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	caadria2023_65
id	caadria2023_65
authors	Rubinowicz, Pawe³
year	2023
title	Separation of Tall Greenery Component in 3D City Models Based on Lidar Data
doi	https://doi.org/10.52842/conf.caadria.2023.1.515
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 515–524
summary	The research analyses the possibility of separating tall greenery as a spatial component of the city in a 3D model based on LiDAR data. This applies in particular to Digital Surface Models (DSMs). The paper presents methods to generate theoretical DSMs of a city without tall greenery and tall greenery only as a component extracted from the 3D model. The first method is based on the use of GIS data, including 2D building outlines. The second method requires additional manual contouring of tall greenery. Both methods have been applied by the author in the planning practice in several cities in Europe. Results of the research are discussed in the article based on the example of Szczecin, Poland. It includes the preparation process, visualisations of theoretical DSM models (buildings without tall greenery and tall greenery only) and their application in urban analyses concerning e.g. protection and development of the cityscape. All simulations have been performed using C++ software developed by the author.
keywords	3D city models, digital cityscape analysis, urban planning, visual impact, DSM, LiDAR
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	ecaade2023_71
id	ecaade2023_71
authors	Austern, Guy, Yosifof, Roei and Fisher-Gewirtzman, Dafna
year	2023
title	A Dataset for Training Machine Learning Models to Analyze Urban Visual Spatial Experience
doi	https://doi.org/10.52842/conf.ecaade.2023.2.781
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 781–790
summary	Previous studies have described the effects of urban attributes such as the Spatial Openness Index (SOI) on pedestrians’ experience. SOI uses 3-dimensional ray casting to quantify the volume of visible space from a single viewpoint. The higher the SOI value, the higher the perceived openness and the lower the perceived density. However, the ray casting simulation on an urban-sized sampling grid is computationally intensive, making this method difficult to use in real-time design tools. Convolutional Neural Networks (CNN), have excellent performance in computer vision in image processing applications. They can be trained to predict the SOI analysis for large urban fabrics in real-time. However, these supervised learning models need a substantial amount of labeled data to train on. For this purpose, we developed a method to generate a large series of height maps and SOI maps of urban fabrics in New York City and encoded them as images using colour information. These height map - SOI analysis image pairs can be used as training data for a CNN to provide rapid, precise visibility simulations on an urban scale.
keywords	Visibility Analysis, Machine Learning, CNN, Perceived Density
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ijac202321307
id	ijac202321307
authors	Yang, Qing; Chu-Fan Cao; Hai-Miao Li; Wai-Shan Qiu; Wen-Jing Li; Dan Luo
year	2023
title	Is greenery green? An analytical comparison between the planned, visual, and perceived green
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 3, 498–515
summary	This research established a comprehensive evaluation system for urban ecological assessment. Through research in the fields of urban planning, urban design, and cognitive psychology, this paper defines three ecological evaluation indexes correspondingly. They measure the vegetation coverage of land (planning green), the visibility of vegetation from the pedestrian’s viewpoint (visual green), and the psychological perception of greenery by human (perceived green). This study uses computerized parametric analysis, computerized deep learning, data visualization, and statistical methods to achieve an accurate description of the three evaluation indicators. This study assumes that the three green values may behave consistently or inconsistently at each point. Therefore, this study, on the one hand, tries to analyze the potential factors affecting each green indicator. On the other hand, by analysing the consistency or discrepancy of the three green values, this research revealed the potential link between urban spatial type and integrated ecological properties. Four areas of Brisbane dominated by different functions were selected for this study (Red Hill and Bardon for residential areas, Brisbane City for downtown CBD, and Woolloongabba for industrial areas). The results of the study demonstrate the credibility and applicability of the three green indicators in different areas, examine the various factors affecting ecology, and provide new design strategies and ideas for urban designers
keywords	Urban green space, urban ecology, street view image, green perception, subjective measurement
series	journal
last changed	2024/04/17 14:30

_id	caadria2023_234
id	caadria2023_234
authors	Chundeli, Faiz Ahmed and Berger, Tania
year	2023
title	Thermal Performance Evaluation of Low-Income Housing Units Using Numerical Simulation
doi	https://doi.org/10.52842/conf.caadria.2023.1.645
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 645–654
summary	The thermal performance of buildings is measured as heat energy transfer between the buildings and the surrounding environment, and there are several heat exchange possibilities. This paper presents the thermal performance of 12 non-air-conditioned low-income single dwellings in warm-humid climates. The Building and material characteristics of the dwellings, including field measurements of the 12 cases, were meticulously documented through a primary survey. The critical indicator for assessing and evaluating the performance of the dwelling unit was hourly simulated indoor temperature data for an entire year. Further, potential planning and design components, viz. building orientation, roof and wall insulation, window size, property & locations, clerestory window, increased floor-to-ceiling height, site setback, and roof profile, were iterated to improve the thermal performance of low-income dwellings. Indoor temperatures as high as 45.9 C were recorded, the mean indoor temperature for the summer months (March-July) was over 34.64 C, and it was always higher than 30 C for the rest of the month. The findings show that the inhabitants are subjected to temperatures exceeding 34 degrees Celsius for more than half of the year. The paper concludes with some suggested design measures to improve the thermal performance of low-income houses. The study also emphasizes the importance of refined early design phase assessment and decision-making to improve the indoor thermal environment.
keywords	Thermal performance, low-income housing, building simulation, heatwaves, natural ventilation
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	sigradi2023_387
id	sigradi2023_387
authors	Dong, Jiahua, Lin, Shuiyang and van Ameijde, Jeroen
year	2023
title	Predicting Network Integration Based on Satellite Imagery Around High-Density Public Housing Estates Through Machine Learning
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 795–806
summary	In studies focusing on environmental and health aspects of urban planning, the integration of road networks within the built environment emerges as an important metric for assessing the livability and healthiness of neighborhoods. The complexity and diversity of the road networks are significant for shaping vibrant streets. In Hong Kong’s ongoing construction program of large-scale public housing estates, the design prioritizes the connectivity of pedestrian circulation to foster social interaction among residents and encourage the utilization of recreational facilities. In this study, an analytical framework is developed to interpret public housing estate spatial layout based on satellite imagery. It extracts road networks using neural networks and vectorizes results to analyze network integration around estates to predict social interactions. The aim of this process is to employ a machine learning workflow to analyze options for newly planned estates, where the design configuration can be further optimized based on its potential to stimulate social engagement and community interaction. Due to the scalability and universality of the method, the research can contribute to improved road networks and sociable housing complexes in Hong Kong, or in other international cities of similar density and vibrancy.
keywords	Network Integration, Spatial Structure, Satellite Imagery, Machine Learning, Hong Kong Public Housing
series	SIGraDi
email
last changed	2024/03/08 14:07

_id	caadria2023_303
id	caadria2023_303
authors	Hu, Anqi, Yabuki, Nobuyoshi and Fukuda, Tomohiro
year	2023
title	Development of a Method for Assessing the View Index of Plants of Interest Using Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2023.1.585
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 585–594
summary	Urban planning often overlooks the diversity of plant species and the perspectives of pedestrians. This study introduces the View Index of Plants of Interest (VIPI) as a new index for evaluating street plants from a pedestrian perspective. VIPI uses image classification and semantic segmentation techniques and was applied to four popular ornamental street plants: cherry trees, maple trees, magnolia trees, and ginkgo trees. The model used achieved a high level of accuracy with a mean intersection over union (mIoU) of 81.06. And the VIPI satisfaction criteria were used to evaluate several cases. The results provide valuable insights for urban planners and policymakers, allowing for a more detailed and accurate evaluation of urban plants from a pedestrian perspective and can guide urban greening actions. Additionally, this study demonstrates the potential of utilizing computer science techniques to inform urban planning and design decisions.
keywords	Deep learning, Image classification, Semantic segmentation, View Index of Plants of Interest (VIPI), Street view images, Urban green space
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	ijac202321202
id	ijac202321202
authors	Koehler, Daniel
year	2023
title	More than anything: Advocating for synthetic architectures within large-scale language-image models
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 2, 242–255
summary	Large-scale language-image (LLI) models have the potential to open new forms of critical practice through architectural research. Their success enables designers to research within discourses that are profoundly connected to the built environment but did not previously have the resources to engage in spatial research. Although LLI models do not generate coherent building ensembles, they offer an esthetic experience of an AI infused design practice. This paper contextualizes diffusion models architecturally. Through a comparison of approaches to diffusion models in architecture, this paper outlines data-centric methods that allow architects to design critically using computation. The design of text-driven latent spaces extends the histories of typological design to synthetic environments including non-building data into an architectural space. More than synthesizing quantic ratios in various arrangements, the architect contributes by assessing new categorical differences into generated work. The architects’ creativity can elevate LLI models with a synthetic architecture, nonexistent in the data sets the models learned from.
keywords	diffusion models, large-scale language-image models, data-centric, access to data, discrete computation, critical computational practice, synthetic architecture
series	journal
last changed	2024/04/17 14:30

_id	caadria2023_161
id	caadria2023_161
authors	Zhao, Mingming, Ding, Cao and Crossley, Tatjana
year	2023
title	Integration of EEG and Deep Learning on Design Decision-Making: A Data-Driven Study of Perception in Immersive Virtual Architectural Environments
doi	https://doi.org/10.52842/conf.caadria.2023.1.089
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 89–98
summary	Immersive virtual reality(IVR) as an emerging architectural design tool is utilized by many architecture firms to assist in better design decision-making. It allows users to immersively experience the simulated architectural environment prior to real construction. However, compared to conventional computational design tools, IVR faces more challenges in assessing the perception of designed simulations and visualizations. This paper attempts to examine the possibilities for incorporating human biological data and deep learning technology into the process of immersive visualization in architectural design. It aims to objectively understand human perception in an immersive virtual architectural environment, and ultimately assist in design decision-making and human-centered architectural design. The study proposes a novel and multidisciplinary use of techniques derived from psychology, computer science, and architecture disciplines to explore how biological data might be understood architecturally and vice versa. It also provides an opportunity to explore ways of using IVR-based computational design in the new metaverse era. The experiment results illustrate that there is a significant correlation between environmental experience and brain activation. It indicates the integration of EEG and deep learning is helpful to perform as complementary tools for better understanding human perception in immersive virtual architectural environments.
keywords	Architectural Design Decision-Making, Eye Tracking, Electroencephalogram(EEG), Convolutional Neural Networks(CNN), Virtual Reality(VR)
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	caadria2023_57
id	caadria2023_57
authors	Alva, Pradeep, Mosteiro-Romero, Martin, Pei, Wanyu, Bartolini, Andrea, Yuan, Chao and Stouffs, Rudi
year	2023
title	Bottom-Up Approach for Creating an Urban Digital Twin Platform and Use Cases
doi	https://doi.org/10.52842/conf.caadria.2023.1.605
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 605–614
summary	Smart city initiatives have been a driving force for city-level dataset collection and the development of data-driven applications that benefit effective city management. There is a need to demonstrate use cases for effective city management using the available dataset. Urban Digital Twin (UDT) is a 3D city model that can integrate multi-disciplines and improve systems operability on a digital platform. However, UDTs are developed within organisations, and there is only limited availability of authoritative open 3D datasets to explore the potential of UDT concepts. This paper reports a methodology for creating a UDT platform for visualising and querying city energy data. We demonstrate a bottom-up approach to constructing an integrated 3D city dataset and create a query system for rapid access and navigation of the 3D city dataset through a visualisation platform using Cesium Ion. Various use cases are explored based on the dataset, such as building material stock management, energy demand simulation, electric vehicles (EV) demand and flexibility, and estimation of greenhouse gas (GHG) emissions. These use cases can help decision-makers and stakeholders involved in city planning and management. Furthermore, it provides a guideline for developers willing to create UDT applications for smart city initiatives.
keywords	Energy modelling, City dataset, Urban analytics, Building Stock Management, Decarbonisation
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	ecaade2023_378
id	ecaade2023_378
authors	Araya, Sergio, Fuentes, Cesar, Strahlendorff, Mikko, Camus, María Jesus and Kröger, Anni
year	2023
title	Three-Dimensional Realtime Air Quality Mapping using Astronomical algorithms on Urban Environments
doi	https://doi.org/10.52842/conf.ecaade.2023.2.811
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 811–820
summary	The OMS estimates that over 7 million people die every year of complications attributed to atmospheric pollution. Air quality has degraded progressively and dramatically in urban environments over the last couple of decades, being a current concern in most metropolitan areas, and the focus of public policy as well as public/private scientific innovation for better diagnostics and better solutions. At SIC we are developing a method for 3D mapping the sources, affected locations, density, motion, translation, and potential composition of polluted air masses in close to real-time. We do this by leveraging a multidisciplinary approach that encompasses urban and architectural simulation with data science and astronomical techniques, producing a data visualization that enables novel research in air quality, urban policy, private investment, sustainability efforts, and smart transportation. Our approach, Sit-C, combines satellite remote sensing of air masses and atmospheric conditions, with data obtained from traffic and urban surveillance cameras deployed throughout the city of Santiago, in Chile. These cameras, oftentimes open to public access, are usually placed linearly along main avenues, or scattered around urban milestones, providing walk-though perspectives and locally situated POVs to observe the city, analog to series of cross-sections through urban areas. Satellite sensing provides a large-scale plan view, allowing for precise location of specific conditions across a region. This collaboration between architects, designers, engineers, and meteorologists, from Chile and Finland, combines digital design, data science, and remote sensing techniques to study air quality. We study suspended particulate matter (SPM) and other molecules, and its spatial behavior over time, through light-occlusion analysis, producing a three-dimensional map of the air over a city.
keywords	Air Quality, Pollution, 3D mapping, Data Science, Astronomy, Sustainable Cities, Smart Cities, Machine Learning
series	eCAADe
email
last changed	2023/12/10 10:49

_id	caadria2023_172
id	caadria2023_172
authors	Bachtiar, Naomi Marcelle and Ortner, F. Peter
year	2023
title	A Multiplayer Game for Participatory Planning
doi	https://doi.org/10.52842/conf.caadria.2023.2.421
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 421–430
summary	This paper presents a digital urban design game, ’Katakita’, as a tool for multiple non-expert participants to generate options for equitable transit-oriented development in Jakarta. It is set in the context of the ongoing MRT development and addresses the risk of transit-induced displacement for the lower income group. A preliminary study is done on the risk level of displacement based on historical data of displacement and vulnerable communities are then mapped out. The potential of using a game as a platform for discussion, evaluation and consensus-building is investigated in this paper. The game permits players to choose different roles to play and make design decisions by placing various building blocks in the multiplayer environment. Game scores such as equitability and profitability are tracked to encourage discussions and negotiations. Game session consisting of participants with relevant profiles has been conducted and results of which will be shared in this paper.
keywords	Participatory Planning, Serious Games, Game Design, Multi-criteria Decision Making, Optimisation, Urban Design
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	caadria2023_60
id	caadria2023_60
authors	Bai, Zishen and Peng, Chengzhi
year	2023
title	Convolutional Neural Network (CNN) Supported Urban Design to Reduce Particle Air Pollutant Concentrations
doi	https://doi.org/10.52842/conf.caadria.2023.1.505
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 505–514
summary	PM2.5 has become a significant factor contributing to the haze outbreak in mainland China, which has negative impacts for public health. The current agility of CFD-based modelling to reveal in real-time the changes in PM2.5 concentrations in response to (proposed) changes in urban form limits its practical applications in the design processes. To support urban design for better air quality (AQ), this study presents a machine learning approach to test: (1) that the spatial distribution of PM2.5 concentrations measured in an urban area reflects the area’s capacity to disperse particle air pollution; (2) that the PM2.5 concentration measurements can be linked to certain urban form attributes of that area. A Convolutional Neural Network algorithm called Residual Neural Network (ResNet) was trained and tested using the ChinaHighPM2.5 and urban form datasets. The result is a ResNet-AQ predictor for the city centre area in Beijing which had one of the highest air pollution levels within the Beijing-Tianjin-Hebei region. The urban area covered by the ResNet-AQ predictor contains 4,000 grid cells (approx. 25.3 km x 25.3 km), of which 1,200 (30%) cells were selected randomly for testing. The ResNet-AQ prediction accuracy achieved 87.3% after 100 iterations. An end-use scenario is presented to show how a social housing project can be supported by the AQ predictor to achieve better urban air quality performance.
keywords	PM2.5, urban form indicators, image classification, Convolutional Neural Network, open urban data
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	sigradi2023_46
id	sigradi2023_46
authors	Barashkov, Julia
year	2023
title	Customising Urban Joy: Urban Planning Mechanisms for the Mass - Customisation of Cities, through the Quantifiable Nature of Joy Using Geo-tagged Social Media Data
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 31–42
summary	The paper examines citizen participation in a digitally-driven society and the disparity between desired and existing cities. It emphasises the need to transform cities into adaptable environments that respond to the needs of residents. Traditional top-down urban planning often fails to match the flexible nature of digitised urban residents. To address this, an agent-based model is employed, evaluating urban environments based on individual sentiment derived from social media API. The study case of Wittenberge, Germany, showcases the methodology, including the creation of a 3D digital twin using open data sources and generating agents with unique personalities from social media keywords. These agents' "life satisfaction score" reflects their ability to fulfil daily needs and preferences within a 20-minute walking radius.
keywords	Data-based urban design, Citizen participation, Agent-based modelling, Social media sentiment analysis, Co-creation in cities
series	SIGraDi
email
last changed	2024/03/08 14:06

_id	ecaade2023_31
id	ecaade2023_31
authors	Canli, Ilkim, Gursel Dino, Ipek and Kalkan, Sinan
year	2023
title	Useful Daylight Illuminance Prediction Under Data Imbalance in an Urban Context
doi	https://doi.org/10.52842/conf.ecaade.2023.2.599
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 599–608
summary	Optimal daylight illumination can aid sustainable design by improving occupants’ psychological and physical health, visual and thermal comfort and decreasing electrical lighting energy usage in buildings. However, dense urban areas can result in restricted daylight access in buildings. Therefore, daylight analysis considering surrounding buildings is important for implementing daylighting strategies. Useful Daylight Illuminance (UDI) is a performance metric that can quantify the annual illuminance levels within certain illumination classes (UDIfell-short, UDIsupplementary, UDIautonomous, and UDIexceeded). UDI can be predicted using machine-learning (ML) methods. However, the calculated data is typically unevenly distributed, generally following a power-law distribution, which causes ML models to underperform for UDI classes with less data. Simulations can be utilized to increase the less dispersed data in the dataset; however, at the urban scale, the computational cost of collecting simulation data for daylighting analysis makes it difficult to augment data with simulations. To undertake this challenge, in this study, SMOTE (Synthetic Minority Oversampling Technique) was applied to augment data to increase the prediction performance of the ML model. The results showed that augmenting the data in the classes which are unevenly distributed leads to an increase in ML model prediction performance. This method shows that SMOTE can be used to increase the performance of ML models during UDI estimation at the urban scale.
keywords	Daylight Illumination, Machine Learning Prediction, Useful Daylight Illuminance, Data Imbalance
series	eCAADe
email
last changed	2023/12/10 10:49

_id	sigradi2023_404
id	sigradi2023_404
authors	Carvalho, Tainah, Becker, Newton, Guedes, Joana, Medeiros, Joao Victor, Deodato, Joao Pedro and Appleyard, Maria
year	2023
title	Landscape Information Modeling for vulnerable landscape recovery: the case of Bom Jardim in Fortaleza, Ceará, Brazil
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 113–124
summary	Even with the difficulty of implementing NBS for EbA as a solution due to the inconsistent documentation of past experiences and heavy dependence on local, ecological and social conditions, would it be possible to implement NBS that incorporate the needs of each place? This paper shows the experience of the implementation of a NBS in an urban area, in the context of “Present City Project”, using parametric modeling to simulate outcomes during the planning process. The algorithm used inputs to develop a multi-criteria analysis capable of translating urban complexity. The result of this process is a comprehensive map identifying the most efficient locations for implementing GI based on the provided data and the streets suitable for interventions with NBS as well as their water absorption capacity. Throughout the process of submitting the "Present City Project," the algorithm played a pivotal role as an essential tool for raising public awareness.
keywords	Parametric Analysis, Nature Based Solutions, Landscape Information Modeling, Sustainable Design, Water Resources
series	SIGraDi
email
last changed	2024/03/08 14:06

_id	caadria2023_84
id	caadria2023_84
authors	Chen, Bowen, Lao, Pui Kuan, Dou, Zhiyi, Qiu, Wai-Shan and Luo, Dan
year	2023
title	Analyst Patterns of Influence Between a Commercial Distribution and Neighbourhood Dynamic in a Residential Neighbourhood
doi	https://doi.org/10.52842/conf.caadria.2023.1.525
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 525–534
summary	The spatial distribution of urban commercial spaces significantly impacts the overall efficiency and vibrancy of adjacent neighbourhoods. As such, it is an important factor to consider during urban development. This study aims to examine the patterns of impact between commercial distributions and neighbourhood dynamics in a residential neighbourhood, based on the case study of a highly populated, thriving commercial, and culturally rich area situated in Mong Kok, Hong Kong. In this research, a series of numeric evaluations and statistical analyses of liveability and vibrancy metrics are presented, uncovering the tension created by existing commercial forms and local living patterns. This research started with multi-dimensional data mining, such as accessing planning data using Geographic Information Systems (GIS), perception data using Street View Images (SVI), and business performance data from Google; secondly, analysing the data via machine learning (ML) algorithm and statistical correlation to identify correlations overlaid with a mapping of spaces of measurable characteristics. The goal is to establish a measurable evaluation of the relationship between commercial vibrance and urban features that can further inform the impact of urban design strategies on fostering the vitality of community commercial centres.
keywords	Mong Kok, Model Learning Machine (ML), SVM, PSPnet, MaskRCNN, POI, Commercial vibrance, Heatmap correlation, Visualization, QGIS, Google Maps Information
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	caadria2023_296
id	caadria2023_296
authors	Choi, Yoonjung and Lee, Hyunsoo
year	2023
title	Geographic Information System Based Analysis on Walkability of Commercial Streets at Growing Stage
doi	https://doi.org/10.52842/conf.caadria.2023.1.575
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 575–584
summary	From the perspective of human-centered urban planning, walkability is a crucial concept for enhancing the quality of the neighborhood environment experienced in day-to-day life. Commercial facilities have the greatest impact on a walkable neighborhood environment. However, few studies analyzed the walkable environment's characteristics in consideration of local businesses' economic growth. This study aims to classify commercial areas according to vitality level and to analyze the correlation between store density and walkability factors through a case study on Seongsu district, Seoul, a commercial district where small businesses are growing. First, a Geographic Information System (GIS) based hotspot analysis is performed using the commercial area vitality index to select a target area for the case study. Second, through the Seongsu district case study, the walkability features of the cluster at the street level are evaluated and compared based on 3D (density, diversity, design). The results show that store density is correlated with walkability factors in growing commercial areas, and that there are distinct spatial differences depending on the factors. Based on this study's results, it is possible to propose a combination of a multi-use main street, a commercial street close to life, and a specialized street adjacent to green spaces.
keywords	Walkability, Commercial Area, GIS, Hotspot Analysis, Density Based Clustering, Multi-source Data
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	sigradi2023_375
id	sigradi2023_375
authors	Consalter Diniz, Maria Luisa, Polverini Boeing, Lais, dos Santos Carvalho, Wendel and Bertola Duarte, Rovenir
year	2023
title	Natural Language Processing, Sentiment Analysis, and Urban Studies: A Systematic Review
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 1761–1772
summary	This paper discusses the potential of using data from social media and location data platforms to create cartographies that enhance our understanding of urban dynamics. Natural Language Processing (NLP) and sentiment analysis are highlighted as essential tools for comprehending and categorizing this data. The study conducted a systematic review of NLP and sentiment analysis applications in urban studies, covering 27 peer-reviewed journals and conference papers published between 2018 and 2023. The research classified applications into six categories: urban livability, governance and management, user and landscape perception, land use and zoning, public health, and transportation and mobility. Most studies primarily relied on data from social media platforms like Twitter and location data sources such as Google Maps and Trip Advisor. Challenges include dealing with irrelevant or misleading information in publicly available data and limited accuracy when analyzing sentiments of non-English-speaking populations.
keywords	Natural language processing, Sentiment analysis, Urban studies, Digital cartographies, Systematic review.
series	SIGraDi
email
last changed	2024/03/08 14:09

_id	ijac202321406
id	ijac202321406
authors	da Silva Ruiz, Paulo Roberto; Claudia Maria de Almeida, Marcos Benedito Schimalski, Veraldo Liesenberg and Edson Aparecido Mitishita
year	2023
title	Multi-approach integration of ALS and TLS point clouds for a 3-D building modeling at LoD3
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 4, 652-678
summary	Registering, documenting, updating, revitalizing, expanding, and renovating old urban buildings require proper documentation. The adoption of 3D survey techniques is essential to grant efficiency and agility to such purposes. This article discusses a multi-approach integration of Light Detection and Ranging (LiDAR) data collected by aerial and terrestrial platforms, meant for the 3D modeling of a building at Level of Detail 3. The selected building presents challenging elements for modeling, such as blocks with different heights and indented facades. It is located on the campus of the Federal University of Paraná (UFPR) in Curitiba, Brazil, on a site with irregular terrain and surrounded by trees, what made the terrestrial laser scanning process difficult. For its three-dimensional reconstruction, data from an Aerial Laser Scanning system were integrated with data from a Terrestrial Laser Scanner (TLS). Based on the 3D modeling, an as-is Building Information Modeling model of the building’s exterior was created. To validate the results, measurements of the building were obtained by means of an Electronic Distance Measurement (EDM) device and they were then compared with measurements extracted from the point cloud-based BIM model. The results demonstrate that there was a correspondence between the EDM and the LiDAR-derived measures, attaining a satisfactory statistical agreement. The article focuses on the accuracy of LiDAR models for the cadastral update of buildings, providing information for decision making in documentation projects and construction interventions. The main contribution of this work consists in a multi-approach workflow for delivering an effective and precise solution for accomplishing an as-is BIM documentation, highlighting advantages, drawbacks, and the potential of this set of methods for integrating multi-source LiDAR point clouds.
keywords	3D Modelling, BIM, Aerial Laser Scanner, Terrestrial Laser Scanner, LiDAR
series	journal
last changed	2024/04/17 14:30

_id	caadria2023_305
id	caadria2023_305
authors	Deshpande, Rutvik, Vijay Patel, Sayjel, Weijenberg, Camiel, Nisztuk, Maciej, Corcuera, Miriam, Luo, Jianxi and Zhu, Qihao
year	2023
title	Generative Pre-Trained Transformers for 15-Minute City Design
doi	https://doi.org/10.52842/conf.caadria.2023.1.595
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 595–604
summary	Cities globally are adopting “The 15-Minute City” as an urban response to various crises, including the Covid-19 Pandemic and climate change. However, the challenge of linking location-specific requirements with potential design solutions hinders its effective implementation. To bridge this gap, this paper introduces a novel urban 15 Minute City concept generation tool that applies an artificial intelligence (AI) method called a pre-trained language model (PLM). The PLM model was fine-tuned with structured examples based on 15-Minute City principles. Using a PLM, the tool maps 15-Minute City concepts to a location and project specific prompt, automatically generating neighbourhood design concepts in the form of natural language.
keywords	15-Minute City, neighbourhood design, data-driven design, urban design, natural language generation, Generative Pre-trained Transformer
series	CAADRIA
email
last changed	2023/06/15 23:14

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