Journal of Geographical Sciences, Volume. 30, Issue 5, 794(2020)
A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan
Figures & Tables(20)
Fig. 1. Fukushima prefecture and its administrative boundaries, topographic features, transportation lines, and evacuation zones after the Fukushima Daiichi Nuclear Plant disaster (as of September 2015)
Fig. 2. Changes in land prices averaged by land type in Fukushima prefecture (2005-2018)
Fig. 5. Fitted semi-variograms for the kriging models for the year 2015: (a) Exp: Exponential (b) Gau: Gaussian (c) Sph: Spherical. The nugget, range, and sill values and the mathematical models are shown in the bottom right corner
Fig. 6. The results of the regression kriging for the year 2015 using the exponential model (upper), Gaussian model (middle), and spherical model (lower). On the left are the estimated log-transformed land prices using regression kriging. On the right are the validation errors in the training samples. Capital letters denote major cities within Fukushima prefecture, which are A: Fukushima, B: Koriyama, C: Iwaki, D: Aizuwakamtsu, and E: Shirakawa
Fig. 7. Land price maps for the year 2015 predicted from officially published land price observations using regression kriging based on three mathematical models (ordered from left to right): (1)
Fig. 8. Boxplots of performance of machine learning methods in terms of the MAE, the RMSE, and R2 for the year 2015
Fig. 9. Observed land prices vs. predicted land prices for the year 2015 in the testing samples by different machine learning methods (ordered from left to right, up to down): (1)
Fig. 10. Land price maps for the year 2015 predicted from officially published land price observations using machine learning algorithms (ordered from left to right, up to down): (1)
Fig. 11. Maps of differences in the 2015 land prices between the best-performing machine learning algorithms: (1)
Fig. 12. Area percentage of RF- and krig.EXP-based estimated land price for the year 2015 distributed by predefined ranges in Fukushima prefecture and its subregions
Table 1.
Descriptive list of reviewed literature regarding land price estimation/mapping grouped by estimation approach: (1) hedonic models, (2) geostatistical methods, (3) machine learning algorithms, and (4) comparison of various approaches
View tableView in ArticleTable 1.
Descriptive list of reviewed literature regarding land price estimation/mapping grouped by estimation approach: (1) hedonic models, (2) geostatistical methods, (3) machine learning algorithms, and (4) comparison of various approaches
Estimation approach Study Study area Method(s) Mapping Objective Highlighted results Hedonic models ( Löchl, 2006 )Canton Zurich, Switzerland Hedonic regression Yes Developing an estimation model of rent and land prices Two classified maps of land prices for residential and commercial uses ( Kim and Kim, 2016 )Seoul, South Korea OLS and spatial regression models No Estimation of land value using OLS and generalized regression models Spatial error model (SEM) found to be the best of the tested models ( Hilal )et al. , 2016Côte-d’Or, France OLS No Estimation of the price of agricultural lands at cadastral levels based on previous real estate transactions Hedonic prices were calculated based on a range of attributes influencing agricultural lands most notable time effects Geostatistical methods ( Luo and Wei, 2004 )Milwaukee, Wisconsin, USA Kriging No Predicting urban land values of different land use categories using kriging models Overall average standard error of 2% ( Chica-Olmo, 2007 )City of Granada, Spain Kriging and cokriging Yes Estimating and mapping housing prices using kriging and cokriging approaches Cokriging has a lower standard error compared with that of kriging ( Inoue )et al. , 2007Tokyo 23 wards, Japan Kriging Yes Mapping estimated land prices in Tokyo’s 23 wards from 1975 to 2004 Kriging model-based results were more accurate than those for OLS with the average error ranging from 2% to 10% Geostatistical methods ( Tsutsumi )et al. , 2011Tokyo metropolitan area, Japan Regression kriging Yes Developing a system to estimate and map residential land price in the Tokyo metropolitan area 10% was the average error ratio for the exponential model but 18.3% for the Gaussian model ( Kuntz and Helbich, 2014 )Metropolitan area of Vienna, Austria Kriging and cokriging Yes Mapping predicted real estate prices Universal cokriging showed better results in terms of cross-validation results ( Chica-Olmo )et al. , 2019City of Grenada, Spain Regression and universal cokriging Yes Spatiotemporally estimating housing price variations 1988-2005 Regression cokriging was found to be slightly better ( Palma )et al. , 2019Italy Jackknife kriging No Predicting real estate prices based on socioeconomic factors for the period 2014-2016 Accuracy of the model improved when considering the spatio-temporal correlation Machine learning algorithms ( Gu )et al. , 2011A district of Tangshan city, China Hybrid genetic algorithm and support vector machine model (G-SVM), Grey Model (GM) No Forecasting housing prices G-SVM outperformed GM in many aspects ( Antipov and Pokryshevskaya, 2012 )Saint Petersburg, Russia Machine learning algorithms No Estimating residential apartments Random forest was found to be the most robust among all methods ( Wang )et al. , 2014Chongqing city, China SVM optimized by particle swarm optimization (PSO), BP neural network No Forecasting real estate price based on PSO-optimized SVM compared to other BP neural network PSO-SVM showed higher forecasting accuracy than BP neural network ( Park and Bae, 2015 )Fairfax County, Virginia, USA Machine learning algorithms (C4.5, RIPPER, Naïve Bayesian, and AdaBoost) No Prediction of housing prices using different machine learning methods RIPPER model outperformed all selected methods Comparison of various approaches ( Bourassa )et al. , 2010Jefferson County, Kentucky, USA OLS, nearest neighbors, geostatistical and trend surface models No Comparing the outcomes of several methods estimating house prices The geostatistical model showed better results in terms of prediction errors ( Sampathkumar )et al. , 2015Chennai metropolitan area, India Multiple regression and neural network No Modeling and estimation of land prices based on economic and social factors Neural network and multiple regression performed well with a slight superiority of the former ( Hu )et al. , 2016Wuhan city, China Empirical Bayesian kriging (EBK), GWR, OLS Yes Modeling and visualizing dependency of urban residential land price and the influential variables Estimated coefficients of variables impacting land prices depend on the location based on GWR results which outperformed OLS ( Schernthanner )et al. , 2016Potsdam, Germany Hedonic regression, kriging, and random forest Yes Comparing estimated rental prices by three methods and visualize the outcome RF found to be the most accurate method
Table 2.
The three mathematical models used for kriging and their abbreviations
View tableView in ArticleTable 2.
The three mathematical models used for kriging and their abbreviations
Category Model Abbreviation R package Geostatistical Universal kriging Exponential krig.EXP gstat ( Pebesma, 2004 )Gaussian krig.GAU Spherical krig.SPH
Table 3.
Summary of spatial prediction models used in this study: Linear, nonlinear, and regression trees models are grouped as proposed by Kuhn and Johnson (2013). Abbreviations are used to refer to each method in the manuscript
View tableView in ArticleTable 3.
Summary of spatial prediction models used in this study: Linear, nonlinear, and regression trees models are grouped as proposed by Kuhn and Johnson (2013). Abbreviations are used to refer to each method in the manuscript
Category Model Abbreviation R package Linear Generalized linear model GLM base Generalized additive model using splines GAMS mgcv Support vector machines with linear kernel SVMLinear kernlab Nonlinear Multivariate adaptive regression spline MARS earth k-nearest neighbors kNN base Support vector machines with radial basis function kernel SVMRadial kernlab Regression trees Cubist Cubist Cubist Stochastic gradient boosting GBM gbm ( Ridgeway, 2005 )Random forest RF randomForest ( Breiman, 2001 )
Table 4.
List of explanatory variables selected in this study with their data sources and the related abbreviations
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List of explanatory variables selected in this study with their data sources and the related abbreviations
Explanatory variables Data GIS function Variable description Abbreviation Distance to the nearest railway station (m) Railway stations Near Calculated using the railway stations layer Distance Area of rice fields [m2] Land uses within a square kilometer Spatial Join The areas of different land-uses within one square kilometer classified according to the National Land Numerical Information Paddy Area of other agricultural land (m2) Agricultural Area of forests (m2) Forests Area of uncultivated land (m2) Uncultivated Area of roads (m2) Roads Area of railways (m2) Railways Area of other land uses (m2) Other uses Area of water bodies (m2) Water Area of seashore (m2) Seashore Area of the surface of the sea (m2) Sea Area of golf courses (m2) Golf Dummy variable for urbanization promoting area Promoted urbanization areas Spatial Join A dummy variable; if the point location falls inside the area, the variable value receives 1, else 0 Promotion Population density (persons/km2) Population Spatial Join Calculated using the population data of 2015 for every minor municipal district Density Number of enterprises Enterprises Spatial Join Statistical GIS data of 2015 for every minor municipal district Enterprises Number of employees Employees Employees Elevation (m) DEM Extract Multi Values to Points Elevation of the point location Elevation
Table 5.
Overview of datasets used in the study, their sources, and the year of release
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Overview of datasets used in the study, their sources, and the year of release
Data layers Source Year Land price observations (published and prefectural) National Land Numerical Information 2015 Railway stations 2015 Land uses within 1 km2 area and their areas 2014 Promoted urbanization areas 2011 Population of every minor municipal district Statistics Bureau of Japan 2015 Number of enterprises and employees of every minor municipal district DEM USGS -
Table 6.
Regression results with detailed explanatory variables and their estimated coefficients
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Regression results with detailed explanatory variables and their estimated coefficients
Variables Unit Coefficients’ estimate Intercept - 4.439 *** Distance to the nearest railway station m -2.09 × 10-5 *** Population density persons/km2 3.104 × 10-5 *** Area of rice fields m2 -3.935 × 10-7 *** Area of other agricultural land m2 -4.731 × 10-7 *** Area of forests m2 -2.733 × 10-7 *** Area of uncultivated land m2 -7.437 × 10-7 . Area of roads m2 7.211 × 10-7 ** Area of railways m2 -3.301 × 10-8 Area of other land uses m2 -8.97 × 10-8 Area of water bodies m2 -3.086 × 10-7 *** Area of seashore m2 -1.922 × 10-6 Area of the surface of the sea m2 -1.25 × 10-7 Area of golf courses m2 -5.843 × 10-8 Dummy variable for urbanization promoting area - 1.819 × 10-1 *** Elevation m -1.556 × 10-4 ** Number of enterprises - 3.363 × 10-4 ** Number of employees - -2.951 × 10-5 * Number of samples = 1092; residual standard error = 0.1683, multiple R 2 = 0.7408, adjustedR 2 = 0.7349;F-statistic = 125.7, p-value = < 2.2 × 10-16 *** = sign. at 1% level ** = sign. at 5% level
Table 7.
Prediction errors of validation and cross-validation tests for the three kriging models
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Prediction errors of validation and cross-validation tests for the three kriging models
Mathematical models Validation Cross-validation RMSEV (%) RMSECV (%) Exponential 15.32 15.1 Gaussian 15.86 15.57 Spherical 15.57 15.5
Table 8.
Prediction errors and accuracy of machine learning methods
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Prediction errors and accuracy of machine learning methods
Method 10-fold cross-validation Testing samples Difference MAE (%) RMSE (%) R2CV (%) R2test (%) R2CV (%) - R2test (%) Linear GLM 13.50 17.29 72.47 59.94 +12.53 GAMS 12.03 15.37 78.13 68.72 +9.41 SVMLinear 13.38 17.25 72.73 59.12 +13.61 Nonlinear MARS 12.11 15.52 77.90 70.78 +7.12 kNN 13.38 17.35 72.24 68.03 +4.21 SVMRadial 12.55 16.27 75.53 70.02 +5.51 Regression tree Cubist 12.19 15.60 77.72 72.74 +4.98 GBM 12.16 15.68 77.40 70.83 +6.57 RF 11.39 14.97 79.17 77.68 +1.49
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Ahmed DERDOURI, Yuji MURAYAMA. A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan[J]. Journal of Geographical Sciences, 2020, 30(5): 794
Paper Information
Category: Research Articles
Received: Feb. 19, 2019
Accepted: Sep. 9, 2019
Published Online: Sep. 30, 2020
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