Download Learning from Main Streets: A Machine

Learning from Main Streets:
A Machine Learning Approach Identifying Neighbourhood Commercial Districts
DDSS 2006
International Conference on Design & Decision Support
Systems in Architecture and Urban Planning
Jean Oh
Stephen F. Smith
School of Computer Science,
Carnegie Mellon University
Jie-Eun Hwang
Graduate School of Design,
Harvard University
Kimberle Koile
Computer Science and Artificial Intelligence Laboratory,
Massachusetts Institute of Technology
Motivation
Design
• Data analysis
• Physical, social, &
economic constraints
• Interdisciplinary “multiple
views” problem
A.I.
• Information retrieval,
machine learning
• Constraint reasoning
• Distributed AI (Multiagent
systems)
Urban typology
• Formalize urban components for better
communication among diverse interest groups
• Classification
• Human experts sanctuary
Intelligent Urban Design Assistant
• GIS and beyond
• Efficient urban typology: machine learning

E.g., Main Streets experiment
• Learning in a distributed environment
ARTISTS:
•
•
•
•
Arterial Streets Towards Sustainability
(Svensson et al. 2004)
Human experts
Duration: 3 years (~2004)
Budget: 2.2 million euros
Classified 40 streets in 9 countries into 5 categories
ARTISTS typology of
arterial streets
Narrow Inactive
Old Street
Metropolitan Arterial
Low Intensity Street
Shopping Street
Suburban
Residential Arterial
Main Streets
• the generic street name of the primary retail
street of an urban area, especially a village or
town, in many parts of the world. It is usually a
focal point for shops and retailers in the city
centre, and is most often used in reference to
retailing
Historiography of Townscape
Icon of Townscape
Design Process of Townscape
Finding Main Streets
A machine learning approach!
• Why Main Streets Matter:



A series of individual structures become townscape.
Diverse participants have various perspectives on community
development.
Historic preservation brings controversial issues.
Need Heuristic Process to interpret existing context!
• Information sources: GIS data
• Criteria (features)



Building/parcels structural data
Land use
Business types, etc.
Machine Learning Approach
• Clustering: unsupervised learning
• Classification: supervised learning
• Active Learning: fast learning
Finding Main Streets
Data export
Buildings, parcels, tuple data
GIS
Building Parcel
Feature space modeling (survey)
Public-ness
Built
Form
Use
Patterns
Function
Popularity
Streetscape
Lot
(Parcels)
Building
Quality of
Maintenance /
Service
Types of
User
Groups
Business
Type
Types of
Activities
Massing
Frontage
Yard
Entrance
Architectural
Style
Front
Transparency
Population of
People
Legends
Sign
Abstract
Class
Type of
Signage
Visibility
Semantic
Class
Awareness
of Content
Feature
From DB
Height,
Area,
Periphery,
Distance,
.
.
Distance,
Area,
Vegetation,
.
.
Num of Door,
Stair Size,
.
.
Num of
Window,
Dimension of
Windows,
Material of
Windows
Location,
Size,
Material
.
.
.
Feature
By User
Bold Line :
User
Annotatable
Feature
Intangible
Finding Main Streets
Unsupervised
Buildings data
Learning:
Clustering
Form
candidate
districts
Data export
GIS
Building Parcel
Clustering (single linkage)
What defines “distance”
between two data points?
Main Street Candidates (Boston)
90,649 buildings
99,897 parcels
4,049 commercial
76 candidate districts
Finding Main Streets
Unsupervised
Learning:
Clustering
Data export
Buildings data
GIS
Form
candidate
districts
Building Parcel
candidate districts
Supervised
Learning:
Classification
Main Street
Prediction
Classification
Active Learning with SVM
(Support Vector Machine)
?
Support vectors
Finding Main Streets
Unsupervised
Learning:
Clustering
Data export
Buildings data
GIS
Form
candidate
districts
Building Parcel
Initial train data
Supervised
Learning:
Classification
candidate districts
Main Street
Prediction
Active Learning
with SVM
predictions
Active Learner
Next district to be labeled
Evaluation Metrics
•
•
•
•
n : total # of examples
m: total # of Main Streets in Boston
a: # of examples classified as Main Streets
c: # of correct Main Streets in the answers
Precision: p = c/a
Recall: r = c/m
F1 = 2pr / (p + r)
Results
LOOCV
Precision
Recall
F1
0.842
0.762
0.800
Leave-One-Out-Cross-Validation
Conclusion and future directions
• Urban design decision support system can
benefit from machine learning approaches.
• The need for such support has been
underscored after series of failures of recent
post-disaster management.
• Comparison with morphological approaches
• Learning in a multiagent environment
Thank you!