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• W2162213595 abstract "Hedonic price modelling attempts to uncover information on the determinants of prices - in this case the pricesare those of houses in the Greater London area for the period between 1980 and 1998. The determinants of houseprices can include house attributes (such as size, type of building, age, etc.), neighbourhood attributes (such asproportion of unemployed people in the neighbourhood or local tax rates) and geographic attributes (such asdistance from the city centre or proximity to various amenities) (Orford 1999).Almost all applications of hedonic price models applied to housing are in the form of multiple linear regressionmodels where price is regressed on various attributes. The parameter estimates from the calibration of this typeof regression model are assumed to yield information on the relative importance of various attributes ininfluencing price. One major problem with this approach is that it assumes that the determinants of prices are thesame in all parts of the study area. This seems particularly illogical in this type of application where there couldeasily be local variations in preferences and also in supply and demand relationships. Hence, it seems reasonableto calibrate local hedonic price models rather than global ones – that is, to calibrate a model form which isflexible enough to allow the determinants of house prices to vary spatially. Geographically Weighted Regression(GWR) (Fotheringham et al. 2002) is a statistical technique that allows local calibrations and which yields localestimates of the determinants of house prices. GWR was recently used to investigate spatial variations in houseprice determinants across London separately for each of the years between 1980 and 1998 (Crespo et al. 2007).The result of the GWR analysis is a set of continuous localised parameter estimate surfaces which describe thegeography of the parameter space. These surfaces are typically visualised with a set of univariate choroplethmaps for each surface which are used to examine the plausibility of the stationarity assumption of the traditionalregression and different possible causes of non-stationarity for each separate parameter (Fotheringham and al.2002). The downside of these separate univariate visualisations is that multivariate spatial and non-spatialrelationships and patterns in the parameter space can not be seen. In an attempt to counter this inadequacy, in aprevious study we suggested to treat the result space of one single GWR analysis as a multivariate dataset andvisually explore it (Demsar et al. 2007). The goal was to identify spatial and multivariate patterns that theseparate univariate mapping could not recognise. In this paper we extend this approach with the temporaldimension: we use Geovisual Analytical exploration to investigate the spatio-temporal dynamics in a time seriesof GWR hedonic price models. The idea is to merge the time series of GWR result spaces (one space per year)into one single highly-dimensional spatio-temporal dataset, which we then visually explore in an attempt touncover information about the temporal and spatio-temporal behaviour of parameter estimates of GWR andconsequently of underlying geographical processes." @default.
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• W2162213595 date "2008-01-01" @default.
• W2162213595 modified "2023-10-08" @default.
• W2162213595 title "Combining Geographically Weighted Regression and Geovisual Analytics to investigate temporal variations in house price determinants across London in the period 1980-1998" @default.
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