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A novel computational knowledge-base framework for visualization and quantification of geospatial metadata in spatial data infrastructures

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Abstract

Advances in Metadata research have been instrumental in predictions and ‘fitness-of-use evaluation’ for the effective Decision-making process. For the past two decades, the model has been developed to provide visual assistance for assessing the quality information in metadata and quantifying the degree of metadata population. Still, there is a need to develop a framework that can be generic to adopt all the standards available for Geospatial Metadata. The computational analysis of metadata for specific applications remains uncharted for investigations and studies. This work proposes a computational framework for Geospatial Metadata by integrating TopicMaps and Hypergraphs (HXTM) based on the elements and their dependency relationships. A purpose-built dataset extracted from schemas of various standardisation organisations and existing knowledge in the discipline is utilised to model the framework and thereby evaluate ranking strategies. Hypergraph-Helly Property based Weight-Assignment Algorithm (HHWA) have been proposed for HXTM framework to calculate Stable weights for Metadata Elements. Recursive use of Helly-property ensures predominant elements, while Rank Order Centroid (ROC) method is used to compute standard weights. A real corpus using case studies from FGDC’s Standard for Geospatial Metadata, INSPIRE Metadata Standards, and ISRO Metadata Content Standard (NSDI 2.0) is used to validate the proposed framework. The observations show that the Information Gain (Entropy) of the proposed model along with the algorithm proves to be computationally smart for quantification purposes and visualises the strength of Metadata Elements for all applications. A prototype tool, ‘MetDEVViz- MetaData Editor, Validator & Visualization’ is designed to exploit the benefits of the proposed algorithm for the case studies that acts as a web service to provide a user interface for editing, validating and visualizing metadata elements.

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Notes

  1. Richness of Information is a metric that reflects the combination of Accessibility, Readability and Information Content metrics.

  2. Rajaram, Gangothri (2016), “Synthetic Geospatial Metadata set_FGDC”, Mendeley Data, v1 https://doi.org/10.17632/p9w8w9mg78.1

  3. https://data.mendeley.com/datasets/p9w8w9mg78/1#file-f1ae5c20-b876-4e8e-8073-04583152ae84

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Acknowledgments

The authors thank SASTRA University for their financial and research support. The third and fifth author thanks the Department of Science and Technology - Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions Government of India (SR/FST/MSI-107/2015) for their financial support. The authors thank Dr.P.S.Roy, NASI Senior Scientist Platinum Jubilee Fellow in Centre for Earth & Space Sciences at the University of Hyderabad for the fruitful discussions, ample interactions and advice for the completion of the manuscript. The authors thank Dr. G. Ravishankar, Head, Land Use and Cover Monitoring Division, National Remote Sensing Centre, ISRO, Hyderabad for his valuable discussions.

Author information

Authors and Affiliations

  1. School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India

    Gangothri Rajaram & K. R. Manjula

  2. Remote Sensing and Geoinformatics Group, Indian Institute of Remote Sensing, Indian Space Research Organization, 4, Kalidas Road, Dehradun, 248001, India

    Harish Chandra Karnatak

  3. Discrete Mathematics Research Laboratory, Srinivasa Ramanujan Centre, SASTRA University, Kumbakonam, Tamil Nadu, India

    Swaminathan Venkatraman

  4. School of Humanities & Sciences, SASTRA University, Thanjavur, Tamil Nadu, India

    Kannan Krithivasan

Authors
  1. Gangothri Rajaram
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  2. Harish Chandra Karnatak
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  3. Swaminathan Venkatraman
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  4. K. R. Manjula
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  5. Kannan Krithivasan
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Correspondence to Harish Chandra Karnatak.

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Appendices

Appendices

1.1 Appendix 1

Table 2 Glossary
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1.2 Appendix 2

Table 3 Literature of Metadata Quality Metrics (MQM)
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1.3 Appendix 3

Table 4 Weight calculation concerning each metadata element in FGDC content standard for geospatial metadata
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1.4 Appendix 4

Table 5 Weight calculation with respect to each metadata element in INSPIRE geospatial metadata standards
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1.5 Appendix 5

Table 6 Weight calculation concerning each metadata element in ISRO (NSDI 2.0) geospatial metadata standards
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1.6 Appendix 6: Evaluation of the proposed HXTM framework using Munzer’s Nested Model

The proposed HXTM framework is analysed using Munzer’s Nested Model [56] for visualization design and validation. The HXTM framework addresses all four levels of the model with validation at three of the levels as described below:

figure f
  1. i.

    Domain Problem Characterization: There is an explicit characterisation of the Geospatial Metadata Domain. Validation at this level is done implicitly through detailed study and discussion on literature study that could be construed as observing longstanding needs of users.

  2. ii.

    Data abstraction Design: The proposed framework includes a detailed list of abbreviated terms of Metadata Elements along with metrics and content nature. The methodology transforms raw metadata into visualization using Topic Maps. It involves vocabulary of computer science and geoinformation. Validation: The evidence of utility is proved by MetDEVViz tool creation and illustration.

  3. iii.

    Visual Encoding and Interaction Design: There is a thorough discussion of the original encoding design to use node D3 graph view to visualize the metadata. There exists discussion on Hypergraph partitioning and binding of elements based on metrics. There is also an extensive downstream validation of this level using qualitative representation on pie and bar chart in the tools. Validation: The chi-square test is performed to justify the need for the encoding/interaction design (TopicMap). Qualitative and Quantitative result image analysis is also done.

  4. iv.

    Algorithm Design: At the algorithm level, the framework emphases on HHWA weight assignment algorithm to analyze the quality of metadata record. Validation: Computational Complexity of the algorithm is calculated as\( \Theta \left(\mathfrak{m}\ \mathfrak{n}\right) \).

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Rajaram, G., Karnatak, H.C., Venkatraman, S. et al. A novel computational knowledge-base framework for visualization and quantification of geospatial metadata in spatial data infrastructures. Geoinformatica 22, 269–305 (2018). https://doi.org/10.1007/s10707-018-0317-6

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