A Strategic Approach to Smart Cities through CA and Shape

Grammars

Fakiri Ioanna

and Tsoumpri Dimitra

School of Architecture, NTUA, Athens, Greece

Keywords: Smarter City, Urban Metabolism, Tank, Conduits, Affordance, Indicators, GIS, SLEUTH, Shape Grammar.

Abstract: In recent years we have seen a gradually increasing concern for the urban landscape and the way it is designed

and evaluated. This concern, a result of the emergence of digital technologies and convergence of different

scientific disciplines, is based on the ability of design tools to support and reinforce the discussion on urban

landscape as an open process for action. But, how do we design a new urban space employing these design

tools? So far the discussion on the design and form of the city placed emphasis on the creation of a

communication platform that functions either through the development of interpersonal and interactive

relationships of the users, or as an entity for configuring and displaying visual messages and communication

to society. The term "smart city", has been linked with digital applications, sensors, and software to produce

the city of the future. However, the real challenge is to develop a "smart city," that starts from the city of today

and enables the combination of these smart practices by activating infrastructure that may reform the spatial

structure of the urban morphology. This paper will introduce a "reformer," the natural landscape, based on

which a new methodological approach shall be established, in order to manage the urban landscape. The paper

presumes that the execution of the cell automata demonstrates, loose coupled with Shape Grammar, provides

a robust and useful application of this reformer in metropolitan planning. The connected techniques shift

locally as a component of the points of interest concerning specific examples and procedures to be advanced.

This will help create a "smarter city," which may find applications in various fields that start from today’s

city, instead of trying to compose an ideal image of the city of tomorrow, that can bridge the gap between

digital, natural and urban environment. The main theme of this paper is part of the extended scope of

Landscape Urbanism, according to which the urban landscape can be redefined / designed through the

remedial procedures of the urban landscape.

1 INTRODUCTION

Before tackling the main issue of the research

presented in this paper, it is necessary to present the

broader context of this research, as this constitutes the

basis, which feeds the research interests, produces

general questions and directs research methods. This

research is being conducted at a time when the focus

of architectural activity shifts from its perception as a

form or (and) operational organization, which

responds to a given architectural program, to its

perception as composition of elements, their

properties and relationships. At the dawn of our late

capitalist era, we are witnessing a paradigm shift that

encourages a new relationship between design and

object, which, according to Michael Hays (Hays,

https://orcid.org/0000-1111-2222-3333

https://orcid.org/1111-2222-3333-4444

1998) is nothing other than the passage from a

"critical history" to a "theory" of architecture.

To this end, the term "smart city" was introduced,

which covers a wide spectrum of research and

development applications. The concept of smart city

involves an emerging market, therefore identifying

and examining the term "smart" is still going on.

Consideration of the particular characteristics of the

smart city is best understood by interpreting its main

conceptual features (Vianna et al., 2004; Hollands,

2008). Accordingly, "Smart cities" are created by the

convergence of two major currents: on the one hand,

the redefinition of the city through its communication

technologies, digital networking and representation,

and, on the other hand, through the understanding of

the city as an environment of creativity and

Ioanna, F. and Dimitra, T.

A Strategic Approach to Smart Cities through CA and Shape Grammars.

DOI: 10.5220/0007771702070214

In Proceedings of the 8th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2019), pages 207-214

ISBN: 978-989-758-373-5

207

innovation. Despite the clear link between society of

creativity and information society, the concept of

"smart city" is still controversial. This occurred

because term "smart" is often associated with digital

functions, and the terms "digital city" and "cyber city"

(Mitchell, 2006) are used alternatively and

equivalently. However, it is certain that, providing a

digital platform or a digital representation of the city

does not adequately justify the description of an urban

system as innovative. In the following sections, the

main framework of the research presented in this

paper elaborates on the above questions and proposes

a new method for a smarter city.

1.1 Research Framework

Digital applications, sensors, and software often

interact towards the creation of the city of the future.

However, the real challenge in contemporary reality

is to develop a "smart city", which starts from the city

of today and enables the combination of smart

practices. The current research attempts to define the

concept of the smart city based on the structure of an

existing city. Due to this, Landscape is introduced as

a parameter, which in the proposed method acts as a

key reformer of the urban fabric.

This paper is organized in two main parts. The first

part addresses methodological issues: the importance

of landscape ecology and its application to

metropolitan planning; and the range of model

approaches available in order to create a smarter city.

The second part of the paper addresses a new

methodological approach to design with emphasis on

the landscaping component; the implementation of

CA in this strategy; followed by an extended

discussion; and ending with some conclusions.

2 THE STATE OF THE ART

Landscape Urbanism is a neologism, introduced in

1996 by Charles Waldheim that attempts to describe

the landscape as an urban phenomenon, on an effort

to reduce the conflicts between the man-made and

natural environment of the cities. Landscape

Urbanism is today a thriving interdisciplinary

practice that emerges as a renewed perception for

recording, dealing with, and strategically examining,

or designing, towards contemporary problems of the

structure of the urban landscape.

Waldheim, through a presentation of two projects

from the 1930’s and 40’s, presents the early

emergence of an "organic urbanism" which can be

viewed as early versions of landscape urbanism

principles. His account in these projects becomes the

basis for a brief look into the rise of this organic way

of thinking – that is the rise of landscape urbanism.

2.1 Defining the Idea of a Smarter City

based on the Landscape

As mentioned earlier, the natural landscape, on the

one hand, is the lens through which we can describe

and visualize the smarter city, while on the other

hand, the appreciation of the natural landscape is

linked to a search for the landscapes dynamic

capabilities as a design standard. Therefore, the

landscape comes into the public scene as an indicator

of the sustainable growth of the urban fabric, and as

an indicator of sustainability to the extent that it can

control the delicate dynamic balance between the

natural space and the urban fabric. This renders

landscape an attractive intervention environment

worthy of a smarter city. In this paper, we will try to

outline methods and strategies that can manage the

dynamic conditions of the natural landscape.

Specifically, the aim of the presented research is to

propose a method for managing the landscape in the

form of a diagram, and an approach, which will be

linked with the concept of a smarter city, that is based

and builds on the city of today.

3 THE MEASURING AND

MINING URBAN DATA

The need to incorporate the ecological component in

regional planning and landscape ecology ideas is

broadly acknowledged as the fitting reason for

environmental planning in urbanizing , however there

is as yet a gap between the hypothetical originations

of scene biology, the advancement of demonstrating

approaches and the genuine usage of coordinated

metropolitan planning. Such a comprehensive

methodology requires a complex framework

approach, so as to more readily comprehend the

procedures included and to guarantee that future

urban structures are practical and ecologically benign.

Then, and most likely because of the absence of

incorporated investigation and planning, there is an

absence of instruments accessible to lead dynamic

recreations of metropolitan land use change that

coordinate landscape ecological concepts and

principles.

SMARTGREENS 2019 - 8th International Conference on Smart Cities and Green ICT Systems

208

3.1 Scope of Model Methodologies

Accessible

A few studies have been done utilizing geological

information systems (GIS). Nevertheless, few have

endeavored to incorporate scene methodologies

concentrated on scene availability, or having

ecological segments collaborating progressively with

urban pressure(s), or time development.

To analyze pattern and process all through a

metropolitan landscape requires models that are

delicate and receptive to heterogeneous nearby

conditions and changeability. Shape Grammar and

Cell automata (CA) are useful in doing this since they

are cell-situated in their examination and results,

sensitive and versatile to nearby conditions.

4 LANDSCAPE ECOLOGY AND A

NEW APPLICATION TO

METROPOLITAN PLANNING

In this section we attempt to define the research

methodology. The notion of the smarter city as a

hybrid field between the natural and the urban fabric

permits the exploration of processes that will lead to

the effective management of the properties of both

systems (natural and urban). According to Herbert

Simon (Simon, 1996), the dominant directions in

landscape management are: a) prevision and b) the

homeostatic and feedback adjustment. Prevision

presupposes understanding the initial conditions, the

selection of appropriate variables, and decoding of

the relations between them. On the other hand,

homeostasis refers to the flexibility of a system to

absorb environmental changes remaining unchanged,

while feedback presupposes a kind of dynamic

adjustment of the system. Thus, we will try to outline

a strategy for the hybrid development of smarter cities

based on these two basic directions.

Specifically, depending on the issue raised each

time, the dynamic planning tool is organized in three

levels, namely tanks (provision), conduit

(homeostatic), affordance (feedback adjustment).

Along this policy lines, the first level regards the data

configuration. Specifically, all data - parameters

derived from the reading of the landscape are

identified, recorded and assessed, as well as feed the

system and affect its evolutionary path. The data

collected, are classified into three categories, which

will be constituted as “tanks”. The information

entered in these tanks include the investigation of

parts (for example number and sort of spatial

components and species), the investigation of

examples (for example natural connections that

assistance set up and support species) and the

investigation of procedures (for example natural

capacities after some time).

4.1 Measuring and Mining Urban

Data: Configuration

This paper proposes that a reasonable comprehension

of ecological planning approaches and a clear

understanding of spatial configuration of the

landscape systems components is central

Understanding spatial structure is one of the key

activities in picking up a comprehension of patterns

and process and in accurately getting ready for their

working. In terms of full operational models of the

spatial configuration, only two approaches have been

widespread in use, due to their flexible

implementation. In this way the above mentioned

data are identified through Shape Grammar and CA.

4.1.1 Shape Grammar and Measuring

Urban Data

Shape grammars were introduced by Stiny (Stiny,

1980) in A shape grammar consists of a set of rules

regarding the embedding and analysis of a shape and

its replacement by another. The rules are formed in

the following manner:

Transformation Rule

C' = [ [C - t(g(x))] + t(g(y)) in which C= initial

shape, t= transformation, x=Rule condition, y= Rule

conclusion

Table 1: Shape rule syntax.

C' = [C - t(g(x))] + t(g(y))

New Shape

(visual)

Initial Shape

transformation

Rule condition

Rule Conclusion

Why shape grammars in cities?

Cities have been shaped through the century not only

by landscape or climate but by culture, social

A Strategic Approach to Smart Cities through CA and Shape Grammars

209

composition and daily life as well. All that create a

unique character that defines each city expressing all

the things that make it familiar to its citizens and

express their way of life.

Shape grammars can be helpful at the visual

representation of an existing condition that affects the

spatial interrelations of a city’s components, such as

the Built Environment - Natural Environment. After

the research parameters are set, the grammar can be

be applied analytically to a defined region in order to

discover the spatial rules that have been formed up to

that moment in the region. The problem can then be

decomposed to its basic requirements such as

physical elements, physical landscape, geodynamics,

buildings and urban expansion. Shape rules can be

derived from the interaction of the above and can be

used to study the elements’ behavior.

Table 2: Shape rule for Natural – Urban transformation.

C' = [C - t(g(x))] + t(g(y))

Modern State

Initial Condition/ Natural State

transformation

Natural element

Urban Element

4.1.2 The SLEUTH Cellular Automaton

Model and Measuring Urban Data

SLEUTH is a CA model created to figure urban

development and land use change. SLEUTH is an

acronym for Slope, Land Use, Excluded Areas,

Urbanization, Transportation, and Hill shade, the

information layers that make the model run.

SLEUTH requires five inputs maps: urbanization,

transportation, areas excluded from urbanization,

slopes, and a hill shaded map (prepared using GIS and

then converted to 8 bit GIF images). For all these

layers, 0 is a null value, while all the values in-

between 1 and 255 are a measured value. The model

also requires that the input layers have the same

number of rows and columns and are correctly

dereferenced, as the model is sensitive to layer

misregistration. Urbanization is the most important

layer in the model, and for statistical calibration of the

model at least four urban time periods or spatial

extent ‘snapshots’ must be used.

The model works in the following way: after

reading the input layers, initializing random numbers

and controlling parameters, a predefined number of

interactions takes place that corresponds to the same

number of years. An outer loop executes each growth

history and retains statistical data, while an inner loop

executes the growth rules for a single year. After each

model run, sets of descriptive statistics are computed

and saved to a file for the purpose of calibration.

There are thirteen scores, and include: r2 population

(least squares regression score for the number of

urban pixels compared to actual urbanization for the

control years), edge r2 (least squares regression score

for the length of the urban-rural edge compared to

actual urbanization for the control years), r2 clusters

(least squares regression score for modelled average

urban cluster size compared to known mean urban

cluster size for the control years).

Table 3: Sleuth required input files.

MODEL

SLEUTH

REQUIRED INPUT

FILES

1.urbanization,

2.transportation,

3.areas excluded

from urbanization,

4.slopes, (besides the

slope, a constraint or

exclusion map

represents water

bodies, or natural and

agricultural reserves)

4.2 Conduit

4.2.1 How Shape Grammar Can Derive

Sleuth Rules for Spatial Configuration

As mentioned in (4.1.1) shape grammars can be used

analytically to visually express spatial properties of a

given problem and form shape rules that express

them, for example how does built environment

develop itself around existing rivers in a specific city?

The formation of such rules can help establish a

certain vocabulary of the case study region.

p.e

If ( C = initial condition){

If (river_not_covered){

riverbank();

builtEnvi();

SMARTGREENS 2019 - 8th International Conference on Smart Cities and Green ICT Systems

210

If (r_bank && b_Envi) {

If ((r_bank ∩b_Envi) ≠0){

Shape Rule Y1 = (Overlap_Shape);

Shape w1 = (r_bank – b_Envi);

Shape b1= (b_Envi-r_bank);

}

Else if ((r_bank ∩b_Envi) =0){

inBetween();

Shape Rule Y2 = (Inbetween_Shape);

}

Else () {

neighboring= true

}

Else() {

urbanEnvironment();

}

Table 4: Shape rules syntax for intervention strategy.

C' = [C - t(g(x))] + t(g(y))

Desired condition

Current Condition

transformation rule based on Shape

Grammars

Element to be replaced

New element

Y1 and Y2 are shape rules while {w1,b1} are

subsets. More steps can be used in order to acquire

more properties such as riverBank (); which can be

used to define shape transformation of the river bank,

the kind of material that exists on the bank. In order to

complete the analysis and create a “language” one

needs syntax. Cellular automata come in to logically

define the neighboring properties of the shape’s

interrelations. More specifically, SLEUTH CA, can

help create a list of neighboring Logic Rules based on

the data of the SLEUTH method. The data are used as

parameters for the shape grammars. The purpose of

this cross referenced method is to create one, final, list

of rules, a language. This way the set of rules is

multilayered and holds properties spatial interrelations,

neighboring, materials, physical elements or even

social properties.

Once the analysis is completed and the problem has

been defined one can study the best strategy of

intervention and composition by using the language of

the existing region, creating a landscape that is familiar

to the city and works with it.

4.2.2 How a CA Model Can Direct Sleuth

Urban Rules

This Understanding spatial structure is one of the key

activities in picking up a comprehension of patterns

and process and in effectively anticipating their

working. CVCA builds on SLEUTH and builds up a

lot of countervailing methodologies to the urban

development dispensed by SLEUTH. This dynamic

association is valuable, since it permits urban

development produced by SLEUTH to be headed to

different territories where the techniques are not

connected. CVCA starts by surveying the underlying

condition of the scene, creating a few scene

measurements dependent on the underlying state, and

utilizing these measurements to choose and execute

the proper scene procedures. CVCA then interfaces

yearly with SLEUTH so as to have the scene

techniques upheld, along these lines managing

SLEUTH forward to 'great to-develop' regions, and

buffering essential environmental regions

distinguished by CVCA from anticipated urban

improvement. (Silva, Ahern, Wileden , 2008)

This is the principle motivation behind why the

CVCA show is named 'countervailing cellular

automaton'. It utilizes a lot of scene biological

methodologies to balance urban development to great

to-develop territories.

The CVCA demonstrate requires a similar info

layers as SLEUTH (Slope, Hill shade, Transportation,

Urban, Excluted). The excludes layer must be

changed altogether, in any case, so as to recognize an

alternate class that represents to every one of the

territories outside the boundary of the metropolitan

zones. These zones are avoided from urbanization,

yet for CVCA purposes they can't be viewed as a

similar kind of prohibition, since they don't figure in

the use of the natural techniques. The measurements

utilized were picked by the spatial indicators that

have been referenced in various landscape ecological

studies .

SLEUTH will work, in this way, as the

background of urban elements, where a lot of natural

elements needs to countervail. Through time, this

round of cooperation of urban– landscape will create

an alternate picture of the metropolitan territory,

where the requirements of urban development are

A Strategic Approach to Smart Cities through CA and Shape Grammars

211

fulfilled, however where environmental necessities

are likewise kept up, expanded or enhanced.

4.3 Affordance

The movement of the conduit is double at the level of

the switching, depending on the sign of the vector

objects located therein. Since the vector objects with

negative sign (-) are considered those whose size

deviation from the permissible limits (indicators) is

the highest, as indicated by the indices. In the second

selection step from said selected vector objects, we

chose to consider first those which the chart has

indicated from the outset that require special attention

against the particular way in which they are studies.

The CVCA yields four classes that coordinate five

diverse scene arranging methodologies (protective,

defensive, offensive, opportunistic, let it grow). As

examined in the presentation, it is imperative to build

up this sort of model, where urban and ecological

elements can be coordinated, taking into

consideration reenactments where both urban and

natural needs are considered and designated in a

feasible future.

5 LARISSA AREA

Larissa, Greece is the territory of examination of the

exploration (Fig. 1). So as to more readily

comprehend the subsequent measurements inside the

alignment results and in this manner in the situations.

The case study was a landfill outside the city that was

meant to be an Agricultural Center and a Park. Since

the landfill was not occupied the stage of analysis

gave way to analytical approach of composition

through the use of Cellular Automata.

Figure 1: Sleuth and Shape Grammar required input files

and indicators.

Figure 2: Offensive strategy. Set up an external support to

the fix and a passage to its neighbor.

Figure 3: Defensive strategy. 50% (all cells are low

probability of change).

Figure 4: Opportunistic strategy. Establish corridor

between the fix and its neighbor.

Figure 5: Protective strategy. Establish corridor and create

outer buffer.

Figure 6: Output files.

The application of CA formed cells of 8 different

qualities, built areas, hard ground, soft ground, urban

water, non urban water, urban green, free space and

sports which are set with specific neighboring

properties. The next step was to use Shape Grammars

to bring these qualities into shape and turn them into

human scale spaces.

The main part of that study was the imprint of the

underwater veins on the surface and how that could

be a living space. Each Shape Rule is connected to a

neighboring condition of the CA.

SMARTGREENS 2019 - 8th International Conference on Smart Cities and Green ICT Systems

212

Figure 7: Shape rules for water points, the initial condition

is represented on the left. The initial condition is the

hexagon cell defined by its nodes. The first step is the

intersection of the cells with the water vein imprint which

gives us new nodes. The second rule is the application of

the triangle shape through transformation to the shape result

from the intersection.

Figure 8: Shape result.

Following the urban water cells, came the formation

of the ‘banks’ the neighboring cells according to the

CA results. The shape rules that were applied were

aimed at the formation of a landscape supporting the

strategy.

Figure 9: Shape rules for the landscape formation, initial

condition is on the left and shape result is on the right. After

the shape has been finalized there is one more step, that of

node interpolation. The shape results must not include more

than 3 neighbouring cells and must not intersect with more

than 3 of their segments.

Figure 10: Shape result example.

6 CONCLUSION

To sum up, if today the cultural consideration changes

looking for a smarter city, then design strategies

should move to manufacturing techniques that

manage change through ecological evolving and

developing platforms. On an effort to form a smarter

city, natural landscape should not be a backdrop on

which the urban articulation will be placed, but a

dynamic field of study, management and recovery of

the urban fabric. On this basis the research presented

in this paper a first conceptual approach to a

mechanism that may monitor the transformation of

natural space, fed with data obtained from its

analysis, in order to compose them and redefine the

urban space. It could be said that this mechanism acts

as a filter which not only receives information but

also checks if this information can be changed and

also produces connections and forms supported by

computer generated programs. Unlike traditional

urban fabric design methods, or the digitized form of

smart cities, this mechanism aims to produce a

smarter city through a renewed perception of

convergence of the aspect between man-made

environment and natural space.

This paper sets up the need to incorporate the natural

part in region planning, and featured landscape

ecology concepts as the proper reason for

environmental planning in urbanizing districts. The

gap between the hypothetical originations and the

practise of landscape is apparent; the advancement of

displaying approaches and the real usage of

coordinated metropolitan planning will in general be

ignored. While we can point to a few explanations

behind this gap, it is imperative to feature one of its

plausible main drivers: an holistic methodology

requires a complex system approach, so as to all the

more likely comprehend the procedures included and

to ensure that future urban structures are practical and

naturally generous; and the truth of the matter is that

we are utilized to deal with the issues and solutions.

By proposing an urban model (SLEUTH) that can

cooperate with landscape ecology model (CVCA)

and with a social-economic tool (Shape Grammar) so

as to propose plenty of arranging techniques that can

guide urban development, this paper is contributing

to the progress of research in and practise with

regards to landscape planning.

REFERENCES

Batty, M. (1999). Modelling urban dynamics through GIS-

based cellular automata. Computer Environment and

Urban Systems, 23, 205–233.

Batty, M., Xie, Y., and Zhanli, S. (1999). Modellng urban

dynamics through GIS-based cellular automata.

Computers, Environment and Urban Systems, 23, 205–

233.

Batty, M. (1996). Fractal cities. London: Academic

Press.Batty, M. (2005). Cities and complexity.

Cambridge, MA: The MIT Press.

Corner, J. 2003. Landscape Urbanism. In Landscape

Urbanism: A Manual for the Machinic Landscape.

Architectural Association.

Clarke, K. (1998). Loose-coupling a cellular automaton

model and GIS: Long-term urban growth prediction for

A Strategic Approach to Smart Cities through CA and Shape Grammars

213

San Francisco and Washington/Baltimore. International

Journal of Geographic Information Science, 12(7),

699–714.

Clarke, K., Hoppen, S., and Gaydos, L. (1997). A self-

modifying cellular automaton model of historical

urbanization in the San Francisco Bay area.

Environment and Planning B: Planning and Design, 24,

247–261.

Hays, M., 1968. Architecture Theory since, Cambridge

MA: the MIT Press Journal, London, England.

Lister, N-M., 2007. Sustainable Large Parks. In Czerniak,

J. & Hargreaves, G. (ed.) Large Parks. New York:

Princeton Architectural Press.

Macdonald, C., 2006. Machines of Loving Grace. In On

Landscape Urbanism. Austin TX: Center for American

Architecture and Design University of Texas at Austin

School of Architecture.

Mitchell, W. J., 2006. Smart City 2020. Metropolis,

April.Availableathttp://www.metropolismag.com/story

/20060320/smart-city- 2020.

Moore, R., Lopes, J., 1999. Paper templates. In

TEMPLATE’06, 1st International Conference on

Template Production. SCITEPRESS.

Νeuman, M., 2006. Infiltrating infrastructures. Journal of

Urban Technology,131.

Kotsopoulos 2008, From Design Concepts to design

descriptions, International Journal of Architectural

Computing, issue 3, vol.6

Simon, H.A., 1996.The Science of the artificial. MIT Press:

Cambridge, Mass, 3rd edition.

Silva, E. A. (in press). ‘Complexity and CA, and application

to metropolitan areas’. In: G. de Roo, and E. A. Silva,

A planner’s meeting with complexity. Aldershot, UK:

Ashgate Publishers Ltd.

Silva, E. A. Ahern, Wileden (in press). ‘Strategies for

landscape ecology: An application using cellular

automata models ’.

Smith, J., 1998. The book, The publishing company.

London, 2

edition.

Stiny, G., 1980. Introduction to shape and shape grammars.

Environment and planning B: planning and design,

7(3), pp.343-351.

Steiner, F. R., 2006. Living Urban Landscapes, In On

Landscape Urbanism. Austin TX: Center for American

Architecture and Design University of Texas at Austin

School of Architecture.

Speller, T. H., Whitney, D., and Crawley, E. (2007). Using

shape grammar to derive cellular automata rule

patterns. COMPLEX SYSTEMS-CHAMPAIGN-

, 17(1/2), 79

Vianna, M.M.B., Portugal, L.S., Balassiano, R., 2004.

Intelligent transportation systems and parking

management: implementation potential in a Brazilian

city, Cities, 21(2).

Waldheim, Ch., 2006. Landscape as Urbanism. In the

Landscape Urbanism Reader. Princeton Architectural

Press.

SMARTGREENS 2019 - 8th International Conference on Smart Cities and Green ICT Systems

214