Scholarly article on topic 'Pre-assessment of Metropolitan Areas’ Smart Growth through Agent Based Modelling'

Pre-assessment of Metropolitan Areas’ Smart Growth through Agent Based Modelling Academic research paper on "Agriculture, forestry, and fisheries"

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Procedia Environmental Sciences
OECD Field of science
{"Smart Growth" / "Agent Based Modelling" / "Urban Facility Management" / "Decision Making" / "Walkable Communities"}

Abstract of research paper on Agriculture, forestry, and fisheries, author of scientific article — Laila M. Khodeir, Aya Elsisy, Muhammad Nagy

Abstract Smart Growth (SG) is an approach for sustainable urban development, it emerged to restrain the drawbacks of Metropolitan Areas’ urban sprawl. This approach enhances social equity and environmental justice through policies covering micro and macro levels, in addition, it discusses sustainability aspects. However, there are few attempts that simulate the effect of applying principles of SG on emergent behaviour within different context. This study aims at initiating a platform for building a SG Agent Based Model SGABM which focuses on the social behaviour, in order to assist in management of growth dynamics and build an information database for Urban Facility Management. The paper reviews literature and undergoes an application on Walkability principle.

Academic research paper on topic "Pre-assessment of Metropolitan Areas’ Smart Growth through Agent Based Modelling"

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Procedía Environmental Sciences 34 (2016) 245 - 257

Environmental Sciences

Improving Sustainability Concept in Developing Countries

Pre-assessment of Metropolitan Areas' Smart Growth through

Agent Based Modelling

Lailaa M.Khodeir*, Ayab Elsisy, Muhammadc Nagy

aAssociate Professor, Dept. of Architecture,Faculty of Engineering, Ain Shams University, Cairo,Egypt bTeaching Assistant, Dept. of Architecture,Faculty of Engineering, Ain Shams University, Cairo,Egypt cTeaching Assistant, Dept. of Architecture,Faculty of Engineering, Ain Shams University, Cairo,Egypt


Smart Growth (SG) is an approach for sustainable urban development, it emerged to restrain the drawbacks of Metropolitan Areas' urban sprawl. This approach enhances social equity and environmental justice through policies covering micro and macro levels, in addition, it discusses sustainability aspects. However, there are few attempts that simulate the effect of applying principles of SG on emergent behaviour within different context. This study aims at initiating a platform for building a SG Agent Based Model SGABM which focuses on the social behaviour, in order to assist in management of growth dynamics and build an information database for Urban Facility Management. The paper reviews literature and undergoes an application on Walkability principle.

©2016 The Authors.PublishedbyElsevierB.V. Thisis an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of IEREK, International experts for Research Enrichment and Knowledge Exchange Keywords: Smart Growth, Agent Based Modelling, Urban Facility Management, Decision Making, Walkable Communities

1. Introduction

Urbanization has become an uprising phenomenon, where about 70% of World's population will be living in cities by 2050,[1].This rapid urbanization caused a number of consequences; starting with the emergence of Urban Sprawl over the fringes of Metropolitan Areas, forming excessive pressure on environmental resources and infrastructure, in addition to negatively affecting the surrounding environment. This phenomenon is also generally associated with common problems like the growth of informal settlements, the increase social segregation, and the lack of social equity

* Corresponding author. Tel.: +02-01002647601; fax: E-mail address .■

1878-0296 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (

Peer-review under responsibility of IEREK, International experts for Research Enrichment and Knowledge Exchange doi:10.1016/j.proenv.2016.04.023

and the unfair distribution of resources. Most of the attempts to control Urban Sprawl were unsuccessful, in fact, the growing patterns of cities are still uncontainable, which is highly evident in most developing countries.

Smart Growth is a proactive development approach that has been introduced by the American Planning Association in 2002, [2] to restrain urban sprawl of the Metropolitan Areas and to achieve urban sustainability goals as well. It aims at enhancing the communities' quality of life, whereas achieving environmental justice and social welfare in general. The approach of SG has been negatively criticized owing to its need to be supported with a simulation model which is able to validate the application of SG in different urban areas before applying it on real areas. This Validation procedures are performed in order to avoid any unexpected outcomes.

This research focuses on the urban development strategies that are involved in the planning phase for existing Metropolitan Areas. The emphases is on the principle of Creating Walkable Communities and its correlation with the scope of work of Urban Facility Management. The researchers utilized the tool of Agent Based Modelling ABM, as a simulation tool that drives input data developed by planners for the application on the principle of Smart Growth.

This paper has a double -fold objective, first it aims to construct a platform of measurable indicators and input variables, which can be utilized by ABM, to value the effect of applying principles of SG on social interactions and behaviors of different communities. Second, to build a SG model that supports information acquisition and resources management for efficient practicing of Urban Facility Management.

2. Methodology

In order to achieve the objective of this paper, the author undergone literature review for concepts of Smart Growth (SG), Urban Facility Management (UFM) and Agent Based Modelling (ABM), which form the key terms of this study. Afterwards, the study attempted to transform the principles of SG into simulation attributes and variables that were applicable through ABM. An application was finally done on the principle of Walkability .The output of the analysis and application, shown in figure 1, was considered as an input to the Urban Facility Management process. This was proved evident in terms of supporting UFM information database and resources management application techniques, along with assigning roles and responsibilities of different stakeholders.



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3. Emerging of Metropolitan Areas

Due to the rapid urbanization of developing and developed countries, cities are expanding and growing in the most complex manners. Their growing dynamics are based on layers of historic events, upgrading policies and urban transformations. These cities are classified according to their population densities, as well as their participation in the World's economy and their ability to attract tourists and investments. According to United Nations (UN) report,

Metropolitan Areas are "A formal local government area comprising the urban area as a whole and its primary commuter areas, typically formed around a city with a large concentration of people" These areas comprises two sub-areas; the surrounding territory and some additional lower-density neighboring sub-areas that are linked to the city,[3]. These areas are recognized by their high densities, major economic hubs and noticeably complex administrative systems. However, their growth patterns differ from one context to another, as shown in Fig. 2, which introduces different challenges and potentials.

Fig. 2 . (a) Greater Cairo Urban Growth (Google Earth) - by Authors; (b) Las Vegas Urban Growth by United States Geographical Survey (USGS)

3.1. Challenges of Metropolitan Areas

According to the UN report, 28 regions have been recognized as cities with more than 10 million inhabitants in 2014, and they are expected to reach 41 region by 2030, [1]. These cities house almost 8% of World's population and 25% of its economy, [4]. The rapid expansion of these metropolises brought challenges on environmental, socio-economical and managerial aspects. The development strategies and upgrading policies haven't been able to cope with the increasing rate of urbanization, causing Urban Sprawl to become the most common trend of urban growth. Urban Sprawl is a scattered trend of horizontal spatial urban growth, which is specified by being poorly planned and automobile dependent, [3]. From environmental aspect, this type of growth resulted in excessive use of non-renewable resources and high rate of energy consumption. The long routes of traffic congestion between destinations increased levels of air pollution, moreover, the continuous horizontal spread threatened the wildlife of rural areas.

On the economic level, irreplaceable assets have been lost in the urban sprawl process, [5]. Also, the globalization and modernization of these municipalities minimized the local character and weakened the identity of places. The invasion of western lifestyle and technologies increased the pressure resulted from growing needs of community, in order to match up with trends of urbanization. This is associated with increasing rate of family breakdowns, social interaction degradation and high crime rates. Social inequality and segregation became more common, where a gap between the poor and the wealth is getting wider with time. These cities suffer from unfair distribution of job opportunities, lack of delivered services or affordable housing, [6]. As a result, social exclusion of some groups appeared, it took the form of failing to satisfy the basic needs for the majority of the lower classes. It also made their participation in making decisions related to designing their context or future growth patterns more challenging.

3.2. Managing Urban Development

Urban Sprawl has followed two different themes according to their contexts; formal and informal. The formal theme is the one sponsored by the private sector and approved by the government. It takes the form of residential communities with low densities, wide private open spaces, luxuries houses and automobile dependent routes, [6]. In contrast, the informal theme is an illegal form of urban expansion over the rural fringes of metropolitan areas. These slum areas are characterized by being high dense, unplanned residential compact communities. These areas suffer from urban degradation, lack of services, poverty and inappropriate living conditions, [7]. Both themes generally lay pressure on the central city infrastructure and services, provoke social segregation and inequity, and make it difficult for mixing

and integrating different social classes, [8]. Managing facilities of this complex urban system face a lot of challenges as there is an absence of a platform that can integrate all these entities altogether.

The concept of Urban Management was discussed by academics long before it was introduced in 1986 by the Urban Management Program. It focused on land management, urban environment, poverty, infrastructure and municipal finance. It works on adapting the managing themes of the urban patterns to the rapid urbanization of population. Its main objective is to promote sustainable development of growing cities both socially and economically, [9]. On the other hand, The term Urban Facility Management (UFM) was introduced as an integrating concept for Facility Management (FM) field of work, which was defined by the International Facility Management Association (IFMA) as "a profession which encompass multiple disciplines to ensure functionality of the work environment by integrating people, space, process and technology[10].

UFM is thus considered a sustainable methodology for planning, operating and controlling public facilities. Therefore, UFM is characterized by being an integrated platform that allows different stakeholders to participate in the process of management through efficient framework and well-designed network of rules and responsibilities, [11]. The main difference between UM and UFM is that the former represents theoretical background for the different aspects in the urban context that need to be managed and the deliverables that need to be achieved, while the latter is a mechanism to transfer strategic plans into actual operational applications.

3.3. Approaches for Sustainable Growth

Since the Industrial Revolution and its consequences on urban degradation of cities, planners and developers have introduced theories and models to retain this damage. Most of these initiatives aimed to upgrade the growth patterns and sustain the environment and the community ties. However, New Urbanism (NU) can be argued as the most comprehensive concept of sustainable development. Although it started in the early 90's, it has driven its concepts and principles from theories discussed and implemented in the beginning of the 20th century. These principles were formalized by New Urbanism Charter, which defined the main themes and goals for this approach. It covered environmental, socio-economical and legal aspects from the scale of city/region to the scale of buildings, [6].

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From this perspective, different approaches have emerged to upgrade the work of New Urbanism. Some of these approaches worked on macro scale like the Transit-Oriented Development (TOD) approach, other worked on micro scale like the Traditional Neighborhood Design (TND) approach. On the other hand, Smart Growth (SG) and Urban Villages (UG) approaches worked on both scales (Error! Reference source not found. - a). These different approaches have worked through varied perspectives; physical, economic, social and managerial ones. The TOD approach worked through physical and environmental perspective, it focused on creating transitional nodes linking

economic hubs together and providing public transportation alternatives. The Urban Villages (UG) approach worked on socio-economical aspect, by provoking community participation and social responsibility, it encouraged conservation of resources, protection of local economy and sustaining community development, [6].

Smart Growth is the most appropriate for this study because it maintained a balance between all perspectives as demonstrated in Fig. 3- b. It is a recent approach that detained a holistic perception for all the factors involved and worked through all the levels of development. It embedded within its principles, the strategies of the other different approaches like those of TOD, TND and eco-cities. It also worked on strategic plans and policies development besides working on implementation tools and techniques, [12]. Moreover, Smart Growth was initiated for American experiences and developed by their academics and practitioners, so it is an appropriate way of analogy to use this approach with its various practices that could match up with different settings around the world, [2]. However, this approach need to be examined carefully regarding the nature of urban context it is applied on.

4. Reviewing Smart Growth (SG)

Smart Growth (SG) was first introduced by the American Planning Association in 2002, [2]. Although it was discussed earlier by academics and planners in 1990, but it became more formalized when the Smart Growth Network has issued the principles of SG and their application strategies. SG is an attempt to manage the growth of urban communities and limit its unplanned expansion. It promotes urban patterns that encourage compact zoning, high densities, mixed uses and walkable communities, in addition, it supports integration between policy making and social inclusion to achieve more sustainable development for existing communities, [6]. The compact development promoted by SG is essential to the process of limiting the sprawl on rural lands. The aim of SG is to decrease the consumption of environmental resources and safe ecological balance. It works on minimizing the vehicle trips between residents and different services, in turn decreases air pollution, [13]. This works by applying mixed land use patterns, introducing access to variety of public transportation and providing a range of housing opportunities for different social classes. These principles affect local participation and social inclusion of the community in a good way. It creates a balanced community, with accessible economic opportunities and services which, in turn, contributes to upgrading the sustainable development economically, socially and environmentally, [6].

4.1. Principles of Smart Growth

Smart Growth has 10 main principles, each principle includes 10 key strategies or policies for implementation. Although these principles are fixed, their implementation policies are continuously upgraded to adapt with the changing nature of urban communities, [12]. The following section reviews these principles from the social aspect point of view.

1. Mixed Land Use: this principle is based on the relevance between achieving livability and heterogeneity and the integration between different aspects of life. The mixed zoning of spaces enhance social inclusion and interactions, as well as providing access to different services for different social classes.

2. Compact Building Design : The compact design has to follow appropriate desirable design standards which sustain privacy for families. It should also maintain an adequate ratio between buildings and street scales increasing the quality of life and sense of security.

3. Housing Opportunities: To ensure equity and fair distribution of resources, a diverse options for housing sector should be available for different types of families with different income levels. The diversity in social classes within the community should attract skillful workers, enhances local economy of the neighborhoods, promotes the environmental justice and decreases social segregation.

4. Walkable Communities: Walkability promotes a healthy society which interact repeatedly with local shops and services, as well as, open areas and public spaces. That decreases the excessive use of automobile, reducing the green gases emission and, enhances social relationships among citizens, as well as, local business.

5. Foster Distinctive, Attractive Communities with a Strong Sense of Place : The strong sense of place provokes civic pride and sense of identity among citizens which in return makes them more interactively participate in enhancing the community quality of life, involved in development of decisions, as well as in the protection of the assets of the place.

6. Preserve Open Space, Farmland, and Natural Beauty and Critical Environmental Areas: Open spaces and Farmlands are considered to be a great asset environmentally, economically and socially. SG initiates programs and solutions to support investments in agriculture, and increase the awareness of communities with the importance of the ecological systems affecting social inclusion and enhancing their social interactions and quality of life.

7. Direct Development toward Existing Communities: Infill development and rehabilitation of existing buildings is much appreciated by existing communities than new communities' development. It directs the investments towards deteriorated zones and enhance the quality of life for local residents with different social classes.

8. Provision of Variety of Transportation Options: It is essential to study carefully the regular trips performed daily by citizens, to design and connect routes and different modes of transportation adapting with the community needs, as well as providing guiding instructions for users. The objective of this is to provide citizens with equal access to these transportation nodes, as well as services, jobs and housing opportunities, and finally enhancing social equity.

9. Predictable, Fair and Cost Efficient Development Decisions : The production of feasibility studies for development projects, enhances the chances of their success and decreases their negative impact on the exiting culture / social interactions. The main concept included is to develop self-sustained communities that encourage investments and acquire appropriate standard of living.

10. Community and Stakeholder Collaboration in Development Decisions: Community members have accurate information about their neighborhoods, and they can introduce innovative solutions for complex problems that respond to their values and needs. Enhancing social inclusion by community participation through capacity building programs, is an effective strategy to meet the expectations of people.

4.2. Assessment of Smart Growth (SG)

The Principles of Smart Growth applied on high dense communities and compact urban patterns have been criticized. The argument was that applying SG would increase traffic congestion and land prices which would affect housing affordability, [14]. The UN-Habitat report on Planning Sustainable Cities recommended that Smart Growth policies have to be implemented in a cautious manner for different local contexts. The report mentioned that SG improves sustainability and encourages integrated communities. Yet it questioned the applicability of its strategies and whether it would slow down the urban sprawl as it claims, [9]. Although SG showed successful progress and achieved many of its objectives, nevertheless, the majority of implementation strategies took place in similar urbanized context, mostly developed. For instance, there was no real attempt to apply SG on urban areas that suffer from informalities or high crime rates. Since most of the applications took place in the Northern United States of America, a simulation procedure for their applicability and their expected outcomes have to perform before any actual implementation.

Different attempts to simulate SG development or one of its principles are carried out by researchers for different contexts. For example, urban growth simulation using Fuzzy-Constrained Cellular Automata model, for the city of Gold Coast in South-East Queensland in Australia was introduced by Liu, [15]. He made a comparison between SG development, Planned Eco-Growth and unconstrained natural development. His work was based on macro scale level concerning land development accelerators and constraints factors, like land accessibility to transportation nodes and urban centers, growth rates, and land capability for development. Liu achieved an accuracy of 84% for his model. Another attempt was performed by Preuss, [16] to simulate the effect of decision policies in land development on citizens' Quality of Life in Montgomery County in Maryland, northwest of Washington, DC. Also researches have been established for current situation analysis, like the declination of growth rate simulation in Ruhr, Germany by Rienow, [17], the housing mobility and simulation of social ties affecting the growth dynamics of cities by Metcalf, [18] and the Land Use Transportation Models for rapid assessment to predict urban futures introduced by Batty, [19].

All of these attempts aimed at studying growth dynamics from different perspectives to explain urban growth and its effect on different aspects. Their utmost objective was to predict dynamics and patterns to validate responsive policies that efficiently address existing challenges.

5. Evolution of Agent Based Modelling (ABM)

Urban modelling is a process in which theoretical urban theories are translated into mathematical algorithms. The main function of this process is to find a tool that is able to calibrate and predict the evolvement of urban theories on reality based models, [20]. Urban Simulation; a specific type of urban modelling, is used for theory developments, where users insert input data and output data are extracted from simulation. It is important in tracking the behavioral process performed by individuals; social simulation, to understand specific phenomenon or predict the progression of one. In general, the social science simulation started in the 1960s, and was based on differential equations and statistical modules. Through time different approaches were introduced to adapt with the complexity of city systems and social interactions, presenting bottom up simulation strategies and individual based approaches, [21].

5.1. Overview on Agent Based Modelling:

Agent Based Modelling Simulation (ABMS) has direct historical roots in complex adaptive systems (CAS) and the underlying notion that "systems are built from the ground-up". The whole term started long ago with the name Social agent-based modeling, which was defined by Sakoda's publication of the checkerboard model of social interaction in the 1970th as "modeling social processes starting at the individual level", [22]. This model was essentially a cellular automata model. More recently, Epstein and Axtell (1996) introduced the idea of artificial societies in their SugarScape model, which used ABM to represent an entire society "from the ground up" by modeling its individuals and their interactions. Epstein and Axtell showed how an extensive number of social processes could be credibly modeled including life, death, disease, war, reproduction, and wealth. This seminal work continued to offer a blueprint for many agent-based models of social processes, [23].

In general, ABMS represents a model in which dynamic processes of agent interaction are simulated repeatedly over time, in other words, the ABM produces a model in which agents repeatedly interact. ABM is typically used when the absolute purpose is to achieve a desired end-state, i.e., the optimized system, rather than to simulate a dynamic process for its own sake. The decision of using ABM among other simulation tools is related to the potentials that ABM holds as follows:

• Their ability to cope with the complexity of systems that are used in analyzing and modelling in terms of their interdependencies.

• They enable Data to be collected and organized into databases at finer levels of granularity.

• Their adaptability with the rapid advance of computational power, where large-scale micro-simulation models could be computed easily.

5.1.1 Defining the "Agent"

Defining ABM is highly coordinated with introducing the meaning of the term" Agent". Thus, Agent is defined as an entity which has a number of distinctive characteristics, these characteristics could be summarized in the following points:

• Attributes/ Properties: Agent could be autonomous and self-directed, modular or self-contained and social and interacting with other agents. In addition, agent may live in an environment, have explicit goals that drive its behavior or may have the ability to learn and adapt.

• Resources: Agent often have resource attributes that indicate its current stock of one or more resources, e.g., energy, wealth, information, etc. on its experiences.

• Memory/ State: Memory is considered the agent internal data representation. Agent has memory for Individual learning and adaptation, usually in the form of a dynamic agent attribute.

• Perception: This means for modifying agent's internal data representation

• Decision making sophistication: An agent's behavioral rules can vary in their sophistication, how much information is considered in the agent's decision (this is referred to as cognitive load), [23].

5.1.2 Rules for Agent Based Modelling

Agent-based modeling concerns itself with modeling agent relationships and agent interactions, as much as, it does modelling agents and agent behaviors. The primary issues of modeling agent interactions are specifying who is, or could be connected to who, and the dynamics governing the mechanisms of the interactions. The application of ABM includes a number or related rules. These rules are either behavioral rule (Base -level rules), and rules to modify behavioral rules (Higher level rules). The former represents means for modifying Agent's environment, they also provide responses to the environment. The later higher level rules represents the "rules to change the rules" in order to provide adaptation.

5.2. Agent Based Modelling (ABM) vs Equation Based Modelling (EBM)

Equation Based Modelling is a theme for statistical models which depend on mathematical equations to relate different parameters with emergent phenomenon. It can explain correlation between variables measured at single point in time, but it can neither work with multiple agents at the same time, nor simulate the process of decision making performed by different individuals. EBM models social relations on the macro scale, however it is not assigned to simulate the behavioral decisions. On the other side, Agent Based Modelling can introduce a distinctive understanding for the behavioral aspect and its process of emergency. It runs a program with different attributes and typologies within different levels and scales, instead of depending on statistical relations. It works with different agents at the same time, each of them has different perception and behavior rules that resembles the complexity of human nature, and all the agents together have interacting rules based on their connections, [21, and 24].

In order to compare between both tools, ABM and EBM, the authors demonstrate an example of the application of both tools on the principles of SG, table 1.

According to this comparison, taking principle (3) as an example, where SG assumes that providing variety of housing opportunities for different social classes is going to increase social equity and decrease social segregation, through enhancing social interactions between different social groups. If we run a simulation with EBM, a transformation for this phenomenon into statistical equations is needed to study, for example, the increase of diversity in social classes and the decrease of social segregation and racism behavior. It formulates these equations based on historical data available for these zones and mentions them as facts without tracking the reasons for emergence of such behaviors. On the other hand, for the same SG principle (3), ABM can work with the perception of every agent in the system, simulating its cognitive behavior and the process of decision making along with the analysis of root causes for these decisions. It also can adapt to collective behavior dynamics, tracking the emergency of different attitudes, explaining the rules of decision making, and analyzing priorities and values that affect the choices made from a bottom-up approach.

After detailed analysis for SG principles, a set of defining variable for input data and expected deliverables can be driven to build a comprehensive model for SG simulation. The input data are formed from developed strategies that are implemented by planners or developers to achieve SG objectives. The output data represent measuring indicators for the level of success in achieving these objectives when calibrated with desirable outcomes. Both inputs and outputs are presented in the form of Rate of Change, like the rate of change in population density, the rate of change in housing units, the rate of change in the deviation of social classes with a specific spatial area, etc.

Based on the comparison presented in table 1, it can be concluded that EBM fails to address some of the SG indicators, especially those having a social nature or an expected emergent behavior for groups or individuals. ABM was found to be an appropriate tool to stimulate the SG dynamics owing to its adaptive nature, having the ability to modify this representation, and change the perception of the agent to the surrounding environment. In addition to having the ability to modify its interaction with hosting environment, so as to adapt to its dynamics, [25]. A number of rules govern the agents' interactions (topologies) with their neighbors, these can be summarized as follows:

• Movement in a free continuous space

• Interaction of agents with their local neighbors,

• Connection by networks while being static or dynamic

• Movement over Geographical Information Systems (GIS) tiling, [23].

LailaM. Khodeir et al. / Procedia Environmental Sciences 34 (2016) 245 — 257 Table 1. Input/output analysis for SG principles and simulation capability of ABM and EBM, by authors

Smart Growth Principles

Input Data (Developed Strategies by Planners) presented as Rate of Change

Output Data (Outcomes need to be measured to ensure success of

implementation) presented as Rate of Change

Simulation Approach

1. Mixed Land Use

Land uses percentages (%)

Land uses distribution patterns

(standard deviation)

Buildings with different uses (% & no

of uses in each building)

Jobs diversity ( % & deviation)

Population density Land value

Diversity in social classes Income of small business Level of community satisfaction

Yes Yes Yes Yes Yes

Yes Yes Yes Yes No

2. Compact Building Design

Number of house hold units Residential capacity Lot sizes

Surface parking area

Ratio between building heights &

street width

Population density Income of small business Construction cost per house hold unit Diversity in social classes Social security (crime rates) Safety perception Social inclusion Resources consumption

Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes No No Yes

3. Creation of Housing Opportunities

Housing types variation (types & sizes)

Housing prices variation (value & way of payment)

Supply & demand relation Transportation alternatives (affordability & capacity)_

Diversity in social classes Job occupation diversity Social equity (each person /family has its share from different services, like housing, schools, public services...) Perception of equity by citizens

Yes Yes

Yes Yes

Yes Yes

Yes No

4. Creation of Walkable Communities

Walking distances Transportation alternatives (no of nodes in specific area, capacity distribution patterns, diversity) Concentration of critical services around transitional points

Walking preference Public transportation densities Roads congestion (average speed) Car provision percentage (%) Greenhouse gases emission Income of small business

Yes Yes Yes Yes Yes Yes

No Yes Yes Yes Yes Yes

5. Foster Distinctive, Attractive Communities with a Strong Sense of Place

- Public accessible open spaces

- Funding distinctive local economy

- Preserving green areas

- Preserving historical buildings

- Design codes protecting context identity (respecting main features)

Level of community participation Level of community satisfaction Number of local organized public events

Success of distinctive local economy

Yes Yes

Yes Yes

Yes Yes

. Preserve Open Space, Farmland, Natural Beauty & Critical Environmental Areas

- Open spaces share for each

- Awareness of ecological system

- Change in open spaces sizes & numbers.

Better quality of life

Less air pollution

Percentage of open space using

Yes Yes Yes

No Yes Yes

7. Strengthen & Direct Development toward Existing Communities

- Percentage of vacant lands & abandoned buildings

- Subsidizing infill development

- Revolving loans

Infrastructure utilization Income of small business

Yes Yes

Yes Yes

8. Provide a Variety of Transportation Options

- Transportation alternatives (no of nodes in specific area, capacity distribution patterns, diversity)

- Concentration of critical services around transitional points

- Comprehensive network design (connections between different modes of transportation)

- Public transportation densities

- Roads congestion (average speed)

- Car provision percentage (%)

- Greenhouse gases emission

- Number of road accidents

- Citizens satisfaction with public


- Time average for daily trips

Yes Yes Yes Yes Yes

Yes Yes

Yes Yes Yes Yes Yes

No Yes

9. Make Development

Decisions Predictable, Fair & Cost Efficient

Business modules for Smart Growth principles application

Benefit/Cost ratio Payback period Projects economic sustainability

Yes Yes Yes

Yes Yes Yes

10. Encourage Community & Stakeholder Collaboration in Development Decisions

- Capacity building programs

- Codes & regulations awareness

Community participation in decision making (level of participation & frequency) Public awareness

Community - based initiatives_

Yes Yes Yes

No No Yes

6. Application: Walkable Communities Simulation by ABM

The application of ABM model on all SG principles requires extant work either to establish theoretical or practical findings. This paper focuses on one of those principles; Walkable communities, and examine the application of AGB on it. In fact, Creation of walkable communities is a core element in Smart Growth agenda and is connected with most of SG principles. Walkable communities depend on having multiple pedestrian routes with safe crossing connections and pleasant landscape design, respecting the needs for elderly citizens, handicapped and children. Walkability promotes a healthy society which interact frequently with local shops and services as well as open areas and public spaces. That decreases the excessive use of automobile and enhances the social relationships among citizens as well as local business. The strategies were modified by the Smart Growth Network to adapt with the uprising needs and values.

Table 2 - Goals, strategies and expected outcomes for creating walkable communities

Walkable Communities Goals Input Strategies Output Indicators

1. Create friendly, healthy, safe living neighborhoods. 2. Different services are accessible from pedestrian routes. 3. Enhance social interactions & social inclusion. 4. Reduce the dependency on using vehicles. 1. Reducing walking distances 2. Increasing accessibility to pedestrian routes (decreasing critical intersections with vehicle routes) 3. Enhancing environment of pedestrian routes: a) Increasing shaded areas & soft cape elements b) Increasing daily activities 4. Enhancing safety of pedestrian routes: a) Increasing separated buffer zones b) Increasing guiding signs & visual cues 5. Providing diversity of transportation nodes: a) Increasing number of transportation nodes b) Increasing diversity of transportation nodes (decreasing standard deviation between number of different nodes) c) Increasing capacity of public transportation (decreasing users density to maximum comfortable utilization) d) Increasing safe accessibility to transportation nodes e) Enhancing the pattern of distribution (decreasing the concentration of nodes in one place) 6. Concentrating critical services around transitional points Physical/Statistical: 1. Adequate public transportation densities (Increase to maximum utilization level) 2. Decrease roads congestion (increase average speed) 3. Decrease Car provision percentage Environmental: 1. Decrease levels of greenhouse gases emission 2. Decrease respiratory health problems for citizens 3. Decrease noise Socio-economical: 1. Increase users' satisfaction. 2. Increase income of small business

The main outcome: Increasing Walking Preference

6.1. Walkable Communities Simulation

To perform a pre-assessment for a specific setup; Walkable Communities, the first step is to define the goals of Walkability, its related measuring indicators and the followed strategies. The next step is to transform all these data in term of variables, used as input data to build up the simulation model. This step was previously discussed in section 5.2 (Table 1), where the strategies and measuring indicators have been introduced as variables and it is detailed in (Table 2) for this application. It is clear that the key target for creating walkable communities, is for people to choose walking or using public transportation, rather than using cars. This is an emergent behavior that can be tracked by ABM to predict whether people are going to choose to walk, in case these strategies are implemented. A decision chart is introduced in Fig. 4 to explain the process of user decision making.

Fig. 4 Decision Chart for Walkability Principle - by Authors

6.2. Application on Walkable Communities Discussion

Behavioral decisions are a complex process, where different factors and attributes are connected. As walkable preference is main target, the proposed chart is designed to make it the 1st route/path for the decision process. The 2nd route represents the use of public transportation for long distances. Both routes are considered preferable for the simulation model. The 3rd and 4th routes are the undesirable decisions. An agent has to pass through the given questions, in the given order, and take decisions based on its given attributes, perception and memory. The sequence of transition rules (questions) and the identifying character of the agent changes from one context/setup to another according to priorities of the community individuals, their values, perception background and definition of their needs.

In this setup, the physical constrain was the 1st transition rules that determines whether the agent is going to walk or not, that is based on the memory given to the agent for the distance between the two points to be tested and its defining character of how long it can walk. The results coming from this transition rule will benefit the planners to identify the acceptable distance between 2 points that the agent will choose to walk, if achieved. The last transition rule in the Walking route is a high level of perception which depends on human self-awareness and self-pride (a social constrain). It is a more complex decision that can be stated with one question, and has multiple cross cutting affecting factors related to the collective behavior, public awareness, level of education and community definition of being wealth or respected. This rule can overrun all the other rules and transfer the agent directly to choosing to ride a car, stating that even if all factors are in favor of walking, the agent might take a decision to take a car anyway.

This chart track the decisions made by agent to give a reasonable explanation for the reasons people choose to walk or not. Any closed loop in the chart that forces the agent to get back to the main route clears out that this is not the agent 1st choice, therefore it isn't its favorable choice. These can be considered as secondary problems that can be addressed on another level. In further research work, the model of walkable communities can be driven into a real

simulation ABM that targets a distinctive context and test its accuracy. Also a diversity of models is to be introduced to give the researches an opportunity to run a simulation that translates communities' priorities, needs and values.

7. Findings

From the previous literature, analysis and discussion, Urban Facility Management can be improved on different levels as the simulation models are in great benefit for understanding the choices of individuals. Adding information about their preferences and basic needs, as well as the mechanism of their action and decision dynamics will improve the methodology of addressing needs, in order to attain users satisfaction on the service provided, in addition to the control of their emergent behavior. A parallel chart for the responsible actors for every transition rule in the decision chart should be provided, so each problem is addressed by the corresponding responsible stakeholder. That enhances the process of decision making and accelerates response to different problems, improving monitoring and controlling techniques of different resources, and attaining maximum utilization from them without excessive using. That can be summarized into three main points: l.Enhancing social information database, 2.Defining responsibilities of different stakeholders and 3.Assisting resources management controlling and monitoring techniques.

8. Conclusion

Smart Growth is a proactive approach that addresses social concerns and plans for enhancement of social inclusion and environmental justice. However, in developing countries, strategies are needed to undergo a spectrum of analysis to validate its ability in addressing real problems, with applicable and effective solutions, especially. Agent Based Modelling is an adequate tool for collecting information about current situation, analyzing data to give a comprehensive understanding for social behavior and clarifying the reasons for its emergency. Involvement of Urban Facility Management in adjusting policy making needs to embrace both sustainable strategies and social values, in order to act as an effective tool addressing challenges of growing Metropolitan Areas.


[1] UN, 2014. World URbanization Prospects: The 2014 Revision Highlights, New York: United Nations-Department of Economic and Social


[2] EDWARD J. JEPSON, M. M. E., 2010. How Possible is Sustainable Urban Development? An Analysis of Planners' Perceptions about New

Urbanism, Smart Growth and the Ecological City. Planning Practice and Research, Vol. 25, No. 4, pp. 417-437

[3] UNICEF, 2012. Children in an Urban World: The State of the World's Children, New York: United Nations

[4] Richard Florida, C. M. T. G., 2012. Global Metropolis: The Role of Cities and Metropolitan Areas in the Global Economy. Toronto: University

of Toronto.

[5] Bruegmann, R., 2015. Urban Sprawl. International Encyclopedia of the Social and Behavioral Sciences, 2nd edition, Volume 24, Elsevier, pp.


[6] Davies, W. K., 2015. Theme Cities: Solutions for Urban Problems. New York: Springer.

[7] Roy, A., 2005. Urban Informality: Toward an Epistemology of Planning. Journal of the American Planning Association, Volume 71, pp. 147-

[8] Arku, G., 2009. Rapidly Growing African Cities Need to Adopt Smart Growth Policies to Solve Urban Development Concerns. Canda, Springer,

pp. 253-270

[9] UN-Habitat, 2009. Planning Sustainable Cities: Global Report on Human Settlements, London: Earthscan

[10] IFMA, 2015. Facility Management Knowledge Base. [Online] Available at:

[11] Mohammad Tammo, M. N., 2012. A Critical Review on the Concepts of Facilities Management in Community-Based Context. Edinburgh, UK, Association of Researchers in Construction Management, pp. 1379 - 1388

[12] Smart Growth Network, 2003. Getting to Smart Growth II. s.l.:International City/County Management Association

[13] Smart Growth Network, 2002. Getting to Smart Growth. USA: International City/County Managment Association

[14] Litman, T., 2015. Evaluating Criticism of Smart Growth. s.l.:Victoria Transport Policy Institute

[15] Liu, Y., 2012. Modelling Sustainable Urban Growth in a Rapidly Urbanising Region Using a Fuzzy-Constrained Cellular Automata Approach.

International Journal of Geographical Information Science, 26(1), p. 151-167

[16] llana Preuss, A. W. V., 2004. "Smart Growth" and Dynamic Modeling: Implications for Quality of Life in Montgomery County, Mar yland. Ecological Modelling, Volume 171, pp. 415-432

[17] Andreas Rienow, D. S., 2014. Geosimulation of Urban Growth and Demographic Decline in the Ruhr: A Case Study for 2025 Using the Artificial Intelligence of Cells and Agents. Geographical Systems, Volume 16, p. 311-342

[18] Metcalf, S. S., 2014. Modeling Social Ties and Household Mobility, Annals of the Association of American Geographers,104(1), p 40,59

[19] Michael Batty, C. V. D. S. J. S. J. R. A. J., 2013. SIMULACRA: Fast Land-Use-Transportation Models for the Rapid Assessment of Urban Futures. Environment and Planning B: Planning and Design, Volume 40, p. 987 - 1002

[20] Batty, M., 2009. Urban Modelling. International Encyclopedia of Human Geography, Elsevier, pp. 51-59.

[21] Nigel Gilbert, K. G. T., 2005. Simulation for the Social Scientist, 2nd Edition. UK: Open University Press

[22] Sakoda, J. M., 1971. The checkerboard model of social interaction, Journal of Mathematical Sociology. 1:119-132

[23] Charles M. Macal, M. J. N., 2009. Agent Based Modelling and Simulation. Texas, IEEE, pp. 86-98

[24] H. Van Dyke Parunak, R. S. R. L. R., 1998. Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide. LNAI

1534, Springer, pp. 10-25

[25] Shanthi.M, D., 2012. Agent Based Cellular Automata: A Novel Approach for Modeling Spatiotemporal Growth Processes. International Journal of Application or Innovation in Engineering and Management, Volume 1, pp. 56-62