Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	ecaade2022_168
id	ecaade2022_168
authors	Abdulmawla, Abdulmalik, Schneider, Sven, Koenig, Reinhard, Bielik, Martin and Fuchkina, Ekaterina
year	2022
title	Parametric Urban Data Structuring and Spatial Query - Advanced data mapping and selection methods for parametric modelling environments
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 277–286
doi	https://doi.org/10.52842/conf.ecaade.2022.2.277
summary	This paper presents a method for organising urban data inside the CAD environment into a hierarchical structure, which promotes the ease of transferring information between all available urban elements, from streets to buildings passing by the plots and blocks. This is done using parametric methods that map the urban data using the available CAD and GIS records. Finally, the paper presents a couple of example scenarios where such methods are most needed and how much they could facilitate more detailed and complex data to be accessed, compared, and analysed.
keywords	Urban Query, Urban Geometry, Spatial Mapping
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_33
id	caadria2022_33
authors	Alva, Pradeep, Mosteiro-Romero, Martin, Miller, Clayton and Stouffs, Rudi
year	2022
title	Digital Twin-Based Resilience Evaluation of District-Scale Archetypes
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 525-534
doi	https://doi.org/10.52842/conf.caadria.2022.1.525
summary	District-scale energy demand models can be powerful tools for understanding interactions in complex urban areas and optimising energy systems in new developments. The process of coupling characteristics of urban environments with simulation software to achieve accurate results is nascent. We developed a digital twin through a web map application for a 170ha district-scale university campus as a pilot. The impact on the built environment is simulated with pandemic (COVID-19) and climate change scenarios. The former can be observed through varying occupancy rates and average cooling loads in the buildings during the lockdown period. The digital twin dashboard was built with visualisations of the 3D campus, real-time data from sensors, energy demand simulation results from the City Energy Analyst (CEA) tool, and occupancy rates from WiFi data. The ongoing work focuses on formulating a resilience assessment metric to measure the robustness of buildings to these disruptions. This district-scale digital twin demonstration can help in facilities management and planning applications. The results show that the digital twin approach can support decarbonising initiatives for cities.
keywords	Digital twin, City Information Modelling, Planning Support System, energy demand model, SGD 11, SGD 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_293
id	cdrf2022_293
authors	Amal Algamdey, Aleksander Mastalski, Angelos Chronis, Amar Gurung, Felipe Romero Vargas, German Bodenbender, and Lea Khairallah
year	2022
title	AI Urban Voids: A Data-Driven Approach to Urban Activation
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_26
summary	With the development of digital technologies, big urban data is now readily available online. This opens the opportunity to utilize new data and create new relationships within multiple urban features for cities. Moreover, new computational design techniques open a new portal for architects and designers to reinterpret this urban data and provide much better-informed design decisions. The “AI Urban Voids'' project is defined as a data-driven approach to analyze and predict the strategic location for urban uses in the addition of amenities within the city. The location of these urban amenities is evaluated based on predictions and scores followed by a series of urban analyses and simulations using K-Means clustering. Furthermore, these results are then visualized in a web-based platform; likewise, the aim is to create a tool that will work on a feedback loop system that constantly updates the information. This paper explains the use of different datasets from Five cities including Melbourne, Sydney, Berlin, Warsaw, and Sao Paulo. Python, Osmx libraries and K-means clustering open the way to manipulate large data sets by introducing a collection of computational processes that can override traditional urban analysis.
series	cdrf
email
last changed	2024/05/29 14:02

_id	caadria2022_167
id	caadria2022_167
authors	Aman, Jayedi, Matisziw, Timothy C, Kim, Jong Bum and Luo, Dan
year	2022
title	Sensing the City: Leveraging Geotagged Social Media Posts and Street View Imagery to Model Urban Streetscapes Using Deep Neural Networks
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 595-604
doi	https://doi.org/10.52842/conf.caadria.2022.1.595
summary	Understanding the relationships between individuals and the urban streetscape is an essential component of sustainable city planning. However, analysis of these relationships involves accounting for a complex mix of human behaviour, perception, as well as geospatial context. In this context, a comprehensive framework for predicting preferred streetscape characteristics utilizing deep learning and geospatial techniques is proposed. Geotagged social media posts and street view imagery are employed to account for individual sentiment and geospatial context. Natural Language Processing (NLP) and computer vision (CV) are then used to infer sentiment and model the visual environment within which individuals make posts to social media. An application of the developed framework is provided using Instagram posts and Google Street View imagery of the urban environment. A spatial analysis is conducted to assess the extent to which urban attributes correlate with the sentiment of social media postings. The results shed light on sustainable streetscape planning by focusing on the relationship between users and the built environment in a complex urban setting. Finally, limitations of the developed methodology as well as future directions are discussed.
keywords	Urban sustainability, data mining, pedestrian sentiments, transportation behavior, street level imagery, transformers, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_47
id	caadria2022_47
authors	An, Yudi
year	2022
title	Impact of Covid-19 on Associations between Land Use and Bike-Sharing Usage
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 605-614
doi	https://doi.org/10.52842/conf.caadria.2022.1.605
summary	Bike-sharing as a human-centred, zero-emission, sustainable, alternative, and easily accessible transport mode has been implemented globally and consistently contributing to communities and the environment by alleviating consumption of natural sources, traffic congestion, and air pollution, which is considered a solution for future cities. The appearance of Covid-19 significantly impacts public transportation modes, including the bike-sharing system. The intention of this study was to investigate the spatiotemporal impact of the Covid-19 pandemic on associations between urban factors and bike-sharing usage in Los Angeles, United States, by analysing a sizeable actual trip dataset and employing geographically weighted regression (GWR) models. GWR was conducted for examining the varying spatial association between bike infrastructure, public transport, and urban land use factors, and bike-sharing trip volume. The results indicated that bike-sharing usage significantly decreased during the pandemic and essential service as restaurant was found consistently and positively associated with bike-sharing use. GWR provided clear spatial patterns of bike usage based on urban land use and big user databases. The outcomes of this study could inspire policymakers and shared mobility operators to support these safe, sustainable transport alters (such as rebalancing bike stations), help city resilience, and shape a sustainable future of mobility in the post-Covid-19 era.
keywords	Bike-Sharing, Covid-19, Land Use, Geographically Weighted Regression, Big Data, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_150
id	cdrf2022_150
authors	Ana Zimbarg
year	2022
title	Mapping Plant Microclimates on Building Envelope Using Environmental Analysis Tools
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_13
summary	Can we build our cities not only for humans but also for all living systems? How can we consider other species occupants of the built environment? Planning cities as an element of the natural domain can reshape our relationship with nature and help redefine sustainability in architecture. Although current design strategies of reducing energy use does not rectify past/continuing im-balances in the natural environment. Landscape architect John Tillman Lyle expanded the regenerative design concept based on a range of ecological concepts. The environment's complexity, and the urge to use resources smartly, encouraged him to think about architecture and the environment as a whole system. John Lyle's regenerative design strategies scaffold a conceptual framework of treating the building as part of the landscape. Environmental tools such as Ladybug can map out the different conditions surrounding the building's envelope. This information can assist in selecting and populating a building façade with suitable plant species. The framework presents the building as a feature in the landscape, creating microclimatic conditions for various plant habitats. This conceptual workflow has the potential to become a tool to include regenerative principles in the urban context.
series	cdrf
email
last changed	2024/05/29 14:02

_id	caadria2022_336
id	caadria2022_336
authors	Araujo, Goncalo, Santos, Luis, Leitao, Antonioand Gomes, Ricardo
year	2022
title	AD-Based Surrogate Models for Simulation and Optimization of Large Urban Areas
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 689-698
doi	https://doi.org/10.52842/conf.caadria.2022.2.689
summary	Urban Building Energy Model (UBEM) approaches help analyze the energy performance of urban areas and predict the impact of different retrofit strategies. However, UBEM approaches require a high level of expertise and entail time-consuming simulations. These limitations hinder their successful application in designing and planning urban areas and supporting the city policy-making sector. Hence, it is necessary to investigate alternatives that are easy-to-use, automated, and fast. Surrogate models have been recently used to address UBEM limitations; however, they are case-specific and only work properly within specific parameter boundaries. We propose a new surrogate modeling approach to predict the energy performance of urban areas by integrating Algorithmic Design, UBEM, and Machine Learning. Our approach can automatically model and simulate thousands of building archetypes and create a broad surrogate model capable of quickly predicting annual energy profiles of large urban areas. We evaluated our approach by applying it to a case study located in Lisbon, Portugal, where we compare its use in model-based optimization routines against conventional UBEM approaches. Results show that our approach delivers predictions with acceptable accuracy at a much faster rate.
keywords	urban building energy modelling, algorithmic design, machine learning in Architecture, optimization of urban areas, SDG 7, SDG 12, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_85
id	ecaade2022_85
authors	Ataman, Cem, Herthogs, Pieter, Tuncer, Bige and Perrault, Simon
year	2022
title	Multi-Criteria Decision Making in Digital Participation - A framework to evaluate participation in urban design processes
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 401–410
doi	https://doi.org/10.52842/conf.ecaade.2022.1.401
summary	Data-driven urban design processes consist of iterative actions of many stakeholders, which require digital participatory approaches for collecting data from a high number of participants to make informed decisions. It is important to evaluate such processes to justify the necessary costs and efforts while continuously improving digital participation. Nevertheless, such evaluation remains a challenge due to the involvement of different stakeholders including participants, designers, and policymakers in decision-making processes, and the lack of a systematic method to generalize participation outputs that are mostly situated and context based. By addressing this challenge, this paper introduces a Multi-Criteria Decision Analysis (MCDA) based framework to measure the effectiveness and quality of digital participation systematically and quantitatively. To achieve such evaluation, we conducted a digital participation experiment and investigated such processes with the help of participants, designers, and policymakers from Singapore and Hamburg. By formulating this framework, we aim to reveal perspectives of different stakeholders towards digital participation and enable the evaluation and comparison of digital participation processes based on the introduced digital participation criteria.
keywords	Data-Driven Urban Design, Digital Participation, Stakeholder Involvement, Multi-Criteria Decision Analysis (MCDA), Participation Quantification
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_139
id	caadria2022_139
authors	Ataman, Cem, Tuncer, Bige and Perrault, Simon
year	2022
title	Asynchronous Digital Participation in Urban Design Processes: Qualitative Data Exploration and Analysis With Natural Language Processing
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 383-392
doi	https://doi.org/10.52842/conf.caadria.2022.1.383
summary	This paper aims to improve the usability of qualitative urban big data sources by utilizing Natural Language Processing (NLP) as a promising AI-based technique. In this research, we designed a digital participation experiment by deploying an open-source and customizable asynchronous participation tool, "Consul Project‚, with 47 participants in the campus transformation process of the Singapore University of Technology and Design (SUTD). At the end of the data collection process with several debate topics and proposals, we analysed the qualitative data in entry scale, topic scale, and module scale. We investigated the impact of sentiment scores of each entry on the overall discussion and the sentiment scores of each introduction text on the ongoing discussions to trace the interaction and engagement. Furthermore, we used Latent Dirichlet Allocation (LDA) topic modelling to visualize the abstract topics that occurred in the participation experiment. The results revealed the links between different debates and proposals, which allow designers and decision makers to identify the most interacted arguments and engaging topics throughout participation processes. Eventually, this research presented the potentials of qualitative data while highlighting the necessity of adopting new methods and techniques, e.g., NLP, sentiment analysis, LDA topic modelling, to analyse and represent the collected qualitative data in asynchronous digital participation processes.
keywords	Urban Design, Digital Participation, Qualitative Urban Data, Natural Language Processing (NLP), Sentiment Analysis, LDA Topic Modelling, SDG 10, SDG 11.
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_59
id	caadria2022_59
authors	Banihashemi, Farzan, Reitberger, Roland and Lang, Werner
year	2022
title	Investigating Urban Heat Island and Vegetation Effects Under the Influence of Climate Change in Early Design Stages
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 679-688
doi	https://doi.org/10.52842/conf.caadria.2022.2.679
summary	Different criteria need to be considered for optimal strategies in the early design stages of urban developments. Under the influence of climate change, the urban heat island effect (UHI) is a phenomenon that gains importance in the early design stages. Here, different parameters, for instance, vegetation ratio in the city district and building density, play a significant role in the UHI effect. These parameters need to be quantified through different simulation tools for optimal climate adaptation and mitigation measures on the urban district scale. However, not all parameters and their influence are clear to the decision-makers and actors in the early design stages. Hence, we propose a Monte Carlo based sensitivity analysis (SA) and uncertainty analysis (UA) to show the significance of different parameters and quantify them. The SA aims to identify the major influencing parameters, whereas the UA quantifies the effect on the energy performance and indoor thermal comfort of occupants. The workflow is integrated into a collaborative design platform and applied in a case study to support decision-makers in the early design stages for new developments, densification, or refurbishment scenarios.
keywords	Monte Carlo Simulation, Sensitivity Analysis, Uncertainty Analysis, Building Energy Simulation, SDG 13, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_102
id	sigradi2022_102
authors	Barreto, Joao; Becker, Newton; Guedes, Joana; Cidrack, Renata
year	2022
title	A Parametric Approach to Efficient Implementation of Green Infrastructure in the Urban Field.
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 249–260
summary	Water availability has a key role in their process of occupation. However, accelerated urbanization had several detrimental impacts, increasing the vulnerability of urban communities. Because of the limitations of traditional planning, an alternative approach is emerging to respond to the constant changes in the landscape. Now, green infrastructure (GI), an ecosystem-based approach (EbA), is being used combined with traditional solutions to increase the resilience of the cities. In this paper, we proposed the use of an algorithm to determine the best place to implement GI. The algorithm used the inputs to develop a multi-criteria analysis capable of translating urban complexity. Results show that the GI solution can’t be efficiently implemented without context evaluation. However, the algorithm has the potential to become an informative tool in the decision-making process of urban planning.
keywords	Parametric Analysis, Bioretention, Sustainable Design, Green Infrastructure, Water Resources
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	caadria2022_45
id	caadria2022_45
authors	Boim, Anna, Dortheimer, Jonathan and Sprecher, Aaron
year	2022
title	A Machine-Learning Approach to Urban Design Interventions In Non-Planned Settlements
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 223-232
doi	https://doi.org/10.52842/conf.caadria.2022.1.223
summary	This study presents generative adversarial networks (GANs), a machine-learning technique that can be used as an urban design tool capable of learning and reproducing complex patterns that express the unique spatial qualities of non-planned settlements. We report preliminary experimental results of training and testing GAN models on different datasets of urban patterns. The results reveal that machine learning models can generate development alternatives with high morphological resemblance to the original urban fabric based on the suggested training process. This study contributes a methodological framework that has the potential to generate development alternatives sensitive to the local practices, thereby promoting preservation of traditional knowledge and cultural sustainability.
keywords	Non-planned settlements, Cultural Sustainability, Machine Learning, Generative Adversarial Networks, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_211
id	ecaade2022_211
authors	Bonafede, Andrea and Erioli, Alessio
year	2022
title	Versus Habitat - Multi agent spatial negotiation for topology-aware, large scale architectural assemblages
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 113–122
doi	https://doi.org/10.52842/conf.ecaade.2022.2.113
summary	With the burst of automation in the AEC industry, modular design for collective living is having a reissue; as for industrial construction in the post WW2 era, the economies of a construction system trigger urban models, but an exploration of non-standard spatial models based on computational methods is still lacking. This research proposes a competition-based process for the design of large scale (urban) collective habitats as topology-aware architectural assemblages of spatial (as in including constructive elements + void) components. Two competing multi-agent systems negotiate spatial occupancy, leveraging the morphological computation capabilities of individual and combined components at increasing scales. Localized information stored in the environment by the agents is converted in architectural components, resulting in a multi- level spatial organization that transcends typical typological classification. Space syntax techniques are used to map the assemblage properties and support design inferences on spatial occupation such as potentially implementable functional programmes.
keywords	Multi-agent System, Automation, Assemblages, Stigmergy, Space Syntax
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_450
id	ecaade2022_450
authors	Braumann, Johannes, Gollob, Emanuel and Singline, Karl
year	2022
title	Visual Programming for Interactive Robotic Fabrication Processes - Process flow definition in robotic fabrication
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 427–434
doi	https://doi.org/10.52842/conf.ecaade.2022.2.427
summary	Visual, flow-based programming environments in architecture and design are built to control data flow but not process flow. However, controlling the process flow is essential for interacting with robotic fabrication processes, so that they can react to input such as user interaction or sensor data. In this research, we combine two visual programming environments, utilizing Grasshopper for defining complex, robotic toolpaths, and Unity Visual Scripting for controlling the overall process flow and process interaction. Through that, we want to enable architects and designers to define more complex, interactive production processes, with accessible, bespoke user-interfaces allowing non-experts to operate these processes - a crucial step for the commercialization of innovations. This approach is evaluated in a case study that creates a mobile, urban microfactory that prototypically fabricates location-specific objects through additive manufacturing.
keywords	Visual Programming, State Machine, Industrial Robotics, Unity Visual Scripting
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_68
id	caadria2022_68
authors	Carta, Silvio, Turchi, Tommaso and Pintacuda, Luigi
year	2022
title	Measuring Resilient Communities: an Analytical and Predictive Tool
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 615-624
doi	https://doi.org/10.52842/conf.caadria.2022.1.615
summary	This work presents the initial results of an analytical tool designed to quantitatively assess the level of resilience of urban areas. We use Deep Neural Networks to extract features of resilience from a trained model that classifies urban areas using a pre-assigned value range of resilience. The model returns the resilience value for any urban area, indicating the distance between the centre of the selected area and relevant typologies, including green areas, buildings, natural elements and infrastructures. Our tool also indicates the urban morphological characteristics that have a larger impact on the resilience score. In this way we can learn why a neighbourhood is successful (or not) and how to improve its level of resilience. The model employs Convolutional Neural Networks (CNNs) with Keras on Tensorflow for the computation. The outputs are loaded onto a Node.JS environment and bootstrapped with React.js to generate the online demo.
keywords	sustainable cities and communities, resilient communities, CNN, urban morphology, SDG 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_210
id	sigradi2022_210
authors	Cavalcante, Teane; Cardoso, Daniel; Alexandrino, Joao Victor; Fiuza, Rebeca; de Sousa, Eugenio
year	2022
title	City information modeling (CIM) applied to urban planning: the urban indicator of reachness
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 297–308
summary	This work is part of an extension and investigation project dedicated to studying solutions related to urban, social and economic innovation with the purpose of developing a Health Innovation District (HID). Purposing to define the HID’s intervention area, a group of urban indicators was developed and categorized in four layers: reachness, integrability, use diversity and social validation. This article will explain the first layer: reachness. To achieve this, it aims to appropriate a generic framework that incorporates 1) a Relational Database Management System (PostgreSQL), 2) a Geographic Information System (QGIS) and 3) a CAD software associated to an algorithmic modelator (Rhinoceros3D + Grasshopper3D), associated to computer solutions to assess if the shortest way possible between the residential lots and the points of interest has an adequate distance.
keywords	City Information Model, Urban Planning, Urban Indicators, Parametric Analysis
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	ijac202220205
id	ijac202220205
authors	Charitonidou, Marianna
year	2022
title	Urban scale digital twins in data-driven society: Challenging digital universalism in urban planning decision-making
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 238–253
summary	The article examines the impact of the virtual public sphere on how urban spaces are experienced andconceived in our data-driven society. It places particular emphasis on urban scale digital twins, which arevirtual replicas of cities that are used to simulate environments and develop scenarios in response to policyproblems. The article also investigates the shift from the technical to the socio-technical perspective withinthe field of smart cities. Despite the aspirations of urban scale digital twins to enhance the participation ofcitizens in the decision-making processes relayed to urban planning strategies, the fact that they are based on alimited set of variables and processes makes them problematic. The article aims to shed light on the tensionbetween the real and the ideal at stake during this process of abstracting sets of variables and processes in thecase of urban scale digital twins
keywords	Data-driven society, urban scale digital twins, digital universalism, democracy, big data, cyber–physical–social ecosystems, sovereignty, socio-technical perspective, smart cities, mobility justice, data-driven decisionmaking
series	journal
last changed	2024/04/17 14:29

_id	caadria2022_270
id	caadria2022_270
authors	Chen, Guoyi, Choi, Seungcheol, Makki, Mohammed and Mathers, Jordan
year	2022
title	Parasite City: Retaining the Industrial District of Alexandria, Sydney as an Integral Part of Urban Regeneration
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 161-170
doi	https://doi.org/10.52842/conf.caadria.2022.1.161
summary	Industrial lands are the most vulnerable urban typologies in areas undergoing urban regeneration. They are considered less adaptive to integrated residential typologies, and their legacies are threatened under fast gentrification. The goal of this paper is to explore a sustainable strategy to address the conflict between urban sprawl and industrial conservation in Alexandria, Sydney. Through the application of a sequential evolutionary simulation, the presented research proposes a potential mixed-use scheme to rejuvenate the existing industrial district of Alexandria in an integrative manner without necessitating its destruction. This paper provides a prototype of urban regeneration, optimised by a multi-objective evolutionary algorithm, that demonstrates the necessity of industrial integration in the pursuit of true mixed use urban typologies.
keywords	GeGentrification, Mixed-use, Urban Development, Sequential eGentrification, Mixed-use, Urban Development, Sequential evolutionary simulation, SDG 9, SDG 10, SDG 11, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_153
id	caadria2022_153
authors	Cheng, Cesar, Li, Yuke, Deshpande, Rutvik, Antonio, Rishan, Chavan, Tejas, Nisztuk, Maciej, Subramanian, Ramanathan, Weijenberg, Camiel and Patel, Sayjel Vijay
year	2022
title	Realtime Urban Insights for Bottom-up 15-minute City Design
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 435-444
doi	https://doi.org/10.52842/conf.caadria.2022.1.435
summary	This paper introduces a real-time neighbour scoring system, using data collected from various web-based APIs, to facilitate "15-minute city‚ designs. The system extends on the current state of the art in three ways; first, it incorporates a multi-source urban API, to automate the extraction of location-based information from online sources; second, it provides a quantitative method to calculate and index "15-minute city‚ performance; and third, it provides a web-based application, to allow real-time feedback of neighbourhood design performance complementing the design refinements at a building and tenancy level. In addition to discussing its theoretical basis, and technical implementation, this paper provides a case study to demonstrate how the neighbourhood scoring system is incorporated into the design of a hypothetical mixed-use urban development.
keywords	Industry Innovation and Infrastructure, Sustainable Cities and Communities, Urban Walkability, Urban Accessibility, 15-minute City, Spatial Analysis, SDG 9, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_522
id	caadria2022_522
authors	Cheng, Sifan, Leung, Carson Ka Shut and van Ameijde, Jeroen
year	2022
title	Evaluating the Accessibility of Amenities toward Walkable Neighourhoods: an Integrated Method for Testing Alternatives in a Generative Urban Design Process
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 495-504
doi	https://doi.org/10.52842/conf.caadria.2022.1.495
summary	Studies have shown that walkable communities reduce traffic-related pollution and the risk of chronic illnesses, promote economic growth and prosperity, and stimulate community participation and the growth of social capital. To assess the walkability of urban areas, various methodologies have been developed around shortest-distance calculations between various points of interest (POIs), yet their outcomes do not guide potential urban design improvements. The absence of appropriate measurements and procedures that may give quantitative and actionable feedback to support design decision-making is one of the primary issues in building walkable neighborhoods. The work presented in this paper revolves around a new workflow, that employed Urbano, a mobility simulation and assessment tool, and integrated it within a generative design process to allowing for the quantitative evaluation on amenity accessibility for several alternative design scenarios for a case study site in Mong Kok, Hong Kong. The results show how this data-driven urban design process benefits from generative techniques to produce solutions with improved contextual connectivity, energy-efficient urban form, and good quality public spaces that contribute to the walkability of neighbourhoods.
keywords	Generative Urban Design, Walkability, Urbano, SDG 3, SDG 11
series	CAADRIA
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last changed	2022/07/22 07:34

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