DDSS 2006

1. 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. 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

2. Data analysis Physical, social, & economic constraints Interdisciplinary “multiple views” problem Information retrieval, machine learning Constraint reasoning Distributed AI (Multiagent systems) Motivation Design A.I.

3. Urban typology • Formalize urban components for better communication among diverse interest groups • Classification • Human experts sanctuary

4. Intelligent Urban Design Assistant • GIS and beyond • Efficient urban typology: machine learning • E.g., Main Streets experiment • Learning in a distributed environment

5. 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

6. Low Intensity Street ARTISTS typology of arterial streets Shopping Street Narrow Inactive Old Street Metropolitan Arterial Suburban Residential Arterial

7. 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

8. 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.

9. Machine Learning Approach • Clustering: unsupervised learning • Classification: supervised learning • Active Learning: fast learning

10. Data export Building Parcel Finding Main Streets Buildings, parcels, tuple data GIS

11. Feature space modeling (survey)

12. Unsupervised Learning: Clustering Data export Finding Main Streets Buildings data GIS Form candidate districts Building Parcel

13. Clustering (single linkage) What defines “distance” between two data points?

14. Main Street Candidates (Boston) 90,649 buildings 99,897 parcels 4,049 commercial 76 candidate districts

15. Data export Supervised Learning: Classification Finding Main Streets Unsupervised Learning: Clustering Buildings data GIS Form candidate districts Building Parcel candidate districts Main Street Prediction

16. Classification

17. Active Learning with SVM(Support Vector Machine) ? Support vectors

18. Data export Initial train data Active Learning with SVM predictions Active Learner Next district to be labeled Finding Main Streets Unsupervised Learning: Clustering Buildings data GIS Form candidate districts Building Parcel candidate districts Main Street Prediction Supervised Learning: Classification

19. 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)

20. Results Leave-One-Out-Cross-Validation

21. 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

22. Thank you!