On the Discovery of Urban Typologies: Data Mining the Multi-dimensional Character of Neighbourhoods

Gil, Jorge; Montenegro, Nuno; Beirão, José Nuno; Duarte, José Pinto

doi:10.52842/conf.ecaade.2009.269

Cited by 9 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hägglöf and Salminen (2015) suggest that CIM can provide cities with more efficient planning processes by bringing together tools and numerous data sets in a common environment. Thompson et al (2016) echo the words of different authors (Duarte et al, 2012;Gil et al, 2009;Hamilton et al, 2005) in synthesising the CIM vision as a global and transversal philosophy generating spatial data models. Furthermore, CIM could be used to model changes over time, to observe the different impacts on the urban system (Sielker & Sichel, 2019).…”

Section: Origin and Actual Definitionsmentioning

confidence: 90%

Exploring Digital Twin Adaptation to the Urban Environment: Comparison With Cim to Avoid Silo-Based Approaches

Deprêtre

Jacquinod²,

Mielniczek³

2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. The use of digital models and tools to support a more sober, sustainable and human-centred spatial planning is constantly expanding. Among those, digital models of buildings and territories are considered useful by scientists and practitioners and used for a wide range of purposes. Several labels are currently used to characterise those digital tools and models, partly reflecting on technological developments: 3D city models, Planning support systems, Smart Cities, urbanism 3.0., City Information Model (CIM), Digital Twins (DT), etc. First used in industry, the label DT is now both used by practitioners and researchers, in relation to the development of innovative city models. Nevertheless, this label remains fuzzily defined and designates heterogeneous models from a technical standpoint. In this paper, we propose an exploration of the definitions and technical contents of DT at the city scale and a comparison with CIM approaches, as CIM is also used to label similar city models. Our analysis is based on a literature review of both DT and CIM definitions and applications to the urban context, an exploratory survey conducted with 13 practitioners about their views on DT and its potential regarding urban planning and management and a comparison of a few real-world projects either labelled CIM or DT by practitioners. Our analysis leads us to pinpoint several of the remaining challenges for a DT approach to be developed at the city scale. We also shed light on potential shortcomings of future research, if based on too narrow DT definitions.

show abstract

Section: Origin and Actual Definitionsmentioning

confidence: 90%

Exploring Digital Twin Adaptation to the Urban Environment: Comparison With Cim to Avoid Silo-Based Approaches

Deprêtre

Jacquinod²,

Mielniczek³

2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…There are several studies applying data mining in the context of architectural and urban research such as; analyzing multidimensional characteristics of German communities by Ultsch (2008, 2009), identification of Saudi Arabian architectural typologies by Reffat (2008), investigation of the way location affects property prices by Christopoulou ( 2009), explorations for identifying potential relationships between incident locations and other factors in Helsinki by Demsar ( 2006), extracting descriptions of street and block typologies by by Gil, et al (2009), modeling urban growth based on spatial data mining by Liu and Seto (2008).…”

Section: Data Mining As a Methodology For Knowledge Discovery In Urba...mentioning

confidence: 99%

“…Data mining is defined as to extract important patterns and trends from raw data (Witten and Frank, 2005). When applied to discover relationships between urban attributes, data mining can constitute a methodology for the analysis of multi-dimensional relational complexity of urban environments (Gil, Montenegro, Beirao and Duarte, 2009) To clarify what data mining is and how it can contribute, there will be an introduction of data mining as a methodology for urban feature analysis, in the following section.…”

Section: Motivation and Hypothesesmentioning

confidence: 99%

Exploring the Patterns and Relationships of Urban Attributes by Data Mining

Sökmenoglu¹,

Çaǧdaş²,

Sarıyıldız³

2010

eCAADe Proceedings

View full text Add to dashboard Cite

“…Meanwhile, statistical and machine learning models are applied to morphology index analysis, expanding the visual analysis capabilities of multidimensional morphological data with large sample sizes. For example, Gil et al used the K-means algorithm to analyze the typical urban block morphological dataset, realizing the objective classification of block morphology types [ 27 ]. SEM [ 28 ] and hierarchical clustering [ 29 ] also provide practical tools for a comprehensive analysis of morphological data.…”

Section: Relevant Researchmentioning

confidence: 99%

Quantitative Morphology of Polder Landscape Based on SOM Identification Model: Case Study of Typical Polders in the South of Yangtze River

Han

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Landscape morphology is a significant area of landscape architecture research. One of the scientific and technological issues in recent landscape morphology research is the use of quantitative analysis technology driven by morphology indexes and computational models to describe, compare, and analyze form features. This article focuses on the form features of the polder landscape, based on existing theoretical and practical achievements in landscape morphology. First, we choose five landscape morphology indexes based on the morphological constituent units of the landscape (elongation, rectangular compactness, concavity, ellipse compactness, and fractal dimension). Then, using the self-organizing map (SOM), we create an identification model for clustering the types of constituent units. The experimental results show that the identification model can classify polder morphology and analyze the distribution of units using typical polders in the Yangtze River’s south bank as study cases. This article presents a technical approach to polder landscape morphology classification as well as a reference and developable quantitative analysis method for landscape morphology research.

show abstract

On the Discovery of Urban Typologies: Data Mining the Multi-dimensional Character of Neighbourhoods

Cited by 9 publications

References 4 publications

Exploring Digital Twin Adaptation to the Urban Environment: Comparison With Cim to Avoid Silo-Based Approaches

Exploring Digital Twin Adaptation to the Urban Environment: Comparison With Cim to Avoid Silo-Based Approaches

Exploring the Patterns and Relationships of Urban Attributes by Data Mining

Quantitative Morphology of Polder Landscape Based on SOM Identification Model: Case Study of Typical Polders in the South of Yangtze River

Contact Info

Product

Resources

About