Multi-layered hinterland classification of Indian ports of containerized cargoes using GIS visualization and decision tree analysis
Jean-Claude Thill and
Kailas Venkitasubramanian
Additional contact information
Jean-Claude Thill: Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, North Carolina 28223, USA. E-mails: kvenkita@uncc.edu, Jean-Claude.Thill@uncc.edu
Kailas Venkitasubramanian: Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, North Carolina 28223, USA. E-mails: kvenkita@uncc.edu, Jean-Claude.Thill@uncc.edu
Maritime Economics & Logistics, 2015, vol. 17, issue 3, 265-291
Abstract:
In this study, we develop a multi-layered hinterland classification of major Indian ports for containerized shipments using a novel approach that integrates geographic information system visualizations and data mining methods. Strong export-oriented business sectors and growing industrial diversity have been prompting developing economies like India to move towards the containerization of maritime transport. We recognize the dearth of systematic research on Indian ports’ hinterlands and therefore seek to understand how the hinterland of container shipping markets in India has evolved in recent years, within the context of ongoing privatization and modernization efforts in this sector. Using disaggregate shipping data of exports from India to the United States, we conduct origin-destination analysis to describe the patterns of container traffic flows from major Indian ports. Furthermore, we develop a decision tree model of hinterland structure and overlap that explains the nature of inter-port competition from three dimensions: space, commodity types and shipment values. This article also attempts to assess the competition posed by private ports like Mundra and Pipavav on major Government ports, for which data have largely been unavailable. The results demonstrate the utility of this data mining method in conceptualizing the port hinterland as a dynamic spatial object and in unravelling multidimensional relationships.
Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.palgrave-journals.com/mel/journal/v17/n3/pdf/mel201424a.pdf Link to full text PDF (application/pdf)
http://www.palgrave-journals.com/mel/journal/v17/n3/full/mel201424a.html Link to full text HTML (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:pal:marecl:v:17:y:2015:i:3:p:265-291
Ordering information: This journal article can be ordered from
http://www.springer. ... nt/journal/41278/PS2
Access Statistics for this article
Maritime Economics & Logistics is currently edited by Hercules E. Haralambides
More articles in Maritime Economics & Logistics from Palgrave Macmillan, International Association of Maritime Economists (IAME) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().