Optimal discretization for geographical detectors-based risk assessment

被引：258

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 50 条

[31] Risk Assessment of Underground Gas Pipeline Leakage Incorporating Geographical Information System (GIS)
Seli, Vanessa Ak
Zardasti, Libriati Binti
Noor, Norhazilan Bin Md
[J]. SMART ENVIRONMENTAL SCIENCE, TECHNOLOGY AND MANAGEMENT, 2022, : 76 - 82
[32] Health impact evaluation of boron in drinking water: a geographical risk assessment in Northern France
Yazbeck, C
Kloppmann, W
Cottier, R
Sahuquillo, J
Debotte, G
Huel, G
[J]. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH, 2005, 27 (5-6) : 419 - 427
[33] Geographical distribution and risk assessment of heavy metals: a case study of mine tailings pond
Zhang, Mingjiang
Sun, Minjie
Wang, Jianlei
Yan, Xiao
Hu, Xuewu
Zhong, Juan
Zhu, Xuezhe
Liu, Xingyu
[J]. CHEMISTRY AND ECOLOGY, 2020, 36 (01) : 1 - 15
[34] Geographical origin differentiation of Chinese Angelica by specific metal element fingerprinting and risk assessment
Sun, Lei
Ma, Xiao
Jin, Hong-Yu
Fan, Chang-jun
Li, Xiao-dong
Zuo, Tian-Tian
Ma, Shuang-Cheng
Wang, Sicen
[J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2020, 27 (36) : 45018 - 45030
[35] The Optimal Re-sampling Strategy for a Risk Assessment Model
Tong, L. I.
Wei, W. Y.
Wu, P. Y.
[J]. PROCEEDINGS 18TH ISSAT INTERNATIONAL CONFERENCE ON RELIABILITY & QUALITY IN DESIGN, 2012, : 293 - 295
[36] Inference Based on Kernel Estimates of the Relative Risk Function in Geographical Epidemiology
Hazelton, Martin L.
Davies, Tilman M.
[J]. BIOMETRICAL JOURNAL, 2009, 51 (01) : 98 - 109
[37] Application of Multiple Geographical Units Convolutional Neural Network based on neighborhood effects in urban waterlogging risk assessment in the city of Guangzhou, China
Shu, Yuqin
Zheng, Guibing
Yan, Xiawan
[J]. PHYSICS AND CHEMISTRY OF THE EARTH, 2022, 126
[38] Flood-Resilient Smart Cities: A Data-Driven Risk Assessment Approach Based on Geographical Risks and Emergency Response Infrastructure
Peixoto, Joao Paulo Just
Costa, Daniel G.
Portugal, Paulo
Vasques, Francisco
[J]. SMART CITIES, 2024, 7 (01): : 662 - 679
[39] Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture
Liu, Chenli
Li, Wenlong
Zhu, Gaofeng
Zhou, Huakun
Yan, Hepiao
Xue, Pengfei
[J]. REMOTE SENSING, 2020, 12 (19)
[40] Epidemiology-based risk assessment using the benchmark dose/margin of exposure approach: the example of ethanol and liver cirrhosis
Lachenmeier, Dirk W.
Kanteres, Fotis
Rehm, Juergen
[J]. INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2011, 40 (01) : 210 - 218

← 1 2 3 4 5 →