Editorial: City Information Modelling: Digital Planning for Sustainable Cities
About this issue
City Information Modelling may be in its infancy, but as the papers in this issue show it presents a unifying conceptual framework that offers context and purpose for transdisciplinary research and development, both in academia and industry, in the digital technology disciplines that drive the field of Smart Cities. The practice of CIM will contribute to moving forward the digitalization of the built environment development process towards safe, resilient, sustainable and inclusive cities.
City Information Modelling: Digital Planning for Sustainable Cities
The world is facing unprecedented challenges affecting our environment, economy, and society at large, as expressed in the United Nations’ Sustainable Development Goals (https://sustainabledevelopment.un.org/). These challenges must be addressed – particularly in cities and urban regions where most people live and most problems arise (e.g. energy consumption, waste, pollution, poverty, inequality). This is especially reflected in Goal 11 on sustainable cities and communities, and in the United Nations’ New Urban Agenda (NUA) (United Nations, 2017).
The challenges and goals are complex because they are interdependent and cities are based on multiple interdependent systems, e.g. energy, resources, mobility, social, economic, or ecological. To address them, we must plan, manage, and develop our cities in an integrated and transdisciplinary way. Local and national governments in charge of meeting the UN’s targets have a responsibility to conduct urban planning processes transparently, to collect and provide detailed information on their cities, and to monitor the outcomes of planning to ensure that the sustainability targets are being met.
Today, sectorial expertise is not enough, and the urban planning process must involve all stakeholders and actors through information and knowledge exchange, active collaboration, and public engagement in participatory processes. Having the right information accessible to the relevant stakeholders, at the right time, for the various stages of the planning process is a pre-requisite. To help in achieving this, the NUA proposes the development of new digital technologies to support urban design and planning in the transition to safe, resilient, sustainable and inclusive cities: a smart cities approach (Morphet and Morphet, 2019).
Aligned with this proposition, the concept of City Information Modelling (CIM) has quietly emerged, so far by-passing the hype that several other concepts related to the smart cities approach – such as Big Data, Digital Twins, or Urban Analytics – have experienced. The term CIM has rather seamlessly been adopted by the software industry and in urban design and planning research and practice circles. It can be found in industry and technology blogs, in presentations and job descriptions, in software demos and videos, and in conference and seminar lists of topics. Despite this acceptance of the term, it has never been properly defined. What CIM means might just seem rather obvious to everyone, even if, seen from different perspectives, its meaning can differ. This tacit acceptance suggests that CIM represents a useful or even necessary concept, providing a focal point to the various actors involved in digital urban design and planning.
The term CIM originates in a vision of the evolution of Building Information Modelling (BIM) technology to address neighbourhood and city-scale modelling for disaster management and response (Khemlani, 2005). More generally, the term has been used to describe the practice of planning and designing the city with the support of rich geospatial information and digital tools. Such a concept had already been proposed a decade earlier by Huang (1995) as a prototype ‘Dynamic Urban Information Model’ to support strategic urban redevelopment and define architectural interventions, applied in the context of a university’s architectural design studio. Since then we have witnessed developments in numerous digital technologies that set the scene for the implementation of CIM practice and tools, namely GIS, BIM, 3D City Models, Big Data, Urban Analytics, Planning Support Systems, Parametric Design, Internet of Things, to name a few. The digital planning of cities is a complex field and, despite the advances in various related disciplines of science and technology, the implementation of CIM faces challenges on different fronts, some of them highlighted in what follows.
There is currently a surge of data availability, e.g. authoritative, crowdsourced, remotely sensed, or generated by numerous Internet of Things (IoT) sensors and devices developed in the context of Smart City initiatives. The use of these data is key to the success of CIM: how can the potential of these data sources be leveraged to feed applications that are relevant for city planning?
There is an increasing use of Geographic Information Systems (GIS) in urban design and planning practice, but GIS does not inherently offer a knowledge model of the urban environment, nor of its production and management, in the way that Building Information Modelling (BIM) offers a rich, integrated, semantic model of the various components of the buildings and infrastructure, its functions and performance. In addition, a model of the city, besides an arrangement of physical features, must include the flows of people, goods and resources, and its political, social and economic dimensions: how can an integrated and comprehensive information model of the city be created, that is more than a collection of BIM models of buildings and infrastructure?
New platforms offer 3D city models and Digital Twins of cities for visualization and simulation, and parametric design platforms are extensible to support urban design and analysis features. However, the planning process involves many different stakeholders (e.g. city planners, architects and urban designers, real-estate developers, infrastructure engineers, citizens, utility providers, landscape architects, sociologists, emergency services), with different roles and expertise: how can applications cater for specific stakeholder’s needs, and ensure consistent knowledge sharing between all those involved in the urban planning process?
Academic curricula in architecture, civil engineering and urban planning need to prepare students for a more cross-disciplinary practice, to face the challenges of sustainable development and digital practice: in what ways can CIM support this cross-disciplinary education?
It is time to take stock and set an agenda for the coming years, coordinating the innovation efforts of academia and the IT industry to meet the requirements of urban design and planning practice, and education. The seven papers in this special issue explore some of these challenges of City Information Modelling, and of the practice of digital urban design and planning in general.
It starts with a necessary review of the academic literature on CIM in a search for a comprehensive understanding of the concept and its application. A general picture of CIM emerges from a set of recurring key concepts leading to a concise definition:
CIM is an ecosystem of interdependent practices and digital technologies for the process of urban design and planning, used interactively and collaboratively by all stakeholders, connected to a data rich and integrated city information database.
This is further developed into a conceptual framework which provides a road map and common focus for the development of the underlying disciplines of research and practice.
But the journey is challenging, and the paper by Kemp (2020) provides important lessons from the BIM implementation journey in the UK. This detailed narrative looks at the role of the government and other organizations in the process of the digital transformation of the building industry, including the importance of standards for its implementation. Further, it offers critical reflections on important emerging trends linked to CIM, namely Digital Twins and Artificial Intelligence, highlighting the need for a humanistic perspective.
Ketzler et al. (2020) continue on this path, offering a state-of-the-art review of the emerging concept of Digital Twins for cities. The authors reflect on its relation to the dominant concept of 3D City Models and explore a range of applications that Digital Twins can offer cities, identifying outstanding challenges for their development and implementation.
Considering the application of CIM to address complex urban questions, Lido et al. (2020) showcase a holistic, data-led and analytical approach that leverages a diverse range of urban data sets, and integrates them to gain insight into multiple social dimensions of urban inclusion, providing evidence that can help policymakers, city planners and other stakeholders make informed planning decisions.
The three remaining papers shift the focus of the application of CIM from city planning to urban design practice, taking different and complementary perspectives. Koenig et al. (2020) return to the topic of Artificial Intelligence, developing a framework for classifying the level of automation of different urban design tools and methods, similar to the levels of automation developed for the car industry. These are then demonstrated through a series of advanced parametric urban design solvers, reflecting on what is achieved and how human capabilities assisted by computational intelligence should be able to tackle the wicked problems commonly found in urban design and planning.
Contrasting with the cutting edge parametric urban design applications developed in research labs of universities and leading architectural practices, Gösta et al. (2020) report on the challenges of adopting a CIM approach faced by the typical architectural practice, which represents the vast majority of cases. Through interviews and a workshop, they discuss the ‘simple’ data-informed digital design methods that are standard in their own disciplines but still too demanding for everyday practice, and reflect on the limitations and challenges faced by practice. In turn, this also signals the set of skills that architectural and planning students should be developing through their education, in order to introduce this digital knowledge into practice.
The issue concludes with a viewpoint by Stojanovsky et al. (2020), reflecting on the role of AI and generative algorithms in the traditional urban design discipline. The authors explore and question how urban morphology, environmental perception, and design theories can be integrated in CIM tools to support the digitalization of urban design practices.
The subject of CIM is certainly complex, involving multiple disciplines, and only some pieces of the puzzle were touched on in this issue. What we find is that CIM is emerging as an instrumental concept, providing a holistic, data-driven and user centred framework for the digital technologies at our disposal to support the planning and design of the sustainable city.
- Gil, J. (2020) City Information Modelling: a conceptual framework for research and practice in digital urban planning. Built Environment, 46(4), pp. 501–527.
- Gösta, A., Agi, A., Flårback, J., Karlsson, J. and Simonsson, E. (2020) The BIM implementation journey: lessons learned for developing and disseminating City Information Modelling (CIM). Built Environment, 46(4), pp. 620–636.
- Huan, J. (1995) Dynamic urban information model: integrated approach to strategic urban development, in Proceedings of the Sixth International Conference on Computer-Aided Architectural Design Futures. Singapore: National University of Singapore, pp. 399–408. Available at: http://papers.cumincad.org/data/works/att/c7ef.content.pdf.
- Kemp, A. (2020) The BIM implementation journey: lessons learned for developing and disseminating City Information Modelling (CIM). Built Environment, 46(4), pp. 528–546.
- Ketzler, B., Naserentin, F., Latino, C., Zangelidis, Thuvander and Logg (2020) Digital twins for cities: a state of the art review. Built Environment, 46(4), pp. 547–573.
- Khemlani, L. (2005) Hurricanes and their aftermath: how can technology help? AECbytes, 29 September.
- Koenig, R., Bielik, M., Dennemark, M., Fink, T., Schneider, S. and Siegmund, N. (2020) Levels of automation in urban design through artificial intelligence: a framework to characterize automation approaches. Built Environment, 46(4), pp. 599–619.
- Lido, C., Mason, P., Hong, J., Gorash, N., Anejionu, O.C.D. and Osborne, M. (2020) Integrated multimedia city data: exploring learning engagement and greenspace in Glasgow. Built Environment, 46(4), pp. 574–598.
- Morphet, J. and Morphet, R. (2019) New Urban Agenda: New Urban Analytics. A report prepared for the MacArthur Foundation. London: The Bartlett Centre for Advanced Spatial Analysis, University College London. Available at: https://www.ucl.ac.uk/bartlett/casa/sites/bartlett/files/casa_publicatio....
- Stojanovsky, T., Partanen, J., Samuels, I., Sanders, P. and Peters, C. (2020) Viewpoint: City Information Modelling (CIM) and digitizing urban design practices. Built Environment, 46(4), pp. 637–646.
- United Nations (2017) New Urban Agenda. Available at: http://habitat3.org/wp-content/uploads/NUA-English.pdf.