| 基于克里格回归分析和机器学习算法的日本福岛县土地价格估算与制图的比较分析 |
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| 引用本文： | DERDOURlAhmed,村山佑司.基于克里格回归分析和机器学习算法的日本福岛县土地价格估算与制图的比较分析[J].地理学报(英文版),2020,30(5):794-822. |
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| 作者姓名： | DERDOURlAhmed 村山佑司 |
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| 作者单位： | Division of Spatial Information Science;Faculty of Life and Environmental Sciences |
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| 摘 要： | Finding accurate methods for estimating and mapping land prices at the macro-scale based on publicly accessible and low-cost spatial data is an essential step in producing a meaningful reference for regional planners.This asset would assist them in making economically justified decisions in favor of key investors for development projects and post-disaster recovery efforts.Since 2005,the Ministry of Land,Infrastructure,and Transport of Japan has made land price data open to the public in the form of observations at dispersed locations.Although this data is useful,it does not provide complete information at every site for all market participants.Therefore,estimating and mapping land prices based on sound statistical theories is required.This paper presents a comparative study of spatial prediction of land prices in 2015 in Fukushima prefecture based on geostatistical methods and machine learning algorithms.Land use,elevation,and socioeconomic factors,including population density and distance to railway stations,were used for modeling.Results show the superiority of the random forest algorithm.Overall,land prices are distributed unevenly across the prefecture with the most expensive land located in the western region characterized by flat topography and the availability of well-connected and highly dense economic hotspots.
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| 关 键 词： | LAND PRICE spatial estimation KRIGING machine learning FUKUSHIMA prefecture Japan |
| 收稿时间： | 2019-02-19 |
A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture,Japan |
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| DERDOURI Ahmed,MURAYAMA Yuji.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):794-822. |
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| Authors: | DERDOURI Ahmed MURAYAMA Yuji |
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| Institution: | 1. Division of Spatial Information Science, Graduate School of Life and Environmental Sciences, Uni-versity of Tsukuba, Tennodai, Tsukuba, Ibaraki, Japan;2. Faculty of Life and Environmental Sciences, University of Tsukuba, Tennodai, Tsukuba, Ibaraki, Japan |
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| Abstract: | Finding accurate methods for estimating and mapping land prices at the macro-scale based on publicly accessible and low-cost spatial data is an essential step in producing a meaningful reference for regional planners. This asset would assist them in making economically justified decisions in favor of key investors for development projects and post-disaster recovery efforts. Since 2005, the Ministry of Land, Infrastructure, and Transport of Japan has made land price data open to the public in the form of observations at dispersed locations. Although this data is useful, it does not provide complete information at every site for all market participants. Therefore, estimating and mapping land prices based on sound statistical theories is required. This paper presents a comparative study of spatial prediction of land prices in 2015 in Fukushima prefecture based on geostatistical methods and machine learning algorithms. Land use, elevation, and socioeconomic factors, including population density and distance to railway stations, were used for modeling. Results show the superiority of the random forest algorithm. Overall, land prices are distributed unevenly across the prefecture with the most expensive land located in the western region characterized by flat topography and the availability of well-connected and highly dense economic hotspots. |
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| Keywords: | land price spatial estimation kriging machine learning Fukushima prefecture Japan |
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