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Multi-Perspective Urban Optioneering


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This paper investigates the state-of-the-art with respect to simulation-based planning support systems in order to draw a set of requirements and best practices for an urban planning and design framework that enables multiple stakeholders with differing perspectives to systematically explore design options, leveraging the latest analysis and simulation techniques. From these requirements and best practices, the foundations and structure of such an urban planning and design framework are developed. A number of technological and methodological challenges are identified for future investigation.
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Multi-Perspective Urban Optioneering
Patrick Janssen1, Rudi Stouffs2
1,2National University of Singapore
This paper investigates the state-of-the-art with respect to simulation-based
planning support systems in order to draw a set of requirements and best
practices for an urban planning and design framework that enables multiple
stakeholders with differing perspectives to systematically explore design options,
leveraging the latest analysis and simulation techniques. From these
requirements and best practices, the foundations and structure of such an urban
planning and design framework are developed. A number of technological and
methodological challenges are identified for future investigation.
Keywords: Urban planning and design, optioneering, simulation-based planning
support systems
With the increasingly complex nature of future mas-
ter plans, there is a need for comprehensive urban
planning and design frameworks that leverage the
latest analysis and simulation techniques in order to
enable planning and design options to be systemat-
ically developed, evaluated, and analysed. In order
to be effective in supporting collaborative decision-
making, such frameworks must concurrently address
the varying spatial and temporal scales of urban plan-
ning and the varying requirements and interests of
different stakeholders. Currently, no such frame-
works exists and, as a result, collaborative decision
makers are unable to effectively reconcile their dif-
ferent viewpoints and objectives and only a very lim-
ited number of suboptimal planning options are ex-
plored using mainly ad-hoc manual approaches that
are time-consuming and error-prone.
Such frameworks fall under what are referred to
as Planning Support Systems, which are systems fully
dedicated to support and improve the performance
of those involved in undertaking specific planning
tasks (Batty 1995; Klosterman 1997). They usually
consist of a combination of planning-related theory,
data, information, knowledge, methods and instru-
ments that take the form of an integrated framework
with a shared graphical user interface. They are typ-
ically used to provide projections forward to some
point in the future or may involve some estimation
of the impacts that result from some form of devel-
opment (Geertman and Stillwell 2003; 2009).
This research focuses on planning support sys-
tems that enable stakeholders to apply complex
types of simulation at varying spatial and temporal
scales. Below, existing simulation-based planning
support systems are reviewed and an approach re-
ferred to as 'Urban Optioneering' is described. A
framework for simulation-based planning support
systems is then proposed, consisting of a method
and a platform. Finally, the discussion section high-
lights a number of technological and methodologi-
cal challenges that have to be addressed in order to
achieve and implement the proposed framework.
Towards Smarter Cities - Volume 1 - eCAADe 32 |79
Current urban planning and design practices typ-
ically use off-the-shelf impact-analysis software,
mainly based on point scoring systems that use
checklists and other methods that do not provide
adequate feedback for planning sustainable envi-
ronments. For example, AECOM's Sustainable Sys-
tems Integration Model (SSIM; Brown and Kellen-
berg 2009) is a method and tool for analysing urban
plans using a set of key sustainability indicators. The
method requires a small number of alternative plans
to be developed, and then allows users to make se-
lections for a variety of predefined systems and op-
tions, and to interactively see a cost-benefit analysis
based on these indicators. However, the indicators
rely on relatively simplistic calculations performed in
MS Excel. For non-spatial indicators (such as energy
use, water use, carbon emissions, and development
costs), the calculations make extrapolations based
on representative data defined by experts. For spa-
tial indicators (such as connectivity, access to local
services, and access to transit), the analysis consists
mainly of various types of network analysis that do
not take into account the complex dynamics of urban
Even when adopting planning support systems
that have the ability to run more advanced types
of simulations, the feedback they provide focuses
on isolated impacts of land-use planning; the inter-
dependencies between different aspects as well as
holistic overall considerations have to be done 'by
hand' by the planning teams. For example, one of the
more accessible systems is the CommunityVIZ plan-
ning and analysis platform (Walker and Daniels 2011),
available as an extension to ArcGIS. CommunityVIZ
has a comprehensive modelling framework that in-
cludes a set of built-in models for the dynamic sim-
ulation of complex urban phenomena, and also sup-
ports the ability to plug-in custom models defined by
end users. However, the coreof the modelling frame -
work is proprietary and as a result the interactions
between the various models cannot be controlled by
end-users. Modelling complex phenomena by creat-
ing networks of linked models is therefore not possi-
The need for more comprehensive methods that
integrate simulation-based planning support sys-
tems is increasingly recognised and accepted by
practitioners. The complexity of planning urban de-
velopment arises by the interaction of its compo-
nents, and the fact that these interactions can lead to
unexpected, counterintuitive results. Unfortunately,
most of the existing solutions oversimplify such inter-
This insight has led to numerous recent efforts in
the development of systems that integrate a number
of domain specific models (e.g., land-use, transporta-
tion and energy-supply). However, these mono-
lithic systems hard-code these domain specific mod-
els in ways that are not easily modified by end-
users, thereby essentially limiting the use of these
systems to a very narrow range of planning ques-
tions. SynCity system (Keirstead et al. 2009) imposes
an energy perspective; CitySim system (Robinson et
al. 2009) imposes a resource-flow perspective; and
UrbanSim system (Waddell 2002) imposes a trans-
port and land-use perspective.
In addition, these types of systems can typically
only be applied within a narrow range of scales,
thereby hindering a truly multi-scale approach to ur-
ban design. Such monolithic models reach the lim-
its of feasibility and practical usability due to huge
data demand, limited life span and costs of mainte-
nance (Conway and McClain 2003; Davis and Ander-
son 2004). Most of the academic approaches lack the
continuity and support to achieve a level of maturity
and industrialization that can make it usable for prac-
Multi-Perspective Optioneering
Optioneering is a collaborative decision making
methodology that systematically explores a wide
spectrum of options early on in the design process
by iteratively developing, evaluating, and analysing
alternatives (Holzer and Downing 2010, Gerber at al.
80 |eCAADe 32 - Towards Smarter Cities - Volume 1
2012). Urban optioneering applies this methodology
at an urban scale, allowing urban planning and de-
sign options to be developed in a more rigorous man-
In the design of complex engineering systems,
optioneering has become an important tool to guide
the interaction between different experts, to test the
consistency of proposed technical solutions and to
evaluate their impacts. Variants of the optioneer-
ing approach, such as virtual prototyping, have been
applied in many fields of design and engineering,
from the design of airplanes and cars to the design
of complex building constructions (El Khaldi et al.
2010). Leading architectural offices and develop-
ment companies are using optioneering in their col-
laborative design processes, enabling highly special-
ized experts from all over the world to contribute to
the design solutions. In this context optioneering has
been proven as a model that supports complex deci-
sion making.
For the application of optioneering at the urban
level, methods and systems have to be developed
that are capable of engaging with the high levels
of complexity inherent in urban planning and de-
sign. Urban planning and design processes have to
integrate economic, environmental and social issues,
have to cover different scales in space and time, and
have to address multi-actor environments.
These processes therefore require various per-
spectives (e.g., financial, environmental, social, oper-
ational) to serve the different actors involved. Fur-
thermore, due to the fact that urban plans are typi-
cally related to long-term decisions, they also have to
face changing conditions and demands, with unfore-
seen and unexpected phenomena being the norm.
From that point of view, current modelling and sim-
ulation approaches are lacking in open system think-
ing and "the capability of covering multiple system
perspectives at once and in different levels of details
to provide better understanding of the systems" (Tek-
inay et al. 2010).
In the scientific debate about modelling of com-
plex systems many authors emphasize the limita-
tions of single-perspective models (Lane 2006; Morin
2005; Mikulecky 2001). Single-perspective models
are reducing the complexity to certain aspects and in
this way are losing relations outside the partitioned
frame, which cannot be brought back to life, even
when different models are applied in parallel to each
other. According to Seck and Honig (2012) a major
reason for these limitations is caused by the fact that
simplified hierarchical structures of technical systems
are imposed on non-technical systems and thus in-
herit "a strong reductionist world view".
To overcome the principal limitations of single-
perspective models, the development of multi-
perspective models is widely discussed (Seck and
Honig 2012; Kingston 2007; Frank 2002). Although
the discussion until now did not result in concrete
applications, various proposals have been made at a
conceptual level.
Figure 1
Modelling complex
through multiple
perspectives (After
Seck and Honig,
Seck and Honig (2012) propose a complex sys-
tem (the natural system) as an expansible collection
of perspectives, where each perspective, although
associated with its own formal aspect system, is re-
lated to "multiple non-isomorphic decompositions
that may influence each other" (see Figure 1). In this
way, the fixed hierarchy is replaced by flexible modu-
larity; different formal aspect systems are connected
by bridges, involving different steps of decoding and
encoding. Such multi-perspective modelling is in-
Towards Smarter Cities - Volume 1 - eCAADe 32 |81
tended to "capture the tangledness of the systems
that result when we observe the world from different
perspectives" (Seck and Honig 2012).
The challenge is to elaborate the theoretical con-
cept of multi-perspective urban optioneering into a
practical framework for urban planning and design
that reflects the multi-actor environment of the ur-
ban system.
One of the fundamental characteristic of multi-
perspective optioneering is that it is impossible to
predict in advance the perspectives that will be most
relevant to the stakeholders involved. This unpre-
dictability results from the fact that urban plan-
ning and design is fundamentally an unstructured
or 'wicked' process characterised by (1) multiple ac-
tors with differing, legitimate values and opinions; (2)
high uncertainty; (3) aspects of irreversibility; (4) no
clear solutions; (5) being fraught with contradictions;
(6) being persistent and unsolvable (Rutledge et al.
2008). Any optioneering framework that is a closed
system, based on the assumed relevance of a prede-
fined set of perspectives, is therefore bound to fail.
In order to support multi-perspective optioneer-
ing, a radically different type of framework is pro-
posed that is inherently open (Axelos 2006). The
openness of the framework hinges on enabling
stakeholders to define their own customized mod-
els that reflect the perspectives that are most rele-
vant to them, and to the scenario and context being
explored. These customized models are defined as
networks of loosely coupled components, including
a variety of domain specific simulation engines and
data sets (Altintas 2011; Deelman at al. 2008; Curcin
and Ghanem 2008).
The proposed framework consists of two parts:
an Urban Optioneering Method and an Urban Op-
tioneering Platform. The method and the platform
are shown in Figure 2, and will be described in more
detail in due course.
Figure 2
The proposed
consisting of an
method and an
Urban Optioneering Method
An Urban Optioneering Method is proposed that en-
ables questions relating to specific urban planning
and design scenarios to be explored using multi-
perspective models. The perspectives can be related
to the tasks of different planning agencies, to specific
interests of different stakeholders or to different po-
litical, social, economic or environmental priorities.
For each question to be explored, customized
models reflecting the differing perspectives need to
be created, and the outcomes of those models need
to be explored. The method consists of an adaptive-
iterative process consisting of two nested loops: the
82 |eCAADe 32 - Towards Smarter Cities - Volume 1
adaptive loop and the iterative loop (see Figure 2).
The adaptive loop comprises three key activities:
defining questions, creating models, and exploring
Urban questions are defined, possibly by dif-
ferent stakeholders in the planning process,
with differing and possibly conflicting con-
• Urban models are created as executable
models, consisting of coupled legacy single-
domain urban models and data sets.
Urban outcomes are explored by iteratively
executing models and analysing results, in
particular, including cross-perspective analy-
sis of data and result inconsistencies.
The iterative loop is part of the process of exploring
model outcomes. For any given multi-perspective
model, many variants can be explored based on
differing assumptions and using differing data sets.
These outcomes may involve the estimation of im-
pacts on the present and projected into the future
based on differing assumptions and data sets. Fur-
thermore, the results from running model variants
can be investigated using a variety of data analytics
techniques. This process of iterative exploration will
result in the accumulation of evidence and will culmi-
nate in actionable feedback, which may then trigger
new or modified questions to be posed.
The proposed method enables stakeholders to
ask complex questions. For example, the question
may be posed: to what extent would the electri-
fication of road transport reduce air conditioning
use in residential flats? Since electric cars are qui-
eter and cleaner, they will produce less noise and
air pollution, thereby resulting in more people opt-
ing for natural ventilation over air conditioning. The
model would have to include a complex set of do-
main specific tools and data sets. First, the behaviour
of residents within flats may be predicted using a be-
haviour model, with input requirements that include
localised environmental conditions (including tem-
perature, humidity, and ventilation) and localised
pollutant levels (including noise and air pollution). In
order to predict the environmental conditions, en-
vironmental simulations would be required, using
weather data and detailed urban models. These sim-
ulations may also include micro-climate simulations,
such as heat island simulations. In order to predict
the pollution levels, noise mapping and pollutant
transport simulations would be required. Both the
environmental simulations and the pollutant simula-
tions may in turn require data from other simulations,
such as traffic simulations and wind simulations. This
original question therefore results in a complex cas-
cading network of simulation engines and data sets.
Urban Optioneering Platform
An Urban Optioneering Platform is proposed that
will enable stakeholders to fluidly build and explore
computable multi-perspective models, consisting of
loosely coupled legacy simulation engines and data
sets. This process of exploration includes the abil-
ity to analyze and compare various partial models at
varying temporal and spatial scales, thereby allowing
conflicts and inconsistencies to be discovered.
Models are divided into three layers: compo-
nents, networks, and dashboards.
Model components are the basic elements
from which models are built and may include
legacy simulation engines, data sets, and data
mappers. Such components are central to the
modularity feature of the platform, as they al-
low additional components to be added to
the platform as they become available and rel-
Model networks are executable networks that
reflect sets of values and beliefs specific to the
perspectives being applied. Such models are
created by coupling together selected sets of
model components, including legacy simula-
tion engines.
Model dashboards are customised graphical
Towards Smarter Cities - Volume 1 - eCAADe 32 |83
user interfaces and data-mashups associated
with one or more models, where each model
may reflect a different perspective. Such
dashboards will allow input and output data
from multiple perspectives to be analysed in
an integrated environment. At its simplest,
this may consist of some sliders for defining
input data and some graphs and charts for dis-
playing output data. However, it may also in-
clude complex spatiotemporal data manipu-
lation and data analytics.
The Urban Optioneering Platform consists of two ap-
plications: one application for building models and
another application for exploring models. They are
defined as distinct applications since they represent
fundamentally different modes of working, requiring
different skill sets.
The Model Builder application provides tools
building all three layers of a model. For building
model components, a set of tools is required to help
users to wrap existing computational objects such as
legacy simulation engines, data sets, and data map-
pers. The resulting components will be archived as li-
braries of components to be embedded within larger
models. For building model networks, a set of tools is
required to help users to create multi-domain model
networks from selected sets of components. These
tools will allow models to be visually constructed as
a network of components interconnected by wires.
Features such as advanced type checking and de-
bugging will need to be provided to help users build
valid models. For building model dashboards, a set
of tools is required to help users build customized
dashboards from predefined user interface building
blocks using visual drag-and-drop techniques.
Once a multi-perspective model has been built,
the outcomes of the model then need to be ex-
plored. The Model Explorer application is conceived
as a cloud-based application for deploying, execut-
ing, and managing multi-domain, multi-perspective
models. On the front end, the application provides
a graphical user interface for deploying models. On
the back end, the application provides automated
scheduling and data management procedures for ro-
bust fault-tolerant parallel execution of models and
data analytics tasks.
The Urban Optioneering Platform aims to radi-
cally improve the way with which stakeholders are
able to leverage the latest computational tools and
techniques to explore critical questions that impact
decisions in urban planning and design. The system
enables diverse stakeholders to gather the evidence
required to take positions, which can then be used as
a basis for further discussions and negotiations.
In order to implement the proposed Urban Option-
nering Framework, a number of technological and
methodological challenges need to be tackled. Be-
low, three fundamental challenges are discussed:
building models; linking components; and making
Challenge 1: Building models
The challenge of model building focuses on develop-
ing a system that enables stakeholders to build com-
plex models from a set of re-usable modular compo-
An approach needs to be developed that does
not require advanced technical skills, as stakehold-
ers, such as urban planners and designers, cannot
be expected to have such skills. Previously, a dis-
tinction was made between two modes of working:
building a model and exploring a model. As a result,
it might be suggested that the stakeholders should
refrain from getting involved in the model building
process, which might be better left to people with
more advanced technical skills. However, the pro-
cess of building models cannot be subcontracted, for
two reasons: the people building models need to un-
derstand the urban planning and design issues; sec-
ond, the people exploring models need to under-
stand the technological issues, such as the limits and
constraints of the models being explored. It is there-
fore important to try and minimize the gap between
building models and exploring them.
84 |eCAADe 32 - Towards Smarter Cities - Volume 1
One approach to achieving this is to create a
set of tools that allow stakeholders to be directly in-
volved in the building of models from predefined
components. With this approach, a distinction is
made between building model components and
building model networks. Model components, in-
cluding various legacy domain-specific analysis and
simulation programs and data sets, are built and
tested by researchers who have the technical skills
and are specialists in their corresponding field of sci-
ence. Model networks are then built by stakeholders
by assembling predefined components using graph-
ical interfaces that do not require any programming
or other advanced technical skills.
With regards to building model networks, two
approaches can be identified, referred to as tightly
coupled component models versus loosely coupled
component models. Tightly coupled component
models are developed in a standard programming
language using a set of modular programming li-
braries for different single-domain models and the
resulting program is then compiled into a single ex-
ecutable. (For example: Leavesley et al. 1996; Dah-
mann 1997; Watson et al. 1998; Rizzoli et al. 1998;
Reed et al. 1999; Krahl 2000; David et al. 2002;
Voinov et al. 2004; Rahman et al. 2004; Kolbe at
al. 2005; Ahuja et al. 2005; Müller 2009). Loosely
coupled component models are developed by cou-
pling various analysis and simulation programs at the
data level. These components are typically legacy
programs developed as stand-alone executables that
are wrapped in order to enable data exchange via
input and output files (For example: Sydelko et al.
2001; Babendreier and Castleton 2005; Bernholdt et
al. 2006; Fortube et al. 2008; Tan et al. 2012). The
problem with the tightly coupled approach is that it
still requires a significant amount of software engi-
neering and programming knowledge. The loosely
coupled approach is therefore seen as being prefer-
Challenge 2: Linking components
The challenge of linking components focuses on how
to link together domain-specific analysis and simula-
tion programs.
An approach needs to be developed that allows
components to be interactively linked in complex
ways. One promising approach is scientific workflow
systems (Deelman et al. 2008, Altintas 2011; Toth et
al. 2012). Such systems exhibit a common reference
architecture that consists of a graphical user interface
(GUI) for authoring workflows, along with a work-
flow engine that handles invocation of the applica-
tions required to run the solution (Yu and Buyya 2005,
Curcin and Ghanem 2008). Nearly all workflow sys-
tems are visual programming tools in that they allow
processes to be described graphically as networks of
nodes and wires that can be configured and recon-
figured by users as required (McPhillips 2009). Nodes
may represent analysis or simulation programs, while
wires represent the flow of data, linking an output of
one node to an input of another node.
With regards to the dataflow between such
nodes, a more difficult problem is the interoper-
ability issues that invariably exist between various
analysis and simulation programs (Janssen et al. In
press). Such programs typically require independent
domain-specific data models that are efficient within
the domain, but are difficult to share across domains.
Existing interoperability efforts such as IFC, CityGML,
and gbXML attempt to bridge domains, and there are
also efforts to extend or amalgamate such schemas
to improve generality. However the challenge is non-
trivial given the large set of domains that may be rel-
In the domain of urban planning and design,
such interoperability issues include in particular the
ability to step up and down between different spa-
tial and temporal scales of the data (also known as
multi-resolution modelling). In general, it is feasi-
ble to overcome such incompatibilities using a range
of data aggregation and compensation techniques
(Reynolds et al. 1997). For example, data aggre-
gation may involve combining sets of data at the
Towards Smarter Cities - Volume 1 - eCAADe 32 |85
precinct level in order to characterize the neighbour-
hood, while data disaggregation may do the reverse
by taking data at the neighbourhood level and ap-
portioning it to precincts based on various heuris-
tics. Data compensation comes into play when cer-
tain data sets are missing or deficient. For exam-
ple, when aggregating data from the precinct level to
the neighbourhood level, missing data may need to
be synthetically generated based on typical patterns
and distributions.
Finally, an important element of the proposed
framework is the development of bridges that con-
nect distinct model networks, for example by using
the outputs of two or more perspectives as inputs for
other perspectives, in this way replacing the fixed hi-
erarchical order of a single perspective model by flex-
ible modularity.
Challenge 3: Making decisions
The challenge of making decisions focuses on how
models can be used to support decision making in
urban planning and design.
An approach needs to be developed that cre-
ates models that have the potential to generate ac-
tionable feedback. At a building scale, this can
be achieved by analyzing complete design options.
Due to the fact that design constraints and per-
formance targets are often relatively well defined,
actionable feedback can be generated by various
techniques for optimizing and ranking alternative
options, such as Evolutionary Multi-objective Opti-
mization (EMO), Multiple-Criteria Decision Analysis
(MCDA), and Multi-disciplinary Design Optimization
(MDO). However, at an urban scale this may not be
feasible due to the fact the complexity of urban prob-
lems means that reductive techniques for optimizing
and ranking are not applicable, which in turn leads to
feedback that is often ambiguous and contradictory.
In order to be able to manage the complexity,
urban optioneering methods may need to focus on
questions that are more narrowly defined, but that
have a direct impact on how design options are de-
veloped and that are still relevant to a range of dif-
fering perspectives. It is proposed that through a
more complex process of modelling and counter-
modelling (Greenberger at al. 1976) using a variety of
different types of models reflecting distinct perspec-
tives, stakeholders will gradually gather the evidence
required to take positions on questions that directly
impact decision making in urban planning and de-
sign. An important part of this evidence is an analy-
sis of the inevitable conflicts and inconsistencies that
will arise between the different models.
The state-of-the-art in planning suppor t systems was
presented in order to draw requirements and best
practices for the design and development of a frame-
work that supports the complexity of the urban plan-
ning and design process. The foundations and struc-
ture of such an urban planning and design frame-
work were drawn. Finally, the major challenges that
have to be addressed in order to achieve and imple-
ment this framework were identified as an agenda for
future research.
The approach proposed in this research emerged out
of discussions within the Urban Prototyping Group
[1] at the National University of Singapore. The group
was set up in June 2011 as a cross-faculty collabo-
ration between the School of Design and Environ-
ment (SDE) and the School of Computing (SoC). The
group aims to develop collaborative methods and
systems for urban information and simulation mod-
elling, leveraging growing computing power and in-
creasing availability of urban metabolic data.
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88 |eCAADe 32 - Towards Smarter Cities - Volume 1
One of the most important consequences of digitalization and the progress of artificial intelligence is automation in all areas of life. In this paper we investigate the automation of urban design. Based on four levels of automation, we provide a conceptual framework for the classification and comparison of various urban design automation approaches and consider the scope of their applicability and the division of tasks between humans and computers. The proposed framework is applied in two demonstration projects. Finally, we discuss the technical needs and possibilities for increasing urban design automation, as well as the implications these are expected to have for the profession of urban designers and architects.
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Custom digital workflows aim to allow diverse, non-integrated design and analysis applications to be custom linked in digital workflows, created by a variety of users, including those who are not expert programmers. With the intention of introducing this in practice, education and research, this paper focuses on critical aspects of overcoming interoperability hurdles, illustrat- ing the use of property graphs for mapping data between AEC software tools that are not connected by common data formats and/or other interoperability measures. A brief exemplar design scenario is presented to illustrate the concepts and methods proposed, and conclusions are then drawn regarding the feasibility of this approach and directions for further research.
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Flexible information exchange is critical to successful design-analysis integration, but current top-down, standards-based and model-oriented strategies impose restrictions that contradict this flexibility. In this article we present a bottom-up, user-controlled and process-oriented approach to linking design and analysis applications that is more responsive to the varied needs of designers and design teams. Drawing on research into scientific workflows, we present a framework for integration that capitalises on advances in cloud computing to connect discrete tools via flexible and distributed process networks.We then discuss how a shared mapping process that is flexible and user friendly supports non-programmers in creating these custom connections. Adopting a services-oriented system architecture, we propose a web- based platform that enables data, semantics and models to be shared on the fly.We then discuss potential challenges and opportunities for its development as a flexible, visual, collaborative, scalable and open system.
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As asserted by the Institute of Medicine, sound health policy and investment decisions require use of "what if" simulation models to analyze the potential impacts of alternative decisions on health outcomes. The challenge is that high-level health decisions require understanding complex interactions of diverse systems across many disciplines both inside and outside of healthcare, creating a need for experts across widely different domains to combine their data and models. Splash - the Smarter Planet Platform for Analysis and Simulation of Health - is a novel decision support framework that facilitates combining heterogeneous, pre-existing simulation models and data from different domains and disciplines. Splash leverages and extends data integration, search, and scientific-workflow technologies to permit loose coupling of models via data exchange. This approach avoids the need to enforce universal standards for data and models, thereby facilitating both model interoperability and reuse of models and data that were independently created or curated by different individuals or organizations. In this way Splash can help domain experts from different areas collaborate effectively and efficiently to attack complex health problems. We illustrate Splash's architecture and capabilities using a simple, proof-of-concept model of community obesity. We show how models of transportation, eating habits, food-shopping choices, exercise, and human metabolism can be combined with geographic, store location, and population data to play "what if," asking, for instance, how community obesity measures would change if tax incentives are used to encourage grocery chains selling healthy and inexpensive food to open stores near obesity "hot spots."
Planning Support Systems: Technologies that are Driving Planning Michael Batty Centre for Advanced Spatial Analysis (CASA), University College London, 1-19 Torrington Place, London WC 1 E 6BT, United Kingdom I had always thought the term 'Planning Support Systems', abbreviated to PSS, had been coined by the father of land use modelling, Britton Harris, in his article 'Beyond Geographic Information Systems: computers and the planning professional' published in the Journal of the American Planning Association in 1989 (Harris 1989). Until I asked hirn, that iso In a response to a paper he gave to the Urban and Regional Information Systems Association (URISA) in the summer of 1987, he told me that someone in the audience who he cannot quite remember, actually coined the term, referring to 'planning support systems' as that constellation of digital techniques (such as GIS) which were emerging to support the planning process. In fact, the predecessor term 'decision support systems' (DSS) from which this unknown originator obviously defined PSS by analogy, was coined as far back as the late 1970s in the management literature for a loose assemblage of techniques, usually computer-based, which aided management decisions. The term slowly entered the geographicallexicon as 'spatial decision support systems' (SDSS) and this is probably first attributable to Lew Hopkins and Mark Armstrong who used it in a paper published in AutoCarto 7 in 1985 (Hopkins and Armstrong 1985).
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Although the software tools to develop simulation models have improved dramatically, relatively little has been written about how to most effectively exercise those models. This paper provides warnings and suggestions to improve simulation studies. Although based primarily on the authors' experience with discrete-event models in manufacturing situations, many of their observations are more widely applicable.
Every day more evidence surfaces about the dire state of the environment. More and more, sustainable development is fundamentally about meeting human needs while restoring balance to the global ecosystem that is failing. Greenhouse gas emissions reductions targets and other new environmental performance targets are rapidly being adopted across the globe to try to shore up this degradation. We are finally entering the era of broad scale environmental accountability. Achieving sustainability performance targets across complex and integrated social, ecological, and economic systems requires new ways of engineering the way we interact with the environment. In turn, ecologically engineering the built environment requires new quantitative performance approaches to planning. It requires understanding and analyzing the complex systematic relationships between the built and natural environment and capitalizing on the efficiencies that can be found through integrated design. The sustainability movement is currently focused on reducing greenhouse gas emissions, but the threats to sustainability run much deeper than that. Climate change is driving changes to nearly all ecosystem services upon which we rely. Reducing carbon emissions has taken a front seat in sustainability programming, but typically through a somewhat focused lens of energy and transportation systems. This paper will discuss an approach to sustainability that is more holistic and ecosystem based. One that optimizes the ecological opportunities of a site and technology to make the built environment more sustainably integrated with the natural environment at the site, region, and global scale.