Cities Knowledge Graph
By Aurel Von RichthofenSemantic Cities not ‘Smart’ Cities!
Cities are becoming more differentiated, complex and diverse systems. Fundamental drivers of city form, such as energy, mobility and information technology have radically changed in less than a decade. With this comes new lifestyles as well as demographic and migration patterns, which demand different social arrangements and the rethinking of spatial organisation. Cities are also exposed to climate change and need to adapt infrastructure and urban form to stay resilient, sustainable and liveable.
Cities and their constituent cyber-physical elements are often conceptualised as static – immobile – entities that have a designated function, life-span, and audience. The underlying digital models of cities still mimic older, physical models and protocols. This is evident in the recent moves towards ‘digital twins’ – virtual models of the status quo – whose updating and management is exponentially complex as the level of detail increases. The ‘smart city’ discourse and much of the hype around artificial intelligence (AI) and optimisation equally reduces cities and citizens to mechanical components of a seemingly controllable urban system. While there is a growing demand to measure, monitor, and manage complex urban systems, a new conceptual approach is needed to digital urban representation.
Semantic Web Technologies developed for the World Wide Web allow to represent ‘The World’ – or subsets of it, depending the perspective taken – by means of relations or graphs. Semantic data can be used to infer knowledge via ontologies – common vocabularies that represent any domain of the city – and reasons based in descriptive logic, e.g. mathematics. This allows us to overcome major challenges in the representation of large and complex systems such as cities: addressing data fragmentation with linked data (e.g. siloes of planning, transport, energy, and other domains), creating interoperability between these domains and data formats with ontologies (Internet of Things, Building Information Modelling, Geo-Information Sciences, etc. data), enabling scalability and sustainability the system with graph architecture, and to handle planning scenarios in the form of base worlds and parallel worlds. This also opens up new modes of linking data and access to information such as participation, open-source urbanism, and digital co-creation.
The Cities Knowledge Graph is a new urban knowledge system built on linked data and Semantic Web Technologies. It can be used for the whole spectrum of urban knowledge formation, including: representation and visualisation of urban data; urban analysis and evaluation by linking in models and simulations; planning and scenario development; as well as to synthesise higher level indicators, such as ‘heart-beats of the city’. The underlying semantic representational concept foregrounds the relation between, the actions, and the meaning of its constituent agents: the citizen. It also foregrounds a political and social dimension vis-à-vis fast developing digital technologies deployed in urban spaces in the discourse on future cities. This is where dynamic spatial knowledge graphs could be used to support democratisation of knowledge and innovation in culture and cities alike.
Literature (or links if any):
[1] Cairns, Stephen, Pieter Herthogs, and Aurel von Richthofen. 2021. “Harness Data Streams.” In Future Cities Laboratory: Indicia 03, edited by Stephen Cairns and Devisari Tunas. Zürich: Lars Müller Publishers.
[2] Chadzynski, Arkadiusz, Nenad Krdzavac, Feroz Farazi, Mei Qi Lim, Shiying Li, Ayda Grisiute, Pieter Herthogs, Aurel von Richthofen, Stephen Cairns, and Markus Kraft. 2021. “Semantic 3D City Database - an Enabler for a Dynamic Geospatial Knowledge Graph.” C4e-Preprint Series, no. 271 (May). https://como.ceb.cam.ac.uk/media/preprints/c4e-preprint-271.pdf.
[3] Clavier, Fabien, and Aurel von Richthofen. 2019. “Looking Behind the Screen of Big Data.” In Future Cities Laboratory: Indicia 02, edited by Stephen Cairns and Devisari Tunas, 93–95. Zürich: Lars Müller Publishers. https://doi.org/10.3929/ethz-b-000334723.
[4] Richthofen, Aurel von. 2019. “Digital Tools, Pipelines and Protocols.” In Future Cities Laboratory: Indicia 02, edited by Stephen Cairns and Devisari Tunas, 80–89. Zürich: Lars Müller Publishers. https://doi.org/10.3929/ethz-b-000334721.
[5] Richthofen, Aurel von. 2020. “Aurel von Richthofen, on Tools, Technology and Society around the Future of AI and Architecture - Artificial Intelligence & Architecture Pavilion de l’Arsenal, Paris, France 27 February – 5 April 2020.” Architectural Research Quarterly 24 (4): 379–81. https://doi.org/10.1017/S1359135521000075.
[6] Richthofen, Aurel von, Pieter Herthogs, Markus Kraft, and Stephen Cairns. 2021. “Semantic City Planning Systems (SCPS): A Literature Review.” C4e-Preprint Series, no. 270 (April). https://como.ceb.cam.ac.uk/media/preprints/c4e-preprint-270.pdf.
[7] Richthofen, Aurel von, Ludovica Tomarchio, and Alberto Costa. 2019. “Identifying Communities Within the Smart-Cultural City of Singapore: A Network Analysis Approach.” Smart Cities 2 (1): 66–81. https://doi.org/10.3390/smartcities2010005.
This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.
Cities are becoming more differentiated, complex and diverse systems. Fundamental drivers of city form, such as energy, mobility and information technology have radically changed in less than a decade. With this comes new lifestyles as well as demographic and migration patterns, which demand different social arrangements and the rethinking of spatial organisation. Cities are also exposed to climate change and need to adapt infrastructure and urban form to stay resilient, sustainable and liveable.
Cities and their constituent cyber-physical elements are often conceptualised as static – immobile – entities that have a designated function, life-span, and audience. The underlying digital models of cities still mimic older, physical models and protocols. This is evident in the recent moves towards ‘digital twins’ – virtual models of the status quo – whose updating and management is exponentially complex as the level of detail increases. The ‘smart city’ discourse and much of the hype around artificial intelligence (AI) and optimisation equally reduces cities and citizens to mechanical components of a seemingly controllable urban system. While there is a growing demand to measure, monitor, and manage complex urban systems, a new conceptual approach is needed to digital urban representation.
Semantic Web Technologies developed for the World Wide Web allow to represent ‘The World’ – or subsets of it, depending the perspective taken – by means of relations or graphs. Semantic data can be used to infer knowledge via ontologies – common vocabularies that represent any domain of the city – and reasons based in descriptive logic, e.g. mathematics. This allows us to overcome major challenges in the representation of large and complex systems such as cities: addressing data fragmentation with linked data (e.g. siloes of planning, transport, energy, and other domains), creating interoperability between these domains and data formats with ontologies (Internet of Things, Building Information Modelling, Geo-Information Sciences, etc. data), enabling scalability and sustainability the system with graph architecture, and to handle planning scenarios in the form of base worlds and parallel worlds. This also opens up new modes of linking data and access to information such as participation, open-source urbanism, and digital co-creation.
The Cities Knowledge Graph is a new urban knowledge system built on linked data and Semantic Web Technologies. It can be used for the whole spectrum of urban knowledge formation, including: representation and visualisation of urban data; urban analysis and evaluation by linking in models and simulations; planning and scenario development; as well as to synthesise higher level indicators, such as ‘heart-beats of the city’. The underlying semantic representational concept foregrounds the relation between, the actions, and the meaning of its constituent agents: the citizen. It also foregrounds a political and social dimension vis-à-vis fast developing digital technologies deployed in urban spaces in the discourse on future cities. This is where dynamic spatial knowledge graphs could be used to support democratisation of knowledge and innovation in culture and cities alike.
Literature (or links if any):
[1] Cairns, Stephen, Pieter Herthogs, and Aurel von Richthofen. 2021. “Harness Data Streams.” In Future Cities Laboratory: Indicia 03, edited by Stephen Cairns and Devisari Tunas. Zürich: Lars Müller Publishers.
[2] Chadzynski, Arkadiusz, Nenad Krdzavac, Feroz Farazi, Mei Qi Lim, Shiying Li, Ayda Grisiute, Pieter Herthogs, Aurel von Richthofen, Stephen Cairns, and Markus Kraft. 2021. “Semantic 3D City Database - an Enabler for a Dynamic Geospatial Knowledge Graph.” C4e-Preprint Series, no. 271 (May). https://como.ceb.cam.ac.uk/media/preprints/c4e-preprint-271.pdf.
[3] Clavier, Fabien, and Aurel von Richthofen. 2019. “Looking Behind the Screen of Big Data.” In Future Cities Laboratory: Indicia 02, edited by Stephen Cairns and Devisari Tunas, 93–95. Zürich: Lars Müller Publishers. https://doi.org/10.3929/ethz-b-000334723.
[4] Richthofen, Aurel von. 2019. “Digital Tools, Pipelines and Protocols.” In Future Cities Laboratory: Indicia 02, edited by Stephen Cairns and Devisari Tunas, 80–89. Zürich: Lars Müller Publishers. https://doi.org/10.3929/ethz-b-000334721.
[5] Richthofen, Aurel von. 2020. “Aurel von Richthofen, on Tools, Technology and Society around the Future of AI and Architecture - Artificial Intelligence & Architecture Pavilion de l’Arsenal, Paris, France 27 February – 5 April 2020.” Architectural Research Quarterly 24 (4): 379–81. https://doi.org/10.1017/S1359135521000075.
[6] Richthofen, Aurel von, Pieter Herthogs, Markus Kraft, and Stephen Cairns. 2021. “Semantic City Planning Systems (SCPS): A Literature Review.” C4e-Preprint Series, no. 270 (April). https://como.ceb.cam.ac.uk/media/preprints/c4e-preprint-270.pdf.
[7] Richthofen, Aurel von, Ludovica Tomarchio, and Alberto Costa. 2019. “Identifying Communities Within the Smart-Cultural City of Singapore: A Network Analysis Approach.” Smart Cities 2 (1): 66–81. https://doi.org/10.3390/smartcities2010005.
This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.
