On the derivation of value from geospatial linked data

Abstract

Linked Data (LD) is a set of best practices for publishing and connecting structured data on the web. LD and Linked Open Data (LOD) are often conflated to the point where there is an expectation that LD will be free and unrestricted. The current research looks at deriving commercial value from LD. When there is both free and paid for data available the issue arises of how users will react to a situation where two or more options are provided. The current research examines the factors that would affect choices made by users, and subsequently created prototypes for users to interact with, in order to understand how consumers reacted to each of the different options. Our examination of commercial providers of LD uses Ordnance Survey (OS) (the UK national mapping agency) as a case study by studying their requirements for and experiences of publishing LD, and we further extrapolate from this by comparing the OS to other potential commercial publishers of LD. Our research looks at the business case for LD and introduces the concept of LOD and Linked Closed Data (LCD). We also determine that there are two types of LD users; non-commercial users and commercial users and as such, two types of use of LD; LD as a raw commodity and LD as an application. Our experiments aim to identify the issues users would find whereby LD is accessed via an application. Our first investigation brought together technical users and users of Geographic Information (GI). With the idea of LOD and LCD we asked users what factors would affect their view of data quality. We found 3 different types of buying behaviour on the web. We also found that context actively affected the users decision, i.e. users were willing to pay when the data was to make a professional decision but not for leisure use. To enable us to observe the behaviour of consumers whilst using data online, we built a working prototype of a LD application that would enable potential users of the system to experience the data and give us feedback about how they would behave in a LD environment. This was then extended into a second LD application to find if the same principles held true if actual capital was involved and they had to make a conscious decision regarding payment. With this in mind we proposed a potential architecture for the consumption of LD on the web. We determined potential issues which affect a consumers willingness to pay for data which surround quality factors. This supported our hypothesis that context affects a consumers willingness to pay and that willingness to pay is related to a requirement to reduce search times. We also found that a consumers perception of value and criticality of purpose also affected their willingness to pay. Finally we outlined an architecture to enable users to use LD where different scenarios may be involved which may have potential payment restrictions. This work is our contribution to the issue of the business case for LD on the web and is a starting point.

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Identifiers

ORCID for Nicholas Gibbins: orcid.org/0000-0002-6140-9956

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