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Spatial Activity Summarization Using Buffers: An Efficient Framework for Network Analysis

The Spatial Activity Summarization Using Buffers (SASB) framework aims to optimize the selection of active buffers in a spatial network to maximize the coverage of activities while keeping computational costs minimal. This NP-Hard problem addresses applications in critical areas such as public safety and disaster relief operations. By incorporating a greedy approach for selecting buffer areas, the project highlights a combination of geometric and network-based methodologies. The study concludes with a comparative performance analysis, demonstrating the potential effectiveness of the SASB model in spatial data analysis.

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Spatial Activity Summarization Using Buffers: An Efficient Framework for Network Analysis

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  1. SASB: Spatial Activity Summarization using Buffers Atanu Roy & AkashAgrawal

  2. Overview • Motivation • Problem Statement • Computational Challenges • Related Works • Approach • Examples • Conclusion

  3. SASB Motivation • Applications in domains like • Public safety • Disaster relief operations

  4. SASB Problem Statement • Input • A spatial network, • Set of activities & their location in space, • Number of buffers required (k), • A set of buffer (β), • Output • A set of k active buffers, where • Objective • Maximize the number of activities covered in the kbuffers • Constraints • Minimize computation costs

  5. Definitions • Constant Area Buffers • Node buffers • Path buffers

  6. Running Example

  7. Computational Challenges • SASB is NP-Hard • Proof: • KMR is a special case of SASB • Buffers have width = 0 • KMR is proved to be NP-Complete • SASB is at least NP-Hard

  8. Related Works

  9. Contributions • Definition SASB problem • NP-Hardness proof • Combination of geometry and network based summarization. • First principle examples

  10. Greedy Approach Choice of k-best buffers • Repeat k times • Choose the buffer with maximum activities • Delete all activities contained in the chosen buffer from all the remaining buffers • Replace the chosen buffer from buffer pool to the result-set

  11. Execution Trace

  12. Execution Trace: Final Solution

  13. Best Case Scenario

  14. Better

  15. Average Case Scenario

  16. Conclusion • Provides a framework to fuse geometry and network based approaches. • First principle examples indicates it can be comparable with related approaches.

  17. Acknowledgements • CSci 8715 peer reviewers who gave valuable suggestions.

  18. Thank you

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