N. Bai
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1
This study introduces a literature review tool for abstract-screening in urban studies. It presents two pipelines that focus on linking geoparsing outputs and hierarchical toponym aggregation. Pipeline1 uses batched matching with GeoNames for fast but coarse aggregation. Pipeline2 enhances the accuracy by incorporating a toponym resolution model to address geo/geo ambiguity, and semantic checks to correct potential resolution errors. Evaluated on 500 abstracts, Pipeline2 achieves a precision of 0.96 and a recall of 0.98 for aggregated toponym output.
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This study introduces a literature review tool for abstract-screening in urban studies. It presents two pipelines that focus on linking geoparsing outputs and hierarchical toponym aggregation. Pipeline1 uses batched matching with GeoNames for fast but coarse aggregation. Pipeline2 enhances the accuracy by incorporating a toponym resolution model to address geo/geo ambiguity, and semantic checks to correct potential resolution errors. Evaluated on 500 abstracts, Pipeline2 achieves a precision of 0.96 and a recall of 0.98 for aggregated toponym output.
Preface
48e European Conference on Information Retrieval (ECIR 2026)
Journal article
(2026)
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Ricardo Campos, Adam Jatowt, Yanyan Lan, Mohammad Aliannejadi, Christine Bauer, Sean MacAvaney, Avishek Anand, Nan Bai, Masoud Mansoury, More Authors
Foreword postscript
(2026)
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Ricardo Campos, Adam Jatowt, Yanyan Lan, Mohammad Aliannejadi, Christine Bauer, Sean MacAvaney, Avishek Anand, Nan Bai, Masoud Mansoury, More Authors
Preface
Proceedings van de 48e European Conference on Information Retrieval (ECIR 2026),
Journal article
(2026)
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Ricardo Campos, Adam Jatowt, Yanyan Lan, Mohammad Aliannejadi, Christine Bauer, Sean MacAvaney, Avishek Anand, Nan Bai, Masoud Mansoury
Introducing a Perceptual–Spatial Landscape Planning Model (PSLPM) for cultural landscapes’ route optimization
The case of Chengde Mountain Resort
As cultural landscapes increasingly evolve within urban environments, a persistent gap has emerged between their heritage spatial characteristics and public heritage value perception, undermining urban identity and sustainability. This study introduces the Perceptual–Spatial Landscape Planning Model, an integrative framework that positions route optimization as a strategic planning intervention to bridge this gap. Taking Chengde Mountain Resort as a case, we constructed a spatial network of 144 scenes and collected 43,879 user-generated social media comments to quantify public perception. We achieved perceptual–spatial coupling through: 1) a few-shot learning paradigm based on a large language model generates five distinct perception scores of each scene; 2) the resulting scores are then integrated with heritage spatial characteristics in graph neural networks using graph attention networks and deep graph infomax. Based on the coupling process, the optimized routes were generated by a multi-objective evolutionary algorithm. The optimized routes outperformed official and greedy baselines, achieving higher spatial diversity, perceptual coherence, lower variance, and broader coverage of marginal yet meaningful scenes. These routes avoided overconcentration in perceptual hotspots as conventional tourism planning while enhancing interpretive richness across various spaces. This model provides strong contextual transferability, offering interpretable framework for integrating public perception into cultural landscape planning, thereby advancing both the methodological rigor and spatial intelligence of cultural landscapes’ development in urban environments.
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As cultural landscapes increasingly evolve within urban environments, a persistent gap has emerged between their heritage spatial characteristics and public heritage value perception, undermining urban identity and sustainability. This study introduces the Perceptual–Spatial Landscape Planning Model, an integrative framework that positions route optimization as a strategic planning intervention to bridge this gap. Taking Chengde Mountain Resort as a case, we constructed a spatial network of 144 scenes and collected 43,879 user-generated social media comments to quantify public perception. We achieved perceptual–spatial coupling through: 1) a few-shot learning paradigm based on a large language model generates five distinct perception scores of each scene; 2) the resulting scores are then integrated with heritage spatial characteristics in graph neural networks using graph attention networks and deep graph infomax. Based on the coupling process, the optimized routes were generated by a multi-objective evolutionary algorithm. The optimized routes outperformed official and greedy baselines, achieving higher spatial diversity, perceptual coherence, lower variance, and broader coverage of marginal yet meaningful scenes. These routes avoided overconcentration in perceptual hotspots as conventional tourism planning while enhancing interpretive richness across various spaces. This model provides strong contextual transferability, offering interpretable framework for integrating public perception into cultural landscape planning, thereby advancing both the methodological rigor and spatial intelligence of cultural landscapes’ development in urban environments.
Diasporic communities and their heritage are vital in shaping cultural diversity in urban planning and management. However, diasporic heritage management overlooked cross-geographical networks that constitute cultural significance. This study develops a workflow to explore diasporic heritage’s spatial and semantic networks. Global social media data about Chinatowns on Flickr is collected and analysed through Named Entity Recognition, and Spatial and Semantic Network Analysis. Findings reveal the diasporic flows based on places frequently recognised as origin and destination of Chinese diasporic heritage, and the distribution of places, zooming in from worldwide to the Netherlands. Semantic networks in different Dutch cities are compared.
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Diasporic communities and their heritage are vital in shaping cultural diversity in urban planning and management. However, diasporic heritage management overlooked cross-geographical networks that constitute cultural significance. This study develops a workflow to explore diasporic heritage’s spatial and semantic networks. Global social media data about Chinatowns on Flickr is collected and analysed through Named Entity Recognition, and Spatial and Semantic Network Analysis. Findings reveal the diasporic flows based on places frequently recognised as origin and destination of Chinese diasporic heritage, and the distribution of places, zooming in from worldwide to the Netherlands. Semantic networks in different Dutch cities are compared.
Preprint
(2025)
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Maarten de Rijke, Bart van den Hurk, Flora Salim, Alaa Al Khourdajie, Nan Bai, Renato Calzone, Declan Curran, Getnet Demil, Lesley Frew, More Authors...
The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and discuss core research problems in information retrieval to assess climate change impacts. The workshop aimed to foster collaboration by bringing communities together that have so far not been very well connected -- information retrieval, natural language processing, systematic reviews, impact assessments, and climate science. The workshop brought together a diverse set of researchers and practitioners interested in contributing to the development of a technical research agenda for information retrieval to assess climate change impacts.
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The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and discuss core research problems in information retrieval to assess climate change impacts. The workshop aimed to foster collaboration by bringing communities together that have so far not been very well connected -- information retrieval, natural language processing, systematic reviews, impact assessments, and climate science. The workshop brought together a diverse set of researchers and practitioners interested in contributing to the development of a technical research agenda for information retrieval to assess climate change impacts.
Journal article
(2025)
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Maarten de Rijke, Bart Van Den Hurk, Flora Salim, Alaa Al Khourdajie, Nan Bai, Renato Calzone, Declan Curran, Getnet Demil, Lesley Frew, More Authors...
The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and discuss core research problems in information retrieval to assess climate change impacts. The workshop aimed to foster collaboration by bringing communities together that have so far not been very well connected - information retrieval, natural language processing, systematic reviews, impact assessments, and climate science. The workshop brought together a diverse set of researchers and practitioners interested in contributing to the development of a technical research agenda for information retrieval to assess climate change impacts.
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The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and discuss core research problems in information retrieval to assess climate change impacts. The workshop aimed to foster collaboration by bringing communities together that have so far not been very well connected - information retrieval, natural language processing, systematic reviews, impact assessments, and climate science. The workshop brought together a diverse set of researchers and practitioners interested in contributing to the development of a technical research agenda for information retrieval to assess climate change impacts.
Unveiling Climate-Adaptive World Heritage Management Strategies
The Netherlands as a Case Study
The Netherlands has established climate-adaptive strategies shaped by its long history of water-related climate events, such as the floods in 1421 and 1953. UNESCO World Heritage (WH) properties in The Netherlands reflect centuries of human intervention and natural processes to adapt and mitigate climate challenges, including spatial design and hydraulic engineering. The Dutch Climate Research Initiative also highlights cultural heritage as an integral component in preparing for the 2026 National Climate Adaptation Strategy. This article aims to unveil climate-adaptive World Heritage management strategies (CAWHMSs), using WH properties in The Netherlands as a case study. It collects textual data from Statements of Outstanding Universal Value, State of Conservation Reports by the State Parties and management plans. Through qualitative coding and keywords aggregation of the documents, the visualised results of a Sankey diagram and two semantic networks confirmed two CAWHMSs: conservation and developing WH properties as collaborative knowledge hubs. Conservation supports regulating urban climate and sustainable water management. As collaborative knowledge hubs, multidisciplinary sectors explore opportunities to align WH properties with broader sustainable development initiatives. They also deepen younger generations’ awareness of cultural and natural significance relevant to mitigating climate threats. The results emphasise WH as a contributor to climate adaptation. Cross-sectoral stakeholders can advance holistic climate adaptation efforts using CAWHMSs.
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The Netherlands has established climate-adaptive strategies shaped by its long history of water-related climate events, such as the floods in 1421 and 1953. UNESCO World Heritage (WH) properties in The Netherlands reflect centuries of human intervention and natural processes to adapt and mitigate climate challenges, including spatial design and hydraulic engineering. The Dutch Climate Research Initiative also highlights cultural heritage as an integral component in preparing for the 2026 National Climate Adaptation Strategy. This article aims to unveil climate-adaptive World Heritage management strategies (CAWHMSs), using WH properties in The Netherlands as a case study. It collects textual data from Statements of Outstanding Universal Value, State of Conservation Reports by the State Parties and management plans. Through qualitative coding and keywords aggregation of the documents, the visualised results of a Sankey diagram and two semantic networks confirmed two CAWHMSs: conservation and developing WH properties as collaborative knowledge hubs. Conservation supports regulating urban climate and sustainable water management. As collaborative knowledge hubs, multidisciplinary sectors explore opportunities to align WH properties with broader sustainable development initiatives. They also deepen younger generations’ awareness of cultural and natural significance relevant to mitigating climate threats. The results emphasise WH as a contributor to climate adaptation. Cross-sectoral stakeholders can advance holistic climate adaptation efforts using CAWHMSs.
Mapping the Global Concerns of Sea Level Rise on Twitter
A Multimodal Framing Perspective for Urban Sensing
Sea level rise (SLR) is a global concern under climate change. Understanding how SLR is discussed, depicted, and framed helps urban management and public governance make more informed decisions towards climate actions and communicate more effectively. A multimodal dataset of tweets containing visuals and texts is collected, mapped, and analysed with multimodal topic modelling. It shows that SLR discussion mainly concentrates on coastal cities from the West and that complex themes are brought to the spotlight, including but not limited to risks, actions, and doubtful objections. The framework can be extended for other climate impacts, informative for climate-responsive urban management.
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Sea level rise (SLR) is a global concern under climate change. Understanding how SLR is discussed, depicted, and framed helps urban management and public governance make more informed decisions towards climate actions and communicate more effectively. A multimodal dataset of tweets containing visuals and texts is collected, mapped, and analysed with multimodal topic modelling. It shows that SLR discussion mainly concentrates on coastal cities from the West and that complex themes are brought to the spotlight, including but not limited to risks, actions, and doubtful objections. The framework can be extended for other climate impacts, informative for climate-responsive urban management.
Understanding historic gardens for the sustainable land management of cultural landscapes
Chengde Mountain Resort (CMR) as a case study
Journal article
(2025)
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J.S. Lian, S. Nijhuis, N. Bai, G. Bracken, H. Zhang, Xiangyan Wu, Dong Chen, Jingyu Li
The concept of historic gardens has gradually expanded to encompass a broader range of landscape meanings. UNESCO's cultural landscape categories have significantly influenced land policy improvements in the context of globalization, with historic gardens being classified as Category 1 cultural landscapes. The other categories are organically evolved landscapes (Category 2) and associative cultural landscapes (Category 3). While existing studies have primarily focused on each of these categories individually, it remains unclear how to characterize a cultural landscape when all three categories coexist and influence each other, as seen in complex cases such as the Chengde Mountain Resort (CMR). Furthermore, strategies for improving sustainable land management based on this understanding are still lacking. This study uses landscape mapping to collect data, digitally reconstruct, and characterize cultural landscapes in the CMR based on four environmental factors: topography, accessibility, visibility, and land use changes. Based on this, we illustrate the evolution of the CMR through reconstruction, capturing four phases detailed in 144 scenes. From this, we identify six distinct groups of scenes with six targeted indicators, each reflecting specific spatial attributes of Category 1. Additionally, statistical and comparative analyses of land use changes illuminate various landscape dynamics of these scenes that correspond to Categories 2 and 3. The discussion presents a systematic sustainable pathway to characterize the interdependencies among UNESCO’s three cultural landscape categories. Based on these findings, this research proposes a three-level management model that connects dynamic authenticity and modern functionality, offering insights for urban policymakers navigating pluralistic cultural landscapes.
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The concept of historic gardens has gradually expanded to encompass a broader range of landscape meanings. UNESCO's cultural landscape categories have significantly influenced land policy improvements in the context of globalization, with historic gardens being classified as Category 1 cultural landscapes. The other categories are organically evolved landscapes (Category 2) and associative cultural landscapes (Category 3). While existing studies have primarily focused on each of these categories individually, it remains unclear how to characterize a cultural landscape when all three categories coexist and influence each other, as seen in complex cases such as the Chengde Mountain Resort (CMR). Furthermore, strategies for improving sustainable land management based on this understanding are still lacking. This study uses landscape mapping to collect data, digitally reconstruct, and characterize cultural landscapes in the CMR based on four environmental factors: topography, accessibility, visibility, and land use changes. Based on this, we illustrate the evolution of the CMR through reconstruction, capturing four phases detailed in 144 scenes. From this, we identify six distinct groups of scenes with six targeted indicators, each reflecting specific spatial attributes of Category 1. Additionally, statistical and comparative analyses of land use changes illuminate various landscape dynamics of these scenes that correspond to Categories 2 and 3. The discussion presents a systematic sustainable pathway to characterize the interdependencies among UNESCO’s three cultural landscape categories. Based on these findings, this research proposes a three-level management model that connects dynamic authenticity and modern functionality, offering insights for urban policymakers navigating pluralistic cultural landscapes.
UNESCO World Heritage (WH) properties are increasingly vulnerable to challenges caused by climate change, which requires them to balance the needs of heritage management with sustainable urban growth and climate change adaptation (CCA). CCA strategies are being developed by stakeholders at all levels. It is, however, not customary to generalise strategies developed in one property to another, since they are assumed to be highly localised. This paper takes the WH properties in the Netherlands as an example and showcases that topics relevant to CCA, such as climate change challenges and water management strategies, are being shared among properties in their heritage management plans, aiming to safeguard their Outstanding Universal Value. Sentence embeddings computed with cutting-edge Natural Language Processing models are used to retrieve similar topics among the properties and evaluate their associations. Common challenges (such as low groundwater level) and strategies (such as coastal dunes and dykes) are found to be mentioned in different properties. The methodological framework proposed in this paper, bridging CCA with WH management, can be repeated in other countries, and eventually at the global level, providing a generalisable integrated knowledge system beneficial and easily applicable in heritage properties from broad geographical and cultural contexts.
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UNESCO World Heritage (WH) properties are increasingly vulnerable to challenges caused by climate change, which requires them to balance the needs of heritage management with sustainable urban growth and climate change adaptation (CCA). CCA strategies are being developed by stakeholders at all levels. It is, however, not customary to generalise strategies developed in one property to another, since they are assumed to be highly localised. This paper takes the WH properties in the Netherlands as an example and showcases that topics relevant to CCA, such as climate change challenges and water management strategies, are being shared among properties in their heritage management plans, aiming to safeguard their Outstanding Universal Value. Sentence embeddings computed with cutting-edge Natural Language Processing models are used to retrieve similar topics among the properties and evaluate their associations. Common challenges (such as low groundwater level) and strategies (such as coastal dunes and dykes) are found to be mentioned in different properties. The methodological framework proposed in this paper, bridging CCA with WH management, can be repeated in other countries, and eventually at the global level, providing a generalisable integrated knowledge system beneficial and easily applicable in heritage properties from broad geographical and cultural contexts.
Journal article
(2025)
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Denise J. Roth, Nan Bai, Robbert Biesbroek, Art Dewulf, Sanne Kruikemeier, Daan de Leur, Mariken A. C. G. van der Velden, Erik de Vries, Rens Vliegenthart
Climate change adaptation occurs within a complex interplay of science, media, politics, policy, and the public. Using comparative longitudinal data (2012–2021) from online newspaper articles, parliamentary debates, policy documents, and social media, we analyze agenda-setting dynamics in the United Kingdom and the Netherlands. Vector autoregression models, incorporating scientific output as an exogenous variable, reveal traditional agenda-setting effects in both countries. In the Netherlands, however, social media positively influences traditional media, while in the United Kingdom, traditional media negatively affects social media. These findings enrich our understanding of the factors shaping public awareness and policy responses to this critical global issue.
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Climate change adaptation occurs within a complex interplay of science, media, politics, policy, and the public. Using comparative longitudinal data (2012–2021) from online newspaper articles, parliamentary debates, policy documents, and social media, we analyze agenda-setting dynamics in the United Kingdom and the Netherlands. Vector autoregression models, incorporating scientific output as an exogenous variable, reveal traditional agenda-setting effects in both countries. In the Netherlands, however, social media positively influences traditional media, while in the United Kingdom, traditional media negatively affects social media. These findings enrich our understanding of the factors shaping public awareness and policy responses to this critical global issue.
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.
Climate change is a heated discussion topic in public arenas such as social media. Both texts and visuals play key roles in the debate, as they can complement, contradict, or reinforce each other in nuanced ways. It is therefore urgently needed to study the messages as multimodal objects to better understand the polarized debate about climate change impacts and policies. Multimodal representation models such as CLIP are known to be able to transfer knowledge across domains and modalities, enabling the investigation of textual and visual semantics together. Yet they are not directly able to distinguish the nuances between supporting and sceptic climate change stances. This paper explores a simple but effective strategy combining modality fusion and domain-knowledge enhancing to prepare CLIP-based models with knowledge of climate change stances. A multimodal Dutch Twitter dataset is collected and experimented with the proposed strategy, which increased the macro-average F1 score across stances from 51% to 86%. The outcomes can be applied in both data science and public policy studies, to better analyse how the combined use of texts and visuals generates meanings during debates, in the context of climate change and beyond.
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Climate change is a heated discussion topic in public arenas such as social media. Both texts and visuals play key roles in the debate, as they can complement, contradict, or reinforce each other in nuanced ways. It is therefore urgently needed to study the messages as multimodal objects to better understand the polarized debate about climate change impacts and policies. Multimodal representation models such as CLIP are known to be able to transfer knowledge across domains and modalities, enabling the investigation of textual and visual semantics together. Yet they are not directly able to distinguish the nuances between supporting and sceptic climate change stances. This paper explores a simple but effective strategy combining modality fusion and domain-knowledge enhancing to prepare CLIP-based models with knowledge of climate change stances. A multimodal Dutch Twitter dataset is collected and experimented with the proposed strategy, which increased the macro-average F1 score across stances from 51% to 86%. The outcomes can be applied in both data science and public policy studies, to better analyse how the combined use of texts and visuals generates meanings during debates, in the context of climate change and beyond.
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.
How can we assess the ergonomic comfort of a sizeable spatial configuration such as the indoor space of a complex building or an urban landscape when we design, plan, and manage the space? Is there a fundamental difference between indoor [architectural] spatial configurations and outdoor [urban] spatial configurations with respect to ergonomics? Can we have a unified approach to the computational study of spatial ergonomics? This paper addresses these fundamental questions while providing a brief taxonomic review of the scholarly literature on these matters from a mathematical point of view, including a brief introduction to the modelling-based approaches to the computational ways of studying the fundamental effects of spatial configuration on human behaviours. Furthermore, the paper proposes a computational approach for ergonomic assessment of spatial configurations that explicitly allows for combined accessibility and visibility analyses in the built environment. The gist of this approach is the conceptualisation of spatial configurations as rasterised (voxelated) 2D manifold walkable terrains whose voxels have 3D vistas, unifying the simulations and analyses of accessibility and visibility. The paper elaborates on how such a representation of space can provide for conducting various sorts of computational queries, analyses, and simulation experiments for research in spatial ergonomics. The paper concludes with a mapping of the computational modelling approaches pertinent to the study and assessment of spatial ergonomics; and marks avenues of future research on various categories of exploratory, generative, and associative models for ex-ante and ex-post assessment of ergonomic matters at spatial scales.
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How can we assess the ergonomic comfort of a sizeable spatial configuration such as the indoor space of a complex building or an urban landscape when we design, plan, and manage the space? Is there a fundamental difference between indoor [architectural] spatial configurations and outdoor [urban] spatial configurations with respect to ergonomics? Can we have a unified approach to the computational study of spatial ergonomics? This paper addresses these fundamental questions while providing a brief taxonomic review of the scholarly literature on these matters from a mathematical point of view, including a brief introduction to the modelling-based approaches to the computational ways of studying the fundamental effects of spatial configuration on human behaviours. Furthermore, the paper proposes a computational approach for ergonomic assessment of spatial configurations that explicitly allows for combined accessibility and visibility analyses in the built environment. The gist of this approach is the conceptualisation of spatial configurations as rasterised (voxelated) 2D manifold walkable terrains whose voxels have 3D vistas, unifying the simulations and analyses of accessibility and visibility. The paper elaborates on how such a representation of space can provide for conducting various sorts of computational queries, analyses, and simulation experiments for research in spatial ergonomics. The paper concludes with a mapping of the computational modelling approaches pertinent to the study and assessment of spatial ergonomics; and marks avenues of future research on various categories of exploratory, generative, and associative models for ex-ante and ex-post assessment of ergonomic matters at spatial scales.
EquiCity game
A mathematical serious game for participatory design of spatial configurations
Journal article
(2024)
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Pirouz Nourian, Shervin Azadi, Nan Bai, Bruno de Andrade, Nour Abu Zaid, Samaneh Rezvani, A. Pereira Roders
We propose a mathematical framework for developing social-choice games that are designed to mediate decision-making processes for city planning, urban area redevelopment, and architectural configuration of urban housing complexes. The proposed framework features a digital serious gaming approach for participatory design to support transparency and inclusion in the process of decision-making and ensure an equitable balance of sustainable development goals in spatial design outcomes. The mathematical process consists of a Markovian design machine for balancing the design decisions of actors, a massing configurator equipped with fuzzy logic and multi-criteria decision analysis, algebraic graph-theoretical accessibility evaluators, and automated solar-climatic evaluators using geospatial computational geometry. We demonstrate the effectiveness of the framework by implementing a multi-player online game that facilitates a participatory decision-making workshop for forming multi-functional building complexes by providing a generative configurator equipped with automated appraisal/scoring mechanisms for revealing the aggregate impact of alternatives. The EquiCity game empowers a group of decision-makers to reach a fair consensual spatial design by mathematically simulating many rounds of reasonable trade-offs between their decisions, with different levels of interest or control over various types of investments. The novelty of the framework is in its capability to encompass decision-making about the most idiosyncratic aspects of a site related to its heritage status and cultural significance to the most generic aspects such as balancing access to sunlight for the site while respecting ‘the right to sunlight’ of the neighbours of the site, ensuring coherence of the entire configuration with regards to a network of desired closeness ratings, the satisfaction of a programme of requirements, and intricately balancing individual development goals in conjunction with communal goals and environmental design codes.
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We propose a mathematical framework for developing social-choice games that are designed to mediate decision-making processes for city planning, urban area redevelopment, and architectural configuration of urban housing complexes. The proposed framework features a digital serious gaming approach for participatory design to support transparency and inclusion in the process of decision-making and ensure an equitable balance of sustainable development goals in spatial design outcomes. The mathematical process consists of a Markovian design machine for balancing the design decisions of actors, a massing configurator equipped with fuzzy logic and multi-criteria decision analysis, algebraic graph-theoretical accessibility evaluators, and automated solar-climatic evaluators using geospatial computational geometry. We demonstrate the effectiveness of the framework by implementing a multi-player online game that facilitates a participatory decision-making workshop for forming multi-functional building complexes by providing a generative configurator equipped with automated appraisal/scoring mechanisms for revealing the aggregate impact of alternatives. The EquiCity game empowers a group of decision-makers to reach a fair consensual spatial design by mathematically simulating many rounds of reasonable trade-offs between their decisions, with different levels of interest or control over various types of investments. The novelty of the framework is in its capability to encompass decision-making about the most idiosyncratic aspects of a site related to its heritage status and cultural significance to the most generic aspects such as balancing access to sunlight for the site while respecting ‘the right to sunlight’ of the neighbours of the site, ensuring coherence of the entire configuration with regards to a network of desired closeness ratings, the satisfaction of a programme of requirements, and intricately balancing individual development goals in conjunction with communal goals and environmental design codes.
Conference paper
(2023)
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N. Bai, Marta Ducci, Rusudan Mirzikashvili, Pirouz Nourian, A. Pereira Roders
The UNESCO 2011 Recommendation on the Historic Urban Landscape promotes to map cultural significance of urban heritage from the perspectives of the general public in pursuit of social inclusion in heritage management. The user-generated information already available on social media platforms in the form of images, comments, and ratings can be considered a rich source for collecting data concerning the tourists’ image of destinations and their collective perception of urban cultural heritage. Considering the large amount of unstructured data, artificial intelligence (AI) can construct structured feature vectors therefrom and significantly aid the analysis and collation processes compared to the traditional manual approach for mapping public perception of cultural heritage. This paper presents an exploratory case study conducted in the area of Testaccio, Rome, showcasing the use of AI to map the perceived and narrated urban heritage images using social media data. An image-sharing platform, Flickr, is used to collect thousands of posts containing images and comments in the area, which are further analysed with pre-trained image recognition, natural language processing, and dimensionality reduction algorithms. Results as the urban heritage images are visualised, showing the most significant elements from a public perspective. Such a methodology provides an alternative perspective of viewing the urban heritage attributes as a collection of depicted and posted content. It can contribute as a tool for the documentation of collective attention for inclusive heritage management and local development planning during the designing and policy-making processes.
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The UNESCO 2011 Recommendation on the Historic Urban Landscape promotes to map cultural significance of urban heritage from the perspectives of the general public in pursuit of social inclusion in heritage management. The user-generated information already available on social media platforms in the form of images, comments, and ratings can be considered a rich source for collecting data concerning the tourists’ image of destinations and their collective perception of urban cultural heritage. Considering the large amount of unstructured data, artificial intelligence (AI) can construct structured feature vectors therefrom and significantly aid the analysis and collation processes compared to the traditional manual approach for mapping public perception of cultural heritage. This paper presents an exploratory case study conducted in the area of Testaccio, Rome, showcasing the use of AI to map the perceived and narrated urban heritage images using social media data. An image-sharing platform, Flickr, is used to collect thousands of posts containing images and comments in the area, which are further analysed with pre-trained image recognition, natural language processing, and dimensionality reduction algorithms. Results as the urban heritage images are visualised, showing the most significant elements from a public perspective. Such a methodology provides an alternative perspective of viewing the urban heritage attributes as a collection of depicted and posted content. It can contribute as a tool for the documentation of collective attention for inclusive heritage management and local development planning during the designing and policy-making processes.
Sensing the Cultural Significance with AI for Social Inclusion
A Computational Spatiotemporal Network-based Framework of Heritage Knowledge Documentation using User-Generated Content
Social Inclusion has been growing as a goal in heritage management. Whereas the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL) called for tools of knowledge documentation, social media already functions as a platform for online communities to actively involve themselves in heritage-related discussions. Such discussions happen both in “baseline scenarios” when people calmly share their experiences about the cities they live in or travel to, and in “activated scenarios” when radical events trigger their emotions. To organize, process, and analyse the massive unstructured multi-modal (mainly images and texts) user-generated data from social media efficiently and systematically, Artificial Intelligence (AI) is shown to be indispensable. This thesis explores the use of AI in a methodological framework to include the contribution of a larger and more diverse group of participants with user-generated data. It is an interdisciplinary study integrating methods and knowledge from heritage studies, computer science, social sciences, network science, and spatial analysis. AI models were applied, nurtured, and tested, helping to analyse the massive information content to derive the knowledge of cultural significance perceived by online communities. The framework was tested in case study cities including Venice, Paris, Suzhou, Amsterdam, and Rome for the baseline and/or activated scenarios. The AI-based methodological framework proposed in this thesis is shown to be able to collect information in cities and map the knowledge of the communities about cultural significance, fulfilling the expectation and requirement of HUL, useful and informative for future socially inclusive heritage management processes.
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Social Inclusion has been growing as a goal in heritage management. Whereas the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL) called for tools of knowledge documentation, social media already functions as a platform for online communities to actively involve themselves in heritage-related discussions. Such discussions happen both in “baseline scenarios” when people calmly share their experiences about the cities they live in or travel to, and in “activated scenarios” when radical events trigger their emotions. To organize, process, and analyse the massive unstructured multi-modal (mainly images and texts) user-generated data from social media efficiently and systematically, Artificial Intelligence (AI) is shown to be indispensable. This thesis explores the use of AI in a methodological framework to include the contribution of a larger and more diverse group of participants with user-generated data. It is an interdisciplinary study integrating methods and knowledge from heritage studies, computer science, social sciences, network science, and spatial analysis. AI models were applied, nurtured, and tested, helping to analyse the massive information content to derive the knowledge of cultural significance perceived by online communities. The framework was tested in case study cities including Venice, Paris, Suzhou, Amsterdam, and Rome for the baseline and/or activated scenarios. The AI-based methodological framework proposed in this thesis is shown to be able to collect information in cities and map the knowledge of the communities about cultural significance, fulfilling the expectation and requirement of HUL, useful and informative for future socially inclusive heritage management processes.