Optimal discretization for geographical detectors-based risk assessment

被引：278

作者：

Cao, Feng ^{[1
]}

Ge, Yong ^{[1
]}

Wang, Jin-Feng ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China

来源：

GISCIENCE & REMOTE SENSING | 2013年 / 50卷 / 01期

关键词：

geographical detectors; discretization; risk assessment; NTD; NEURAL-TUBE DEFECTS; SPATIAL ASSOCIATION; POISSON REGRESSION; HESHUN REGION; BIRTH-DEFECTS; HEALTH; CLASSIFICATION; SUPPORT; DISEASE; SHANXI;

D O I：

10.1080/15481603.2013.778562

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

摘要：

The geographical detectors model is a new spatial analysis method for the assessment of health risks. It is adapted to discrete risk factors. Meanwhile, the geographical detectors model also effectively analyzes the continuous risk factors by discretizing the continuous data into discrete data. The biggest difficulty is in deciding how to discretize continuous risk factors using the most appropriate discretization method. In this paper, we will discuss the selection of an optimal discretization method for geographical detectors-based risk assessment, and exemplify the process using neural tube defects (NTD) from the Heshun County, Shanxi Province, China.

引用

页码：78 / 92

页数：15

共 26 条

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