smart cities

Article
Urban Systems Design: A Conceptual Framework for
Planning Smart Communities
Michael B. Tobey 1,2 , Robert B. Binder 1,3 , Soowon Chang 1,4 , Takahiro Yoshida 1 ,
Yoshiki Yamagata 1, * and Perry P. J. Yang 4
1 Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba,
Ibaraki 305-8506, Japan; mbtobey@uw.edu (M.B.T.); rbinder@usc.edu (R.B.B.); soowonch@gatech.edu (S.C.);
yoshida.takahiro@nies.go.jp (T.Y.)
2 College of Built Environments, University of Washington, Seattle, WA 98195, USA
3 Sol Price School of Public Policy, University of Southern California, Los Angeles, CA 90007, USA
4 Eco Urban Lab, College of Design, Georgia Institute of Technology, Atlanta, GA 30332, USA;
perry.yang@design.gatech.edu
* Correspondence: yamagata@nies.go.jp




Received: 30 August 2019; Accepted: 23 October 2019; Published: 19 November 2019


Abstract: Urban systems design arises from disparate current planning approaches (urban design,
Planning Support Systems, and community engagement), compounded by the reemergence of rational
planning methods from new technology (Internet of Things (IoT), metric based analysis, and big
data). The proposed methods join social considerations (Human Well-Being), environmental needs
(Sustainability), climate change and disaster mitigation (Resilience), and prosperity (Economics) as the
four foundational pillars. Urban systems design integrates planning methodologies to systematically
tackle urban challenges, using IoT and rational methods, while human beings form the core of
all analysis and objectives. Our approach utilizes an iterative three-phase development loop to
contextualize, evaluate, plan and design scenarios for the specific needs of communities. An equal
emphasis is placed on feedback loops through analysis and design, to achieve the end goal of building
smart communities.

Keywords: urban design; Planning Support System; resilience; sustainability; economics; human
factors; big data

1. Introduction
Modern planning, as a unified practice of coherent theory, is an invention of the late 19th to early
20th century which arose to meet the growing challenges and complexities of urbanity. Settlement forms
and organic growth patterns were unable to keep pace with the increasing complexities of urbanization
from the local to global levels. The planning profession’s early theoretical iteration, rational planning,
attempted to mitigate and solve these problems through the application of the scientific method and
statistical modeling [1]. However, contemporaries such as Patsy Healey and Paul Davidoff noted
in their work the failings of these methods for ignoring the social factors and the unmeasurable
nature of these interactions [2,3]. Social, communicative, and advocacy planning were theorized
as direct counters to rational planning, aligning professional aims to empowerment and societal
effects. However, smart cities and their associated technologies seek to revive and refine these older,
static rational modeling approaches in new contexts for the purpose of designing future cities which
better serve humanity and the environment.
Urban systems design (USD) seeks to blend the beneficial elements of systems thinking, rationalism
and metric-based analysis, while tempering them, and to focus on the social interactions and design

Smart Cities 2019, 2, 522–537; doi:10.3390/smartcities2040032 www.mdpi.com/journal/smartcities

Smart Cities 2019, 2 523

interventions that drive cities. Building upon the traditions and collective knowledge of the planning
profession and the existing three pillars of sustainability, outlined by the World Commission in 1987,
the proposed framework seeks to design smart communities balanced on four pillars: Resilience,
Economics, Sustainability, and Human Well-Being [4]. Humans are the fundamental element from
which cities are constructed. Their environment is enacted and changed by their interactions. It is
essential to consider complex relations and nexus of human and environmental needs, given their
foundational qualities in their continued existence.
The paper seeks to outline a conceptual framework planning smart communities based on
the practice of urban systems design. Accomplishing this task requires expounding core principles
upon which this approach is developed: historic practices, the controversies associated with data,
their applications, and system complexity behind. This USD method is built upon three phases:
contextualization, evaluation, and iterative change that are further explored in this paper.

2. Background
Historical cities were planned from an initial event and then allowed to grow organically,
within examples such as Chinese traditions, Roman, etc. [5]. Modern city planning, however, found its
primacy in the industrial cities of Europe and the North America when cities were continuously
planned, regulated, and iterated through design. In this period, the Garden City Movement was
one of the attempts to mitigate the harmful effects of industrialization, while extolling the virtues
and morality of nature and organic systems, by employing rationalization as the basis of design—as
proposed by E. Howard in 1902 [6]. Rationalism became rooted in the field with the idea that with
metrics, new technologies, and the great innovative minds of the early 20th century, the problems of
the cities would be solved [1]. The well-known group of New York City intellectuals that became
known as the Regional Planning Association of America (RPAA) planned and implemented ideas that
were grounded in rationalism, in particular highlighted in Lewis Mumford’s book, The Story of Utopia,
and Clarence Stein’s Sunnyside Homes and Radburn developments [7]. There was some success from
the group’s rational methods, like the Appalachian Trail. However, rational planning was largely
grounded in theory without the backing of research and practice. The destruction of communities,
capital, and social cohesion—following the implementation of many of the early ideals that grew
from rational planning theory within transportation planning practices, suburbanization and housing
practices [7]—and the discrediting of social factors [8], are examples of why rational planning in its
early form created problematic outcomes. These modernist movements were rejected as contemporary
planning diverged into a myriad of thoughts and theories pertaining to the planning profession.
From this turbulent time emerged the general categories of contemporary theories which permeate
planning discourse, such as resilient design, human-centric design and community-driven design
and landscape urbanism approaches. In Eraydin’s article “’Resilience Thinking’ for planning” [9],
resilient communities and design revolve around more than physical systems (flood protection,
adaptive strategies, etc.) and must include social systems (community connections, neighborhood
trust, etc.) which together can aid in adapting to climatic change. Resilience speaks to adaptability
of social and physical systems to handle and respond to structural and environmental pressures [10].
Where resilience focuses on the ability of a system to adapt to changes, sustainability examines the
longevity and long-term effects of a system. The core concern is to prevent the exploitation of the present
at the expense of the future. Human-centered design and community-driven design approaches are
intrinsically motivated by the human experience, as either individuals or in aggregate. Human-centered
design, lauded by Mike Cooley, is an approach to interactive systems development that aims to make
systems usable and useful by focusing on the users, their needs and requirements, and by applying
human factors/ergonomics, usability knowledge, and techniques [11]. Community-driven design,
influenced by Paul Davidoff’s advocacy model of intervention, tends to be a more macroscopic approach
to aggregation and interventions, focusing on empowerment and the collective needs of individuals in
communities [12]. Landscape urbanism took a proposition that landscape can be seen as an essential
Smart Cities 2019, 2 524

block for organizing cities and the urban environment. Grounded on landscape ecology, it emphasizes
not only patterns and forms of the environment, but also its flows and ecological functions behind,
as well as its temporal processes and changes.
The recent proposition in smart cities brings in a new dimension that contains potential links
to the ideas of rationalism, in which the reliance of data, analytics and statistical modeling would
facilitate the creation of better city plans. Several elements are core to the premise: information
and communication technologies, sustainable development, citizen engagement, government system
integration, new modes of transportation, Internet of Things (IoT) and their integration [13].
Each of these design approaches, despite divergent aims, are constructed on the same framework of
problem identification and examination. Modern city planning fails to adequately capture the connected
and complex nexus of system interactions involved in making decisions [14–16]. Synthesizing these
theories through the careful application of data, systematic approaches, and community integration
using consistent metrics will lead to better and more understandable plans. Data, informatics,
and rational methods are again resurgent in the field and discourse of planning. This resurgence has
been brought on by an emphasis on the need to update planning methodologies according to emerging
technologies and increasing computational capacities (e.g., IoT, big data, etc.). The collective theories of
Michael Batty and Juval Portugali, among others, expose the necessity of these theories to address the
betterment of the human conditions [15,17,18]. Sensing technology, the omnipresence of computing
technology, and the availability of many types of personal data would require novel planning methods
to handle urban issues of our time. One of which is the care to which data collection and application is
applied given the ability to perpetuate existing errors forward or discount non-quantifiable metrics.
These issues are pertaining to disruptive technologies that are often connected with smart cities.
Applications of these technologies, autonomous vehicles (AVs) as an example, potentially perpetuate
the same sets of challenges to current planning methods as older ones but with a new gimmick – new
means with old methods reproducing old failures anew. The manner in which these methods and
new technologies are considered, discussed, or evidenced (a storyline), and by who these complex
set of systems are crafted, can greatly impact their outcomes [19], functionality, and the diffusion of
services [20]. Involvement in these new methods must then include the social subsystem at work in
urban environments as a systematic vision—one that has to account for urban agents in a complex
dynamic manner with spatial system consequences from various agents both local (residents) and
foreign (visitors) [21]. One potential way of meeting the challenges of converging demands and
designing within complex systems is treating neighborhoods as the principle unit of design, study,
and basis for these systems. Al-Thani, Skelhorn, Amato, Koc, and Al-Ghamdi argue that sustainable
urbanism, for smart cities and technologies, can be best achieved through the use of mixed-used and
polycentric neighborhoods and cities with technology acting as a support, but not as the governing
component [22]. However, this approach still lacks a finer grain approach and connection below the
neighborhood in reflecting the parts and designs which create it.
Urban systems design deals with a series of nested complex systems—where the nexus of
system interactions can be studied and evaluated. They is a system of systems that all interact and
engage with each other in an inclusive or mutually exclusive system tree. As such, the effects on one
system will influence another or result in the inability to positively affect it. System complexity is
defined as dynamic, unpredictable and multi-dimensional—consisting of a collection of interconnected
relationships and parts. Unlike “cause and effect” or linear thinking, complexity is characterized by
non-linearity [23], as urban systems are a set of complex systems with multiple actors and decision
processes occurring internally to themselves and with adjacent systems. Careful consideration must be
given to deciding which systems to include, to ignore, or to further simplify.
The concept of a holon fits as a framing device for understanding and linking these independent and
interdependent systems. Holons are a way of conceptualizing complex systems that are simultaneously
a part of and a whole in a larger system where their roles change depending on the bounds or objectives.
Every individual holon has three important characteristics: (i) It controls the lower level order of
Smart Cities 2019, 2 525

Smart Cities 2019, 2 FOR PEER REVIEW 4

holons that comprise it. (ii) It is controlled or affected by those on a higher level than it. (iii) It is a
complete system that could be investigated on its own [24,25] (Figure 1).

Figure1.1.Holarchy
Figure Holarchyof
ofurban
urbansystems
systems from
from the
the core
core holon,
holon, individuals
individualsand
andnature,
nature,totomacro
macrosystems.
systems.
Each smart community is then comprised of smaller interlocking sub-holon systems likebuildings,
Each smart community is then comprised of smaller interlocking sub-holon systems like buildings,
transportation,infrastructure,
transportation, infrastructure,and
andinformation.
information.

Studyingthese
Studying thesecomplex
complexsystem
system relationships
relationships in
in isolation
isolation would
woulddiscount,
discount,ororignore,
ignore,the
theripple
ripple
effectthat
effect thataachange
change in
in one
one system
systemcan canhave
haveononanother. This
another. added
This addedlayer exponentially
layer increases
exponentially the
increases
complexity, uncertainty, and difficulty in properly evaluating decisions of the models.
the complexity, uncertainty, and difficulty in properly evaluating decisions of the models. However, However, the
the end results are more complete, justifiable, and grounded in the context for which the plansare
end results are more complete, justifiable, and grounded in the context for which the plans are
developed. While the system components and stratification of its structure is important
developed. While the system components and stratification of its structure is important in providing in providing
understandingand
understanding andaffecting
affecting changes,
changes, the
the primary
primary focus
focus of
of these
these breakdowns
breakdownsare aretotouse
usethem
themforfor
facilitating the creation of smart communities. These systems ultimately exist to serve the needs of
facilitating the creation of smart communities. These systems ultimately exist to serve the needs of
humanity in the built environment and should aim to foster stronger connections between people,
humanity in the built environment and should aim to foster stronger connections between people,
the built environment, and the natural world.
the built environment, and the natural world.

3.3.AAConceptual
ConceptualFramework
Frameworkof ofUrban
Urban Systems
Systems Design
Design (USD)
(USD) Process
Process
Theurban
The urbansystems
systems design
design is
is aaproposition
propositiontotoaddress
addressthe above
the system
above complexity
system problems.
complexity A
problems.
conceptual framework of its processes is discussed here that is aimed to address challenges in a
A conceptual framework of its processes is discussed here that is aimed to address challenges in a
systematic, humanistic, and cohesive manner. It is a model for planners, system designers,
systematic, humanistic, and cohesive manner. It is a model for planners, system designers, stakeholders,
stakeholders, and communities to handle processes of planning complex urban systems and support
and communities to handle processes of planning complex urban systems and support decisions.
decisions. The framework is divided into its three core elements: Contextualization, Evaluation, and
The framework is divided into its three core elements: Contextualization, Evaluation, and Change and
Change and Iterative Continual Design. (Figure 2).
Iterative Continual Design. (Figure 2).
 The urban systems design is a proposition to address the above system complexity problems. A
conceptual framework of its processes is discussed here that is aimed to address challenges in a
systematic, humanistic, and cohesive manner. It is a model for planners, system designers,
stakeholders, and communities to handle processes of planning complex urban systems and support
decisions.
Smart The2framework is divided into its three core elements: Contextualization, Evaluation, and
Cities 2019, 526
Change and Iterative Continual Design. (Figure 2).

Figure 2. A conceptual framework of urban system design processes: Contextualization, Evaluation,

and Change and Iterative Continual Design.

Urban systems design integrates urban design and systems science. It engages emergent
information and communication technologies to address questions such as how cities function,
or what properties emerge from interactive processes of urban systems. Big data, analytic techniques,
and new planning methods are needed. “Urban systems design is an interventional approach regarding
how future cities should be invented, and yet should be grounded in understanding the extent urban system of
systems. Emergent properties of urban complex systems are to be derived to inform urban design [26].”
USD as a process model is divided into three core phases: Contextualization, Evaluation,
and Change and Iterative Continual Design. Contextualization constructs the current dataset and
conditions, combining the bottom-up processes from community engagement with the top-down
approach from governmental policies and performance goals of the overall system. Evaluation employs
a set of evaluative tools to judge the current conditions—tools which are then used in an iterative
testing loop for all future designs. Change and Iterative Continual Design steps through the design and
community engagement processes in selecting final designs and iterating through potential outcomes.
Underpinning these three phases are four components that are essential to the USD model: Abstraction,
Modeling, Evaluation, and Design. Abstraction is a necessary component of complex evaluation in
which simplifying and categorizing information occurs. Examples such as buildings are categorized to
various typologies. Modeling employs the tools, methods, and applications required to run metric-based
evaluation for deriving emergent properties. Evaluation takes place once modeling has been conducted,
comparing disparate models and information. An evaluation of USD can be accomplished through four
pillars: (R)esliency, (E)conomy, (S)ustainability, and (H)uman Well-Being. Defining System Boundaries
within spatial and temporal scale is a key to urban systems design, which refers to the area of interest
(focus of the study), and how issues and problems are determined. Each of these individual components
are expounded upon in this section.

3.1. Abstraction
Abstraction, in the USD framework, refers to two principal methods: scale-based and
typology-based. Scale-based abstraction examines the metrics and granularity of the areas of the study:
a single household, a neighborhood, an urban district, or a larger region. When moving through
them, each of these different scales have differing resolutions and important characteristics that are
essential to condense or expand upon. Modeling often relies on using these different scales in different
manners. Transportation modeling tends to be large in scale, while the models are normally constructed
from household- and block-level data. Urban building energy modeling examines building energy
performance that relies on urban form, building design, systems, occupants and local climate data.
Overall system integration transcends and connects each element together in a unified evaluation
Smart Cities 2019, 2 527

for a given scale and boundary (Figure 3). We define this stratification based on the qualities of
holons arranged hierarchically into a set grouping of categories: Individual Agents (fundamental),
Households (base unit of analysis), Buildings (extra small), Blocks (small), Districts/Superblocks
Smart Cities 2019,
(medium), 2 FOR PEER REVIEW
Neighborhoods (large), Communities (extra-large), and cities. 6

Figure 3.
Figure Various scales
3. Various scales of
of abstraction
abstraction in
in the
the urban
urban environment,
environment, the
the central
central city
city of
of Tokyo
Tokyo (a);
(a);
Communities, Sumida Ward (b) and North Sumida (c); Neighborhood, Kyojima (d);
Communities, Sumida Ward (b) and North Sumida (c); Neighborhood, Kyojima (d); Blocks (e); and Blocks (e);
and Buildings
Buildings (f). (f).

Typologies are
Typologies are important
important for for contextualized
contextualized and and statistical
statistical abstraction,
abstraction, especially
especially in in examining
examining
large areas where individualized discrete data is inadequate, problematic,
large areas where individualized discrete data is inadequate, problematic, or does not exist. The or does not exist. The basic
basic
elements of
elements of typologies
typologies are are within
within one
one of of the
the following
following categories: Form, Context,
categories: Form, Context, Use, Use, and
and Structure
Structure
(FoCUS). Each of these categories carries a different set of criteria that will
(FoCUS). Each of these categories carries a different set of criteria that will make it a good or bad make it a good or bad tool
tool
for categorization when studying a specific problem. Form, sometimes
for categorization when studying a specific problem. Form, sometimes referred to as urban form, is referred to as urban form, is
the physical
the physical shape
shape and and other
other physical
physical characteristics
characteristics of of aa built
built environment.
environment. The The Context
Context refers
refers toto
information needed to understand where and why it exists or what
information needed to understand where and why it exists or what is affecting it. This ranges is affecting it. This ranges from the
from
surrounding
the surrounding buildings,
buildings,zoning, or transit
zoning, lines to
or transit more
lines to macro-scale
more macro-scale environments like rivers,
environments like forests,
rivers,
and mountains.
forests, ContextContext
and mountains. can be used
can beto used
determine the proximity
to determine of the location
the proximity to amenities,
of the location such as
to amenities,
how close
such as howthe food
closestore (food store
the food desert)(food
is, ordesert)
spacingis, in-between
or spacing buildings
in-betweenthat affects
buildings daylighting and
that affects
urban ventilation.
daylighting and urbanUse, ventilation.
or function, Use,is a common
or function,element used in modeling,
is a common element used as almost all transportation
in modeling, as almost
all transportation and energy modeling requires this to run models. Structure refers tocomponents.
and energy modeling requires this to run models. Structure refers to its internal physical its internal
Structuralcomponents.
physical elements include mechanical
Structural elements systems,
includewindow
mechanical types, and age
systems, of the building.
window types, and age of the
Abstractions are undertaken through manual coding and can be aided by computing tools.
building.
To create these categorizations
Abstractions are undertaken when a studymanual
through is conducted,
codinganand individualized
can be aidedcontextual
by computing analysis based
tools. To
on methodological steps is required. Ideally, this process in the future could
create these categorizations when a study is conducted, an individualized contextual analysis based rely on community-based
machine
on learning and
methodological steps edge computing
is required. to more
Ideally, thisaccurately
process inrepresent
the futurethe stochastic
could rely onand dynamic
community-
relationships that exist in cities. In-depth abstractions can be developed
based machine learning and edge computing to more accurately represent the stochastic and dynamic by classifying buildings using
visual analysis
relationships tools
that existlike
in those
cities. employed by street views
In-depth abstractions can be and classification
developed software,buildings
by classifying using land-useusing
visual analysis tools like those employed by street views and classification software, usingmaterials,
data, and utilizing Building Information Modeling (BIM) to record data of building geometry, land-use
use patterns,
data, time scheduling
and utilizing Building and cost . . . etc.
Information Modeling (BIM) to record data of building geometry,
materials, use patterns, time scheduling and cost…etc.
3.2. Modeling
In current practice, there are many issues in the way modeling is completed and how these
3.2. Modeling
models integrate and interact with planning and design principles. Different scales of modeling are
In current practice, there are many issues in the way modeling is completed and how these
completed in different ways for tackling different problems, whether it is for a private development,
models integrate and interact with planning and design principles. Different scales of modeling are
completed in different ways for tackling different problems, whether it is for a private development,
for a community plan, for an extended arterial corridor study, or for an interstate/freeway corridor
study. Analyses are often completed in a silo for site-specific developments. Modelers tend to react
to simple conditions instead of recommending solutions for complex system problems and their
future changes. The USD framework seeks to close the gap between designers and modelers, as these
Smart Cities 2019, 2 528

for a community plan, for an extended arterial corridor study, or for an interstate/freeway corridor
study. Analyses are often completed in a silo for site-specific developments. Modelers tend to react
to simple
Smart conditions
Cities 2019, instead
2 FOR PEER REVIEWof recommending solutions for complex system problems and their7
future changes. The USD framework seeks to close the gap between designers and modelers, as these
tasks
tasks are
are toto be
bejointly
jointlyhandled
handledby bythem.
them. It’s
It’s aa process
process to
to utilize
utilize aa looping
looping method
method ofof redesign
redesign and
and
verification
verification[27].
[27].
The
The USDUSD framework
framework relies
relies on
on blending
blending the the analytics
analytics and
and design.
design. It is
is important
important that
that value,
value,
information,
information,and anddata
datacan bebe
can compared
compared across models
across and and
models methods to meet
methods the accepted
to meet criteriacriteria
the accepted using
the USD.
using theThe performance
USD. modeling modeling
The performance criteria arecriteria
dividedare
intodivided
three modeling methods
into three (Performance;
modeling methods
People Flow; Experiential),
(Performance; People Flow;two design integrated
Experiential), two design approaches
integrated (“Bottom-Up” Context; “Top-Down”
approaches (“Bottom-Up” Context;
System),
“Top-Down” and one overarching
System), and onemodel (Figure model
overarching 4). (Figure 4).

Figure
Figure 4. Anoverall
4. An overallframework
frameworkofof urban
urban systems
systems design
design including
including three
three modeling
modeling methods
methods and
and their
their objectives, indicators (qualitative and quantitative), tools used for modeling, and their relevant
objectives, indicators (qualitative and quantitative), tools used for modeling, and their relevant data
data inputs.
inputs.
3.2.1. Performance Modeling
3.2.1. Performance Modeling
The performance modeling method is goal-driven and objective-oriented. In these models,
The performance modeling method is goal-driven and objective-oriented. In these models, the
the data can range from hard to soft. Hard data is defined as quantitative data in the form of
data can range from hard to soft. Hard data is defined as quantitative data in the form of numbers or
numbers or graphs. Hard data describes the types of data that are measured, traced, validated and
graphs. Hard data describes the types of data that are measured, traced, validated and generated
generated from devices and applications, such as phones, computers, sensors, smart meters,
from devices and applications, such as phones, computers, sensors, smart meters, traffic monitoring
traffic monitoring systems, call detail records, bank transaction records, among others [28]. Soft data is
systems, call detail records, bank transaction records, among others [28]. Soft data is usually derived
usually derived from qualitative measures, and sometimes immeasurable. Quantitative models
from qualitative measures, and sometimes immeasurable. Quantitative models include energy use
include energy use modeling, carbon emissions, energy supply, food supply, logistics chains,
modeling, carbon emissions, energy supply, food supply, logistics chains, etc. For modeling buildings
etc. For modeling buildings and block-scale built environment, tools like Rhino, Grasshopper,
and block-scale built environment, tools like Rhino, Grasshopper, EnergyPlus, and BIM are useful.
EnergyPlus, and BIM are useful. For modeling network, macroscopic, mesoscopic, and microscopic
For modeling network, macroscopic, mesoscopic, and microscopic simulation tools are used. All
simulation tools are used. All modeling is encompassed by statistical modeling. The desired outputs
modeling is encompassed by statistical modeling. The desired outputs of measures-of-effectiveness
of measures-of-effectiveness (MOEs) are based on the chosen goals of the analyses, and the outputs
(MOEs) are based on the chosen goals of the analyses, and the outputs are then compared to the
are then compared to the objectives of the analyses. These results are weighted against the other
objectives of the analyses. These results are weighted against the other modeling methods, and it is
modeling methods, and it is then decided whether the process must loop-back into model adjustment
then decided whether the process must loop-back into model adjustment and redesign.
and redesign.
3.2.2. People-Flow
3.2.2. People-Flow Modeling
Modeling
People-flow modeling
People-flow modeling deals
deals with
with the
the movement
movement of of people
people throughout
throughout urban
urban environment,
environment,
which consists of hard and mixed data types. As mentioned, the models can be macroscopic,macroscopic,
which consists of hard and mixed data types. As mentioned, the models can be mesoscopic,
mesoscopic,
or orinmicroscopic
microscopic in nature,
nature, which which
is usually is usually
decided decided
upon data upon data
availability. availability.
Specifically, Specifically,
the people-flow
the people-flow
modeling modeling
investigates investigates
the effects of the the effects ofand
movements the flows
movements
at the and flows at the
network-level network-level
and most often,
and most often, the transportation/traffic with or without the use of a vehicle in the built
environment. The main aspect of this modeling is to expand and change the focus from
transportation-only foci (vehicle-based modeling) to people/agent-based modeling that can
investigate how walking, autonomous vehicles (AVs), and other new system designs affect the city.
The additional information that is now available with big data creates the possibility for agent-based
simulation which includes network schedules and patterns based on origin-destination data points.
Smart Cities 2019, 2 529

the transportation/traffic with or without the use of a vehicle in the built environment. The main
aspect of this modeling is to expand and change the focus from transportation-only foci (vehicle-based
modeling) to people/agent-based modeling that can investigate how walking, autonomous vehicles
(AVs), and other new system designs affect the city. The additional information that is now available
with big data creates the possibility for agent-based simulation which includes network schedules and
patterns based on origin-destination data points. It is important to note that these data and modeling
methods can also reach other metrics and data included in the performance and experiential modeling
methods. This overlapping modeling methods can enrich our understanding of people flow patterns,
urban forms and their interactions that generate human experiences. An example is the outdoor human
comfort modeling, which is a hard metric that also impacts the modeling and data of people flow.

3.2.3. Experiential Modeling

Experiential modeling refers to modeling methods, tools, and metrics that can quantify and
qualify the human or sensory experiences that impact or originate from the city or object of study.
Often data and information studied by these models include soft/mixed data types with the use of
harder (quantitative) information being a proxy for actual human experiences. Examples of this would
be thermal comfort in outdoor spaces and the visual quality of street views. Thermal comfort is highly
dependent on the individual locations and movements based on a number of factors that can be studied
and combined as a proxy (i.e., radiation, temperature, wind and humidity, etc.). One way of assessing
human visual experience in urban environment is through public outreach, surveys, and field studies
to determine what qualities that users find appealing. The data can then be used to train algorithms to
potentially approximate users’ preference. However, this should be applied with care given the ease of
miss training or errors in machine learning. Together with performance and people-flow modeling,
experiential modeling reaches the human-scale and human-effect of the current and future decisions
made for and within the built environment.

3.2.4. Design
Design, or system change, focuses less on the immediate use of analytical modeling tools and
instead seeks to blend the modeling and planning methods into a unified and coherent new system
through a synthesis approach. Where the analytical methods are to provide the basic metrics for
evaluation, design aims to understanding contexts, and focuses on social interactions, stakeholders
engagement, and creative thinking through inductive reasoning.
Design as a change model tends to reply on a top-down approach. Designers and planners identify
problems, state their intent, find potential opportunities and use anticipation to generate concepts and
solutions to these issues they have identified. Design often relies on a priori thought and deductive
reasoning that can be judged and needs to work in tandem with contexts. An example is the push
for mass adoption of autonomous vehicles into communities prior to understanding the potential
effects. Another example is the introduction of photovoltaic (solar) panels that must first be corrected
and gauged in context. It is also managed in dialogue flows between modeling and design processes.
Through iterations, professionals can evaluate outcomes based on the goals and objectives through
community engagement to make final design decisions.
From the three modeling methods, performance, people flow, experiential, and their integration
in design, an overall framework of urban systems design would emerge. The framework offers a
process to address how urban system changes would arise from complex and non-linear system
interactions. The selection of modeling tools and techniques is to be contextualized, and to a certain
degree, determined based on issues defined by the system boundary. Evaluation of the modeling
results will inform iterative and continual design processes for moving urban systems change.
Smart Cities 2019, 2 530

3.3. Resilience, Economic Success, Sustainability, and Human Well-Being (RESH) Evaluation
Resilience, Economic Success, Sustainability, and Human Well-Being (RESH) are the four pillars
of this framework—used as the key evaluation and validation criteria. Each of the four pillars of RESH
act as a three-dimensional trade-off matrix for evaluating current and projected future conditions
(Figure 5), but by themselves can be nebulous in their definition depending on the context, field of study,
and application. Resilience examines the ability of a system to resist or recover from stress—indicators
Smart
of Citiescan
which 2019,
be2 based
FOR PEER REVIEW
on the assessment of an area’s vulnerability to flooding, fire risks, or earthquakes.9
Economic success, or further aggregated to mean individual and community prosperity, is built upon
prosperity, is built upon the needs of individuals and businesses in cities. Given our current social
the needs of individuals and businesses in cities. Given our current social structures—economic
structures—economic success can be measured by worker-to-housing balance, the transportation
success can be measured by worker-to-housing balance, the transportation network, or economic
network, or economic growth. Sustainability can be defined in many ways, but for USD specifically,
growth. Sustainability can be defined in many ways, but for USD specifically, it is used to explore
it is used to explore environmental sustainability—via solar potential, energy supply, energy
environmental sustainability—via solar potential, energy supply, energy demand, or food and waste
demand, or food and waste cycles. Human Well-Being is the measure which contains all social and
cycles. Human Well-Being is the measure which contains all social and tactile systems that are
tactile systems that are fundamental to how humans interact, live, and use the city—metrics like heat-
fundamental to how humans interact, live, and use the city—metrics like heat-island effect, walkability,
island effect, walkability, thermal comfort, or heat risk assessments are all of use. With these
thermal comfort, or heat risk assessments are all of use. With these definitions and methods, RESH can
definitions and methods, RESH can be utilized by evaluating the quantitative indicators from
be utilized by evaluating the quantitative indicators from modeling, their trade-offs and how they
modeling, their trade-offs and how they are operated and managed by considering inputs and
are operated and managed by considering inputs and outputs within a particular urban context.
outputs within a particular urban context. While quantitative metrics are needed, qualitative metrics
While quantitative metrics are needed, qualitative metrics are crucial for a better understanding of how
are crucial for a better understanding of how humans, nature, and urban spaces work together as
humans, nature, and urban spaces work together as interdependent systems. To further aggregate the
interdependent systems. To further aggregate the RESH concept into evaluation and validation
RESH concept into evaluation and validation criteria, it was necessary to consider existing literature
criteria, it was necessary to consider existing literature that looks deeply at each of the pillars.
that looks deeply at each of the pillars.

Figure 5.5.Three-dimensional
Figure Three-dimensionaltrade-off matrix
trade-off between
matrix resilience,
between economics,
resilience, sustainability,
economics, and human
sustainability, and
well-being (RESH) evaluation metrics (created based upon [29]).
human well-being (RESH) evaluation metrics (created based upon [29]).

3.3.1. Resilience
3.3.1. Resilience
The concept of resilience in the urban context is defined as the ability for a particular geographic
The concept of resilience in the urban context is defined as the ability for a particular geographic
area to return or recover to its original status after experiencing shocks either physically, socially,
area to return or recover to its original status after experiencing shocks either physically, socially, or
or psychologically [30]. “Shocks” (or stresses) can be caused naturally or intentionally, and it may
psychologically [30]. “Shocks” (or stresses) can be caused naturally or intentionally, and it may cause
cause urban systems to fail [31]. “Recovery” from the damages can include restoration to the original
urban systems to fail [31]. “Recovery” from the damages can include restoration to the original
positions
positionsororanan
update to develop
update or redevelop
to develop systemssystems
or redevelop [31]. Since potential
[31]. hazards such
Since potential as hurricanes,
hazards such as
earthquakes, heat waves, etc., can be increased more by climate change [32] and these types
hurricanes, earthquakes, heat waves, etc., can be increased more by climate change [32] and these of disaster
risks
typesinfluence the risks
of disaster urbaninfluence
systems’ failure [33], systems’
the urban resiliencefailure
becomes significant
[33], forbecomes
resilience human settlements
significant[34].
for
To summarize,
human settlements [34]. this study categorizes the constituent elements of urban systems into
governance, infrastructure
To summarize, this studyand categorizes
the built environment,
the constituentsocioeconomics,
elements of urban and systems
the natural
into
governance, infrastructure and the built environment, socioeconomics, and the natural environment
(Figure 6). The elements and sub-elements are defined by cross-validating different literature
[10,27,28,30–33,35–39].
Smart Cities 2019, 2 531

environment (Figure 6). The elements and sub-elements are defined by cross-validating different
SmartCities
Cities2019,
literature
Smart 2019,2 2FOR
FORPEER
PEERREVIEW
REVIEW
[10,27,28,30–33,35–39]. 1010

Figure6.6.6.Stocks
Figure
Figure Stocksand
Stocks andflows
and flowsconstituting
flows constitutingthe
constituting theurban
the urbansystems.
urban systems.
systems.

The evaluation
Theevaluation
The criteria
evaluationcriteria of
criteriaof urban
ofurban resilience
urbanresilience should
resilienceshould
shouldbe be applicable
beapplicable for
applicablefor any
forany given
anygiven types
giventypes of
typesof stresses,
ofstresses,
stresses,
systems, and
systems,and
systems, recovery.
andrecovery. By
recovery.By considering
Byconsidering resilient
consideringresilient phases
resilientphases and
phasesand given
andgiven conditions,
givenconditions, this
conditions,this study
thisstudy develops
studydevelops the
developsthethe
framework
framework for
forevaluating
evaluating the
the urban
urban resilience
resilience [10,34]
[10,34](Figure
(Figure
framework for evaluating the urban resilience [10,34] (Figure 7). 7).
7).

Figure7.7.7.Evaluation
Figure
Figure Evaluationframework
Evaluation frameworkfor
framework forurban
for urbanresilience.
urban resilience.
resilience.

3.3.2. Economics
3.3.2.Economics
Economics
3.3.2.
An urbaneconomy
Anurban
urban economy canbecan be defined
bedefined
defined as an economy that sustainably
marketbuilds market
An economy can asasan
aneconomy
economy thatsustainably
that sustainably buildsmarket
builds structures
structures and
and
structures
business and business
environments environments
into those that caninto those
acquire that
economic can acquire
growth, economic
prosperity, growth,
and prosperity,
competitiveness
business environments into those that can acquire economic growth, prosperity, and competitiveness
and competitiveness
[40].The
Theurban
urbaneconomy [40].can
economy The urban
beevaluatedeconomy
evaluated can be
withinputs
inputs evaluated
(cost) with inputs
andoutputs
outputs (cost)
(benefits) and outputs
[40]. can be with (cost) and (benefits) ofofurban
urban systems
systems
(benefits)
[41], but of urban
must systems
also include [41], but must alsoof
considerations include
equity. considerations
Economic of equity.
growth can Economic
be driven growth
by can
human
[41], but must also include considerations of equity. Economic growth can be driven by human
capital,physical
capital, physicalcapital
capital(i.e.,
(i.e.,infrastructure
infrastructureand andbuilt
builtenvironment),
environment),innovation
innovation(in (inpublic
publicand
andprivate
private
sectors),and
sectors), andspatial
spatialconditions
conditions[42]. [42].Hammer
Hammeretetal. al.(2011)
(2011)identified
identifiedindicators
indicatorsforforeconomic
economicgrowth
growth
as job creation, demands of products or services, and urban attractiveness for
as job creation, demands of products or services, and urban attractiveness for investment [42]. The investment [42]. The
outcomes of urban economies are defined by job creation (employment), demands of products oror
outcomes of urban economies are defined by job creation (employment), demands of products
Smart Cities 2019, 2 532

be driven by human capital, physical capital (i.e., infrastructure and built environment), innovation (in
public and private sectors), and spatial conditions [42]. Hammer et al. (2011) identified indicators
Smart Cities 2019, 2 FOR PEER REVIEW 11
for
Smarteconomic
Cities 2019, 2growth
FOR PEERasREVIEW
job creation, demands of products or services, and urban attractiveness 11
for investment
services, supply [42].
of The outcomes
products of urbanshared
or services, economies are defined
prosperity, by job creation
and investment (employment),
opportunity. These
services,
demands supply
of of
products products
or or
services, services,
supply ofshared
productsprosperity,
or and
services, investment
shared
outcomes can be designated as the evaluation criteria for the urban economy in the opportunity.
prosperity, and These
investment
proposed
outcomes
opportunity.
framework can be designated
These
(Figure outcomes
8). as be
can thedesignated
evaluation criteria
as the for thecriteria
evaluation urbanforeconomy ineconomy
the urban the proposed
in the
framework
proposed (Figure 8).(Figure 8).
framework

Figure8.8.Evaluation
Figure Evaluationframework
frameworkfor
forurban
urbaneconomy
economygrowth
growth(Adapted
(Adaptedfrom
from[42]).
[42]).
Figure 8. Evaluation framework for urban economy growth (Adapted from [42]).
3.3.3.
3.3.3.Sustainability
Sustainability
3.3.3. Sustainability
Sustainable
Sustainablesystems
systemscancanbebedefined
definedas asthe
thesystems’
systems’processes
processesand andconditions
conditionsbeing
beingmaintained
maintained
Sustainable
forever
foreverwithout systems
withoutdecreasing can be
decreasingvalue defined as the
valueororperformance systems’
performance[43]. processes and
[43]. Sustainability conditions
Sustainability has has often being maintained
oftenencompassed
encompassed the
the
forever
environment,without
environment, decreasing
economy,
economy, and value
societyor
andsociety performance
[44].
[44]. Urban [43]. Sustainability
Urbansustainability
sustainability can
canbe has often
beachieved
achieved by encompassed
byactively
actively the
integrating
integrating
environment,
or
orcollaborating
collaborating economy,
between
betweenand society [44].
subsystems
subsystems toUrban
to sustainability
minimize
minimize can be achieved
harmfulconsequences
harmful consequences by
onon actively
thethe integrating
ecosystem
ecosystem [45].
[45]. To
or
To collaborating
integrate
integrate thethe between
diverse
diverse subsystems
subsystems,
subsystems, to
thetheminimize
urban
urban harmful consequences
sustainability
sustainability framework
framework on the ecosystem
requires
requires four
four [45]. To
stages:
stages: (1)
integrate
(1)
diagnosis, the
diagnosis, (2) diverse
vision subsystems,
(2) vision
and priorities theactions,
and priorities
for urban sustainability
for actions,
(3) framework
(3) financing
financing the plan,the requires
andplan,
(4) and four stages:
(4) monitoring
monitoring framework (1)
diagnosis,
framework
[40]. In this (2) vision
[40]. Inand
respect, thispriorities
urban respect, for
urbanactions,
sustainability (3)evaluated
financing
sustainability
can be can the
by be plan, and
the(4)
evaluated
whether bymonitoring
whether
inflows framework
the inflows
and outflows over
[40].
and
urban In this
outflows respect,
systems over urban sustainability
urban from
deviate systems can
deviate from
manageable be evaluated
manageable
ranges, by
and within whether
ranges,
thisand the inflows and
within thisshould
framework outflows
framework a over
have should
focus
urban
have
towardsa systems
focus deviate
towards
environmental from manageable
environmental
sustainability ranges,
sustainability
within and
within
the urban within this
the urban
context framework
context
(Figure should
9). (Figure 9). have a focus
towards environmental sustainability within the urban context (Figure 9).

Figure 9. Figure 9. Evaluation

Evaluation frameworkframework for the
for the urban urban sustainability
sustainability (adapted (adapted from [40]).
from [40]).
Figure 9. Evaluation framework for the urban sustainability (adapted from [40]).
3.3.4. Human Well-Being
3.3.4. Human Well-Being
3.3.4.Human
Humanwell-being
Well-Beingis the key purpose of urban systems [33]. Da Silva et al. (2012) identified the
Human well-being is the key purpose of urban systems [33]. Da Silva et al. (2012) identified the
components
Human of well-being
well-being as the
is the basic needs
of for survival, security, health, good social identified
relations and
components of well-being as key purpose
the basic needs urban systems
for survival, [33]. Da
security, Silva et
health, al. (2012)
good social relations the
and
esteem, and freedom
components of of choice
well-being as and
the action
basic [33].for
needs Arup (2014)security,
survival, defined health,
humangood health and well-being
social relations as
and
esteem, and freedom of choice and action [33]. Arup (2014) defined human health and well-being as
esteem, andoffreedom
consisting minimalofhuman
choice vulnerability,
and action [33]. Arup (2014)
livelihoods anddefined human and
employment, health and well-being
safeguards to humanas
consisting of minimal human vulnerability, livelihoods and employment, and safeguards
life and health [46]. Human well-being requires the functionality of urban systems with a stable or to human
life and health
increasing [46]. Human
experience well-being
for people requires
in diverse livingthe functionality
conditions [47].of urban systemsinwith
Socioeconomics a stable
urban or
systems,
increasing
including experience
materials and for resources
people in flowing
diverse living
through conditions
the urban[47]. Socioeconomics
systems, in urban attributes
become important systems,
including materials and resources flowing through the urban systems, become important attributes
Smart Cities 2019, 2 533

consisting of minimal human vulnerability, livelihoods and employment, and safeguards to human
life and health [46]. Human well-being requires the functionality of urban systems with a stable or
increasing experience for people in diverse living conditions [47]. Socioeconomics in urban systems,
Smart Cities 2019, 2 FOR PEER REVIEW 12
including materials and resources flowing through the urban systems, become important attributes to
measure
to measure human well-being.
human Other urban
well-being. Othersystems
urban (governance, infrastructure,
systems (governance, and natural environment)
infrastructure, and natural
can support the formation of the evaluation criteria. The evaluation criteria framework for human
environment) can support the formation of the evaluation criteria. The evaluation criteria framework
well-being is presented in Figure 10.
for human well-being is presented in Figure 10.

Figure 10. Framework for measuring human well-being and progress (adapted from [47]).
Figure 10. Framework for measuring human well-being and progress (adapted from [47]).
3.4. System Boundaries
3.4. System Boundaries
The creation of system boundaries, or study boundaries, is the first step toward initiating the
The creation
performance of system
modeling boundaries,
for a planning and or studystudy
design boundaries,
utilizingisthethe first systems
urban step toward design initiating
conceptualthe
performance modeling for a planning and design study utilizing
framework. In studying problems and applying metrics, boundaries serve the purpose of focusingthe urban systems design
conceptual
and providingframework. In studying
restrictions problems and
to what otherwise can applying
be edgeless metrics,
systems. boundaries
For the serveinitialthe purpose
phases of
of the
focusing
study, theand
systemproviding restrictions
boundaries can be to what otherwise
categorized into fivecan be edgeless
types: systems. For the
Study, Infrastructure, initial phases
Administrative,
of the study, the system
Community/Social, boundaries
and Procedural. Thecan
Studybe boundary
categorized into on
focuses five
thetypes:
area that Study, Infrastructure,
is restrained by the
Administrative,
questions Community/Social,
that a study seeks to address. andTheProcedural. The Study
Infrastructure boundary
boundary focuses on theor
uses infrastructure area that to
nature is
restrained by the questions that a study seeks to address. The Infrastructure
form the boundaries of an area. The Administrative boundary uses the bounds set by administrative boundary uses
infrastructure
agencies or nature
to segment space.to The
formCommunity/Social
the boundaries ofboundary
an area. TheusesAdministrative
how communities boundary
and social uses the
areas
bounds set bytoadministrative
self-identify agencies of
inform the conditions to the
segment
study.space. The Community/Social
The Procedural boundary automatesboundarythe uses how
above
communities and social areas self-identify to
methods to generate more localized conditions of boundaries.inform the conditions of the study. The Procedural
boundary automates the
The Procedural above methods
boundary is the mostto generate
powerful more localizedamong
boundary conditions of boundaries.
the categories because it
The Procedural
combines boundary
the other methods underis the
one most powerful
umbrella. boundary
An example of the among the categories
development because it
of the Procedural
combines the other methods under one umbrella. An example of the development
boundary is to choose the three key destinations, and the radii vector between or around them, that are of the Procedural
boundary ismost
considered to choose the three
important key destinations,
in a community and
to create thethe radii vector
centroid between
of a study area or at around them, that
the neighborhood,
are considered most important in a community to create the centroid
or microsimulation, scale. In the Japanese urban context, it is believed that the radii between of a study area at the
the
neighborhood, or microsimulation, scale. In the Japanese urban context, it
local market (or Kira-Kira), the local shrine or community space, and the nearest transit station, is believed that the radii
between
along thethe
with local market
level (or Kira-Kira),
of human the local shrine
comfort experienced alongor community
these space,
radii, create theand the nearest
routine walkshed transit
in a
station, along with the level of human comfort experienced along these
neighborhood. This routine walkshed could make sense as the study boundary for a small-area study. radii, create the routine
walkshed
The in a in
measures neighborhood.
this boundaryThis routine walkshed
development create ancould make connection
additional sense as thetostudy RESHboundary
and act asfor thea
small-area study. The measures in
interaction interface between design spaces. this boundary development create an additional connection to
RESH and act as the interaction interface between design spaces.
A potential addition to the Procedural boundary development utilizes a more community-
centric approach in that it would relate directly to a set of questions to be asked of the residents of a
neighborhood. The home location and the points of interest of the individuals would be mapped. The
walkshed would then be developed from iterations of the radii distances between home and points
of interest found in the individual responses.
Smart Cities 2019, 2 534

A potential addition to the Procedural boundary development utilizes a more community-centric

approach in that it would relate directly to a set of questions to be asked of the residents of a
neighborhood. The home location and the points of interest of the individuals would be mapped.
The walkshed would then be developed from iterations of the radii distances between home and points
of interest
Smart found
Cities 2019, in the
2 FOR individual
PEER REVIEW responses. 13

4.4.USD
USDPhases
Phases
AA conceptual
conceptual framework
framework ofof urban
urban systems
systems design
design isis segmented
segmented into
into three
three core
core phases:
phases:
Contextualization,
Contextualization,Evaluation,
Evaluation,and
andChange
ChangeandandIterative
IterativeContinual
ContinualDesign
Design(Figure
(Figure11).
11).

Figure11.
Figure 11.Urban
Urbansystem
systemdesign
designsystem
systemdiagram
diagramand
andprogrammatic
programmaticsteps
stepsin
inits
itsimplantation
implantationprocess.
process.

Contextualizationdevelops
Contextualization develops the physical
the physical (modeled),
(modeled), social (engagement),
social (engagement), and evaluativeand (objective)
evaluative
(objective)and
properties properties
metrics and metrics
of the area ofofinterest,
the areadefining
of interest,
the defining
limits and thescope
limits
ofand
the scope
project.ofEvaluation
the project.
Evaluation uses modeling tools to generate metrics based on the RESH categories
uses modeling tools to generate metrics based on the RESH categories to establish how trade-offs, to establish how
trade-offs, between mutually exclusive choices, are weighed for current
between mutually exclusive choices, are weighed for current and future scenarios. Typologies are and future scenarios.
Typologies
created are created
and evaluated and
in this evaluated
phase. Changein andthis phase.
Iterative ChangeDesign
Continual and Iterative
incorporatesContinual Design
RESH metrics,
incorporates RESH metrics, human design choices, parametric modeling, and the
human design choices, parametric modeling, and the objective identifiers (noted in phase 1) to create objective identifiers
(noted in phase
alternatives and to1) to createmonitor
constantly alternatives andthrough
and loop to constantly
the USD monitor
framework and once
loop applied
throughtothe USD
a space.
framework once applied to a space. If the alternatives do not meet the goals that
If the alternatives do not meet the goals that were originally set-out by the community, the process were originally set-
out by the community, the process loops back to the existing conditions
loops back to the existing conditions (Contextualization) and typology creation (Evaluation)—an(Contextualization) and
typologyelement
essential creationof (Evaluation)—an essential element
the effort of this framework to achieveofcommunity
the effort goals
of thisand framework to achieve
the goals within the
community goals
RESH evaluation. and the goals within the RESH evaluation.

5.5.Conclusions
Conclusions
Wepresent
We presentaaconceptual
conceptualframework
frameworkofofurban
urbansystems
systemsdesign—driven
design—drivenby bythe
thegoals
goalsof
ofResilience,
Resilience,
EconomicSuccess,
Economic Success,Sustainability,
Sustainability,
andand
HumanHuman Well-Being—which
Well-Being—which reliesrelies onmodeling
on joint joint modeling and
and mixed
mixed evaluations
evaluations of currentofconditions
current conditions
to addresstonew
address new challenges
challenges of urban
of urban design and design
planning and
in planning in
the context
ofthe context
smart ofmovement.
cities smart citiesInmovement. In current
current planning planning
practice, practice,
we often we often
see that seeorthat
a single a single
several or several
performance
performance
criteria criteriarepresent
and indicators and indicators
the RESHrepresent
metrics. the RESHthe
However, metrics.
proposedHowever, the proposed
USD conceptual USD
framework
conceptual framework shows the possibility of combining the performance evaluation and the
engagement of a community while considering impacts of design for changing urban systems. The
implications of this framework for planning and design are significant because it brings together the
often-clashing theories of rational planning and the social and communicative approaches under one
analysis umbrella which considers new and future technologies and methods while utilizing big data
Smart Cities 2019, 2 535

shows the possibility of combining the performance evaluation and the engagement of a community
while considering impacts of design for changing urban systems. The implications of this framework
for planning and design are significant because it brings together the often-clashing theories of rational
planning and the social and communicative approaches under one analysis umbrella which considers
new and future technologies and methods while utilizing big data sources. The methodology is
enhanced by the understanding of the previous failures of the top-down system-driven methods of
planning from 1950s that is to be refined in the new context of emerging technologies and smart cities.
These important aspects of the framework allow those in the profession to better understand the
desires and needs of the average citizen, in relation to mutually exclusive objectives, potentially in
a greater way than current public involvement practices can achieve. This is all made possible by
new data, novel evaluation and design methods that should be used to better manage our current
urban systems, to develop a feedback loop in design using modeling and analysis that enhances
design towards community goals, and to influence how urban systems could be changed in the future.
Rather than solidifying a strict implementation guideline of RESH criteria, we hope that users of this
framework can broaden their opportunities to use new planning methods based on these four pillars.
New technologies are to be driven by community goals that are context-specific and equity-focused.
Interrelationships among the four pillars can be further investigated for a more iterative urban planning
practice and as a governing principle based on the USD concept. Future study will need to explore
the interdependencies among the four pillars to support optimization of the performance of urban
scenarios, while considering trade-offs and other examples of planning support systems as further
justification for the USD approach. The question of how we should apply USD framework to actual
urban contexts remains challenging. Practices and issues should continue to be investigated with local
stakeholders in other locations to strengthen and test the framework.

Author Contributions: Concept development, M.B.T., R.B.B., S.C., T.Y., Y.Y. and P.P.J.Y.; data curation, T.Y.;
writing—draft preparation, M.B.T., R.B.B., S.C., and T.Y.; writing—review and editing, M.B.T., R.B.B., S.C., Y.Y.
and P.P.J.Y.; visualization, M.B.T. and S.C.; projects supervised by Y.Y. and P.P.J.Y.
Funding: This research was funded by National Institute for Environmental Studies and Global Carbon Project.
Acknowledgments: Our team would like to take this opportunity to thank the administrative staff with the Global
Carbon Project at the National Institute for Environmental Studies. Without their hard work and efforts, our team
would not have had the opportunity to work together at the Institute in Tsukuba, Japan. Additionally, we must
acknowledge the hard work of the Spring 2016, 2017, and 2018 International Urban Design Studios from School of
City and Regional Planning and School of Architecture of the Georgia Institute of Technology, who utilized urban
systems design methods in their studio which laid the groundwork. We have special thanks to Akito Murayama
from the Department of Urban Engineering of the University of Tokyo for his advice on Tokyo urban planning.
Finally, we acknowledge Sycamore Consulting in Decatur, Georgia of the U.S., who laid the groundwork for
discussions of community engagement methods that were utilized.
Conflicts of Interest: The authors declare no conflict of interest.

References
1. Taylor, N. Urban Planning Theory since 1945; SAGE Publications Ltd.: London, UK, 1998.
2. Davidoff, P. Advocacy and pluralism in planning. J. Am. Inst. Plan. 1965, 31, 331–338. [CrossRef]
3. Healey, P. Collaborative Planning: Shaping Places in Fragmented Societies; Macmillan International Higher
Education: London, UK, 1998. [CrossRef]
4. World Commission on Environment and Development. Our Common Future—Report of the World
Commission on Environment and Development (The Brundtland Report). Med. Confl. Surviv. 1987.
[CrossRef]
5. Morris, A.E.J. History of Urban form Before the Industrial Revolution, 3rd ed.; Routledge: London, UK, 2013.
6. Howard, E. Garden Cities of To-morrow; Routledge: London, UK, 2013. [CrossRef]
7. Parsons, K.C. Collaborative genius: The regional planning association of America. J. Am. Plan. Assoc. 1994,
60, 462–482. [CrossRef]
8. Jacob, J. The Death and Life of Great American Cities; Random House: New York, NY, USA, 1961.
Smart Cities 2019, 2 536

9. Eraydin, A. “Resilience Think” For Planning. In Resilience Thinking in Urban Planning; Eraydin, A.,
Tasan-Kok, T., Eds.; Springer: Dordrecht, The Netherlands, 2013; pp. 17–37.
10. Meerow, S.; Newell, J.P.; Stults, M. Defining urban resilience: A review. Landsc. Urban Plan. 2016, 147, 38–49.
[CrossRef]
11. ISO 9241-210:2010. Ergonomics of human-system interaction—Part 210: Human-centred design for interactive
systems (ISO 9241-210:2010). Int. Stand. Organ. 2010. [CrossRef]
12. Sanoff, H. Origins of Community Design. PN Plan. Netw. 2005, 166, 14–17.
13. Albino, V.; Berardi, U.; Dangelico, R.M. Smart Cities: Definitions, Dimensions, Performance, and Initiatives.
J. Urban Technol. 2015, 22, 3–21. [CrossRef]
14. Friedmann, J. Why Do Planning Theory? Plan. Theory 2003, 2, 7–10. [CrossRef]
15. Portugali, J. Complexity Theories of Cities: Achievements, Criticism and Potentials. In Complexity Theories of
Cities Have Come of Age; Tan, E., Portugali, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2012; pp. 47–62.
[CrossRef]
16. Scott, A.J.; Storper, M. The nature of cities: The scope and limits of urban theory. Int. J. Urban Reg. Res. 2015,
39, 1–15. [CrossRef]
17. Batty, M.; Axhausen, K.W.; Giannotti, F.; Pozdnoukhov, A.; Bazzani, A.; Wachowicz, M. Smart cities of the
future. Eur. Phys. J. Spec. Top. 2012, 214, 481–518. [CrossRef]
18. Batty, M. The size, scale, and shape of cities. Science 2008, 319, 769–771. [CrossRef] [PubMed]
19. Freudendal-Pedersen, M.; Kesselring, S.; Servou, E. What is smart for the future city? Mobilities and
automation. Sustainability 2019, 11, 221. [CrossRef]
20. Bamwesigye, D.; Hlavackova, P. Analysis of Sustainable Transport for Smart Cities. Sustainability 2019,
11, 2140. [CrossRef]
21. Fistola, R.; Gargiulo, C.; Battarra, R.; La Rocca, R.A. Sustainability of urban functions: Dealing with tourism
activity. Sustainability 2019, 11, 1071. [CrossRef]
22. Al-Thani, S.K.; Skelhorn, C.P.; Amato, A.; Koc, M.; Al-Ghamdi, S.G. Smart technology impact on neighborhood
form for a sustainable Doha. Sustainability 2018, 10, 4764. [CrossRef]
23. Ladyman, J.; Lambert, J.; Wiesner, K. What is a complex system? Eur. J. Philos. Sci. 2013, 3, 33–67. [CrossRef]
24. Yang, P.P.-J.; Wiedenback, A.; Tobey, M.; Wu, Y.; Quan, S.J.; Chauhan, Y. Material Based Urban Modeling:
An Approach to Integrate Smart Materials in a Near-Zero Community Design. Energy Procedia 2017, 105,
3765–3771. [CrossRef]
25. Koestler, A. Beyond atomism and holism—the concept of the holon. Rules Game Interdiscip. Transdiscipl. Anal.
Model. Sch. Thought 2013. [CrossRef]
26. Yang, P.P.; Yamagata, Y. Urban Systems Design: From “science for design” to “design in science”. Environ. Plan.
B Urban Anal. City Sci. 2019, 46, 1381–1386. [CrossRef]
27. Binder, R.B.; Lancaster, Z.; Tobey, M.; Jittrapirom, P.; Yamagata, Y. Transport modeling with a purpose: How
urban systems design can bridge the gaps between modeling, planning, and design. WIT Trans. Built. Environ.
2019, 186, 85–96.
28. Objectivity. Hard Data vs. Soft Data. Industrial IoT; Objectivity Inc.: San Jose, CA, USA, 2015.
29. Tobey, M.B.; Binder, R.B.; Yoshida, T.; Yamagata, Y. Urban Systems Design Case Study: Tokyo’s Sumida
Ward. Smart Cities 2019, 2, 453–470. [CrossRef]
30. Barata-Salgueiro, T.; Erkip, F. Retail planning and urban resilience—An introduction to the special issue.
Cities 2014, 36, 107–111. [CrossRef]
31. Maruyama, H. Taxonomy and General Strategies for Resilience. In Urban Resilience; Yamagata, Y.,
Maruyama, H., Eds.; Springer: Cham, Germany, 2016; pp. 3–21. [CrossRef]
32. Ackerman, F.; Stanton, E.A.; Hope, C.; Alberth, S.; Fisher, J.; Biewald, B. The Cost of Climate Change: What We’ll
Pay if Global Warming Continues Unchecked; Natural Resources Defense Council: New York, NY, USA, 2007.
33. Da Silva, J.; Kernaghan, S.; Luque, A. A systems approach to meeting the challenges of urban climate change.
Int. J. Urban Sustain. Dev. 2012, 4, 125–145. [CrossRef]
34. Sharifi, A.; Yamagata, Y. Resilient Urban Planning: Major Principles and Criteria. Energy Procedia 2014, 61,
1491–1495. [CrossRef]
35. Derrible, S. Complexity in future cities: the rise of networked infrastructure. Int. J. Urban Sci. 2017, 21, 68–86.
[CrossRef]
Smart Cities 2019, 2 537

36. Desouza, K.C.; Flanery, T.H. Designing, planning, and managing resilient cities: A conceptual framework.
Cities 2013, 35, 89–99. [CrossRef]
37. Derrible, S. Urban infrastructure is not a tree: Integrating and decentralizing urban infrastructure systems.
Environ. Plan. B Urban Anal. City Sci. 2017, 44, 553–569. [CrossRef]
38. Sharifi, A. A critical review of selected tools for assessing community resilience. Ecol. Indic. 2016, 69, 629–647.
[CrossRef]
39. Collier, M.J.; Nedović-Budić, Z.; Aerts, J.; Connop, S.; Foley, D.; Foley, K. Transitioning to resilience and
sustainability in urban communities. Cities 2013, 32, S21–S28. [CrossRef]
40. Global Platform for Sustainable Cities (GPSC). Urban Sustainability Framework. Available online: https:
//www.thegpsc.org/usf (accessed on 30 August 2019).
41. Liang, X. Integrated Economic and Financial Analysis of China’s Sponge City Program for Water-resilient
Urban Development. Sustainability 2018, 10, 669. [CrossRef]
42. Hammer, S.; Kamal-Chaoui, L.; Robert, A.; Plouin, M. Cities and Green Growth: A Conceptual Framework;
OECD: Paris, France, 2011. [CrossRef]
43. Alberti, M. Measuring urban sustainability. Environ. Impact. Assess. Rev. 1996, 16, 381–424. [CrossRef]
44. Huang, L.; Wu, J.; Yan, L. Defining and measuring urban sustainability: a review of indicators. Landsc. Ecol.
2015, 30, 1175–1193. [CrossRef]
45. Zhang, X.; Li, H. Urban resilience and urban sustainability: What we know and what do not know? Cities
2018, 72, 141–148. [CrossRef]
46. Arup and the Rockefeller Foundation. City Resilience Framework City Resilience Index; Arup and the Rockefeller
Foundation: London, UK, 2014.
47. OECD. How’s Life? 2017: Measuring Well-Being; OECD Publishing: Paris, France, 2017. [CrossRef]

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).