Stacking-GWR Model for Spatio-temporal Prediction of Urban Land Prices
The pricing of urban land significantly affects territorial and spatial planning,contemporary urban governance,and land market regulation,accurate forecasting of urban land prices is critically important.The price trends for different land uses exhibit notable differences and spatial heterogeneity,making it difficult to predict using a single model.Therefore,this paper es-tablishes a new spatiotemporal prediction model for urban land prices,the Stacking-GWR model.The study focuses on the main urban area of Changzhou City,dividing land into industrial and non-industrial groups based on price trends.The Stacking-GWR model is used for land price prediction and compared with the results from using Stacking,geographically weighted regression(GWR),and geographically and temporally weighted regression(GTWR)models individually.The results are shown as fol-lows.① The Stacking-GWR model,which integrates feature information,spatial information,and temporal information from land price data,improves prediction accuracy.② Prediction accuracy is higher for grouped predictions based on land price trends compared to non-grouped predictions.③ There are significant differences in global and local influencing factors for land prices between industrial and non-industrial land.
land priceland price predictionensemble learninggeographically weighted regressionChangzhou City
万方数据
维普
