Are Obesity and Physical Activity Clustered? A Spatial Analysis Linked to Residential Density
Bibliographic record
Abstract
The aim of this study was to examine spatial clustering of obesity and/or moderate physical activity and their relationship to a neighborhood's built environment. Data on levels of obesity and moderate physical activity were derived from the results of a telephone survey conducted in 2006, with 1,863 survey respondents in the study sample. This sample was spread across eight suburban neighborhoods in Metro Vancouver. These areas were selected to contrast residential density and income and do not constitute a random sample, but within each area, respondents were selected randomly. Obesity and moderate physical activity were mapped to determine levels of global and local spatial autocorrelation within the neighborhoods. Clustering was measured using Moran's I at the global level, Anselin's Local Moran's I at the local level, and geographically weighted regression (GWR). The global-level spatial analysis reveals no significant clustering for the attributes of obesity or moderate physical activity. Within individual neighborhoods, there is moderate clustering of obesity and/or physical activity but these clusters do not achieve statistical significance. In some neighborhoods, local clustering is restricted to a single pair of respondents with moderate physical activity. In other neighborhoods, any moderate local clustering is offset by negative local spatial autocorrelation. Importantly, there is no evidence of significant clustering for the attribute of obesity at either the global or local level of analysis. The GWR analysis fails to improve significantly upon the global model-thus reinforcing the negative results. Overall, the study indicates that the relationship between the urban environment and obesity is not direct.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".