Human development in Morocco: out-of-sample prediction using spatial econometrics and RandomForest

被引：0
|
作者：
Lehnert, Matthew R. [1 ]
Smirnov, Oleg [2 ]
机构：
[1] Al Akhawayn Univ Ifrane, Sch Humanities & Social Sci, Ifrane, Morocco
[2] Univ Toledo, Econ, Toledo, OH USA
来源：
AFRICAN GEOGRAPHICAL REVIEW | 2024年 / 43卷 / 01期
关键词：
Spatial econometrics; random forest; spatial data science; spatial machine learning; human development index; Morocco; HUMAN-DEVELOPMENT INDEX; INEQUALITY;
D O I：
10.1080/19376812.2022.2107547
中图分类号：
P9 [自然地理学]; K9 [地理];
学科分类号：
0705 ; 070501 ;
摘要：
Morocco has integrated the human development paradigm into their country-wide planning goals. The 2004 Moroccan census measured HDI for every commune in Morocco, the smallest spatial unit for which data are available. However, when the 2014 Moroccan census was released HDI values were omitted . To measure the change in HDI over the 10-year period, this article uses spatial econometric models along with the RandomForest algorithm in order to make out-of-sample predictions for 2014 HDI scores at the Moroccan commune level. The article concludes that in certain situations RandomForest can outperform spatial econometric models, and discusses the benefits this approach.
引用
收藏
页码：60 / 79
页数：20
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