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Urban Information Gathering Complexity: Metrics Significance Overview

Explore the challenges and significance of urban information gathering through representation formats, primal-dual metrics, and eigenvector centrality. Discover correlations between data metrics like age, crime, continent, and structure to uncover insights in urban areas. Learn about the factors affecting search ease in cities and the applied models like JIZNA.

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Urban Information Gathering Complexity: Metrics Significance Overview

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Presentation Transcript

  1. The Complexity of Urban Information Gathering Abdulfatai Popoola

  2. Representation Formats Primal Dual

  3. Measuring Difficulty

  4. Eigenvector Centrality

  5. Metrics

  6. Metrics - Significance

  7. Overview

  8. Stage 1: Data Collection • Openstreetmap • Challenges • Extra data: age, crime, continent, structure, area, population

  9. Merging Roads

  10. Artificial data

  11. Processed Data Inter metric correlations

  12. Discoveries…

  13. Correlation Age

  14. Correlation Age

  15. Correlation Crime I

  16. Correlation Crime II

  17. Continental Boxplots Entropy Gini Coefficient

  18. Stats

  19. Structural Boxplots Entropy Gini Coefficient

  20. Stats

  21. No significant Correlations • Area • Population

  22. Contributions • Model • JIZNA • Applications

  23. 2012 Tag Challenge

  24. Tag Challenge Cities Entropy

  25. More to come Data Dynamic characteristics DARPA’s Red Balloon Challenge

  26. Finally… • Just about three major things • The age of a city can influence the ease of search. • City structure has no significant influence on ease of search. • North American cities easier to search than European/Asian cities.

  27. Thank you.

  28. Questions

  29. extras

  30. Projections

  31. Normalization

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