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Spatial Data Analysis for Robust Classification of Network Topology Through Synthetic Combinatorics

Author

Listed:
  • Samrat Hore

    (Tripura University)

  • Stabak Roy

    (University of Gdansk)

  • Malabika Boruah

    (Tripura University)

  • Saptarshi Mitra

    (Tripura University)

Abstract

The measurement of network topology through various spatial topological indices like Alpha, Beta and Gamma are widely used for spatial data analysis. However, explaining the classification of the network topology of a city based on Alpha, Beta and Gamma indices is not conclusive, as the result of individual indices are different. To address an efficient classification of network topology, a Modified Synthetic Indicator (MSI) has been proposed and criticised over existing synthetic indicators based on the Composite Weighted Connectivity Index (CWCI), the linear combination of Alpha, Beta and Gamma indices. Application of the proposed MSI in micro-level (ward level) classification of network topology i.e., road network connectivity, has been verified in Agartala City and calibrates the efficiency of CWCI over Alpha, Beta and Gamma indices. The study reveals that the proposed CWCI is more robust than any individual graph-theoretic measure.

Suggested Citation

  • Samrat Hore & Stabak Roy & Malabika Boruah & Saptarshi Mitra, 2024. "Spatial Data Analysis for Robust Classification of Network Topology Through Synthetic Combinatorics," Annals of Data Science, Springer, vol. 11(4), pages 1341-1359, August.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:4:d:10.1007_s40745-024-00523-6
    DOI: 10.1007/s40745-024-00523-6
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