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	ecaade2020_089
id	ecaade2020_089
authors	Ardic, Sabiha Irem, Kirdar, Gulce and Lima, Angela Barros
year	2020
title	An Exploratory Urban Analysis via Big Data Approach: Eindhoven Case - Measuring popularity based on POIs, accessibility and perceptual quality parameters
doi	https://doi.org/10.52842/conf.ecaade.2020.2.309
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 309-318
summary	The cities are equipped with the data as a result of the individuals' sharings and application usage. This significant amount of data has the potential to reveal relations and support user-centric decision making. The focus of the research is to examine the relational factors of the neighborhoods' popularity by implementing a big data approach to contribute to the problem of urban areas' degradation. This paper presents an exploratory urban analysis for Eindhoven at the neighborhood level by considering variables of popularity: density and diversity of points of interest (POI), accessibility, and perceptual qualities. The multi-sourced data are composed of geotagged photos, the location and types of POIs, travel time data, and survey data. These different datasets are evaluated using BBN (Bayesian Belief Network) to understand the relationships between the parameters. The results showed a positive and relatively high connection between popularity - population change, accessibility by walk - density of POIs, and the feeling of safety - social cohesion. For further studies, this approach can contribute to the decision-making process in urban development, specifically in real estate and tourism development decisions to evaluate the land prices or the hot-spot touristic places.
keywords	big data approach; neighborhood analysis; popularity; point of interest (POI); accessibility; perceptual quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ecaade2020_214
id	ecaade2020_214
authors	Chen, Hsien and Hsu, Pei-Hsien
year	2020
title	Data Mining as a User-oriented Tool in Participatory Urban Design
doi	https://doi.org/10.52842/conf.ecaade.2020.1.011
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 11-18
summary	In this research, we did the datamining to the POI(point-of-interest) of the city, and shows how Popular times data and NPL(Natural language processing) analysis transformed user data into new tools of participatory design of urban planning. After analyzing and visualizing the popular time data of the city POI, we showed the city users' preferred place to go at different point in time. And this will figured out that at some time, same type of POI has different using condition. Based on above mentioned, we used NPL to analyze user reviews to find out the causes and provide planning suggestions. This method can offer planner a chance to understand the experience of city user at the planning stage. Comparing to the traditional method, fetching data from the social platform could be able to get the daily preference, perspective and emotion of the users, and these data can make the result of participatory urban planning accord with the demand of the users.
keywords	Popular times; NLP; Social Media; Urban Design Tool; Smart Cities
series	eCAADe
email
last changed	2022/06/07 07:55

_id	ecaade2020_267
id	ecaade2020_267
authors	Argin, Gorsev, Pak, Burak and Turkoglu, Handan
year	2020
title	Through the Eyes of (Post-)Flâneurs - Altering rhythm and visual attention in public space in the era of smartphones
doi	https://doi.org/10.52842/conf.ecaade.2020.1.239
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 239-248
summary	In the last decade, rapid penetration of smartphones into our everyday life introduced a new kind of urban wanderer named as the 'post-flâneur'. By navigating through the virtual and physical space with a smartphone, and taking and sharing photographs, post-flâneur walks and experiences the city in novel ways. This paper aims to investigate the effects of smartphone use on the human-environment relationship by comparing post-flânerie with flânerie in public space with a focus on two key indicators: alteration of 1) the visual attention and 2) the walking rhythm. In this regard, ten postgraduate Architecture students are asked to perform flânerie and post-flânerie consecutively in the historical city center of Ghent with an eye-tracker and a smartphone. During the flânerie condition, they walked and experienced the city without using a smartphone. In the post-flânerie condition, they used a smartphone, took pictures and uploaded them to an application. By analyzing the eye-tracker (number and duration of fixations) and the smartphone (location data and geolocated photographs) data, altering rhythm and visual attention during the flânerie and post-flânerie were compared. Preliminary results indicate that flânerie and post-flânerie differ in terms of rhythm and visual attention. The average duration of fixations on the environment were significantly lower in the post-flânerie condition while the average walking rhythm was faster but impeded from time to time. In addition, post-flâneurs' visual attention was on the smartphone during a significant part of the stationary activities which point out to an altered state of public space appropriation. The findings are significant because they reveal the novel spatial appropriations and experiences of the (post)public space -particularly "the honeypot effect" which was more significant in the post-flânerie condition. These observations evoke questions on how designers can rethink public space as a hybrid construct integrating the virtual and the physical.
keywords	post-flâneur; rhythm; visual attention; smartphone; eye-tracking
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ijac202018103
id	ijac202018103
authors	Kimm, Geoff
year	2020
title	Actual and experiential shadow origin tagging: A 2.5D algorithm for efficient precinct-scale modelling
source	International Journal of Architectural Computing vol. 18 - no. 1, 41-52
summary	This article describes a novel algorithm for built environment 2.5D digital model shadow generation that allows identities of shadowing sources to be efficiently precalculated. For any point on the ground, all sources of shadowing can be identified and are classified as actual or experiential obstructions to sunlight. The article justifies a 2.5D raster approach in the context of modelling of architectural and urban environments that has in recent times shifted from 2D to 3D, and describes in detail the algorithm which builds on precedents for 2.5D raster calculation of shadows. The algorithm is efficient and is applicable at even precinct scale in low-end computing environments. The simplicity of this new technique, and its independence of GPU coding, facilitates its easy use in research, prototyping and civic engagement contexts. Two research software applications are presented with technical details to demonstrate the algorithm’s use for participatory built environment simulation and generative modelling applications. The algorithm and its shadow origin tagging can be applied to many digital workflows in architectural and urban design, including those using big data, artificial intelligence or community participative processes.
keywords	2.5D raster, actual and experiential shadow origins, generative techniques, participatory built environment simulation, reactive scripting for design
series	journal
email
last changed	2020/11/02 13:34

_id	acadia20_84
id	acadia20_84
authors	Kirova, Nikol; Markopoulou, Areti
year	2020
title	Pedestrian Flow: Monitoring and Prediction
doi	https://doi.org/10.52842/conf.acadia.2020.1.084
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 84-93.
summary	The worldwide lockdowns during the first wave of the COVID-19 pandemic had an immense effect on the public space. The events brought up an opportunity to redesign mobility plans, streets, and sidewalks, making cities more resilient and adaptable. This paper builds on previous research of the authors that focused on the development of a graphene-based sensing material system applied to a smart pavement and utilized to obtain pedestrian spatiotemporal data. The necessary steps for gradual integration of the material system within the urban fabric are introduced as milestones toward predictive modeling and dynamic mobility reconfiguration. Based on the capacity of the smart pavement, the current research presents how data acquired through an agent-based pedestrian simulation is used to gain insight into mobility patterns. A range of maps representing pedestrian density, flow, and distancing are generated to visualize the simulated behavioral patterns. The methodology is used to identify areas with high density and, thus, high risk of transmitting airborne diseases. The insights gained are used to identify streets where additional space for pedestrians is needed to allow safe use of the public space. It is proposed that this is done by creating a dynamic mobility plan where temporal pedestrianization takes place at certain times of the day with minimal disruption of road traffic. Although this paper focuses mainly on the agent-based pedestrian simulation, the method can be used with real-time data acquired by the sensing material system for informed decision-making following otherwise-unpredictable pedestrian behavior. Finally, the simulated data is used within a predictive modeling framework to identify further steps for each agent; this is used as a proof-of-concept through which more insights can be gained with additional exploration.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_259
id	caadria2020_259
authors	Rhee, Jinmo, Veloso, Pedro and Krishnamurti, Ramesh
year	2020
title	Integrating building footprint prediction and building massing - an experiment in Pittsburgh
doi	https://doi.org/10.52842/conf.caadria.2020.2.669
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 669-678
summary	We present a novel method for generating building geometry using deep learning techniques based on contextual geometry in urban context and explore its potential to support building massing. For contextual geometry, we opted to investigate the building footprint, a main interface between urban and architectural forms. For training, we collected GIS data of building footprints and geometries of parcels from Pittsburgh and created a large dataset of Diagrammatic Image Dataset (DID). We employed a modified version of a VGG neural network to model the relationship between (c) a diagrammatic image of a building parcel and context without the footprint, and (q) a quadrilateral representing the original footprint. The option for simple geometrical output enables direct integration with custom design workflows because it obviates image processing and increases training speed. After training the neural network with a curated dataset, we explore a generative workflow for building massing that integrates contextual and programmatic data. As trained model can suggest a contextual boundary for a new site, we used Massigner (Rhee and Chung 2019) to recommend massing alternatives based on the subtraction of voids inside the contextual boundary that satisfy design constraints and programmatic requirements. This new method suggests the potential that learning-based method can be an alternative of rule-based design methods to grasp the complex relationships between design elements.
keywords	Deep Learning; Prediction; Building Footprint; Massing; Generative Design
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	cdrf2022_209
id	cdrf2022_209
authors	Yecheng Zhang, Qimin Zhang, Yuxuan Zhao, Yunjie Deng, Feiyang Liu, Hao Zheng
year	2022
title	Artificial Intelligence Prediction of Urban Spatial Risk Factors from an Epidemic Perspective
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_18
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	From the epidemiological perspective, previous research methods of COVID-19 are generally based on classical statistical analysis. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. We take the Spatio-temporal data of people infected with new coronary pneumonia before February 28 in Wuhan in 2020 as the research object. We use kriging spatial interpolation technology and core density estimation technology to establish the epidemic heat distribution on fine grid units. We further examine the distribution of nine main spatial risk factors, including agencies, hospitals, park squares, sports fields, banks, hotels, Etc., which are tested for the significant positive correlation with the heat distribution of the epidemic. The weights of the spatial risk factors are used for training Generative Adversarial Network models, which predict the heat distribution of the outbreak in a given area. According to the trained model, optimizing the relevant environment design in urban areas to control risk factors effectively prevents and manages the epidemic from dispersing. The input image of the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area.
series	cdrf
email
last changed	2024/05/29 14:02

_id	ecaade2020_131
id	ecaade2020_131
authors	Gortazar-Balerdi, Ander and Markusiewicz, Jacek
year	2020
title	Legible Bilbao - Computational method for urban legibility
doi	https://doi.org/10.52842/conf.ecaade.2020.1.209
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 209-218
summary	Legibility is a core concept in spatial cognition theories since Kevin Lynch published The Image of the City in 1960. It is the ability of a city to be interpreted and easily used, travelled and enjoyed, from the pedestrian's perspective. Following a proposal in the participatory budget process of the city of Bilbao, we wrote a technical report to improve the urban legibility of the city and facilitate wayfinding through innovations in signage. This paper aims to present this project, which is an application of computational methods to measure urban legibility that resulted in a proposal for a new wayfinding strategy for Bilbao. The method is based on GIS data, and it simulates urban processes using dedicated algorithms, allowing us to perform two analyses that resulted in two overlapping maps: a heat map of decision points and a map of visual openings. It allowed us to perceive common urban elements that can help to decide both the location of the wayfinding signage and how it should provide the relevant information. In addition, the research introduces the concept of anticipation points, as a complement to the existing idea of decision points.
keywords	Wayfinding; Urban legibility; Spatial cognition
series	eCAADe
email
last changed	2022/06/07 07:51

_id	acadia20_658
id	acadia20_658
authors	Ho, Brian
year	2020
title	Making a New City Image
doi	https://doi.org/10.52842/conf.acadia.2020.1.658
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 658-667.
summary	This paper explores the application of computer vision and machine learning to streetlevel imagery of cities, reevaluating past theory linking urban form to human perception. This paper further proposes a new method for design based on the resulting model, where a designer can identify areas of a city tied to certain perceptual qualities and generate speculative street scenes optimized for their predicted saliency on labels of human experience. This work extends Kevin Lynch’s Image of the City with deep learning: training an image classification model to recognize Lynch’s five elements of the city image, using Lynch’s original photographs and diagrams of Boston to construct labeled training data alongside new imagery of the same locations. This new city image revitalizes past attempts to quantify the human perception of urban form and improve urban design. A designer can search and map the data set to understand spatial opportunities and predict the quality of imagined designs through a dynamic process of collage, model inference, and adaptation. Within a larger practice of design, this work suggests that the curation of archival records, computer science techniques, and theoretical principles of urbanism might be integrated into a single craft. With a new city image, designers might “see” at the scale of the city, as well as focus on the texture, color, and details of urban life.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_222
id	ecaade2020_222
authors	Ikeno, Kazunosuke, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2020
title	Automatic Generation of Horizontal Building Mask Images by Using a 3D Model with Aerial Photographs for Deep Learning
doi	https://doi.org/10.52842/conf.ecaade.2020.2.271
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 271-278
summary	Information extracted from aerial photographs is widely used in urban planning and design. An effective method for detecting buildings in aerial photographs is to use deep learning for understanding the current state of a target region. However, the building mask images used to train the deep learning model are manually generated in many cases. To solve this challenge, a method has been proposed for automatically generating mask images by using virtual reality 3D models for deep learning. Because normal virtual models do not have the realism of a photograph, it is difficult to obtain highly accurate detection results in the real world even if the images are used for deep learning training. Therefore, the objective of this research is to propose a method for automatically generating building mask images by using 3D models with textured aerial photographs for deep learning. The model trained on datasets generated by the proposed method could detect buildings in aerial photographs with an accuracy of IoU = 0.622. Work left for the future includes changing the size and type of mask images, training the model, and evaluating the accuracy of the trained model.
keywords	Urban planning and design; Deep learning; Semantic segmentation; Mask image; Training data; Automatic design
series	eCAADe
email
last changed	2022/06/07 07:50

_id	cdrf2019_93
id	cdrf2019_93
authors	Jiaxin Zhang , Tomohiro Fukuda , and Nobuyoshi Yabuki
year	2020
title	A Large-Scale Measurement and Quantitative Analysis Method of Façade Color in the Urban Street Using Deep Learning
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_9
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	Color planning has become a significant issue in urban development, and an overall cognition of the urban color identities will help to design a better urban environment. However, the previous measurement and analysis methods for the facade color in the urban street are limited to manual collection, which is challenging to carry out on a city scale. Recent emerging dataset street view image and deep learning have revealed the possibility to overcome the previous limits, thus bringing forward a research paradigm shift. In the experimental part, we disassemble the goal into three steps: firstly, capturing the street view images with coordinate information through the API provided by the street view service; then extracting facade images and cleaning up invalid data by using the deep-learning segmentation method; finally, calculating the dominant color based on the data on the Munsell Color System. Results can show whether the color status satisfies the requirements of its urban plan for façade color in the street. This method can help to realize the refined measurement of façade color using open source data, and has good universality in practice.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_497
id	ecaade2020_497
authors	Kim, Eunsu, Rosenwasser, David and Garcia del Castillo Lopez, Jose Luis
year	2020
title	Urban Emotion - The interrogation of social media and its implications within urban context
doi	https://doi.org/10.52842/conf.ecaade.2020.2.475
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 475-482
summary	This paper presents social media as an analytical tool, helping to transform public policy-making, alongside urban needs by dissecting and evaluating human perception. Using emotion analysis on data gathered from a social media platform, experiments are developed to bring new value to architectural and civic narratives. Emotions from texts collected within social media platforms are extracted and mapped alongside tagged locations to gain a greater understanding of how public spaces are utilized. This project develops a new analytical layer within our built environment, working alongside the urban fabric, mechanical systems, and digital infrastructure. It is offered as an interactive tool for policymakers and designers to glean feedback, creating an informed conversation between citizens and decision-makers. Whereas social media platforms such as Twitter and Yelp have been referenced in past academic contexts, this project moves further by producing quantified emotions, painting a differentiated result from what purely semantic data could deliver.
keywords	Social Media; Mapping; Natural Language Processing
series	eCAADe
email
last changed	2022/06/07 07:52

_id	acadia20_170
id	acadia20_170
authors	Li, Peiwen; Zhu, Wenbo
year	2020
title	Clustering and Morphological Analysis of Campus Context
doi	https://doi.org/10.52842/conf.acadia.2020.2.170
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 170-177.
summary	“Figure-ground” is an indispensable and significant part of urban design and urban morphological research, especially for the study of the university, which exists as a unique product of the city development and also develops with the city. In the past few decades, methods adapted by scholars of analyzing the figure-ground relationship of university campuses have gradually turned from qualitative to quantitative. And with the widespread application of AI technology in various disciplines, emerging research tools such as machine learning/deep learning have also been used in the study of urban morphology. On this basis, this paper reports on a potential application of deep clustering and big-data methods for campus morphological analysis. It documents a new framework for compressing the customized diagrammatic images containing a campus and its surrounding city context into integrated feature vectors via a convolutional autoencoder model, and using the compressed feature vectors for clustering and quantitative analysis of campus morphology.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_113
id	ecaade2020_113
authors	Li, Yunqin, Yabuki, Nobuyoshi, Fukuda, Tomohiro and Zhang, Jiaxin
year	2020
title	A big data evaluation of urban street walkability using deep learning and environmental sensors - a case study around Osaka University Suita campus
doi	https://doi.org/10.52842/conf.ecaade.2020.2.319
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 319-328
summary	Although it is widely known that the walkability of urban street plays a vital role in promoting street quality and public health, there is still no consensus on how to measure it quantitatively and comprehensively. Recent emerging deep learning and sensor network has revealed the possibility to overcome the previous limit, thus bringing forward a research paradigm shift. Taking this advantage, this study explores a new approach for urban street walkability measurement. In the experimental study, we capture Street View Picture, traffic flow data, and environmental sensor data covering streets within Osaka University and conduct both physical and perceived walkability evaluation. The result indicates that the street walkability of the campus is significantly higher than that of municipal, and the streets close to large service facilities have better walkability, while others receive lower scores. The difference between physical and perceived walkability indicates the feasibility and limitation of the auto-calculation method.
keywords	walkability; WalkScore; deep learning; Street view picture; environmental sensor
series	eCAADe
email
last changed	2022/06/07 07:51

_id	ecaade2020_054
id	ecaade2020_054
authors	Liu, Yuezhong, Stouffs, Rudi and Theng, Yin Leng
year	2020
title	Development of Synthetic Patient Data to Support Urban Planning for Public Health
doi	https://doi.org/10.52842/conf.ecaade.2020.1.315
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 315-322
summary	Healthy urban planning is about planning for people, considering the needs of people and communities during the planning process and the implications of decisions for human health and well-being. However, access to real electronic health record (EHR) data is hindered by legal, privacy, security, and intellectual property restrictions. The lack of freely distributable health records has become an important issue for healthy urban planning. This research develops a source of synthetic electronic health records based on reviewed and meta-analysed evidence on the association between built environmental characteristics related to lifestyle chronic diseases. This research uses Type 2 Diabetes Mellitus (T2DM) as health for proof of concept. The results roughly approximate age and gender groups at diagnosis curves (R2 = 0.876), and correctly generated more than 90% of patients for the all age group in Singapore. As a summary, these pilot validated synthetic records could be used as a risk-free (no privacy & security issues) data for supporting healthy urban planning.
keywords	synthetic patient; urban planning; computer simulation; Type 2 Diabetes Mellitus; GIS
series	eCAADe
email
last changed	2022/06/07 07:59

_id	ecaade2020_167
id	ecaade2020_167
authors	Newton, David, Piatkowski, Dan, Marshall, Wesley and Tendle, Atharva
year	2020
title	Deep Learning Methods for Urban Analysis and Health Estimation of Obesity
doi	https://doi.org/10.52842/conf.ecaade.2020.1.297
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 297-304
summary	In the 20th and 21st centuries, urban populations have increased dramatically with a whole host of impacts to human health that remain unknown. Research has shown significant correlations between design features in the built environment and human health, but this research has remained limited. A better understanding of this relationship could allow urban planners and architects to design healthier cities and buildings for an increasingly urbanized population. This research addresses this problem by using discriminative deep learning in combination with satellite imagery of census tracts to estimate rates of obesity. Data from the California Health Interview Survey is used to train a Convolutional Neural Network that uses satellite imagery of selected census tracts to estimate rates of obesity. This research contributes knowledge on methods for applying deep learning to urban health estimation, as well as, methods for identifying correlations between urban morphology and human health.
keywords	Deep Learning; Artificial Intelligence; Urban Planning; Health; Remote Sensing
series	eCAADe
email
last changed	2022/06/07 07:58

_id	caadria2020_384
id	caadria2020_384
authors	Patt, Trevor Ryan
year	2020
title	Spectral Clustering for Urban Networks
doi	https://doi.org/10.52842/conf.caadria.2020.2.091
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 91-100
summary	As planetary urbanization accelerates, the significance of developing better methods for analyzing and making sense of complex urban networks also increases. The complexity and heterogeneity of contemporary urban space poses a challenge to conventional descriptive tools. In recent years, the emergence of urban network analysis and the widespread availability of GIS data has brought network analysis methods into the discussion of urban form. This paper describes a method for computationally identifying clusters within urban and other spatial networks using spectral analysis techniques. While spectral clustering has been employed in some limited urban studies, on large spatialized datasets (particularly in identifying land use from orthoimages), it has not yet been thoroughly studied in relation to the space of the urban network itself. We present the construction of a weighted graph Laplacian matrix representation of the network and the processing of the network by eigen decomposition and subsequent clustering of eigenvalues in 4d-space.In this implementation, the algorithm computes a cross-comparison for different numbers of clusters and recommends the best option based on either the 'elbow method,' or by "eigen gap" criteria. The results of the clustering operation are immediately visualized on the original map and can also be validated numerically according to a selection of cluster metrics. Cohesion and separation values are calculated simultaneously for all nodes. After presenting these, the paper also expands on the 'silhouette' value, which is a composite measure that seems especially suited to urban network clustering.This research is undertaken with the aim of informing the design process and so the visualization of results within the active 3d model is essential. Within the paper, we illustrate the process as applied to formal grids and also historic, vernacular urban fabric; first on small, extract urban fragments and then over an entire city networks to indicate the scalability.
keywords	Urban morphology; network analysis; spectral clustering; computation
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	sigradi2020_484
id	sigradi2020_484
authors	Pavan, Luís Henrique; Oliveira, Lucas Fernandes de; Rosa, Gabriel Machado da; Kos, José Ripper
year	2020
title	The Privacy of the Academic Community in Mapping Usage Patterns over Wi-Fi Connections
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 484-489
summary	Data sourced by mobile devices has gained importance in urban research techniques. We have conducted two different studies on the usage pattern of a university campus using Wi-Fi connections. We have evaluated this data using anonymization methods more restricted than the Brazilian Data Protection Law. One of our studies is an ongoing work on the campus reopening after the coronavirus crisis. We sought to obtain data that may constitute resources to resilient urban projects on campus. Our results highlight the liability of the more restricted anonymization method and the quality maintenance of the data representation simultaneously.
keywords	Wi-Fi connections, Data protection, Data visualization, University campus, Social infrastructure
series	SIGraDi
email
last changed	2021/07/16 11:49

_id	ecaade2020_009
id	ecaade2020_009
authors	Reaver, Kai
year	2020
title	After Imagery - Evaluating the use of mixed reality (MR) in urban planning
doi	https://doi.org/10.52842/conf.ecaade.2020.1.187
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 187-196
summary	While many researchers have developed interesting use cases for Mixed Reality (MR) in urban environments, the paper argues that determining the long-term viability of such applications as planning tools will likely require evaluating whether such applications are compatible with the democratically mandated procedures in Urban Planning. The paper compares this claim to current debates regarding the legality of the use of digital imagery in Urban Planning today. The paper elaborates these arguments through case studies done in Oslo, Norway in the context of developing the "Nordic Digital City". The case studies involve the use of MR in 1) a public competition, 2) a regulation plan, and 3) a building permit. The study thus presents some of the benefits and challenges of using these technologies in such a manner, particularly regarding accuracy, user feedback, and robustness as a common interface. The paper concludes that MR offers several benefits to Urban Planning, but will likely require a highly digitized competent public sector in order to function, in addition to requiring negotiation between the required user data and user privacy rights, suggesting that MR development may migrate from a primarily technical domain to a matter of public policy.
keywords	Mixed Reality; Urban Planning; Urbanism; Augmented Reality
series	eCAADe
email
last changed	2022/06/07 08:00

_id	acadia20_220p
id	acadia20_220p
authors	Rieger, Uwe; Liu, Yinan
year	2020
title	LightWing II
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 220-225
summary	LightWing II is an immersive XR installation that explores hybrid design strategies equally addressing physical and digital design parameters. The interactive project links a kinetic structure with dynamic digital information in the form of 3D projected imagery and spatial sound. A key component of the project was the development of a new rendering principle that allows the accurate projection of stereoscopic images on a moving target screen. Using simple red/cyan cardboard glasses, the system expands the applications of contemporary AR headsets beyond an isolated viewing towards a communal multi-viewer event. LightWing`s construction consists of thin flexible carbon fibre rods used to tension an almost invisible mesh screen. The structure is asymmetrically balanced on a single pin joint and monitored by an IMU. A light touch sets the delicate wing-like object into a rotational oscillation. As a ‘hands-on’ experience, LightWing II creates a mysterious sensation of tactile data and enables the user to navigate through holographic narratives assembled in four scenes, including the interaction with swarms of three winged creatures, being immersed in a silky bubble, and a journey through a velvet wormhole. The user interface is dissolved through the direct linkage between the physical construction and the dynamic digital content. The project was developed at the arc/sec Lab at the University of Auckland. The Lab explores user responsive constructions where dynamic properties of the virtual world influence the material world and vice versa. The Lab’s vision is to re-connect the intangible computer world to the multisensory qualities of architecture and urban spaces. With a focus on intuitive forms of user interaction, the arc/sec Lab uses large-scale prototypes and installations as the driving method for both the development and the demonstration of new cyber-physical design principles.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

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