A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture,Japan

作     者：DERDOURI Ahmed MURAYAMA Yuji DERDOURlAhmed;村山佑司

作者机构：Division of Spatial Information ScienceGraduate School of Life and Environmental SciencesUniversity of TsukubaTennodaiTsukubaIbarakiJapan Faculty of Life and Environmental SciencesUniversity of TsukubaTennodaiTsukubaIbarakiJapan 

出 版 物：《Journal of Geographical Sciences》 (地理学报（英文版）)

年 卷 期：2020年第30卷第5期

页      面：794-822页

核心收录：

学科分类：12[管理学] 1204[管理学-公共管理] 082802[工学-农业水土工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0828[工学-农业工程] 0835[工学-软件工程] 120405[管理学-土地资源管理] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：land price spatial estimation kriging machine learning Fukushima prefecture,Japan 

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 *** asset would assist them in making economically justified decisions in favor of key investors for development projects and post-disaster recovery *** 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 *** this data is useful,it does not provide complete information at every site for all market ***,estimating and mapping land prices based on sound statistical theories is *** paper presents a comparative study of spatial prediction of land prices in 2015 in Fukushima prefecture based on geostatistical methods and machine learning *** use,elevation,and socioeconomic factors,including population density and distance to railway stations,were used for *** show the superiority of the random forest ***,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.