Data

Industry Type, Skilled Employment and Income: Kelvin Grove Urban Village, Macquarie Park Innovation District and Monash Technology Precinct, Australia

Queensland University of Technology
Adu McVie, Rosemary ; Yigitcanlar, Tan ; Xia, Bo ; Erol, Isil
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25912/RDF_1673400515569&rft.title=Industry Type, Skilled Employment and Income: Kelvin Grove Urban Village, Macquarie Park Innovation District, Monash Technology Precinct&rft.identifier=10.25912/RDF_1673400515569&rft.publisher=Queensland University of Technology&rft.description=This dataset is a summary of  industry types, skilled employment,  sales income, and  number of employees collected from Kelvin Grove Urban Village (Brisbane), Macquarie Park Innovation District (Sydney), and Monash Technology Precinct (Melbourne), Australia. It was compiled from the data extracted from Dunn & Bradstreet directory,  relevant business websites, Google Earth (2018 imagery date) and Google My Map (2021 Imagery date). The dataset is presented in excel spreadsheets (tables 1-4) which consist of separate data for multinational, large national , and small and medium enterprises operating at the study areas at the time of the data collection from February 2021 to April 2022. The dataset is used in conjunction with other datasets in a pilot study which adopts a multidimensional innovation district performance framework to develop an innovation district typology matrix and evaluates its practicality with real innovation district data. The dataset contributes toward classification of Australia’s three eminent innovation districts ─Macquarie Park Innovation District, Monash technology Precinct and Kelvin Grove Urban Village based on their performance. &rft.creator=Adu McVie, Rosemary &rft.creator=Yigitcanlar, Tan &rft.creator=Xia, Bo &rft.creator=Erol, Isil &rft.date=2022&rft.edition=1&rft.relation=https://eprints.qut.edu.au/235113/&rft.coverage=144.975784,-37.941129&rft.coverage=151.216019,-33.885427&rft.coverage=153.061722,-27.513346&rft_rights=© Rosemary Adu McVie, 2022&rft_rights=Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/4.0/&rft_subject=Innovation district&rft_subject=Kelvin Grove Urban Village&rft_subject=Typology matrix&rft_subject=Innovation district classification&rft_subject=Macquarie Park Innovation District&rft_subject=Monash Technology Precinct&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

Open Licence view details
CC-BY

Creative Commons Attribution 3.0
http://creativecommons.org/licenses/by/4.0/

© Rosemary Adu McVie, 2022

Access:

Other

Contact Information

Postal Address:
Mrs Rosemary Adu McVie

rosemary.adu@pnguot.ac.pg

Full description

This dataset is a summary of  industry types, skilled employment,  sales income, and  number of employees collected from Kelvin Grove Urban Village (Brisbane), Macquarie Park Innovation District (Sydney), and Monash Technology Precinct (Melbourne), Australia. It was compiled from the data extracted from Dunn & Bradstreet directory,  relevant business websites, Google Earth (2018 imagery date) and Google My Map (2021 Imagery date). The dataset is presented in excel spreadsheets (tables 1-4) which consist of separate data for multinational, large national , and small and medium enterprises operating at the study areas at the time of the data collection from February 2021 to April 2022.

The dataset is used in conjunction with other datasets in a pilot study which adopts a multidimensional innovation district performance framework to develop an innovation district typology matrix and evaluates its practicality with real innovation district data. The dataset contributes toward classification of Australia’s three eminent innovation districts ─Macquarie Park Innovation District, Monash technology Precinct and Kelvin Grove Urban Village based on their performance.

Data time period: 02 2021 to 30 04 2022

This dataset is part of a larger collection

Click to explore relationships graph

144.97578,-37.94113

144.975784,-37.941129

151.21602,-33.88543

151.216019,-33.885427

153.06172,-27.51335

153.061722,-27.513346

Subjects

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Identifiers