Modelling the spatial pattern of housing-renovation employment in Melbourne, Australia: an application of geographically weighted regression
Kiran KC
Prem Chhetri
Colin Arrowsmith
Jonathan Corcoran
10.4225/03/5a13b67a7bcca
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.
2017-11-21 05:15:35
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