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

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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.

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Fig. 1

Source: Prepared by the authors, 2021; Data extracted from DIVAGIS and handheld GPS receiver

Fig. 2

Source: Prepared by the Authors, 2021; data collected from the field through a handheld GPS receiver

Fig. 3

Source: Prepared by the Authors, 2021

Fig. 4

Source: Prepared by the authors, 2022; Data extracted from a field study by using a handheld GPS receiver

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Data Availability

Spatial data (.gpx,.kml/.shp format) share upon reasonable request.

Code Availability

Not Applicable.

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Acknowledgements

The authors are grateful to Department of Science and Technology (DST), Ministry of Science and Technology, Government of India for financial support to carry out the research.

Funding

This publication resulted (in part) from the research project supported by the Department of Science and Technology (DST), Ministry of Science and Technology, Government of India. The project entitled "Identification, Assessment and Model Building of Urban Morphology using Geospatial Techniques: A Study of Border City Agartala"(File No. NRDMS/UG/S.Mitra/Tripura/e-10/2019 (G) dated 15.05.2019). The content is solely the authors' responsibility and does not necessarily represent the official views of the DST, New Delhi, India.

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Authors and Affiliations

  1. Department of Statistics, Tripura University, Tripura, 799022, India

    Samrat Hore & Malabika Boruah

  2. Institute of Socio-Economic Geography and Spatial Management, University of Gdansk, Gdansk, Pomeranian, 80-309, Poland

    Stabak Roy

  3. Department of Geography and Disaster Management, Tripura University, Tripura, 799022, India

    Saptarshi Mitra

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I hereby declare that this manuscript is the result of our independent creation, developed under the guidance and comments of the reviewers. Except for the quoted contents, this manuscript does not contain any research achievements that have been published or written by other individuals or groups. I confirm that there are four authors namely SamratHore, StabakRoy, MalabikaBoruah, SaptarshiMitra of this manuscript. The legal responsibility for this statement shall be borne by me.

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Hore, S., Roy, S., Boruah, M. et al. Spatial Data Analysis for Robust Classification of Network Topology Through Synthetic Combinatorics. Ann. Data. Sci. (2024). https://doi.org/10.1007/s40745-024-00523-6

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  • DOI: https://doi.org/10.1007/s40745-024-00523-6

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