10.4225/03/5a13b67a7bcca
Kiran KC
Kiran
KC
Prem Chhetri
Prem
Chhetri
Colin Arrowsmith
Colin
Arrowsmith
Jonathan Corcoran
Jonathan
Corcoran
Modelling the spatial pattern of housing-renovation employment in Melbourne, Australia: an application of geographically weighted regression
Monash University
2017
Geographic Information Systems (GIS)
Geographically weighted regression
Ordinary least squares regression
Residential housing renovation employment
2014
1959.1/1060130
monash:131140
1832-5505
Geography
Geospatial Information Systems
2017-11-21 05:15:35
Journal contribution
https://bridges.monash.edu/articles/journal_contribution/Modelling_the_spatial_pattern_of_housing-renovation_employment_in_Melbourne_Australia_an_application_of_geographically_weighted_regression/5619754
This paper discusses research aimed at identifying key factors influencing the distribution of residential housing renovation employment in metropolitan Melbourne. Using Geographically Weighted Regression (GWR), employment focused on residential housing renovation is modelled using six parameters representing urban space: distance to the central business district, median household income, distance to highways, the number of nearby shopping centres, distance to public open space and accessibility to railway stations. Of the six different explanatory variables, the estimated value of the Ordinary Least Square model for distance to CBD and open space were statistically significant. Mapping the values of local coefficient estimates of independent variables revealed their extent of influence and variation in residential housing renovation employment.