PN
Pirouz Nourian
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7 records found
1
HDSS
A Hospital Design Support System architecture
Hospital layout design plays a crucial role in ensuring operational efficiency. This study presents a Hospital Design Support System, a data-driven framework that integrates the Four-Step Transportation Model, Discrete-Event Simulation, and Exploratory Network Analysis to systematically assess hospital layouts. The HDSS evaluates four key operational criteria: spatial crowdedness, patient waiting times, patient walking distances, and difficulty in wayfinding. Hospitals exhibit spatial and operational characteristics akin to small cities and factories, making transportation planning and Discrete-Event Simulation highly applicable in evaluating hospital layout performances in terms of the four operational criteria. Exploratory Network Analysis further reveals the inherent structural tendencies that impact hospital efficiency and resilience. Additionally, evaluation mechanisms, including aggregation, relativisation, and interpretation, translate disaggregated simulation outputs into actionable metrics, enabling comparative assessment of design alternatives. This study contributes a systematic approach to hospital layout evaluation, offering valuable insights for architects and policymakers aiming to enhance hospital layout design.
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Hospital layout design plays a crucial role in ensuring operational efficiency. This study presents a Hospital Design Support System, a data-driven framework that integrates the Four-Step Transportation Model, Discrete-Event Simulation, and Exploratory Network Analysis to systematically assess hospital layouts. The HDSS evaluates four key operational criteria: spatial crowdedness, patient waiting times, patient walking distances, and difficulty in wayfinding. Hospitals exhibit spatial and operational characteristics akin to small cities and factories, making transportation planning and Discrete-Event Simulation highly applicable in evaluating hospital layout performances in terms of the four operational criteria. Exploratory Network Analysis further reveals the inherent structural tendencies that impact hospital efficiency and resilience. Additionally, evaluation mechanisms, including aggregation, relativisation, and interpretation, translate disaggregated simulation outputs into actionable metrics, enabling comparative assessment of design alternatives. This study contributes a systematic approach to hospital layout evaluation, offering valuable insights for architects and policymakers aiming to enhance hospital layout design.
IFC2BCM
A Tool for Generating IndoorGML and Building Configuration Model from IFC
IFC2BCM is a novel software tool designed to generate IndoorGML and Building Configuration Models (BCM) from IFC/BIM models. The primary motivation behind IFC2BCM is to develop a tool for generating BCM as the core foundation of a Spatial Design Support System that will evaluate layout designs of complex buildings such as hospitals regarding operational efficiency. The software addresses the need for detailed spatial network analysis and simulation modelling in complex environments, offering a semi-automatic process to convert IFC data into IndoorGML, and subsequently into a comprehensive BCM. The BCM generated by this tool consists of geometric, topological, semantic, and operational information, it supports applications such as space optimization, facility management, ensuring safety, and indoor navigation. More generally, the results are relevant to the study of complex buildings such as airports, transport hubs, public buildings, etc.
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IFC2BCM is a novel software tool designed to generate IndoorGML and Building Configuration Models (BCM) from IFC/BIM models. The primary motivation behind IFC2BCM is to develop a tool for generating BCM as the core foundation of a Spatial Design Support System that will evaluate layout designs of complex buildings such as hospitals regarding operational efficiency. The software addresses the need for detailed spatial network analysis and simulation modelling in complex environments, offering a semi-automatic process to convert IFC data into IndoorGML, and subsequently into a comprehensive BCM. The BCM generated by this tool consists of geometric, topological, semantic, and operational information, it supports applications such as space optimization, facility management, ensuring safety, and indoor navigation. More generally, the results are relevant to the study of complex buildings such as airports, transport hubs, public buildings, etc.
Hospital layout significantly influences hospital service quality, demanding robust tools for informed decision-making during the layout design stage. This study presents a novel Hospital Configuration Model as the foundational component of a Hospital Design Support System, which utilizes simulation modeling to provide evaluation mechanisms on hospital efficiencies and functionalities. The Hospital Configuration Model integrates four critical data types—geometric, topological, semantic, and operational—into a machine-readable digital twin, enabling comprehensive spatial and procedural analyses. The Hospital Configuration Model facilitates simulation modeling to optimize hospital layouts and predict performance metrics such as crowdingness, patient waiting times, patient walking distance, and difficulty in wayfinding. In conclusion, the Hospital Configuration Model is the core and foundation of developing the Hospital Design Support System for evaluating hospital functionalities and efficiencies, and the potential applications of the model include digital twin development, facility management, and safety enhancement. Future research directions should, in particular, include developing the proposed Hospital Design Support System and establishing a standard way of extracting hospital operational information into an industry-standard data model.
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Hospital layout significantly influences hospital service quality, demanding robust tools for informed decision-making during the layout design stage. This study presents a novel Hospital Configuration Model as the foundational component of a Hospital Design Support System, which utilizes simulation modeling to provide evaluation mechanisms on hospital efficiencies and functionalities. The Hospital Configuration Model integrates four critical data types—geometric, topological, semantic, and operational—into a machine-readable digital twin, enabling comprehensive spatial and procedural analyses. The Hospital Configuration Model facilitates simulation modeling to optimize hospital layouts and predict performance metrics such as crowdingness, patient waiting times, patient walking distance, and difficulty in wayfinding. In conclusion, the Hospital Configuration Model is the core and foundation of developing the Hospital Design Support System for evaluating hospital functionalities and efficiencies, and the potential applications of the model include digital twin development, facility management, and safety enhancement. Future research directions should, in particular, include developing the proposed Hospital Design Support System and establishing a standard way of extracting hospital operational information into an industry-standard data model.
This chapter presents methodological reflections on the necessity and utility of artificial intelligence (AI) in generative design. Specifically, the chapter discusses how generative design processes can be augmented by AI to deliver in terms of a few outcomes of interest or performance indicators while dealing with hundreds or thousands of small decisions. The core of the performance-based generative design paradigm is about making statistical or simulation-driven associations between these choices and their consequences for mapping and navigating such a complex decision space. This chapter will discuss promising directions in AI for augmenting decision-making processes in architectural design for mapping and navigating complex design spaces.
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This chapter presents methodological reflections on the necessity and utility of artificial intelligence (AI) in generative design. Specifically, the chapter discusses how generative design processes can be augmented by AI to deliver in terms of a few outcomes of interest or performance indicators while dealing with hundreds or thousands of small decisions. The core of the performance-based generative design paradigm is about making statistical or simulation-driven associations between these choices and their consequences for mapping and navigating such a complex decision space. This chapter will discuss promising directions in AI for augmenting decision-making processes in architectural design for mapping and navigating complex design spaces.
No Report, No Densification?
A Spatiotemporal Analysis of Urban Densification and Reporting Practices in World Heritage Properties
Journal article
(2024)
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Moses Katontoka, Francesca Noardo, Daniela Palacios-Lopez, Thomas Esch, Pirouz Nourian, Fulong Chen, Ana Pereira Roders
As urbanization accelerates, World Heritage properties, critical conservation areas, face a growing threat of urban densification, jeopardizing their Outstanding Universal Value (OUV). States Parties, the countries that have ratified the World Heritage Convention, are responsible for submitting periodic reports on the state-of-conservation of their World Heritage properties. These reports should explicitly address any instances of urban densification that may be occurring. But do they? This research investigates the relationship between urban densification and reporting practices in World Heritage properties over time and space. Through a spatiotemporal analysis, by analyzing changes in the built-up area within the core zones of cultural World Heritage properties from 1985 to 2015. We found that urban development, including housing, infrastructure, and tourism facilities, has significantly impacted World Heritage properties and an increase in built-up area can be observed especially in properties not reporting on urban threats.
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As urbanization accelerates, World Heritage properties, critical conservation areas, face a growing threat of urban densification, jeopardizing their Outstanding Universal Value (OUV). States Parties, the countries that have ratified the World Heritage Convention, are responsible for submitting periodic reports on the state-of-conservation of their World Heritage properties. These reports should explicitly address any instances of urban densification that may be occurring. But do they? This research investigates the relationship between urban densification and reporting practices in World Heritage properties over time and space. Through a spatiotemporal analysis, by analyzing changes in the built-up area within the core zones of cultural World Heritage properties from 1985 to 2015. We found that urban development, including housing, infrastructure, and tourism facilities, has significantly impacted World Heritage properties and an increase in built-up area can be observed especially in properties not reporting on urban threats.
Cultural heritage, especially those inscribed on the UNESCO World Heritage List, is meant to be valued by mankind and protected for future generations. Triggered by radical and sometimes disastrous Heritage-Related Events (HREs), communities around the world are actively involved on social media to share their opinions and emotional attachments. This paper presents exploratory data analyses on a dataset collected from Twitter concerning HREs in World Heritage that triggered global concerns, with cases of the Notre Dame Paris fire and the Venice flood, both in 2019. The spatiotemporal patterns of tweeting behaviours of online communities before, during, and after the event demonstrate a clear distinction of activation levels caused by the HREs. The dominant emotions and topics of people during the online debate are detected and visualized with pre-trained deep-learning models and unsupervised clustering algorithms. Clear spatiotemporal dynamics can be observed from the data collected in both case studies, while each case also demonstrated its specific characteristics due to the different severity. The methodological framework proposed and the analytical outcomes obtained in this paper could be used both in urban studies to mine the public opinions about HREs and other urban events for reducing risks, and by the Geo-AI community to test spatiotemporal clustering algorithms.
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Cultural heritage, especially those inscribed on the UNESCO World Heritage List, is meant to be valued by mankind and protected for future generations. Triggered by radical and sometimes disastrous Heritage-Related Events (HREs), communities around the world are actively involved on social media to share their opinions and emotional attachments. This paper presents exploratory data analyses on a dataset collected from Twitter concerning HREs in World Heritage that triggered global concerns, with cases of the Notre Dame Paris fire and the Venice flood, both in 2019. The spatiotemporal patterns of tweeting behaviours of online communities before, during, and after the event demonstrate a clear distinction of activation levels caused by the HREs. The dominant emotions and topics of people during the online debate are detected and visualized with pre-trained deep-learning models and unsupervised clustering algorithms. Clear spatiotemporal dynamics can be observed from the data collected in both case studies, while each case also demonstrated its specific characteristics due to the different severity. The methodological framework proposed and the analytical outcomes obtained in this paper could be used both in urban studies to mine the public opinions about HREs and other urban events for reducing risks, and by the Geo-AI community to test spatiotemporal clustering algorithms.
Screening the stones of Venice
Mapping social perceptions of cultural significance through graph-based semi-supervised classification
Mapping cultural significance of heritage properties in urban environment from the perspective of the public has become an increasingly relevant process, as highlighted by the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL). With the ubiquitous use of social media and the prosperous developments in machine and deep learning, it has become feasible to collect and process massive amounts of information produced by online communities about their perceptions of heritage as social constructs. Moreover, such information is usually inter-connected and embedded within specific socioeconomic and spatiotemporal contexts. This paper presents a methodological workflow for using semi-supervised learning with graph neural networks (GNN) to classify, summarize, and map cultural significance categories based on user-generated content on social media. Several GNN models were trained as an ensemble to incorporate the multi-modal (visual and textual) features and the contextual (temporal, spatial, and social) connections of social media data in an attributed multi-graph structure. The classification results with different models were aligned and evaluated with the prediction confidence and agreement. Furthermore, message diffusion methods on graphs were proposed to aggregate the post labels onto their adjacent spatial nodes, which helps to map the cultural significance categories in their geographical contexts. The workflow is tested on data gathered from Venice as a case study, demonstrating the generation of social perception maps for this UNESCO World Heritage property. This research framework could also be applied in other cities worldwide, contributing to more socially inclusive heritage management processes. Furthermore, the proposed methodology holds the potential of diffusing any human-generated location-based information onto spatial networks and temporal timelines, which could be beneficial for measuring the safety, vitality, and/or popularity of urban spaces.
...
Mapping cultural significance of heritage properties in urban environment from the perspective of the public has become an increasingly relevant process, as highlighted by the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL). With the ubiquitous use of social media and the prosperous developments in machine and deep learning, it has become feasible to collect and process massive amounts of information produced by online communities about their perceptions of heritage as social constructs. Moreover, such information is usually inter-connected and embedded within specific socioeconomic and spatiotemporal contexts. This paper presents a methodological workflow for using semi-supervised learning with graph neural networks (GNN) to classify, summarize, and map cultural significance categories based on user-generated content on social media. Several GNN models were trained as an ensemble to incorporate the multi-modal (visual and textual) features and the contextual (temporal, spatial, and social) connections of social media data in an attributed multi-graph structure. The classification results with different models were aligned and evaluated with the prediction confidence and agreement. Furthermore, message diffusion methods on graphs were proposed to aggregate the post labels onto their adjacent spatial nodes, which helps to map the cultural significance categories in their geographical contexts. The workflow is tested on data gathered from Venice as a case study, demonstrating the generation of social perception maps for this UNESCO World Heritage property. This research framework could also be applied in other cities worldwide, contributing to more socially inclusive heritage management processes. Furthermore, the proposed methodology holds the potential of diffusing any human-generated location-based information onto spatial networks and temporal timelines, which could be beneficial for measuring the safety, vitality, and/or popularity of urban spaces.