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2024 | Book

City Information Modelling

Editors: Ali Cheshmehzangi, Michael Batty, Zaheer Allam, David S. Jones

Publisher: Springer Nature Singapore

Book Series : Urban Sustainability


About this book

This is the first book focused on City Information Modelling (CIM) that puts together a collection of recent studies related to concepts and trends in CIM, application and digitization processes/methods, and frameworks and practices of CIM. This emerging topic is important to various research and practice under sectors of the built environment, civil engineering, urban planning, urban design, and urban management. CIM aligns well with smart cities, data-driven urban analytics and optimization, information-based city planning, and future development paradigms.

City Information Modelling provides global case study examples in three parts. At first, the contributors offer several examples of ‘Concepts and Trends’, where CIM is explored further in urban management, urban sustainability, and big data studies. In the second part, the book offers various examples of application and digitization processes or methods related to urban planning and design practices. In the third part, the contributors delve into several examples of CIM frameworks and practices critical to contemporary research, planning and design paradigms, and future practices.

This collection is a niche resource for various stakeholders, particularly urban scientists, urban analytics, urban practitioners, and researchers. It will also be a valuable collection for those who work with information-based models, urban optimization models, and big data analytics, particularly from policy and practice perspectives. The findings of this collection help direct future research in CIM and suggest opportunities for big-data urban research, integrated urban models, and holistic frameworks in sustainable cities, smart cities, and future cities.

Table of Contents

Chapter 1. City Information Modelling: An Insight into a New Era for the Built Environment
In this new era of the built environment, City Information Modelling (CIM) is broadly recognized as a multidisciplinary approach that integrates various data sources, technologies, and analytical tools to support urban planning and management (and, to some extent, urban design). Its applications are diverse, multifaceted and highlight potential new directions in urbanism. From many global examples, it is evident that CIM provides a holistic view of the (existing, new, and growing) city, allowing decision-makers to make informed choices for sustainable and resilient urban development. This chapter briefly provides a brief insight into the concept of CIM. It then provides the book’s aim and objectives and a summary of its structure.
Ali Cheshmehzangi, Michael Batty, Zaheer Allam, David S. Jones

Concepts and Trends

Chapter 2. City Information Modelling and Sustainable Development: The Role of CIM in Achieving Sustainable Urbanization
This chapter explores the potential of City Information Modeling (CIM) in achieving sustainable urbanization. Due to the growing concern about sustainability issues in urban life and the environment in recent years, CIM has been widely proposed by various municipal systems as a holistic approach to achieving sustainable urbanization in terms of urban planning, management, and design. This chapter will aim to outline the importance of CIM for urban sustainability in four main sections: (1) Data acquisition and management for CIM will be included a wide range of data acquisition and management techniques utilized for CIM and urban modelling to explore sustainability by examining the multi-aspects of urban components. (2) CIM for urban planning, design, and management: This section will discuss the use of CIM in urban planning, land use management, zoning regulations, infrastructure management, emergency response planning, and public participation, along with reviewing successful case studies. (3) CIM for public participation will explore how CIM can enable citizen participation and engagement in city decision-making. This section will present the planning process of informatics datasets to decision-makers and stockholders to be assessed more comprehensively due to the application of narrow datasets in spatial planning and urban modelling. (4) Future directions of CIM: This section will include the emerging trends and technologies in CIM and their potential impact on the future of cities, particularly smart cities. The chapter will summarize the critical contributions of CIM to achieving sustainable urbanization and suggest future directions for research and practice. The future directions section will discuss emerging trends and technologies in CIM and their potential impact on the future of sustainable urbanization. Overall, the chapter will provide helpful insights into the potential of CIM as a valuable tool in addressing the challenges of sustainable urbanization.
Hadi Soltanifard, Reza Farhadi, Hossein Mansourian
Chapter 3. Enhancing Health Outcomes Through City Information Modeling (CIM): A Case Study of Sydney, Australia
A case study was conducted in Sydney, Australia, to explore the potential of City Information Modeling (CIM) in improving health outcomes. Sydney is a diverse and populous city with over 5 million residents, featuring a range of urban environments, from densely populated inner-city areas to sprawling suburban neighborhoods. The case study focused on how urban interventions impact health outcomes in Sydney, collecting data on the city’s physical, social, and economic characteristics, as well as health outcomes. By using this data, a 3D model of the city was created. CIM has been used for this model, which was utilized to evaluate how various urban interventions, such as the addition of green spaces or improvements to public transportation, affect health outcomes.
The results of the case study analysis demonstrate that CIM can effectively identify areas of the city that are most vulnerable to health risks and assess the impact of urban interventions on health outcomes. However, the study also highlighted the need for better data collection and analysis, improved collaboration between public health professionals and urban planners, and the development of more sophisticated CIM tools. Overall, the case study in Sydney has shown that CIM has great potential for improving health outcomes. To realize this potential, it is crucial to have the right tools and collaboration, enabling CIM to effectively identify areas of the city most in need and evaluate the impact of interventions on health outcomes.
Mohammad Anvar Adibhesami, Hirou Karimi, Borhan Sepehri, Amirmohamad Parvanehdehkordi
Chapter 4. City Information Modeling and Its Applications: A Review
Over the past few decades, there has been a growing interest in the field of City Information Modeling (CIM). CIM is generally considered a digital representation of a city which can empower the identification of optimal approaches to enhance urban environments. CIM is extensively used in various applications, primarily under the umbrella of smart cities. This chapter first provides a brief review of the history and definition of CIM. Subsequently, the structure and modules of CIM are discussed. Based on the literature review, it is evident that integrating Building Information Modeling (BIM) and Geographic Information System (GIS) is a widely adopted approach for CIM generation. This is because BIM and GIS both model spatial information, with BIM focusing on indoor modeling and GIS emphasizing outdoor environment, thus complementing each other effectively. To investigate the feasibility of CIM applications based on BIM–GIS, the main BIM–GIS integration CIM applications are further reviewed in this chapter. It revealed that these applications include but are not limited to urban planning, urban facility management, urban flood hazard assessment, route and evacuation planning, underground space development and underground utility management, building energy analysis and management, and more.
Xiang Zhang

Applications and Digitisation

Chapter 5. Optimizing Urban Design for Pandemics Using Reinforcement Learning and Multi-objective Optimization
The present study demonstrates a novel approach to leveraging reinforcement learning and multi-objective optimization for enhancing urban preparedness against pandemics. The role of urban design in preventing the spread of infectious diseases is significant, as evidenced by the COVID-19 pandemic, highlighting the need for preparedness for potential future pandemics. The method proposed in this study employs a hybrid approach of reinforcement learning and multi-objective optimization to identify optimal solutions for urban design that effectively reconcile diverse objectives, including but not limited to public health, economic viability, and environmental sustainability. The findings obtained from a simulated outbreak demonstrate that the proposed approach exhibits superior performance in comparison to the currently available methods. This suggests that it could be used to help plan cities for future pandemics. The utilization of reinforcement learning has the potential to enhance urban planning by employing a reward-based mechanism to instruct an agent on the prevention of a pandemic outbreak. The consideration of multiple objectives simultaneously can lead to further enhancement in the optimization process, which is commonly referred to as multi-objective optimization. The proposed methodology has the potential to mitigate the transmission of pandemics while taking into account the economic ramifications and the standard of living. The findings of this investigation illustrate the feasibility of utilizing reinforcement learning and multi-objective optimization techniques for the purpose of optimizing urban design interventions aimed at mitigating pandemics.
Mohammad Anvar Adibhesami, Hirou Karimi, Borhan Sepehri
Chapter 6. Sustainable Smart City Application Based on Machine Learning: A Case Study Example from the Province of Tekirdağ, Turkey
This study focuses on the city and risk-hazard interaction, one of the most significant issues of the twenty-first century. Today’s cities have evolved into sizable risk pools due to the urbanization trend, which intensified particularly following the Industrial Revolution and persisted, with more than half of the world’s population residing in urban areas in 2011. Due to this, the theoretical basis of the research is that a machine learning-based strategy for building smart cities can minimize or eliminate current and future potential risks and hazards in urban areas. Tekirdağ in Turkey, which is affected by natural risks and hazards like earthquakes, floods, and tsunamis, as well as human and technological risks and hazards because of population movement, industrialization, and its location on major transportation lines, has been selected as the pilot city to test the hypothesis. The study’s methodology is focused on machine learning, smart cities, and participatory approaches. Data sets will first be compiled through historical and institutional archives, field research, and in-person interviews with representatives of pertinent institutions. Then, a digital system built on machine learning and in accordance with project-specific smart city components will be created. The data sets will be uploaded to the established digital system, where it will be possible to calculate the likelihood that a risk will evolve into a hazard and the potential effects that existing hazards may have. These chances that the digital system will offer as an output will be assessed in light of the obligations of the pertinent institutions and organizations at the pilot province level regarding risk reduction and vulnerability minimization. Thus, the study seeks to accomplish two key goals. First and foremost, it aims to address all environmental risks and hazards at the level of the pilot province with an integrated strategy and to efficiently monitor the performance of local institutions’ and organizations’ obligations. In case comparable circumstances arise, the machine learning-based system is hoped to offer warning information for future hazards.
Serhat Yılmaz, Hasan Volkan Oral, Hasan Saygın
Chapter 7. The Role of City Information Modelling (CIM) in Evaluating the Spatial Correlation Between Vegetation Index Changes and Heat Island Severity in the Last Two Decades in Tehran Metropolis
The aggravation of the urban heat island, especially during summer time, could affect the environment and the quality of life. Studying the dynamics of surface thermal energy and identifying its correlation with human-induced changes is essential for predicting environmental changes as well as policy-making in urban settlement planning. Increasing vegetation is one of the most effective strategies to reduce the effects of urban microclimate. In this regard, a research study was conducted to analyze the trend of surface thermal changes and the spatial correlation of vegetation greenness with this phenomenon due to urbanization and urban planning developments in Tehran city between 2003–2022. Satellite images of Tehran with clear sky were obtained using ASTER satellite in August 2003 and Landsat 8 satellite in August 2022. They were processed through various remote sensing algorithms using Envi software to extract spatial patterns of surface temperature and Normalized Difference Vegetation Index (NDVI) of the Tehran metropolitan area. Satellite outputs show that over the past two decades, the minimum surface temperature and average surface temperature have decreased by 3.67 °C and 0.47 °C, respectively, while the average NDVI has increased from 0.06 to 0.10. The spatial correlation estimate between NDVI and Land Surface Temperature (LST) in twenty-two districts of Tehran in the years 2003 and 2022 is 83% and 81%, respectively. The decline in correlation suggests a heightened influence of human activities and other physical factors associated with urban areas on the intensity of the urban heat island phenomenon.
Hadi RezaeiRad, Narges Afzali
Chapter 8. Exploiting Advantages of VPL in City Information Modelling for Rapid Digital Urban Surveying and Structural Analysis
This research proposes a parametric modelling methodology based on Visual Programming Language (VPL) for creating City Information Models (CIM) to facilitate seismic vulnerability mapping in historic centres. The methodology consists of two innovative methods (Survey-to-CIM and Scan-to-CIM) developed for integrating direct and derived data, using Grasshopper as the VPL parametric computational design environment. The Survey-to-CIM method is a low-cost solution for small urban centres that integrates different data acquisition techniques within a parametric and responsive flow. The Scan-to-CIM method automates the input of survey data using an Artificial Intelligence system that identifies geometric-architectural features within point clouds. The generated CIM adheres to a specific semantic structure defined as CityGH, an innovative format based on CityJSON 3D city model standards but adapted to the data structure of Grasshopper. The semantic structure of the CIM model allows the storage of attributes and metadata that facilitate the information enrichment and management. The CIM model also allows the extraction of structural geometric models (city block scale) necessary for FEM analysis. Specifically, a workflow was developed to enable FEM analysis in the same VPL environment. Overall, this methodology offers an efficient and sustainable approach for creating CIMs that can support seismic vulnerability mapping and analysis actions in historic centres. This research demonstrated the benefits of adopting an explicit parametric modelling approach for City Information Modelling, enabling to manage digital urban survey data, semantic enrichment and management and FEM analyses. The proposed methods can be pursued based on the availability of project resources and the type of urban centre being studied. The outcome of this research contributes to the debate about parametric urbanism and the role of computational design with CIM methodology. Specifically, the case studies developed offer a practical alternative for seismic vulnerability mapping in historic centres.
Federico Mario La Russa

Frameworks and Practices

Chapter 9. Towards Adaptive and Resilient Strategies Using Digital Twins: A Study on the Port of Tyne, UK
Over the course of history, maritime ports and their associated cities have grown in tandem, with the port acting as a catalyst for economic growth and prosperity in the city. The rise of globalization in recent decades has further reinforced this relationship. Understanding the operational risks faced by ports is crucial for assessing their resilience and their impact on the broader urban areas they serve. Currently, maritime ports are embracing digitalization, taking advantage of the abundance of data collection, transmission, and processing tools and networks. The concept of a “Digital Twin” is gaining popularity, with several pilot initiatives already underway in some of the world’s busiest ports. However, most existing Digital Twin implementations heavily rely on data-gathering devices like IoTs and primarily provide a snapshot of the port’s current status. This approach creates significant economic and technical barriers for other ports looking to replicate the same level of digitalization. This research aims to address the disparity in data availability among assets, facilities, and vehicles by proposing an adaptive Digital Twin framework, using the Port of Tyne as a case study. The developed Digital Twin serves as a foundation for implementing resilience strategies, encompassing both emergency response and long-term mitigation plans. It offers valuable insights to port authorities and stakeholders, aiding in the development of resilience strategies, understanding industrial ecology, and managing urban metabolism in port cities.
Jiayi Jin, Mingyu Zhu
Chapter 10. Ecosystem Institutional Maturity: Perspectives for CIM in Urban Management and Planning in Curitiba, Brazil
City information modeling (CIM) is an innovation in information and communication technologies (ICTs) applied to urban management and planning. However, there are still few studies that evaluate the process of diffusion, implementation, and adoption from a sociotechnical perspective. Our objective is to develop an analytical model to assess the levels of multiscale institutional maturity to support the technological diffusion. The model was tested in the context of the BIM/CIM/GIS ecosystem of Curitiba, a city with long trajectory of technology diffusion, and where the municipality has already structured actions and a well-established trajectory to apply GIS, BIM, and CIM technologies and tools. The results show that (i) the institutional maturity of the BIM/CIM/GIS ecosystem is expressed by the constructs practices and processes, previous experiences, diffusion strategies, and awareness; (ii) it is possible to build an institutional maturity assessment tool to guide the dissemination, adoption and implementation processes of the CIM. The analysis allowed the identification and quantitative explanation of an institutional maturity model in line with previous theoretical debates. Theoretical implications are (i) the explanation of an institutional maturity model capable of reading reality qualitatively and quantitatively; (ii) the approximation of theory and practice via testing of the proposed model. Empirical implications are in the constitution of a theoretically grounded diagnostic tool capable of addressing challenges in technology diffusion practices to reduce the current gap between technological evolution and the pace of change of organizations.
Augusto Pimentel Pereira, Mario Prokopiuk
Chapter 11. The Use of City Information Modelling (CIM) for Realizing Zero Energy Community: A Path Towards Carbon Neutrality
City information modelling (CIM) offers a digital depiction of the urban environment, empowering stakeholders to critically review and optimise the performance of energy. In the pursuit towards Zero Energy Cities (ZECs), CIM becomes an essential instrument of this process. However, despite the promise it delivers, the uptake is slow. This research therefore addresses the gap by providing a comprehensive overview of CIM’s potential for facilitating the achievement of ZECs in the built environment. The chapter employs an extensive review of the literature on the subject. The results reveal that, there exist a notable dearth of research concerning the design and execution of the zero-energy concept within the context of community-scale implementation. Moreover, the merging of CIM and UDT opens novel possibilities for establishing zero-carbon communities throughout their life cycle. By harnessing data from various origins such as buildings, energy grids, urban planning, environmental setups, transportation networks, and socio-demographic metrics, it then becomes feasible to construct a holistic digital portrayal of the community. It is consequently imperative to conduct comprehensive cross-sector inquiries that delves into the practical realization of the zero-energy community concept within the wider framework of local sustainability objectives. This entails examining the interplay between climate mitigation measures and sustainability goals, while carefully assessing both potential conflicts and opportunities.
Hossein Omrany, Amirhosein Ghaffarianhoseini, Ali Ghaffarianhoseini, Kamal Dhawan, Abdulbasit Almhafdy, Daniel Oteng
Chapter 12. Conclusions and the Future of City Information Modelling (CIM)
This conclusion chapter synthesizes critical insights from the multifaceted City Information Modelling (CIM) field, highlighting its transformative potential in shaping sustainable, resilient, and equitable urban landscapes. CIM emerges as a critical tool for data-driven decision-making, particularly in achieving sustainable urbanization aligned with global initiatives like the UN’s Sustainable Development Goals. The technology’s impact is far-reaching, extending from enhancing healthcare outcomes to optimizing energy management in buildings, thereby contributing to carbon neutrality. Advanced machine learning and multi-objective optimization techniques further augment CIM’s capabilities, offering nuanced solutions to complex urban challenges, including pandemics. Environmental dimensions are not overlooked; CIM proves invaluable for balancing urban development with nature conservation, particularly in mitigating heat island effects. The chapter also delves into the institutional implications of CIM, emphasizing its role in improving governance mechanisms and fostering participatory decision-making. As we look towards a future marked by rapid technological advancements and urbanization, CIM stands as a robust framework, facilitating cross-disciplinary collaboration and offering dynamic solutions for the complexities of modern urban life.
Ali Cheshmehzangi, Michael Batty, Zaheer Allam, David S. Jones
City Information Modelling
Ali Cheshmehzangi
Michael Batty
Zaheer Allam
David S. Jones
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Springer Nature Singapore
Electronic ISBN
Print ISBN