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A network approach to understanding obesogenic environments for children in Pennsylvania Cover

A network approach to understanding obesogenic environments for children in Pennsylvania

Connections
Volume 38 (2018): Issue 1 (January 2018)
By: Emily A. Knapp,  Usama Bilal,  Bridget T. Burke,  Geoff B. Dougherty and  Thomas A. Glass  
Open Access
|Jan 2019

Figures & Tables

Fig. 1

Network graph for 1,288 communities in Pennsylvania. This shows a graph of the network of connections between attributes of communities in 1,288 communities in Pennsylvania. Each node in the network represents one feature of the communities, and the edges in the network are absolute values of Spearman correlation coefficients. The bivariate correlation between each variable and average body mass index (BMI) z-score is shown by the shading of each node, with darker colors representing stronger absolute correlation with average community BMI z-score. The strength of absolute correlation between two nodes is represented by the darkness and thickness of the lines connecting the variables. A thick, dark line may represent either a strong positive or a strong negative correlation. Modules of highly connected variables were created using the walktrap method.
Network graph for 1,288 communities in Pennsylvania. This shows a graph of the network of connections between attributes of communities in 1,288 communities in Pennsylvania. Each node in the network represents one feature of the communities, and the edges in the network are absolute values of Spearman correlation coefficients. The bivariate correlation between each variable and average body mass index (BMI) z-score is shown by the shading of each node, with darker colors representing stronger absolute correlation with average community BMI z-score. The strength of absolute correlation between two nodes is represented by the darkness and thickness of the lines connecting the variables. A thick, dark line may represent either a strong positive or a strong negative correlation. Modules of highly connected variables were created using the walktrap method.

Fig. 2

Network graphs for 1288 communities in Pennsylvania, by quartile of percent of children at or above the 85th percentile of BMIz. In communities in the lowest quartile of percent of children who are overweight or obese (A: left), community features appear to be less tightly clustered, i.e., co-occur less often, than in communities in the highest quartile of community BMIz (B: right).
Network graphs for 1288 communities in Pennsylvania, by quartile of percent of children at or above the 85th percentile of BMIz. In communities in the lowest quartile of percent of children who are overweight or obese (A: left), community features appear to be less tightly clustered, i.e., co-occur less often, than in communities in the highest quartile of community BMIz (B: right).

Fig. 3

Association of degree centrality of each community feature with prevalence of overweight and obesity among children. Correlation between community features and body mass index is stronger for more central variables of the obesity-related network features (R = 0.51).
Association of degree centrality of each community feature with prevalence of overweight and obesity among children. Correlation between community features and body mass index is stronger for more central variables of the obesity-related network features (R = 0.51).

Obesity-related community features included in network analysis_

Variable identifierFeature
C-1Violent crime per 100,000 population
C-2Crimes against person per 100,000 pop
C-3Crimes against property per 100,000 pop
F-1Grocery stores and supermarkets per square mile
F-2Gas stations and convenience stores per square mile
F-3Snack stores (donuts, pretzels, ice cream) per square mile
F-4All food establishments per square mile
F-5Fast food chain restaurants, count
F-6All retail food establishments per square mile
F-7All food service establishments per square mile
F-8Diversity of food establishments in 9 categories
F-9Limited service restaurants per capita
F-10Full service restaurants per capita
F-11Bars and taverns per capita
F-12Health food and gourmet stores per capita
F-13Fruit and vegetable stores and stalls per sqare mile
F-14Discount stores per square mile
L-1Average block length
L-2Household density
L-3Road intersection density
L-4Road segment length diversity
P-1Diversity of physical activity establishments in 6 categories
P-2Indoor recreational centers per square mile
P-3Outdoor recreational centers per square mile
P-4Public outdoor parks and recreational spaces per street mile
P-5All physical activity establishments per square mile
P-6Indoor fitness and recreational facilities per street miles
P-7Outdoor fitness & recreational facilities per capita
P-8Indoor recreational clubs and organizations per square mile
P-9Outdoor recreational clubs and organizations per square mile
T-1Vehicle miles traveled on main roads (total)
T-2Vehicle miles traveled on main roads per capita

Network modularity and average network degree in the overall network and by quartile of prevalence of childhood obesity_

Quartiles of prevalence of childhood obesity
Quartile 1Quartile 2Quartile 3Quartile 4Overall network
Network modularity0.18910.26810.11810.09820.1496
Average network degree0.33180.35330.35840.36230.3507
DOI: https://doi.org/10.21307/connections-2018-001 | Journal eISSN: 2816-4245 | Journal ISSN: 0226-1766
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Language: English
Page range: 1 - 11
Published on: Jan 18, 2019
Published by: International Network for Social Network Analysis (INSNA)
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Obesity environment networks
Related subjects:
Social sciences,
Social sciences, other

© 2019 Emily A. Knapp, Usama Bilal, Bridget T. Burke, Geoff B. Dougherty, Thomas A. Glass, published by International Network for Social Network Analysis (INSNA)
This work is licensed under the Creative Commons Attribution 4.0 License.

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