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.