Urbane: A 3D framework to support data driven decision making in urban development

Nivan Ferreira, Marcos Lage, Harish Doraiswamy, Huy Vo, Luc Wilson, Heidi Werner, Muchan Park, Cláudio Silva

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Architects working with developers and city planners typically rely on experience, precedent and data analyzed in isolation when making decisions that impact the character of a city. These decisions are critical in enabling vibrant, sustainable environments but must also negotiate a range of complex political and social forces. This requires those shaping the built environment to balance maximizing the value of a new development with its impact on the character of a neighborhood. As a result architects are focused on two issues throughout the decision making process: a) what defines the character of a neighborhood? and b) how will a new development change its neighborhood? In the first, character can be influenced by a variety of factors and understanding the interplay between diverse data sets is crucial; including safety, transportation access, school quality and access to entertainment. In the second, the impact of a new development is measured, for example, by how it impacts the view from the buildings that surround it. In this paper, we work in collaboration with architects to design Urbane, a 3-dimensional multi-resolution framework that enables a data-driven approach for decision making in the design of new urban development. This is accomplished by integrating multiple data layers and impact analysis techniques facilitating architects to explore and assess the effect of these attributes on the character and value of a neighborhood. Several of these data layers, as well as impact analysis, involve working in 3-dimensions and operating in real time. Efficient computation and visualization is accomplished through the use of techniques from computer graphics. We demonstrate the effectiveness of Urbane through a case study of development in Manhattan depicting how a data-driven understanding of the value and impact of speculative buildings can benefit the design-development process between architects, planners and developers.

Original languageEnglish (US)
Title of host publication2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages97-104
Number of pages8
ISBN (Print)9781467397834
DOIs
StatePublished - Dec 4 2015
Event10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Chicago, United States
Duration: Oct 25 2015Oct 30 2015

Other

Other10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015
CountryUnited States
CityChicago
Period10/25/1510/30/15

Fingerprint

Decision making
Computer graphics
Visualization

Keywords

  • architecture
  • city development
  • GIS
  • impact analysis
  • Urban data analysis
  • visual analytics

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Ferreira, N., Lage, M., Doraiswamy, H., Vo, H., Wilson, L., Werner, H., ... Silva, C. (2015). Urbane: A 3D framework to support data driven decision making in urban development. In 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings (pp. 97-104). [7347636] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VAST.2015.7347636

Urbane : A 3D framework to support data driven decision making in urban development. / Ferreira, Nivan; Lage, Marcos; Doraiswamy, Harish; Vo, Huy; Wilson, Luc; Werner, Heidi; Park, Muchan; Silva, Cláudio.

2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 97-104 7347636.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ferreira, N, Lage, M, Doraiswamy, H, Vo, H, Wilson, L, Werner, H, Park, M & Silva, C 2015, Urbane: A 3D framework to support data driven decision making in urban development. in 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings., 7347636, Institute of Electrical and Electronics Engineers Inc., pp. 97-104, 10th IEEE Conference on Visual Analytics Science and Technology, VAST 2015, Chicago, United States, 10/25/15. https://doi.org/10.1109/VAST.2015.7347636
Ferreira N, Lage M, Doraiswamy H, Vo H, Wilson L, Werner H et al. Urbane: A 3D framework to support data driven decision making in urban development. In 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 97-104. 7347636 https://doi.org/10.1109/VAST.2015.7347636
Ferreira, Nivan ; Lage, Marcos ; Doraiswamy, Harish ; Vo, Huy ; Wilson, Luc ; Werner, Heidi ; Park, Muchan ; Silva, Cláudio. / Urbane : A 3D framework to support data driven decision making in urban development. 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 97-104
@inproceedings{84893f76fa184a8bae0feeb34b93e61e,
title = "Urbane: A 3D framework to support data driven decision making in urban development",
abstract = "Architects working with developers and city planners typically rely on experience, precedent and data analyzed in isolation when making decisions that impact the character of a city. These decisions are critical in enabling vibrant, sustainable environments but must also negotiate a range of complex political and social forces. This requires those shaping the built environment to balance maximizing the value of a new development with its impact on the character of a neighborhood. As a result architects are focused on two issues throughout the decision making process: a) what defines the character of a neighborhood? and b) how will a new development change its neighborhood? In the first, character can be influenced by a variety of factors and understanding the interplay between diverse data sets is crucial; including safety, transportation access, school quality and access to entertainment. In the second, the impact of a new development is measured, for example, by how it impacts the view from the buildings that surround it. In this paper, we work in collaboration with architects to design Urbane, a 3-dimensional multi-resolution framework that enables a data-driven approach for decision making in the design of new urban development. This is accomplished by integrating multiple data layers and impact analysis techniques facilitating architects to explore and assess the effect of these attributes on the character and value of a neighborhood. Several of these data layers, as well as impact analysis, involve working in 3-dimensions and operating in real time. Efficient computation and visualization is accomplished through the use of techniques from computer graphics. We demonstrate the effectiveness of Urbane through a case study of development in Manhattan depicting how a data-driven understanding of the value and impact of speculative buildings can benefit the design-development process between architects, planners and developers.",
keywords = "architecture, city development, GIS, impact analysis, Urban data analysis, visual analytics",
author = "Nivan Ferreira and Marcos Lage and Harish Doraiswamy and Huy Vo and Luc Wilson and Heidi Werner and Muchan Park and Cl{\'a}udio Silva",
year = "2015",
month = "12",
day = "4",
doi = "10.1109/VAST.2015.7347636",
language = "English (US)",
isbn = "9781467397834",
pages = "97--104",
booktitle = "2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Urbane

T2 - A 3D framework to support data driven decision making in urban development

AU - Ferreira, Nivan

AU - Lage, Marcos

AU - Doraiswamy, Harish

AU - Vo, Huy

AU - Wilson, Luc

AU - Werner, Heidi

AU - Park, Muchan

AU - Silva, Cláudio

PY - 2015/12/4

Y1 - 2015/12/4

N2 - Architects working with developers and city planners typically rely on experience, precedent and data analyzed in isolation when making decisions that impact the character of a city. These decisions are critical in enabling vibrant, sustainable environments but must also negotiate a range of complex political and social forces. This requires those shaping the built environment to balance maximizing the value of a new development with its impact on the character of a neighborhood. As a result architects are focused on two issues throughout the decision making process: a) what defines the character of a neighborhood? and b) how will a new development change its neighborhood? In the first, character can be influenced by a variety of factors and understanding the interplay between diverse data sets is crucial; including safety, transportation access, school quality and access to entertainment. In the second, the impact of a new development is measured, for example, by how it impacts the view from the buildings that surround it. In this paper, we work in collaboration with architects to design Urbane, a 3-dimensional multi-resolution framework that enables a data-driven approach for decision making in the design of new urban development. This is accomplished by integrating multiple data layers and impact analysis techniques facilitating architects to explore and assess the effect of these attributes on the character and value of a neighborhood. Several of these data layers, as well as impact analysis, involve working in 3-dimensions and operating in real time. Efficient computation and visualization is accomplished through the use of techniques from computer graphics. We demonstrate the effectiveness of Urbane through a case study of development in Manhattan depicting how a data-driven understanding of the value and impact of speculative buildings can benefit the design-development process between architects, planners and developers.

AB - Architects working with developers and city planners typically rely on experience, precedent and data analyzed in isolation when making decisions that impact the character of a city. These decisions are critical in enabling vibrant, sustainable environments but must also negotiate a range of complex political and social forces. This requires those shaping the built environment to balance maximizing the value of a new development with its impact on the character of a neighborhood. As a result architects are focused on two issues throughout the decision making process: a) what defines the character of a neighborhood? and b) how will a new development change its neighborhood? In the first, character can be influenced by a variety of factors and understanding the interplay between diverse data sets is crucial; including safety, transportation access, school quality and access to entertainment. In the second, the impact of a new development is measured, for example, by how it impacts the view from the buildings that surround it. In this paper, we work in collaboration with architects to design Urbane, a 3-dimensional multi-resolution framework that enables a data-driven approach for decision making in the design of new urban development. This is accomplished by integrating multiple data layers and impact analysis techniques facilitating architects to explore and assess the effect of these attributes on the character and value of a neighborhood. Several of these data layers, as well as impact analysis, involve working in 3-dimensions and operating in real time. Efficient computation and visualization is accomplished through the use of techniques from computer graphics. We demonstrate the effectiveness of Urbane through a case study of development in Manhattan depicting how a data-driven understanding of the value and impact of speculative buildings can benefit the design-development process between architects, planners and developers.

KW - architecture

KW - city development

KW - GIS

KW - impact analysis

KW - Urban data analysis

KW - visual analytics

UR - http://www.scopus.com/inward/record.url?scp=84962882045&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84962882045&partnerID=8YFLogxK

U2 - 10.1109/VAST.2015.7347636

DO - 10.1109/VAST.2015.7347636

M3 - Conference contribution

AN - SCOPUS:84962882045

SN - 9781467397834

SP - 97

EP - 104

BT - 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -