Regionalization of Youth and Adolescent Weight Metrics for the Continental United States Using Contiguity-Constrained Clustering and Partitioning
Bibliographic Data
| ID | 14701433 |
|---|---|
| Authors | Samuel Adu-Prah (Sam Houston State University, corresponding author), Tonny J Oyana (0000-0003-0108-2370, Department of Preventive Medicine / College of Medicine / University of Tennessee Health Science Center / Memphis / TN / USA) |
| Year | 2015 |
| Volume | 50 |
| Issue | 2 |
| Pages | 61-70 |
| Publication date | 2015-06-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Cartographica The International Journal for Geographic Information and Geovisualization (JOURNAL) |
| Journal identifiers | ISSN: 0317-7173 • E-ISSN: 1911-9925 |
| Publisher | University of Toronto Press Inc. (UTPress) (PUBLISHER) |
| DOI | 10.3138/cart.50.2.2507 |
| OpenAlex | W2104170855 |
| Language | EN |
| Citations received | 2 |
| References cited | 33 |
Contemporary spatial data collection techniques, analyses, and presentations have created new opportunities for public health analyses that sometimes render existing administrative and statistical boundaries unsuitable. This article presents an applied algorithm, regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP), to create regions other than pre-defined regions. The regions created in the study were based on the weight of youth in the continental United States. The REDCAP algorithm incorporates a spatial contiguity restriction to create regions with the same characteristics and value. The regions created overcome the existing challenge in cartography in which administrative and statistical regions are often used in presenting results. The study generated 10- and 25-class regions that reflected high and low obesity prevalence among US youth without using existing county and state boundaries. The results revealed new insights about regions comprising counties identified as having high obesity prevalence. Some of the counties identified in the established regions interestingly have not been recorded as at risk for high obesity prevalence in previous studies. A crucial advantage of the approach is that it minimizes the bias contained in existing administrative and statistical regions, a challenge in cartography. Furthermore, the approach effectively creates regions based on a specific theme and objective function
Cartography · Cluster analysis · Contiguity · Data mining · Economic geography · Geography · Hierarchical clustering · Regional science · Computer Science · Data-Driven Disease Surveillance · Spatial and Panel Data Analysis · Urban Transport and Accessibility · Artificial Intelligence
Spatial Data Analysis
Prevalence of High Body Mass Index in US Children and Adolescents, 2007-2008
Regionalization with dynamically constrained agglomerative clustering and partitioning (Redcap)
Relationship between Urban Sprawl and Physical Activity, Obesity, and Morbidity
Are American children and adolescents of low socioeconomic status at increased risk of obesity? Changes in the association between overweight and family income between 1971 and 2002
Efficient regionalization techniques for socio‐economic geographical units using minimum spanning trees
Data clustering
Hierarchical Grouping to Optimize an Objective Function
Prevalence of Overweight and Obesity in the United States, 1999-2004
Obesity and Physical Inactivity in Rural America
Urban‐Rural Differences in Overweight Status and Physical Inactivity Among US Children Aged 10‐17 Years
Algorithms for Reengineering 1991 Census Geography
| Citations per year | 0.2 |
|---|---|
| Citation span | 2016 - 2018 (3) |
| Highly cited | No |