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 caadria2016_777
id caadria2016_777
authors Aditra, Rakhmat F. and Andry Widyowijatnoko
year 2016
title Combination of mass customisation and conventional construction: A case study of geodesic bamboo dome
source Living Systems and Micro-Utopias: Towards Continuous Designing, Proceedings of the 21st International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2016) / Melbourne 30 March–2 April 2016, pp. 777-786
summary With the development of advance fabrication, several digi- tal fabrication approaches have been developed. These approaches en- able better form exploration than the conventional manufacturing pro- cess. But, the built examples mostly rely on advance machinery which was not familiar or available in developed country where construction workers are still abundant. Meanwhile, much knowledge gathers in the field practice. This research is aimed to explore an alternative con- struction workflow and method with the combination of mass custom- ization and conventional construction method and to propose the structure system that emphasized this alternative workflow and meth- od. Lattice structure was proposed. The conventional construction method was used in the struts production and mass customization method, laser cutting, and was used for connection production. The algorithmic process was used mainly for data mining, details design, and component production. The backtracking was needed to be pre- dicted and addressed previously. Considerations that will be needed to be tested by further example are on the transition from the digital pro- cess to the manual process. Next research could be for analysing the other engineering aspect for this prototype and suggesting other struc- tural system with more optimal combination of conventional construc- tion and mass customization.
keywords Mass customisation; algorithmic design; digital fabrication; geodesic dome; lattice structure
series CAADRIA
last changed 2016/03/11 09:21

_id acadia17_102
id acadia17_102
authors Aparicio, German
year 2017
title Data-Insight-Driven Project Delivery: Approach to Accelerated Project Delivery Using Data Analytics, Data Mining and Data Visualization
source ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 102-109
summary Today, 98% of megaprojects face cost overruns or delays. The average cost increase is 80% and the average slippage is 20 months behind schedule (McKinsey 2015). It is becoming increasingly challenging to efficiently support the scale, complexity and ambition of these projects. Simultaneously, project data is being captured at growing rates. We continue to capture more data on a project than ever before. Total data captured back in 2009 in the construction industry reached over 51 petabytes, or 51 million gigabytes (Mckinsey 2016). It is becoming increasingly necessary to develop new ways to leverage our project data to better manage the complexity on our projects and allow the many stakeholders to make better more informed decisions. This paper focuses on utilizing advances in data mining, data analytics and data visualization as means to extract project information from massive datasets in a timely fashion to assist in making key informed decisions for project delivery. As part of this paper, we present an innovative new use of these technologies as applied to a large-scale infrastructural megaproject, to deliver a set of over 4,000 construction documents in a six-month period that has the potential to dramatically transform our industry and the way we deliver projects in the future. This paper describes a framework used to measure production performance as part of any project’s set of project controls for accelerated project delivery.
keywords design methods; information processing; data mining; big data; data visualization
series ACADIA
last changed 2017/10/17 09:12

_id cf2017_413
id cf2017_413
authors Aydin, Serdar; Schnabel, Marc Aurel; Sayah, Iman
year 2017
title Association Rule Mining to Assess User-generated Content in Digital Heritage: Participatory Content Making in ‘The Museum of Gamers’
source Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, p. 413.
summary Association rule mining is one of several approaches in game design for discovering correlations among user-generated content items. This paper aims to aid the digital heritage field by analysing user preferences in interactive environments designed for participatory cultural heritage making. Textual and diagrammatic explication of the feedback mechanism introduces the universalization of the knowledge gained in this research that is supported with the outcome of a workshop which offered two gamified interactive environments. Three key pleasures of cyberspace in digital heritage are extended from immersion to meaningful experience and to transformation. User-generated content engenders meaningful correlations that help improve and evaluate digital heritage applications. Qualitative findings explicate the relationship of ‘The Museum of Gamers’ with the authenticity issue. This paper is among the first to investigate the association rule finding methods in relation to indexical authenticity in digital heritage.
keywords Digital heritage, Game analytics, Association rule mining, User-generated content, The Museum of Gamers
series CAAD Futures
email serdar.aydin, marcaurel.schnabel},
last changed 2017/12/01 13:38

_id ddss2004_d-269
id ddss2004_d-269
authors Beetz, J., J. van Leeuwen, and B. de Vries
year 2004
title Towards a Multi Agent System for the Support of Collaborative Design
source Van Leeuwen, J.P. and H.J.P. Timmermans (eds.) Developments in Design & Decision Support Systems in Architecture and Urban Planning, Eindhoven: Eindhoven University of Technology, ISBN 90-6814-155-4, p. 269-280
summary In this paper we are drafting the outline of a framework for a Multi Agent System (MAS) for the support of Collaborative Design in the architectural domain. The system we are proposing makes use of Machine Learning (ML) techniques to infer personalized knowledge from observing a users’ action in a generic working environment using standard tools such as CAD packages. We introduce and discuss possible strategies to combine Concept Modelling (CM)-based approaches using existing ontologies with statistical analysis of action sequences within a domain specific application. In a later step, Agent technologies will be used to gather additional related information from external resources such as examples of similar problems on the users hard disk, from corresponding work of team-members within an intranet or from advises of expert from different knowledge domains, themselves represented by agents. As users deny or reward resulting proposals offered by the agent(s) through an interface the system will be enhanced over time using methods like Reinforced Learning.
keywords Multi Agent Systems, Design & Decision Support Systems, Collaborative Design, Human Computer Interfaces, Machine learning, Data Mining
series DDSS
last changed 2004/07/03 20:13

_id cf2009_626
id cf2009_626
authors Bhatt, Anand; Martens, Bob
year 2009
title The topics of CAAD: An evolutionary perspective – a research for representing the space of CAAD
source T. Tidafi and T. Dorta (eds) Joining Languages, Cultures and Visions: CAADFutures 2009, PUM, 2009, pp. 626- 641
summary This paper is concerned with the ongoing changes and the evolution of CAAD using machine-assisted techniques. It is feasible to create a hypothesis of the identity of the discipline depicted in the archived published output, and semantically link it to the data cloud. This allows for expansion of the meanings embodied in the research and inference about what CAAD is, and to trace back how the field progressed. We present several case-studies showing how this inference is done and finally, observations are elaborated and an outlook on further work is discussed.
keywords Ontology, clustering, metrics, machine learning, data mining
series CAAD Futures
type normal paper
last changed 2010/07/01 05:19

_id ecaade2018_164
id ecaade2018_164
authors Chang, Mei-Chih, Buš, Peter, Tartar, Ayça, Chirkin, Artem and Schmitt, Gerhard
year 2018
title Big-Data Informed Citizen Participatory Urban Identity Design
source Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 669-678
summary The identity of an urban environment is important because it contributes to self-identity, a sense of community, and a sense of place. However, under present-day conditions, the identities of expanding cities are rapidly deteriorating and vanishing, especially in the case of Asian cities. Therefore, cities need to build their urban identity, which includes the past and points to the future. At the same time, cities need to add new features to improve their livability, sustainability, and resilience. In this paper, using data mining technologies for various types of geo-referenced big data and combine them with the space syntax analysis for observing and learning about the socioeconomic behavior and the quality of space. The observed and learned features are identified as the urban identity. The numeric features obtained from data mining are transformed into catalogued levels for designers to understand, which will allow them to propose proper designs that will complement or improve the local traditional features. A workshop in Taiwan, which focuses on a traditional area, demonstrates the result of the proposed methodology and how to transform a traditional area into a livable area. At the same time, we introduce a website platform, Quick Urban Analysis Kit (qua-kit), as a tool for citizens to participate in designs. After the workshop, citizens can view, comment, and vote on different design proposals to provide city authorities and stakeholders with their ideas in a more convenient and responsive way. Therefore, the citizens may deliver their opinions, knowledge, and suggestions for improvements to the investigated neighborhood from their own design perspective.
keywords Urban identity; unsupervised machine learning; Principal Component Analysis (PCA); citizen participated design; space syntax
series eCAADe
last changed 2018/08/22 13:38

_id caadria2013_220
id caadria2013_220
authors Chaszar, André and José Nuno Beirão
year 2013
title Feature Recognition and Clustering for Urban Modelling – Exploration and Analysis in GIS and CAD
source Open Systems: Proceedings of the 18th International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2013) / Singapore 15-18 May 2013, pp. 601-610
wos WOS:000351496100059
summary In urban planning exploration and analysis assist the generation, measurement, interpretation and management of the modelled urban environments. This frequently involves categorisation of model elements and identification of element types. Such designation of elements can be achieved through attribution (e.g. ‘tagging’ or ‘layering’) or direct selection by model users. However, for large, complex models the number and arrangement of elements makes these approaches impractical in terms of time/effort and accuracy. This is particularly true of models which include substantial numbers of elements representing existing urban fabric, rather than only newly generated elements (which might be automatically attributed during the generation process). We present methods for identification and categorisation of model elements in models of existing and proposed urban agglomerations. We also suggest how these methods can enable exploration of models, discovery of identities and relationships not otherwise obvious, and acquisition of insights to the models’ structure and contents which are not captured, and may even be obscured, by manual selection or automated pre-attribution.  
keywords City information modelling, Data mining, Feature recognition, Geometric-content-based-search, Urban typologies 
series CAADRIA
last changed 2016/05/16 09:08

_id ecaade2016_096
id ecaade2016_096
authors Chen, Nai Chun, Nagakura, Takehiko and Larson, Kent
year 2016
title Social Media as Complementary Tool to Evaluate Cities - Data Mining Innovation Districts in Boston
source Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 2, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 447-456
wos WOS:000402064400044
summary High tech industries are playing an important role in the economic development in the United States. While some cities are shrinking, the "innovation" cities are growing. The attributes that cause some cities to successfully become innovative is a very relevant 21st century topic and will be investigated here.Previous work conduct city analysis through conventional government GIS or census data but such analyses do not answer questions about the perception of citizens inhabiting the city, and the activities they conduct. The novelty of this current project is to make use of large-scale bottom-up data available from social media. Several social media sources-CrunchBase, Twitter, Yelp, and Flickr- were data mined pertaining to four innovation districts in Boston. We found that the success of innovation districts in Boston were correlated with several important variables: the most successful districts tended to occur near research institutions, in very "mixed use" areas, and were unexpectedly not correlated with land and labor prices, unlike technology districts in the past. Based on our study, we make recommendations for the urban design that cities should put in place to increase the potential for "innovation".
keywords Smart Cities; Social Media; Innovation District; Spatial Analysis; Data Mining; Natural Language Processing
series eCAADe
last changed 2017/06/28 08:46

_id caadria2017_070
id caadria2017_070
authors Chen, Nai Chun, Xie, Jenny, Tinn, Phil, Alonso, Luis, Nagakura, Takehiko and Larson, Kent
year 2017
title Data Mining Tourism Patterns - Call Detail Records as Complementary Tools for Urban Decision Making
source P. Janssen, P. Loh, A. Raonic, M. A. Schnabel (eds.), Protocols, Flows, and Glitches - Proceedings of the 22nd CAADRIA Conference, Xi'an Jiaotong-Liverpool University, Suzhou, China, 5-8 April 2017, pp. 685-694
summary In this study we show how Call Detail Record (CDR) can be used to better understand the travel patterns of visitors. We show how Origin-Destination (OD) Interactive Maps can provide transportation information through CDR. We then use aggregation of CDR to show the differences between the travel patterns of visitors from different countries and of different lengths of stay. We also show that visitors move differently during event periods and non-event periods, reflecting the importance of real-time data available by CDR. From CDR, we can gain more detailed and complete information about how tourists move compared to traditional surveys, which can be used to aid smarter transportation systems and urban resource planning.
keywords Machine Learning; Call Detail Record; Original-Destination Matrix; Urban Design Tool
series CAADRIA
last changed 2017/05/09 08:05

_id cf2017_101
id cf2017_101
authors Chen, Nai Chun; Zhang, Yan; Stephens, Marrisa; Nagakura, Takehiko; Larson, Kent
year 2017
title Urban Data Mining with Natural Language Processing: Social Media as Complementary Tool for Urban Decision Making
source Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, pp. 101-109.
summary The presence of web2.0 and traceable mobile devices creates new opportunities for urban designers to understand cities through an analysis of user-generated data. The emergence of “big data” has resulted in a large amount of information documenting daily events, perceptions, thoughts, and emotions of citizens, all annotated with the location and time that they were recorded. This data presents an unprecedented opportunity to gauge public opinion about the topic of interest. Natural language processing with social media is a novel tool complementary to traditional survey methods. In this paper, we validate these methods using tourism data from Trip-Advisor in Andorra. “Natural language processing” (NLP) detects patterns within written languages, enabling researchers to infer sentiment by parsing sentences from social media. We applied sentiment analysis to reviews of tourist attractions and restaurants. We found that there were distinct geographic regions in Andorra where amenities were reviewed as either uniformly positive or negative. For example, correlating negative reviews of parking availability with land use data revealed a shortage of parking associated with a known traffic congestion issue, validating our methods. We believe that the application of NLP to social media data can be a complementary tool for urban decision making.
keywords Short Paper, Urban Design Decision Making, Social Media, Natural Language Processing
series CAAD Futures
last changed 2017/12/01 13:37

_id ecaade2007_211
id ecaade2007_211
authors Cheng, Nancy Yen-wen
year 2007
title Mining a Collection of Animated Sketches
source Predicting the Future [25th eCAADe Conference Proceedings / ISBN 978-0-9541183-6-5] Frankfurt am Main (Germany) 26-29 September 2007, pp. 447-456
summary How can we make a set of digital assets useful for teaching and research? As we amass data, it is crucial to select and interpret what is presented. This paper describes how a collection of animated drawings has been made accessible through an iterative development process. It describes a Web matrix interface, interpreted lesson formats and an assessment method. The assessment method of tallying achievement on design criteria before a lesson reveals inherent challenges of the problem, tallying afterwards reveals the effectiveness of the lesson in addressing those challenges. Using space-planning layout problems, we found that students readily picked up simple graphic devices such as measurement grids, adjacency diagrams and thumbnail sketches. Students showed less immediate improvement on skills that require juggling of multiple criteria, such as meeting all programmed area size requirements.
keywords Sketching, design process, architectural education, animation, instruction
series eCAADe
last changed 2007/09/16 15:55

_id 204caadria2004
id 204caadria2004
authors Chieh-Jen Lin, Mao-Lin Chiu
year 2004
title Design Knowledge Discovery in Cases - The Machine View Vs. the Human View
source CAADRIA 2004 [Proceedings of the 9th International Conference on Computer Aided Architectural Design Research in Asia / ISBN 89-7141-648-3] Seoul Korea 28-30 April 2004, pp. 265-274
summary In the previous study, we had applied the data mining techniques and ontology methodology to develop a keyword-based schema to extract and represent the implicit information within a case library, Case Base for Architecture (CBA). To improve the ability of our keyword-based schema on extracting and representing design knowledge within cases, we proceeded some experiments to understand the design’s mental behaviors in extracting knowledge from cases. Through protocol analysis, we attempted to establish a knowledge discovery model of extracting design knowledge from cases, and to propose methods to apply this model to improve our keyword-based schema. Through collecting adjective keywords to restructure our design dictionary, we attempt to make our system more sensitive to design knowledge, and more sensitive to user’s intensions by extending the ontology of our keyword list.
series CAADRIA
last changed 2004/05/20 16:46

_id caadria2005_a_7a_d
id caadria2005_a_7a_d
authors Chieh-Jen Lin, Mao-Lin Chiu
year 2005
title Ontology Based Design Knowledge Detective Agent
source CAADRIA 2005 [Proceedings of the 10th International Conference on Computer Aided Architectural Design Research in Asia / ISBN 89-7141-648-3] New Delhi (India) 28-30 April 2005, vol. 1, pp. 239-250
summary Design knowledge abstracted from cases is important for designers. This paper is aimed to build an agent to detect those correlations between explicit features of design cases and relevant design problems. Using the data mining algorithm, we have accumulated a list of keywords about design problems and their relevant concept from textual information of a case library, and established their semantic ontology by clustering their semantic and sentence structural relations from previous studies. Meanwhile, we also established another hierarchical ontology of explicit design case features by applying design domain knowledge. Then, through mapping semantic relations of relevant keywords between two ontologies, the system will become more sensitive to the correlations of design case features and relevant design problems. Finally, a graphical interface is built to visualize these correlations and help user to recognize useful design knowledge cached in design cases.
series CAADRIA
last changed 2005/04/30 01:30

_id 09a8
authors Chiu, M.L., Lin, C.J., Jeng, T.S. and Lee, C.H.
year 2002
title Re-Searching The Research Problems with CAAD: Datamining in i-CAADRIA
source CAADRIA 2002 [Proceedings of the 7th International Conference on Computer Aided Architectural Design Research in Asia / ISBN 983-2473-42-X] Cyberjaya (Malaysia) 18–20 April 2002, pp. 031-38
summary This study attempts to develop an online CAAD research archive of conference papers, i-CAADRIA, and apply data mining techniques to find research patterns. Research papers are clustered for building semantic relationship. The system and early feedbacks are presented. This study suggests that smart web query and user interface can enhance our understanding of the research patterns.
series CAADRIA
last changed 2002/04/25 17:26

_id ecaade03_067_10_chiu
id ecaade03_067_10_chiu
authors Chiu, Mao-Lin and Lan, Ju-Hung
year 2003
title Information and IN-formation -Information mining for supporting collaborative design
source Digital Design [21th eCAADe Conference Proceedings / ISBN 0-9541183-1-6] Graz (Austria) 17-20 September 2003, pp. 67-74
summary Collaborative design has become a research paradigm in design studies. To make effective collaborative design, an information service mechanism for helping collaborators to access related information of specific design situation is getting important. This paper presents an approach of applying data mining techniques to reveal information patterns for managing collaborative design information. A visual interface of linking design information based on revealed patterns are presented and issues are discussed.
keywords Information, data mining, collaborative design, web-based system
series eCAADe
last changed 2003/09/18 07:13

_id 7ffb
authors Ciftcioglu, Özer and Durmisevic, Sanja
year 2001
title Knowledge management by information mining
source Proceedings of the Ninth International Conference on Computer Aided Architectural Design Futures [ISBN 0-7923-7023-6] Eindhoven, 8-11 July 2001, pp. 533-545
summary Novel information mining method dealing with soft computing is described. By this method, in the first step, receptive fields of design information are identified so that connections among various design aspects are structured. By means of this, complex relationships among various design aspects are modeled with a paradigm, which is non-parametric and generic. In the second step, the structured connections between various pairs of aspects are graded according to the relevancy to each other. This is accomplished by means of sensitivity analysis, which is a computational tool operating on the model established and based on a concept measuring the degree of dependencies between pairs of quantities. The degree of relationships among various design aspects so determined enables one to select the most important independent aspects in the context of design or decision-making process. The paper deals with the description of the method and presents an architectural case study where numerical and as well as non-numerical (linguistic) design information are treated together, demonstrating a ranked or elective information employment which can be of great value for possible design intervention during reconstruction.
keywords Knowledge Management, Information Mining, Sensitivity Analysis
series CAAD Futures
last changed 2006/11/07 06:22

_id ecaade2018_255
id ecaade2018_255
authors Danesh, Foroozan, Baghi, Ali and Kalantari, Saleh
year 2018
title Programmable Paper Cutting - A Method to Digitally Fabricate Transformable, Complex Structural Geometry
source Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 489-498
summary This paper presents a computational approach to generating architectural forms for large spanning structures based on a "paper-cutting" technique. Using this approach, a flat sheet is cut and scored in such a way that a small application of force prompts it to expand into a three-dimensional structure. Our computational system can be used to estimate optimal cutting patterns and to predict the resulting structural characteristics, thereby providing greater rigor to what has previously been an ad-hoc and experimental design approach. To develop the model, we analyzed paper-cutting techniques, extracted the relevant formative parameters, and created a simulation using finite element analysis. We then used a data-mining approach through 400 simulations and applied a regression analysis to create a prediction model. Given a small number of input variables from the designer, this model can rapidly and precisely predict the transformation volume of a paper-cutting pattern. Additional structural characteristics will be modelled in future work. The use of this tool makes paper-cut design approaches more practical by changing a non-systematic, labor-intensive design process into a more precise and efficient one.
keywords Paper-cut?; Transformable geometry; Design method; Model prediction; Data mining; Regression analysis
series eCAADe
last changed 2018/08/22 13:38

_id sigradi2014_234
id sigradi2014_234
authors Ferreira de Arruda, Guilherme; Ana Paula Baltazar dos Santos
year 2014
title Interface urbana digital em Catas Altas (MG): apontamentos para criação de redes plurais e dialógicas [Digital urban interface in Catas Altas (MG): notes for creating plural and dialogical networks]
source SiGraDi 2014 [Proceedings of the 18th Conference of the Iberoamerican Society of Digital Graphics - ISBN: 978-9974-99-655-7] Uruguay - Montevideo 12 - 14 November 2014, pp. 604-608
summary This paper discusses the process of production of the prototype of R.I.C.A. (Catas Altas Ideas Network), a digital interface to trigger social transformation in the town of Catas Altas, Minas Gerais, Brazil, which is part of the Masters “From discourse to dialogue: urban digital interfaces for the recovery of the public realm”. It introduces Catas Altas and its social inequality showing the imbalance between the power of the mining companies that explore the town and its powerless population. It then presents the action research steps that informed the design of the interface R.I.C.A., both technically and socially, and the first use of the prototype empowering the community to discuss the public space.
keywords Interface digital; esfera pública; diálogo; relações socio-espaciais
series SIGRADI
last changed 2016/03/10 08:51

_id ddssup0207
id ddssup0207
authors Geurts, K., Wets, G., Brijs, T. and Vanhoof, K.
year 2002
title The Use of Rule-Based Knowledge Discovery Techniques to Profile Black Spots
source Timmermans, Harry (Ed.), Sixth Design and Decision Support Systems in Architecture and Urban Planning - Part two: Urban Planning Proceedings Avegoor, the Netherlands), 2002
summary In Belgium, traffic safety is currently one of the highest topics on the list of priorities of the government. The identification of black spots and black zones and profiling them in terms of accident related data and location characteristics must provide new insights into the complexity and causes of road accidents which, in turn, provide valuable input for government actions. Data mining is the extraction of information from large amounts of data. The use of data mining algorithms is therefore particularly useful in the context of large datasets on road accidents. In this paper, association rules are used to identify accident circumstances that frequently occur together. The strength of this descriptive approach lies within the definition of different accident types and the identification of relevantvariables that make a strong contribution towards a better understanding of accident circumstances. An analysis of the produced set of rules, describing underlying patterns in the data, indicates that fiveaspects of traffic accidents can be discerned: collision with a pedestrian, collision in parallel, sideways collision, week/weekend accidents and weather conditions. For each of these accident types, different variables play an important role in the occurrence of the accidents.
series DDSS
type normal paper
last changed 2008/11/01 06:38

_id ecaade2009_148
id ecaade2009_148
authors Gil, Jorge; Montenegro, Nuno C.; Beirão, José Nuno; Duarte, José Pinto
year 2009
title On the Discovery of Urban Typologies: Data Mining the Multi-dimensional Character of Neighbourhoods
source Computation: The New Realm of Architectural Design [27th eCAADe Conference Proceedings / ISBN 978-0-9541183-8-9] Istanbul (Turkey) 16-19 September 2009, pp. 269-278
wos WOS:000334282200033
summary In sustainable urban development the first stage of the urban design process should consist of a pre-design phase where the context of the site is analysed both qualitatively and quantitatively. In this paper we present a methodology for data mining an urban Geographic Information System (GIS) data set, consisting of three main phases: representation, analysis and description. The process reveals a series of block and street typologies at various levels of detail that highlight the different character of two neighbourhoods. This methodology is demanding in the preparation phase and requires a high level of GIS and statistics expertise in the analysis phase. However, it successfully addresses the complex multi-scale and multi-level nature of cities in a systematic way, providing a tool for systematic profiling of neighbourhoods, which is site and problem specific.
keywords Data mining, GIS, sustainable development, urban typologies, urban context
series eCAADe
last changed 2016/05/16 09:08

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