales-y que intenta abarcar todo el espectro de actividades urbanas, independientemente de la naturaleza del espacio en el que se realizan. Cerrone et al. (2018) proponen una clasificación basada en dos categorías ("Necesaria" y "Opcional") y diez subcategorías, cinco en cada uno de esos dos grupos, recogidas en la tabla 2. A estas diez subcategorías se añade otra sin definir ("Otro"), la cual engloba aquellos lugares sin una actividad primaria definida, como barrios, calles o edificios entendidos como "un lugar" en su conjunto. ...
... Estas referencias son tomadas por Cerrone et al. (2018) en el desarrollo de una clasificación de actividades en el espacio urbano basada de manera primaria en la necesidad de acción -con origen en la primera clasificación de Gehl (1987) en actividades necesarias y opcionales-y que intenta abarcar todo el espectro de actividades urbanas, independientemente de la naturaleza del espacio en el que se realizan. Cerrone et al. (2018) proponen una clasificación basada en dos categorías ("Necesaria" y "Opcional") y diez subcategorías, cinco en cada uno de esos dos grupos, recogidas en la tabla 2. A estas diez subcategorías se añade otra sin definir ("Otro"), la cual engloba aquellos lugares sin una actividad primaria definida, como barrios, calles o edificios entendidos como "un lugar" en su conjunto. Estos lugares existen en la base de datos, también registran visitas por los usuarios y son accesibles a la búsqueda por nombre, por lo que a pesar de no describir una actividad concreta, su utilidad en el estudio de hitos y toponimia no queda en absoluto descartada. ...
... De los 4264 lugares registrados en los desarrollos urbanos estudiados podemos extraer diversas características generales, partiendo de la tipología de actividad de los lugares, según el trabajo desarrollado por Cerrone et al. (2018). En cuanto a tendencias generales, la tabla 3 muestra la agregación de datos cuantitativos por cada una de las categorías, presentes en todas las piezas de estudio. ...
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... Social media entices to share highlights of everyday lifethus pleasant disruptions in the everyday flow are shared. The content of social media has recently provided a new, still developing method to map urban experiences and opinions (Cerrone et al., 2018). Following the logic of social media, a disruptive element in urban space has potential to circulate beyond its physical location. ...
... On the field of culture-led urban development, research results often end up qualitative and subtle. In order to conduct a quantitative social media analysis (Cerrone et al., 2018), a research strategy for reaching more users and posts would be necessary. ...
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... From Tripadvisor, the hotel, restaurant, and attractions data for the Alessandria district were taken, and were provided with qualitative (rating, ranking, number of reviews) and quantitative (the content of reviews, categories) attributes, and geolocated as punctual geometries through WGS84 coordinates. In addition, referring to some experiments in the urban analysis [51,52] that work on Location-Based Social Media (LBSM) data to investigate the perception and use of public spaces of both local citizens and temporary communities, Instagram, Foursquare, and Flickr were included as sources. ...
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The design of urban public open spaces plays a key role in the development of micro-scale reactions to global phenomena (pandemic, climate change, etc.) that are currently reshaping the human habitat. Their transformability and healthy influence on the urban environment make them strategic nodes for acupunctural regeneration with systemic effects. Several methods, models, and indicators have been developed to face the complexity of these spaces, made up of tangible and intangible layers; however, there is a gap between theoretical investigation and the need for public administrations to devise feasible solutions, strategies, and guidelines. The paper focuses on this mediation, presenting, as a case study, an adopted methodology and the first results achieved according to guidelines for the regeneration of the system of squares in the historical center of Alessandria (Piedmont, Italy). In this case, a multidisciplinary approach and a Multi-Criteria Analysis (MCA) method, supported by geospatial analysis and GIS technology, have been employed to work as mediators for a participatory process which will involve public administration, stakeholders, experts, and researchers. The paper presents an overview of the workflow, with a focus on the first set of thematic indicators and an open conclusion. It will explain how they have been defined, integrated, and turned into a dialogic tool, with the aim of laying the foundation for the next stage of involvement by the public administration and stakeholders. Specific attention will be paid to the key role of vegetational and environmental parameters, which represents the requalification strategy’s backbone, for both local and systemic scales.
... Secondary data are commonly retrieved using passive monitoring of data collection while primary data can only be collected using active monitoring (de Castro Neto & Melo Cartaxo, 2019). Means for passive monitoring may include, environmental sensors, IoT, crowdsensing, social media (Yabe & Ukkusuri, 2019;Cerrone et al., 2018), cellular phone activity (Kim, 2020) to monitor user activities, computer vision (Ibrahim et al., 2020), cyber-physical sensing (Celes et al., 2019), drones (Barmpounakis & Geroliminis, 2020) or lidar systems (Shirowzhan et al., 2019) to monitor traffic congestions. While, primary data are generated through active monitoring via methods involving the direct and intentional input by humans, like GIS-web tools, collaborative platforms, eparticipation systems or social media. ...
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... Los estudios en este dominio persiguen objetivos diversos dada la gran diversidad de plataformas que se han consolidado durante la última década. 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). ...
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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.
... Los estudios en este dominio persiguen objetivos diversos dada la gran diversidad de plataformas que se han consolidado durante la última década. 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). ...
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... Cities have become very powerful generators of information, which remains stored in immaterial layers of data overlapping physical places. Location-based data services can be used to extract such information and facilitate the understanding of the urban and social dynamics found in them by further analysis (Cerrone et al. 2018) and research methodologies across several disciplines, namely social sciences, computational methods, and urban studies. ...
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This dissertation investigates retail location patterns in urban settings -- a domain that has received relatively little attention in recent decades. We analyze which land use, urban form, and agglomeration factors explain observed retail patterns in an empirical case study of Cambridge and Somerville, MA. We are particularly interested in whether and how the distribution of retailers is affected by the spatial configuration of the built environment -- the physical pattern of urban infrastructure, the spacing and sizes of buildings, and the geometry of circulation routes. We argue that understanding retail location patterns in urban settings is not only important for improving retail location theory, but also essential for designing economically, socially, and environmentally sustainable urban neighborhoods. The dissertation proposes a novel graph-analysis framework in which retail location patterns can be represented under realistic constraints of urban geometry, land use distribution, and travel behavior. A series of spatial accessibility metrics, which we hypothesize to affect retail location choices, are introduced and applied in this framework using individual buildings as units of analysis. In order to test the statistical significance of these different metrics on retail location choices, we adopt the strategic interaction methodology from spatial econometrics and apply it for the first time in the context of location studies. We specify a linear probability model with a binary dependent variable and estimate how buildings' probabilities to accommodate retail establishments relate endogenously to other retailers' location choices and exogenously to both land use and urban form characteristics around each building. We apply the model to all retail and food-service establishments as a group and to different three-digit NAICS establishment categories individually. The results confirm that retail location choices in our study area are significantly related to both other retailers' endogenous location choices and exogenous land use characteristics around each building. However, controlling for both of these factors, we find that the spatial distribution of retail activity is also significantly related to the geometry of the built environment. By setting constraints on accessibility, visibility, adjacency, and density, the geometry of the built environment produces a rich landscape of information that appears to guide opportunities for business from building to building. The findings inform economists and planners about factors that attract retailers in urban settings, and urban designers about how the seemingly basic act of laying out streets, parcels and buildings can affect the location choices retail and service land uses, thereby shaping the economic structure of the city in important ways.
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How do we accommodate a growing urban population in a way that is sustainable, equitable, and inviting? This question is becoming increasingly urgent to answer as we face diminishing fossil-fuel resources and the effects of a changing climate while global cities continue to compete to be the most vibrant centers of culture, knowledge, and finance. Jan Gehl has been examining this question since the 1960s, when few urban designers or planners were thinking about designing cities for people. But given the unpredictable, complex and ephemeral nature of life in cities, how can we best design public infrastructure vital to cities for getting from place to place, or staying in place for human use? Studying city life and understanding the factors that encourage or discourage use is the key to designing inviting public space.
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Foursquare is a location-based social network (LBSN) that combines gaming elements with features conventionally associated with social networking sites (SNSs). Following two qualitative studies, this article sets out to explore what impact this overlaying of physical environments with play has on everyday life and experiences of space and place. Drawing on early understandings of play, alongside the flâneur and ‘phoneur’ as respective methods for conceptualizing play in the context of mobility and urbanity, this article examines whether the suggested division between play and ordinary life is challenged by Foursquare, and if so, how this reframing of play is experienced. Second, this article investigates what effect this LBSN has on mobility choices and spatial relationships. Finally, the novel concept of the ‘phoneur’ is posited as a way of understanding how pervasive play through LBSNs acts as a mediating influence on the experience of space and place.
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Identifying changes in the spatial structure of cities is a prerequisite for the development and validation of adequate planning strategies. Nevertheless, current methods of measurement are becoming ever more challenged by the highly diverse and intertwined ways of how people actually make use of urban space. Here, we propose a new quantitative measure for the centrality of locations, taking into account not only the numbers of people attracted to different locations, but also the diversity of the activities they are engaged in. This ‘centrality index’ allows for the identification of functional urban centres and for a systematic tracking of their relative importance over time, thus contributing to our understanding of polycentricity. We demonstrate the proposed index using travel survey data in Singapore for different years between 1997 and 2012. It is shown that, on the one hand, the city-state has been developing rapidly towards a polycentric urban form that compares rather closely with the official urban development plan. On the other hand, however, the downtown core has strongly gained in its importance, and this can be partly attributed to the recent extension of the public transit system.
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Online social platform, such as Wikipedia and Foursquare, has been increasingly exploded due to not only various useful services provided but also social gaming mechanisms that can keep users actively engaged. For example, users are awarded ”virtual goods” like badges and points when they contribute to the community in the network by voluntarily sharing ideas and other information. In this paper, we aim to examine the effectiveness of a social gamification mechanism, named user scores, designed in Foursquare which is one of most popular location-based social networks. A user’s score in Foursquare is an aggregate measure based on recent check-in activities of the user, which reflects a snapshot summary of the user’s temporal and spatial behaviors. Whenever a user checks in to a venue, a list of scores of the user’s friends are visible to the user via a ”leaderboard” which ranks these users’ scores in a descending order. Given a pair of friends who participate in a score competition in such a gimification mechanism, we identify if one user’s scores have significant influence on the other user’s scores by utilizing the Granger Causality Test. To understand what types of users and what types of friends tend to participate in the score competition (i.e., their check-ins are more likely driven by such a gamification mechanism), we extract users’ features (e.g. user’s degree) as well as the features of pairs of friends (e.g., number of common friends, score similarity and ranking difference) to examine whether these features have correlations with those pairs of users who are identified as being involved in the score game. The identified influence on user scores has the important implication on applications including friend and venue recommendations in location-based social networks.