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Innovation District Typology Classification via Performance Framework: Insights from Sydney, Melbourne, and Brisbane

Author(s):
ORCID
ORCID

Medium: journal article
Language(s): English
Published in: Buildings, , n. 9, v. 12
Page(s): 1398
DOI: 10.3390/buildings12091398
Abstract:

As a new land use type, innovation districts are taking prominence in the urban development policies and plans of many cities across the globe. This new urban land use comes in many shapes and forms and offers various features and functions to the users. Despite its increasing popularity, there exist only limited approaches to classify innovation districts, and there are no holistic typologies developed so far. This study focuses on this understudied, but important area of research. The paper aims to develop an innovation district typology matrix and evaluates its practicality with real innovation district data. The methodological approach is three-fold. First, the multidimensional innovation district classification framework is adopted as a performance framework. Second, data from three eminent Australian innovation districts—i.e., Macquarie Park Innovation District (Sydney), Monash Technology Precinct (Melbourne), and Kelvin Grove Urban Village (Brisbane)—are collected. Third, both qualitative and quantitative analysis methods are employed for data analysis. The study finds that innovation district performances can be measured, and typologies can be developed though a novel approach. These, in return, inform property developers and managers, city administrators, and urban planners in their efforts to plan, design, develop, and manage competitive innovation districts.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

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  • About this
    data sheet
  • Reference-ID
    10692564
  • Published on:
    23/09/2022
  • Last updated on:
    10/11/2022
 
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