Next Article in Journal
Assessing Quality of Life Inequalities. A Geographical Approach
Next Article in Special Issue
Radio Base Stations and Electromagnetic Fields: GIS Applications and Models for Identifying Possible Risk Factors and Areas Exposed. Some Exemplifications in Rome
Previous Article in Journal
Simulating Large-Scale 3D Cadastral Dataset Using Procedural Modelling
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Spatial Patterns of Childhood Obesity Prevalence in Relation to Socioeconomic Factors across England

1
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2
Department of Geography, College of Science, Swansea University, Swansea SA28PP, UK
3
Institute of Data Science, German Aerospace Center (DLR), 07745 Jena, Germany
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(10), 599; https://doi.org/10.3390/ijgi9100599
Submission received: 27 August 2020 / Revised: 5 October 2020 / Accepted: 9 October 2020 / Published: 11 October 2020

Abstract

To examine to what extent spatial inequalities in childhood obesity are attributable to spatial inequalities in socioeconomic characteristics across a country, we aimed to investigate the spatial associations of socioeconomic characteristics and childhood obesity. We first explored spatial patterns of childhood obesity prevalence, and subsequently investigated the spatial associations of socioeconomic factors and childhood obesity prevalence across England by selecting and estimating appropriate spatial regression models. As the data used are geospatial data, we used two newly developed specifications of spatial regression models to investigate the spatial association of socioeconomic factors and childhood obesity prevalence. As a result, among the two newly developed specifications of spatial regression models, the fast random effects specification of eigenvector spatial filtering (FRES-ESF) model appears to outperform the matrix exponential spatial specification of spatial autoregressive (MESS-SAR) model. Empirical results indicate that positive spatial dependence is found to exist in childhood obesity prevalence across England; and that socioeconomic factors are significantly associated with childhood obesity prevalence across England. In England, children living in areas with lower socioeconomic status are at higher risk of obesity. This study suggests effectively reducing spatial inequalities in socioeconomic status will plays a vital role in mitigating spatial inequalities in childhood obesity prevalence.
Keywords: childhood obesity; socioeconomic disadvantage; spatial regression model; matrix exponential spatial specification model; eigenvector spatial filtering model childhood obesity; socioeconomic disadvantage; spatial regression model; matrix exponential spatial specification model; eigenvector spatial filtering model

Share and Cite

MDPI and ACS Style

Sun, Y.; Hu, X.; Huang, Y.; On Chan, T. Spatial Patterns of Childhood Obesity Prevalence in Relation to Socioeconomic Factors across England. ISPRS Int. J. Geo-Inf. 2020, 9, 599. https://doi.org/10.3390/ijgi9100599

AMA Style

Sun Y, Hu X, Huang Y, On Chan T. Spatial Patterns of Childhood Obesity Prevalence in Relation to Socioeconomic Factors across England. ISPRS International Journal of Geo-Information. 2020; 9(10):599. https://doi.org/10.3390/ijgi9100599

Chicago/Turabian Style

Sun, Yeran, Xuke Hu, Ying Huang, and Ting On Chan. 2020. "Spatial Patterns of Childhood Obesity Prevalence in Relation to Socioeconomic Factors across England" ISPRS International Journal of Geo-Information 9, no. 10: 599. https://doi.org/10.3390/ijgi9100599

APA Style

Sun, Y., Hu, X., Huang, Y., & On Chan, T. (2020). Spatial Patterns of Childhood Obesity Prevalence in Relation to Socioeconomic Factors across England. ISPRS International Journal of Geo-Information, 9(10), 599. https://doi.org/10.3390/ijgi9100599

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop