Learning from Main Learning from Main Streets: Streets: A Machine Learning Approach Identifying Neighbourhood A Machine Learning Approach Identifying Neighbourhood Commercial Districts 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
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 - PowerPoint PPT Presentation
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Learning from Main Learning from Main Streets:Streets:A Machine Learning Approach Identifying Neighbourhood Commercial A Machine Learning Approach Identifying Neighbourhood Commercial DistrictsDistricts
DDSS 2006International Conference on Design & Decision Support Systems in Architecture and Urban Planning
Jean OhStephen F. SmithSchool of Computer Science, Carnegie Mellon University
Jie-Eun HwangGraduate School of Design, Harvard University
Kimberle KoileComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
MotivationMotivation
• Data analysis• Physical, social, &
economic constraints• Interdisciplinary “multiple
views” problem
• Information retrieval, machine learning
• Constraint reasoning• Distributed AI (Multiagent
systems)
Design A.I.
Urban typologyUrban typology
• Formalize urban components for better communication among diverse interest groups
• 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 StreetsFinding Main Streets
• Why Main Streets Matter: A series of individual structures become townscape. Diverse participants have various perspectives on community
Main Street Candidates Main Street Candidates (Boston)(Boston)
90,649 buildings99,897 parcels
4,049 commercial
76 candidate districts
Finding Main StreetsFinding Main StreetsBuildings data
Main Street Prediction
candidate districts
Parcel
GIS
Building
Form candidate districts
Unsupervised Learning: Clustering
Supervised Learning: Classification
Data export
Classification Classification
Active Learning with SVMActive Learning with SVM(Support Vector Machine)(Support Vector Machine)
Support vectors
?
Finding Main StreetsFinding Main Streets
Buildings data
Main Street Prediction
candidate districts
Parcel
GIS
Building
Form candidate districts
Initial train data
Next district to be labeled
predictions
Active Learner
Unsupervised Learning: Clustering
Supervised Learning: Classification
Active Learning with SVM
Data export
Evaluation MetricsEvaluation 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/aRecall: r = c/mF1 = 2pr / (p + r)
Results
Precision Recall F1
LOOCV 0.842 0.762 0.800
Leave-One-Out-Cross-Validation
Conclusion and future Conclusion and future directionsdirections
• 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