Application of spatial statistics in studying the distribution of food contamination.
10.3760/cma.j.issn.0254-6450.2019.02.022
- Author:
X M WANG
1
;
G X XIAO
2
;
J J LIANG
1
;
L X GUO
3
;
Y LIU
4
,
5
Author Information
1. Food Safety Monitoring Section, Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China.
2. Risk Monitoring Department, China National Center for Food Safety Risk Assessment, Beijing 100022, China.
3. Risk Communication Department, China National Center for Food Safety Risk Assessment, Beijing 100022, China.
4. Risk Communication Department, China National Center for Food Safety Risk Assessment, Beijing 100022, China
5. Post-doctoral Station, Guizhou Academy of Sciences, Guiyang 550001, China.
- Publication Type:Journal Article
- Keywords:
Arsenic;
Rice;
Spatial statistics
- MeSH:
Arsenic/adverse effects*;
China;
Cluster Analysis;
Food Contamination;
Food Supply;
Humans;
Spatial Analysis
- From:
Chinese Journal of Epidemiology
2019;40(2):241-246
- CountryChina
- Language:Chinese
-
Abstract:
Objective: Based on data related to arsenic contents in paddy rice, as part of the food safety monitoring programs in 2017, to discuss and explore the application of spatial analysis used for food safety risk assessment. Methods: One province was chosen to study the spatial visualization, spatial point model estimation, and kernel density estimation. Moran's I statistic of spatial autocorrelation methods was used to analyze the spatial distribution at the county level. Results: Data concerning the spatial point model estimation showed that the spatial distribution of pollution appeared relatively dispersive. From the kernel density estimation, we found that the hot spots of pollution were mainly located in the central and eastern regions. The global Moran's I values appeared as 0.11 which presented low spatial aggregation to the rice arsenic contamination and with statistically significant differences. One "high-high" and two typical "low-low" clustering were seen in this study. Conclusion: Results from our study provided good visual demonstration, identification of pollution distribution rules, hot spots and aggregation areas for research on the distribution of food pollutants. Spatial statistics can provide technical support for the implementation of issue-based monitoring programs.
