Big Data Approach as an Institutional Innovation to Tackle Hong Kong’s Illegal Subdivided Unit Problem

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ID: 73644
2018
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Abstract
While applications of big data have been extensively studied, discussion is mostly made from the perspectives of computer science, Internet services, and informatics. Alternatively, this article takes the big data approach as an institutional innovation and uses the problem of illegal subdivided units (ISUs) in Hong Kong as a case study. High transaction costs incurred in identification of suspected ISUs and associated enforcement actions lead to a proliferation of ISUs in the city. We posit that the deployment of big data analytics can lower these transaction costs, enabling the government to tackle the problem of illegal accommodations. We propose a framework for big data collection, analysis, and feedback. As the findings of a structured questionnaire survey reveal, building professionals believed that the proposed framework could reduce transaction costs of ISU identification. Yet, concerns associated with the big data approach like privacy and predictive policing were also raised by the professionals.
Reference Key
yau2018bigsustainability Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yau, Yung;Lau, Wai Kin;
Journal sustainability
Year 2018
DOI
DOI not found
URL
http://www.mdpi.com/2071-1050/10/8/2709
Keywords
environmental effects of industries and plants renewable energy sources environmental sciences social sciences (general) sociology (general)

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