10.6092/issn.2038-3460/17379
In the context of data driven cities, this paper introduces the notion of an “Internet of Buildings” (IoB), and discusses the potential of connected devices, sensor networks and data analysis to support the purposeful design and development of a livable, well-designed urban environment. The key argument of this paper is that today's Internet of Buildings (IoB) permits the collection and analysis of rich data sets on users and usage, on building and city performance, thereby providing a reliable basis for design decisions and strategies that not only improve design processes, but also enable a more user-oriented, participative and human-centric approach. In addition, this article argues for a responsible and reflexive usage of data generated in living environments and for data literacy in the context of urban design and development. The key challenge addressed in this paper is how to translate urban data into design knowledge. To provide an answer to this important question, this article introduces a new methodology that links urban design, urban data, and the operational modelling of cities to an evidence-based, agile urban development process. On that basis, the article introduces the two tools of “BuildingID” and “UrbanOperationsModel” (UOM) – key instruments for data-based development and oriented towards the “good city” of the future.
application/pdf
2038-3460
17379
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en
Jörg Rainer Noennig
https://creativecommons.org/licenses/by-nc-sa/2.5/
Tecnoscienza – Italian Journal of Science & Technology Studies
urban operations
How to Design the Internet of Buildings? An Agile Design Process for Making the Good City
Text.Serial.Journal
https://tecnoscienza.unibo.it/
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Essays
2018-01-29
2023-06-25
2018-01-29
2023-08-24
105-128
TS
Jörg Rainer Noennig
2017
10.6092/issn.2038-3460/17379
105
TU Dresden
2038-3460
2
urban operations
en
128
How to Design the Internet of Buildings? An Agile Design Process for Making the Good City
https://tecnoscienza.unibo.it/article/download/17379/16236
8
                                                                                Essay
                                                                                        105




How to Design the Internet of Buildings?
An Agile Design Process for Making the Good City

     David Hick                         Adam Urban            Jörg Rainer Noennig
      TU Dresden (DE)                      TU Dresden (DE)        TU Dresden (DE)



   Abstract: In the context of data driven cities, this paper introduces the no-
   tion of an “Internet of Buildings” (IoB), and discusses the potential of con-
   nected devices, sensor networks and data analysis to support the purposeful
   design and development of a livable, well-designed urban environment. The
   key argument of this paper is that today's Internet of Buildings (IoB) permits
   the collection and analysis of rich data sets on users and usage, on building
   and city performance, thereby providing a reliable basis for design decisions
   and strategies that not only improve design processes, but also enable a more
   user-oriented, participative and human-centric approach. In addition, this ar-
   ticle argues for a responsible and reflexive usage of data generated in living
   environments and for data literacy in the context of urban design and devel-
   opment. The key challenge addressed in this paper is how to translate urban
   data into design knowledge. To provide an answer to this important question,
   this article introduces a new methodology that links urban design, urban data,
   and the operational modelling of cities to an evidence-based, agile urban de-
   velopment process. On that basis, the article introduces the two tools of
   “BuildingID” and “UrbanOperationsModel” (UOM) – key instruments for
   data-based development and oriented towards the “good city” of the future.

   Keywords: Internet of Buildings; cyber-physical systems; urban data; building
   information; urban operations.

   Submitted: October 15, 2016 – Accepted: November 15, 2017

   Corresponding author: David Hick, TU Dresden, School of Civil and
   Environmental   Engineering, 01062  Dresden,   Germany.    Email:
   David.Hick@tu-dresden.de




1. Introduction

    Approximately a decade ago, the shift towards information society and
ubiquitous data technology originated the term “smart city”. This debate
is supported by various predictions foreseeing a dramatic increase in the

TECNOSCIENZA
Italian Journal of Science and Technology Studies
8 (2) pp. 105-128 – ISSN 2038-3460
www.tecnoscienza.net                                   2017
106                                                                Tecnoscienza – 8 (2)

number of intelligent and networked objects in an urban context. Gartner
Symposium ITxpo1 has announced a 200% increase by 2020 with respect
to 2016, while companies like Cisco announce even higher figures. A cen-
tral tenet of the Smart City, largely technology driven, is the integration of
ICT systems for creating synergies and improved urban quality of life
(Batty et al. 2012, 483-518). Yet, multiple urban challenges accompany this
development: how can Cyber-Physical Systems (CPS), the Internet of
Things (IoT)2 or the Internet of Everything (IoE)3 meaningfully support
the development of well-organized urban systems with a high quality of
living, engagement, and social cohesion? So far, IoT applications are driven
by the IT industry and digital business world. Urban environments and
operations have only recently come into focus as strategic fields of applica-
tion.
    To highlight this new trend towards networked buildings and urban
spaces, we introduce the term “Internet of Buildings” (IoB)4. We maintain
that it is necessary to establish a focused debate on connected buildings
and urban technologies from the perspective of an urban planner and ar-
chitectural designer, given that digital technologies will have a direct im-
pact not only on visual appearance, on functional infrastructures of build-
ings and cities, and on the operations and performance of cities, but also
on professional key practices such as creative design, concept-creation and
planning. Evidence-based practices are emerging that characterize design
and planning activities as services, based on urban and building infor-
mation, and on public and institutional data.
    A telling indication can be given by the example of Sidewalk Labs, a
Google/Alphabet spin-off5. The mother company clearly anticipates prof-
itable opportunities in the field of digital urban services. Among other
powerful applications and technologies, it has established urban mapping
and home sensing systems, as highlighted by the acquisition of the sensor

      1
       See http://www.gartner.com/newsroom/id/3175418 (retrieved March 22, 2017).
      2
       IoT is the vision of a ubiquitous digital machine to machine network conne-
cting a high amount of everyday ‘smart’ objects embedded with sensors and
processors to collect, exchange and combine data. There is no common consensus
on how to systematically transform the data generated into social or economic
value.
     3
       The ‘Internet of Everything’ is an extension of the term ‘Internet of Things’,
established in 2013 by IT company Cisco. In contrast to the Internet of Things, it
implies not only connections of computer systems, but also of people, and the usage
of behavior information measured by smart gadgets (cf. http://ioeassess-
ment.cisco.com/).
     4
       IoB consists of systematic and hierarchical structures with a clearly defined
goal. It is a scalable network of relationships between quarters, streets, buildings,
apartments, social life and quantitative physical factors like climate and air/water
quality, aiming to get a profound understanding of the complex interplay of urban
life. IoB can be considered a subordinate component of an IoT infrastructure.
     5
       See https://www.sidewalklabs.com (retrieved March 22, 2017).
Hick, Urban and Noennig                                                  107
company Nest in 2015. Now, Sidewalk Labs is creating platforms for urban
analysis based on data collected via these services. Apart from the specific
urban services already up and running (e.g. optimizing traffic and trans-
portation flow), the company is expected to commence experiments in
data-driven urban design and city management with full-scale test projects
soon. This is in line with large-scale digital city experiments such as We-
Sense by the Amsterdam Institute for Advanced Metropolitan Solutions6.
This integrated data platform maps citizens’ perceptions, use and evalua-
tion of the public environment in Amsterdam. Another indicative project
is City Keys (2017) which defines citizens’ needs, analyzes results, and gen-
erates design recommendations by way of using performance indicators.
These, in turn, are informed by sensor data from large urban areas in Eu-
rope. Also of note in the aforementioned Dutch city is the world's largest
data bank of Smart city projects, the Amsterdam Smart City Platform7.
    Despite their rapid development in the field of ICT, Smart City projects
and activities have created few links to the classical domains of urban de-
sign, architecture, and spatial planning in relation to procedures and meth-
odologies. There is a surprising disconnection between the emerging digi-
tal city with its ubiquitous ICT components on the one hand, and the tra-
ditional design and planning processes for the physical city on the other
hand. While new communication and information technologies gain ever
more importance in personal and social life, aspects of urban life such as
building construction, the public realm, and the provision of social or cul-
tural facilities remain central concerns of the citizenry.
    The authors perspective derives from a formal architectural training in
various academic institutions (Germany, Austria, Japan, Poland, Czech
Rep.) and design practice at an international level, ranging from architec-
tural competitions to construction projects. With the exception of design
tools like CAD, parametric design software and Building Information
Modelling (BIM), the practice of urban and architectural design rarely in-
tegrates “smart” IoT or CPS technologies8, which are traditionally not re-
garded as components in conventional design practice or education. We
observe two directions; the first seeking to digitalize the built environment
versus the main second direction continuing to revere human intuition and
aesthetics as the sole methodological instruments of design. This latter di-
rection is based on the principle that architecture is a discipline between
science and art, and which can only be truly understood by professionals.
Few research institutions actively engage in cross-disciplinary research
combining design and IT (e.g. MIT Media Lab, HCU City Science Lab or
the Institute for Computational Design and Construction of TU Stuttgart).

    6
      http://www.wesense-app.com/wp-content/uploads/WeSense_artikel–ener-
gie–spektrum_en.pdf (retrieved March 22, 2017).
    7
      See https://amsterdamsmartcity.com/ (retrieved March 22, 2017).
    8
      One example is the ‘Smart Home’ promoted by huge technology companies
like Samsung or Bosch.
108                                                               Tecnoscienza – 8 (2)

Consequently, the greater part of the profession resists current develop-
ments concerning advancing digitalization and automation. Perusing the
latest architectural journals (such as “Bauwelt” 20/2017, “Detail” 10/2017,
“Architectural Review” september 2017 and “Arch+” 229) there is a dis-
proportionally small amount of discussion on ICT in the building sector.
     As a result, the approach presented in this paper attempts to connect
the domains of data technology and data science with planning and design
sciences. The central question is how to inform and support user-oriented
design work at the urban scale with data acquired from buildings and pub-
lic spaces. In other words: how to derive qualitative design decisions from
data collection and analysis?
     As a starting point for the creation of a methodology that comprehen-
sively uses urban data in urban design, we hypothesize that the goal of ur-
ban design and development should be the “making of the good city”. Fol-
lowing Jane Jacobs (1961), Ray Oldenburg (1999) and Edward Glaeser
(2011), a good city can be summarized as a multitude of rich, lively, adapt-
able and organically developing places. Its structures and spaces grow
steadily and incrementally, and possess the capacity to respond to the
changing needs and emotions of its inhabitants. The development of his-
torical cities with high urban quality (e.g. Italian Renaissance towns like
Florence or Siena) is in stark contrast to current ad hoc developments,
which often result in generic neighborhoods with low urban quality, uni-
form typologies and building patterns. Rapid land development and spec-
ulative investments have replaced the organic processes of urban self-or-
ganization and construction. Due to the financialization of the urban envi-
ronment as a means of private property management, the social, emotional,
and cultural needs of residents and inhabitants as key users of cities are
often insufficiently respected. Due to well-established processes in real es-
tate business, large districts are developed in a short timeframe without
reference to local culture, history, or society. This leads to disconnected,
insufficiently integrated urban quarters with low quality of life over long-
time spans.
     As a counter-reaction, however, alternative approaches such as incre-
mental and participative planning have emerged, a trend that can be sup-
ported by advanced information and communication technology (ICT).
For example, a Horizon2020 project called U_CODE Urban Collective
Design Environment9 will deploy a participatory platform for the purpose
of co-creating urban environments on a massive scale enabled by digital
technologies. Here, urban designers, architects and developers will collab-
oratively design and communicate projects with the various public stake-
holder groups.
     In ancient Greek towns, the emergence of agoras triggered communi-
cation between the citizens, pushing forward the public and democratic
debate. Habermas (1989) calls public areas where social life comes together

      9
          See http://www.u-code.eu/ (retrieved March 22, 2017).
Hick, Urban and Noennig                                                      109
as the “public sphere”. Correspondingly, Oldenburg (1999, 22) defines
those areas as the “neutral ground upon which people may gather”, calling
them “the third place”, with the first place being “home” and the second
“work”. The main activity on neutral grounds is the informal conversation
between citizens that forms a collective understanding of society, innova-
tion, and diversity, as well as the “formation of public opinion, conference
about matters of general interest or debate over general rules governing
relations” (Habermas 1989, 27). This kind of social gathering forms the
basis for raising quality of life in cities. Through public deliberation, factors
defining quality of life like recreation areas, functioning infrastructure and
a vibrant economy can evolve.
    There are numerous examples of neutral grounds in different cultures,
e.g. the French bistro, the German beer-garden, the English pub or the
American main street (Oldenburg 1999). These examples show that not
only actual urban places, but also business ventures can enable the public
sphere. Historical towns like Siena or Florence developed a multitude of
(third) places triggering the growth of a diverse society with complex urban
interplay between voids and buildings. This process must develop over
time and cannot be set up in ad-hoc developments. The prerequisite is the
acceptance of those places as neutral grounds by society. Bridging the gap
between the historic and the contemporary town we believe that “third
places” necessarily do not need to be physical. Rather, they can be digital,
and more importantly for future urban development, a combination of
both. This fusion of digital and physical neutral ground can be defined as
“hyperlocal forums”.
    Current digital urban infrastructures and algorithms model citizens
mainly as “living sensors” or network clients within a larger cyber-physical
system, which represents a narrow understanding of the interplay between
human activity and the quality of the urban environment (Farías and Blok
2016). Data-driven urban design which works towards the Good City max-
imizes not only the gains on the side of technology vendors, but also ex-
pands the reach of political power and the roles and responsibilities of the
social groups, neighborhoods, and communities that need to be defined
with respect to urban data generation, governance, and utilization.
    The key approach taken here may be called creative urban data literacy,
as it implies the practical and creative usage of self-generated urban data
by the local community. The goal is to pave a way towards hyperlocal fo-
rums that connect and empower citizens as individual data entrepreneurs,
creative urban hackers, or service providers. Also in digital terms, cities
need to be developed on a district-by-district basis, leading to a replace-
ment of the neoliberal top-down approach. The “right to infrastructure”
(Corsín Jiménez 2014), as derived from Lefebvre’s “right to the city”, also
implies new forms of digital collaboration, as exemplified by fab-labs or
hack-labs, for example. Such infrastructural enablers empower bottom-up
development, or soft digital urbanism. Through the systematic extension
of know-how and information, the simplification of construction processes
110                                                           Tecnoscienza – 8 (2)

and juridical frameworks, citizens can be enabled to design and build cities
on the basis of their own data. Collective experimentation with urban data
renders cities as socio-technical assemblages open to contingent political
contestation (Jiménez 2014), created through digital participation and
knowledge production, and its subsequent validation in terms of urban
products and services. The concept of “urban learning forums” (McFar-
lane 2011) outlines such possibilities in urban planning. Ideally, such meth-
odology might lead to self-sufficient communities even at the level of data-
production and consumption, with a hybrid forum serving as a place for
knowledge exchange between experts and lay people (Callon, Lascoumes
and Barthe 2009).
     Accordingly, the scheme proposed in our paper builds upon the idea
of a hyperlocal community, or a “Quarter Community” which, via digital
technologies, is able to recognize and interpret subjective indicators such
as procedural constraints for urban development. Here, subjective and hy-
brid data generation that does not reduce citizens to passive objects of dig-
ital technology forms the core of the urban data community. We regard the
human subjective dimension an enhancement of the objectivized, Euclid-
ian urban space which still forms the basis for most conventional architec-
tural and urbanist representations (Latour and Yaneva 2008).


2. Data for the Good City

2.1 The Livable City

    The “good city” implies qualitative goals for urban development. Allan
Jacobs and Donald Appleyard have defined a value framework for the
good urban environment with seven characteristics: livability, identity and
control, access to opportunity, imagination and joy, authenticity and mean-
ing, open communities and public life, self-reliance, and justice (Jacobs and
Appleyard 1987, 115-116). This indicates that a Good City shall not be
equated solely with a Livable City, as the latter appears to be a subcategory
among other influential values. A good urban environment balances these
goals on both an individual and collective level (Jacobs and Appleyard
1987, 112-120).
    For livability, there is certainly no universal definition. Charles Landry
(2000, 21) points out that the inhabitants of Northern cities have higher
standards of living and therefore can consider clean air, public realm, or
cultural facilities as key quality of life factors for livability, whereas in
poorer places quality of life is related to work and the education system, or
infrastructure. Taking this relativity into consideration, indexes for livabil-
ity (Quality of Life) have emerged in recent decades, measuring livability
and its associated factors.
Hick, Urban and Noennig                                                        111
2.2 Quality of Life – Objective and Subjective Indicators

    Quality of Life indexes utilize different benchmarking procedures to
rank cities and countries. Relevant indexes for this approach are the Hu-
man Development Index from the United Nations (HDI)10, the Happy
Planet Index from the Economics Foundation (2016), the Morgenstadt In-
dex (Tomorrow’s City Index) from the Fraunhofer Institute (IAO)11, and
the ISO 37120 from the World Council on City Data (WCCD)12. This last
index is the first to work on the development of an international standard
applicable to all cities. Both the Morgenstadt Index and ISO 37120 are
useful for determining data for the “Good City”, as they collect qualitative
city data at the local level.
    The Morgenstadt Index was created through a detailed investigation of
publicly accessible indicators to form a holistic picture of the future viabil-
ity of a city, and as a first basis for an in-depth analysis of urban neighbor-
hoods. The proposed indicators cover four basic pillars on which a city
must be based: quality of life, resilience, environmental protection and in-
novation potential. These pillars were broken down into 28 detailed indi-
cators informing quality of life, and evaluated according to their absolute
and relative values.
    The ISO 37120 index by WCCD encompasses an international network
of innovative cities using open data to create a platform that maps stand-
ardized urban metrics. It has the aim of pushing innovation forward and
envisioning livable cities. Here, the indicators are categorized into 17
themes on city services and quality of life, such as environment, economy,
education, and transportation.
    Our research group has analyzed these indexes to determine which rel-
evant data need to be collected and processed to inform the design and
development of the “Good City”.
    These indexes and indicators gave useful indications on which urban
data to collect and analyze, but they do not fully indicate the dynamics and
progression of urban areas. The indexes are very global in nature; they
measure society as a collective, but do not represent individual subjective
perception. Quality of life and well-being, however, need to be related to
dimensions on which an individual’s living conditions can be measured,
which may range from rather objective indicators (e.g. economic well-be-
ing, human capital) to more subjective indicators (e.g. social capital, per-
sonal satisfaction) (Giap, Thye and Aw 2014, 178). To assign to these hard-

    10
       Human Development Index (2016) [United Nations Development Program-
me], http://hdr.undp.org/en/2016-report/download (retrieved March 22, 2017).
    11
        See the Fraunhofer Institut für Arbeitswissenschaft und Organisation, http-
://www.morgenstadt.de/de/loesungen2/loesungen_staedte/morgenstadt_index.-
html (retrieved March 22, 2017).
    12
       See the World Council on City Data Foundations,
http://www.dataforcities.org/wccd/ (retrieved March 22, 2017).
112                                                            Tecnoscienza – 8 (2)

to-survey subjective data a higher impact, individual feelings concerning
urban surroundings (joy, imagination, opportunity) require a better defini-
tion and description, such as through explicit measures for identity, diver-
sity and social network dynamics (Landry 2000, 21).
    Combined subjective and objective indicators which comprise both in-
dividual and collective experience provide for a meaningful utilization of
urban data for the design of the “Good City”. Although technologies for
the collection of individual subjective information are still in their infancy,
such urban data will represent city dynamics on a higher value level, and
thus positively inform urban interventions. Collected and analyzed by In-
ternet of Building technologies, their very value may arise from short-term
(soft) spatial interventions as well as from long-term, permanent deploy-
ment. In both cases, they supply the development of urban areas with user
experience and citizen knowledge. “Livehoods”13 is a current example for
mapping social dynamics, structure, and character through the analysis of
users’ behavior data in diverse cities. Here, the aim is to observe patterns
in locations across the city to map different dynamic areas using social me-
dia check-ins. Accordingly, the hypothesis is that an individual Livehood’s
character is shaped not only by objective data, but also through the subjec-
tive behavior of citizens.

3. Key Questions

   We have developed our methodology for data-driven urban design with
three questions in mind:
        • Identification: Which data are relevant for designing good ur-
            ban quarters?
        • Acquisition: How to systematically collect relevant data in ur-
            ban environments?
        • Intelligence: How to derive design knowledge from collected
            data?

3.1 Identification: Which Data Are Relevant for Urban Design?

    We examined the Morgenstadt and ISO 37120 indices and devised a
comparative representation. Based on relevant urban design categories, all
related information from the indexes were assembled into one table. The
table has been extended by further data, not yet covered by these indexes
like food quality to mirror broader economic and social relevance in the
districts14. The resulting shortlist of key data to be collected from urban



     http://livehoods.org (retrieved March 22, 2017).
      13

     See Government of Canada, Agriculture and Agri-Food http://www.agr.gc.-
      14

ca/eng/industry-markets-and-trade/statistics-and-market-information/agriculture-
Hick, Urban and Noennig                                                   113
and building environments can serve as a basis for decision-making in ur-
ban design and planning (see Table 1). The table is divided into the City
Data section which comprises information referring to the natural and ar-
tificial environment collected with quantitative methods (e.g. statistics, re-
mote sensing, observations) reported as values or numbers, and the Indi-
vidual Data section which comprises relational information on individuals
and communities connected to their immediate environment collected via
qualitative methods (e.g. surveys, questionnaires, interviews, gamification).
Both data types are interrelated and can be juxtaposed. To get more de-
tailed insights, both data types (City Data & Individual Data) can be re-
combined: e.g. combining data about “Square meters of recreation & green
space” with “Individual perception about the atmosphere of recreation &
green spaces” could lead to the new data set “Efficiency of distribution of
green spaces in a city”. Furthermore, the combination of the juxtaposed
data sets “Number of businesses” with “Individual impressions about
availability of businesses and services in a city”“ could lead to the data set
“Diversity and fair distribution of businesses and services in a city”. Re-
combination and mining for data relations will be necessary to approach a
comprehensive understanding.

3.2 Acquisition: How to Systematically Collect Relevant Data in
Urban Environments?

    A major challenge for data-driven urban design is the definition of ap-
propriate sources from which design-relevant data can be collected. Thus,
we have further differentiated data resources according to their dynamics.
First, there are resources like municipal archives holding data collected and
structured over long periods. Second, there are streaming data of events
and processes, such as comments on social networks or real-time mobility
data.
    A metaphor for stored and structured (Big Urban Data) data collection
is the so-called data lake which constantly accumulates data, having a phys-
ical limit and time delay. In contrast, the real-time data stream (Smart Ur-
ban Data) resembles a river whose items pass by quickly and disable long
term storage or permanent observation.
    On a tentative basis, Table 2 shows data already available (marked
green), data that are only available for authorities, like police or city de-
partments (marked yellow) and data that imply technologies not yet devel-
oped (marked red).




and-food-market-information-by-region/europe/market-intelligence/consumer-
profile-germany/?id=1421933900883 (Retrieved March 22, 2017).
114                                                        Tecnoscienza – 8 (2)




      Figure 1 – Urban data relevant for urban design and development.
Hick, Urban and Noennig                                                115




              Figure 2 – Data resources according to their dynamics:
                          City Data vs. Individual Data.
116                                                                     Tecnoscienza – 8 (2)

     The column “Stored and Structured Data” is of immediate relevance
for city science due to its inferences of categories of urban quality. Open
source platforms such as “Open City Smart”15 allow for extensive collec-
tion and structuring of information, yet these formats and systems widely
lack the standardization that would allow effective integration and pro-
cessing of different kinds of (streaming) data. A serious obstacle is posed
by the complex Graphical User Interfaces of these systems which require
users, mostly urban planners, to work at the level of IT experts. From an
urban design perspective, there is a clear need for data collection and anal-
ysis tools in combination with easy-to-use applications to support design
creativity and decision making.
     As a resource for the “Real Time Streaming Data” column, multiple
sensor solutions already exist that allow real-time data collection. Many of
these systems are designed for system maintenance, resource optimization,
and technical control, but rarely for design intelligence.
     Furthermore, data can be collected through surveying a community or
society, such as in the “Quarter Community” proposed in this paper. Here
it is necessary to first analyze the contexts and target group (e.g. via ques-
tionnaires) and to design an appropriate interface to address the commu-
nity. This phase of (social) data acquisition is of key importance for the
shaping of identity, and for identifying deficits as well as target qualities in
the quarter.

3.3 Intelligence: How to Derive Design Knowledge from Data
Collections?

    In urban design and master planning, current practice is still widely
based on subjective evidence. In most cases, only information relevant to
planners is being considered in planning. Today, however, there is a chance
to comprehensively collect data in response to actual needs. Data-oriented
and evidence-based approaches provide an altogether new perspective in
design disciplines. It is from here that the Internet of Buildings may find
its biggest momentum. The multiplicity of available sensor systems (elec-
tronic, physical, social) in urban and architectural environments allow for
a rich and target-oriented harvesting of design-relevant data. Systematically
collected and structured, they form a reliable basis for design and decision
making. However, the challenge of translating data into design is far from
trivial.
    How design intelligence can be derived from urban and environmental
data is still unclear, despite a multiplicity of ongoing discourses on data-
driven design. Focusing its research on this aspect, the WISSEN-
SARCHITEKTUR Laboratory of Knowledge Architecture at TU Dresden
endeavors not only to inform urban and architectural design, but also tech-


      15
           https://wiki.osgeo.org/wiki/Opencitysmart (retrieved March 22, 2017).
Hick, Urban and Noennig                                                  117
nological development including sensors, communication media, and ana-
lytic systems. The method presented in this paper is a first step towards a
design and development process that acknowledges data as a starting point
for creative urbanist and architectural work, as well as for technological
innovation.

3.3.1 Adopting Operations Modelling in Urbanism

    In the past, many settlements and cities were built to last by the resi-
dents themselves. Today, however, cities and buildings have become ob-
jects of speculation with ever shorter expiration dates. However, cities re-
main places for the long-term production of cultural value, social wellbe-
ing, and community cohesion. New urban operation systems are needed to
maintain the creation and evolution of these urban qualities. From an op-
erational point of view, cities can be seen as social enterprises that run a
multiplicity of social, economic, environmental and other processes. Just as
public or private ventures need to operate on sustainable plans, cities too
must balance their forms of partnership, investments, expenditures, reve-
nue streams, and value creation. It is important to highlight here that value
does not necessarily mean maximizing monetary profit, but rather quality
of life enhanced by technology and innovation. Cities are large-scale social
ventures, and therefore not merely objects of short-term investment, real
estate speculation, and fast-track profit.
    On this assumption, we have adapted methods of operations modelling
from the field of urban management and development and termed it “Ur-
ban Operations Modelling” (UOM). UOM is a method that models com-
plex urban operations and services, and assesses them for their urban qual-
ity as well as their economic feasibility. While UOM may be applied to all
kinds of urban services, this paper holds that urban and architectural de-
sign is a value-creating public service, which may yield greater benefits by
utilizing urban data.
    A key reference for the UOM is a creative method developed in the
context of innovation management that schemes and validates operations
and business design of enterprises (Osterwalder and Pigneur 2010). The
method was re-modelled by its originators into a highly popular “canvas”
tool (see Figure 3). As a decision-making tool, the canvas has become a new
standard for policy makers, public bodies, and enterprises, as it is easy to
comprehend and already works effectively at the prototype stage. The can-
vas gives a well-structured overview of all necessary items for planning a
venture of any kind. The left-hand side (“Enterprise”) and the right-hand
side (“Market”) are connected via a central column “Value Proposition” –
a representation of the values created by the enterprise, and estimated by
user or clients. The aspects on the enterprise section include Key Partners,
Key Activities, Key Resources, and Costs. The parts on the market section
define Customers, Customer Relationships, Channels and Revenue
118                                                             Tecnoscienza – 8 (2)

Streams. The arrangement of the canvas enables rapid definition of the in-
dividual components, and also the quick outline of their connections. As a
result, the canvas sheds light on the level of integrity of the venture at stake.




          Figure 3 – The Business Model Canvas (source: strategyzer.com).

    By viewing the components of the canvas from an urbanist perspective
rather than an entrepreneurial perspective, the tool and method can be
adapted to issues of urban development and management where it may be
applied to all scales. This includes urban micro-business operations as well
as the maintenance of large-scale urban infrastructures needed to fulfill
conditions such as feasibility, value creation, and resource-effectiveness.
    A possible complication of adapting a business model canvas, based on
explicit rules, is that it may inhibit creative design decision-making. By out-
sourcing and decentralizing the decisions to a wider range of participants,
such as a “Quarter Community”, these rules might be supplemented by a
consensus of implicit design ideas.

3.3.2 Urban Operations Model

    The UOM helps to outline the otherwise hidden operational structures
of cities which form the basis for their successful spatial and physical de-
velopment. At the Wissens-Architektur Laboratory of Knowledge Archi-
tecture, we have sampled historical cases of prosperous cities, and demon-
strated how UOM-descriptions can be applied as an analytic tool. We
could show that vital cities usually possess a well-integrated urban opera-
tion system. Examples are plenty: Hellenistic Athens, the cities of the Han-
seatic League, the transcontinental city corridor along the Silk Road, the
creative city of Florence in the 15th century, or the city of Amsterdam as a
Hick, Urban and Noennig                                                      119
                          th
center of trade of the 17 century. These cities were running on operational
models that balanced partnerships, resources, markets, channels to accu-
mulate and amplify knowledge and cultural production as well as wealth
and political power.




  Figure 4 – Urban Operations Models for the Port of Amsterdam, 17th century
  (left) and for the contemporary Friedrichstadt residential quarter in Dresden
                                     (right).

    However, these cities did not build their success on data and infor-
mation technology which may be assumed a key resource and ingredient
for UOMs in the 20th and 21st century. Arguably, no operational model
and development scheme can be composed for cities, quarters, and build-
ings in the future without reference to digital data. The current capacity of
data analytics, legal access to necessary data and the quality of available
data may limit UOMs. We have extended the UOMs by processes of data
acquisition and processing, and have shown how to integrate digital assets
into the overall operations model of individual quarters or buildings.
    The difference between urban and enterprise operations models lies in
their different purpose as well as in the scale and application of the indi-
vidual components. UOM consider socio-cultural benefits as prominent
value propositions. Furthermore, certain original components need to be
appropriated. “Customers” may be replaced with “Citizens”, indicating
the urban context of the models.

3.3.3 Data Exploitation – Building ID and Quarter ID

    The UOM, once established and comprising environmental as well as
social datasets (City Data, Individual Data), needs to be analyzed and eval-
uated on a qualitative and quantitative basis. To do so, we introduced the
“Building resp. Quarter ID” (BID / QID) (see Figure 5) as a visualization
tool to describe this relationship. BID and QID are a kind of passport for
the Internet of Buildings: they summarize all the indicators shown in Table
120                                                                    Tecnoscienza – 8 (2)

1 and rank their values. The aim of BID / QID is to valorize data linkages
and create value for the various stakeholder groups such as citizens, plan-
ners, facility managers. The BID / QID thus functions either as an infor-
mation display, aid, or a decision-making tool. Every new physical inter-
vention, both temporary or permanent, alters the digital image of the BID
/ QID. While QID operates on a District-to-District basis, showing larger
scale notions, BID operates on small-scale urban units, showing a larger
data context16.




           Figure 5 – Quarter ID (left), Building ID (right): Relationship between City
                       and Individual Data at different urban scales.


3.3.4 Beyond Master Planning: Towards Data-driven, Agile Urban Development

    Data collection in the urban Internet of Buildings plus the above-men-
tioned operations modeling may replace the practices of urban master
planning with an agile and incremental development process. Somewhat
paradoxically, data-based UOM may reenable the natural growth of cities
and neighborhoods, eventually leading to high-value living environments.
    Beyond master planning, we have schematized a process that builds ca
pacity into urban quarters to structurally and flexibly react to changing


      16
       As an example, Bert Spaan of the Waag Society created and organized a map
(http://code.waag.org/buildings/) of all buildings in the Netherlands according to
their age and described their brief function and size. This can be considered a basic
Building ID.
Hick, Urban and Noennig                                                   121
needs and environmental conditions. The process – which actively utilizes
ad hoc construction – can be described by a sequence of iterative phases.
     The “Initiation Phase” starts with analyzing given datasets comprising
City Data and setting up a Quarter Community for detailed insights into
the needs of the inhabitants, summarized by Individual Data (see Table 1
above). The Quarter Community shall consist of at least one hundred par-
ticipants who are periodically surveyed on quality of life in the quarter.
This results in an initial Quarter ID (QID). By applying and downscaling
the QID to a specific location or building, a Building ID (BID) is devel-
oped, and then a tentative UOM can be established.
     In the “Seed Phase”, the process starts with a temporary pioneer or
pilot construction in accordance with a first UOM. The site of develop-
ment is equipped with Cyber-Physical-Systems for monitoring the initially
defined usage. Prior to determining the nature of the pilot, qualitative and
development-relevant urban data are collected (see Tables 1 and 2)
through surveys or Soft Urbanism measurements (festivals, events, contain-
ers, light-material structures etc.). These data are collected and interpreted
with the BID. Importantly, the Seed Phase does not necessarily imply con-
crete spatial or structural intervention. Moreover, there is a difference be-
tween greenfield developments and locations within existing urban blocks.
Greenfield development may be more ambitious due to a lack of existing
users, data streams and preestablished linkages to the surroundings.
     By analyzing the BID, planners and analysts clarify whether positive im-
pulses were given to the site, and thereupon decide further development
scenarios. The seed intervention may either be continued, enhanced, or
stopped. Following this feedback, planners outline an updated operations
model which informs the next step of development, possibly leading to
concrete structures. Thus, at the end of the Seed Phase a development brief
is set up in the form of an Urban Operations Model, determining rules and
orientation for the follow-up intervention. This UOM ensures, in any case,
that the next step is socially and economically valuable, responding to the
primary interests of citizens, developers and investors alike. During the
subsequent steps of the development, the quarter develops more UOM as
the demands, needs, and activities of users and citizens evolve and change.
Past developments without sufficient response and attraction from the lo-
cal community will not be followed further. As some activities will certainly
fail, established and existing structures will need re-programming by other
Seeds. Without a final masterplan, this iterative open-ended and poten-
tially open source development continues, and importantly, is validated
with every iteration. Thus, this agile process enables feasibility and calcu-
lability, and secondly, aids demand-matching and user acceptance. For de-
velopers and investors, this process offers an alternative to speculative ad
hoc master plans: Financial risks become minimal through continuous val-
idation (see Figure 6).
122                                                        Tecnoscienza – 8 (2)




      Figure 6 – Urban Data Operations Modelling: iterative steps.
Hick, Urban and Noennig                                                  123
   The needs of cities for long-lived infrastructure networks can be ad-
dressed by iterative decentralization of embedded frameworks. Natural de-
mand-driven city evolution beyond path dependencies at a large scale ne-
cessitates evolutionary and self-organizational processes that enable testing
and experimenting at all levels.

3.3.5 Data Concierge and Urban Legislative

    A key component for agile urban development is a data platform con-
necting different groups of interests in a network. These include municipal
planning offices and municipal providers as the top-down actors, citizen
organizations and entrepreneurs characterized as bottom-up participants
and the investors, and developers and architects as hybrid figures (see Fig-
ure 7). On the one hand, the UOM is a formalization tool of ad hoc top-
down urban planning rules. On the other hand, it supports a bottom-up
dynamic catalyzing urban development, transmitted by processes and data
visualizations. Appropriate data acquisition and evaluation is necessary to
support design decisions and subsequent development. Today, both capi-
talist urban development and government-led master planning usually lead
to undesirable urban conditions. A data platform, supported by UOM re-
moves the discrepancies between experts and non-experts and may im-
prove top-down master planning by harnessing data collection for progres-
sive ends.




              Figure 7 – Data4City: Data Platform and Stakeholders.
124                                                           Tecnoscienza – 8 (2)

    A first step beyond the conventional master planning paradigm towards
evidence-based design is the re-definition of the roles and actions of indi-
vidual participants. The linear workflow between investors, planners, mu-
nicipalities and constructors needs to be replaced by iterative interaction
and continuous evaluation of interventions as well as dialogue with end-
users. Circular processes between the actors, as described in the previous
sections, may eventually lead to an inclusive “Good City” with high quality
of life.




           Figure 8 – Data Concierge: How data lead to design decisions
                               (stakeholder map).

    It is the task of an interdisciplinary team of data scientists, development
experts and planners controlled by democratically elected citizen partici-
pants to take the role of a so-called Data Concierge who is responsible for
managing, evaluating and processing locally generated data (Figure 8). An
overall legal background can be established through an interdisciplinary
team building the Data Concierge to guarantee its independency. Another
important task is to guard against misuse of data. The Data Concierge can
be furthermore seen as a hybrid authority (comprising both top-down and
bottom-up decisions), making the legislative, background and general de-
cisions according to the information derived from the datasets, democratic
decision making.
    A supplementary possibility for navigating and organizing the data con-
cierge is an open-source peer-to-peer network with a flat hierarchy. Every
interested inhabitant has access to most of the data flow and hence power
Hick, Urban and Noennig                                                      125
to decide. The first method can be compared with the structure of a repre-
sentative democracy, whereupon the second method resembles the struc-
tures of direct democracy. Both options have constraints and benefits. On
the one hand, a method with representative elements is faster in decision-
making, but power is not equitably distributed. On the other hand, a
method with direct democratic constituents allows a nearly comprehensive
rendering of inhabitants’ needs and opinions, but impacts on performance.


4. Reflections – Limitations of Data-Driven Approaches to
Urban Design

     The limitations of smart city initiatives and data-informed design, as
argued by Kitchin et al. (2015), lie in the generalization and over-simplifi-
cation of diverse urban systems, disregarding regional and historical differ-
ences and rationalizing urban and social mechanisms. Arguably, the smart
city agenda is driven primarily by corporate interests to capture financial
and governmental opportunities (Hollands 2008; Kitchin et al. 2015). Fur-
thermore, there are ethical consequences when people are categorized and
reduced to mere numbers (Pentland 2014). This brings up the question of
the general openness of any smart city system and how the data are being
harvested. The distinction in the two methods for data collection (quanti-
tative sensor-based vs. qualitative sociological approaches) in the proposed
method may lead to over-simplifications of the terms “objective” and “sub-
jective”. Only the raw data collected by technical systems and sensors
might be observed as fully objective and non-ideological, yet as soon as any
filter is applied the objectivity disappears. Otherwise urban data cannot be
seen as raw; they are always pre-defined for a specific use leading to a spe-
cific cause (Bowker 2005; Gitelman 2013; Kitchin et al. 2015). An approx-
imation might be to define the filters through participatory decision mak-
ing. This would eliminate more design contingencies resulting in a norma-
tive design model with less subjective and personal design decisions, which
are in many cases driven by the ego of the architect.
     Here, data types and resources were chosen that can directly inform
design decisions, i.e. have implications for form, function and construction
of urban structures. Only data resources, which are accessible and digesti-
ble for designers (who are typically not data scientists or statisticians), e.g.,
public data that are easily representable in diagrams and visualisations have
been considered. These data sets are easily translatable to architectural or
urban design decisions. Yet such a simplification of data, already observa-
ble in contemporary digital architecture, needs to go along with hierarchi-
zation and prioritization, leading again to subjectivity. By decentralizing
these decisions through a network of interconnected users and stakehold-
ers, objectivity might yet be achieved. Such a system based on peer-to-peer
sharing might also be difficult to hack by a third party through blockchain
126                                                                 Tecnoscienza – 8 (2)

technology17. Using blockchain, instant decentralized organisations can be
developed, independent of any intermediaries or outside influences18
(Ethereum 2017). This could also lead data-informed urbanism to a net-
worked and possibly fully autonomous urban environment.
    It is necessary to consider social conditions and issues of openness, es-
pecially in the context of data generation, collection, and economization.
In this respect, hackability (modifiability) and open-source code must be
considered central elements of the overall design in the light of technical
democracy. The social impact of data-driven urban design and develop-
ment requires bottom-up oversight (Farías and Blok 2016). Instead of giv-
ing away (personal) data to corporations and governments, the hyperlocal
community presents a model to utilize and valorize data in the community
and place where they are generated. In this model, urban space is being
created by a constant iterative process of change and adaptation in re-
sponse to the current demands and the actual needs of citizens. Latour and
Yaneva (2008) described this idea as an active datascape informing the evo-
lution of urban space, and modifying the social and physical context.


5. Conclusions

     Max Weber (1921) considered that quality of life does not depend on
the density or size of a city, but on its intermixture. This way, people of
different ethnicities, cultural backgrounds and social classes can live to-
gether. Unfortunately, the appearance of radical functionalism and its zon-
ing principles after the second world war rendered it impossible. Digitali-
zation is a tool helping us move towards a healthy and appropriate disper-
sion of urban functions. Yet, there are principles which need to be re-
spected in the context of data-driven cities, protecting the end-users who
supply platforms with sensitive personal data.
     Massive collection of Big Urban Data (individual data) can only be jus-
tified if a process is given for streaming essential data without the necessity
of storing (Smart Urban Data).
     Yet, in urban management and planning, urban data are not tapped as
a major resource for design intelligence. In addition, cities are still not
viewed as social enterprises which could be represented by way of opera-
tions models (Barquet et al. 2011). Addressing these deficits, Urban Oper-
ation Models (UOMs) provide for the purposeful application of urban


    17
       A blockchain is an iteratively growing list of records (blocks), operating on a
cryptographically secure, decentralized peer-to-peer network. Once recorded, the
data in any block cannot be altered retrogressively without the alteration of all
subsequent blocks. The first example of a blockchain is Bitcoin.
    18
       See Ethereum Foundation: https://ethereum.org/ (retrieved October 5,
2017).
Hick, Urban and Noennig                                                      127
data, especially in the design and development of high quality living envi-
ronments. Cities are places of value creation. This claim may be even more
urgent in the digital age given that “wealth is created by turning data into
information’ (Landry 2000, 33). The case presented in this paper advocates
the systematic usage of information and intelligence for valorizing urban
design. The UOM provides a conceptual tool for urban managers, plan-
ners, administrators, and residents to capitalize on the rich urban data
sources generated in the emerging Internet of Buildings (IoB). As a key
component for data-driven urban design, this paper has shown how UOM
can enable agile and secure urban development processes. Policy and de-
cision makers can balance the interests of citizens with those of investors,
developers, and managers through UOM. The primary aim is to get a more
holistic and comprehensive understanding of the urban metabolism and
hence give more power to citizens to design their own city and environ-
ment. The method moves beyond established practices in urban and fore-
stalls real estate speculation driven primarily by expectations of return-on-
investment. The power of the proposed method lies in the simple workflow
procedure and the capacity to strategize and assess future development by
way of operational modelling.


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