Exploring GIS as a Novel Approach to Measuring Distances to Activity Opportunities
Bibliographic record
Abstract
0513 PURPOSE: To determine the predictive value of continuous data provided by Geographic Information Systems (GIS) distances from participants' residences on physiologic outcomes and their relationships between the environmental opportunities for activity. METHODS: The 242 subjects were from a registry of adults 55yrs and older, living in the London area who agreed to participate in an activity program. We included participants who lived less than 4000 meters of any of the available 11 physical activity opportunity (PAO) GIS distances. Baseline physiologic outcomes were used. Multivariate stepwise linear regression modeling was used within three PAO density modes between 300m (close), 300 to 1000m (mid), and over 1000m(far). At times these overlapped when modeling. RESULTS: Multiple correlations of all distances with and without the dependent variables were significantly correlated with each other (Mean(r) = 0.533, .330–.809), suggesting redundancy amongst some individual opportunities. The greatest PAO density was consistently less than 2Km from each resident. Also a pattern of PAO type fell into three density modes used in the modeling to direct explainable variance. “Close” (less than 300m) models included bike and multi-use paths, and parks; “Mid” (300–1000m) models included health clubs, baseball, and tennis; “Far” (over 1000m) models include soccer, dance studios, and golf. Interaction variables were bike*multi and dance*golf. Overall the best models available only provide R2 of 20% or less. The combined distances reduced the redundant information, increasing the explainable variance. A direct relationship between the continuous GIS measures and the outcomes was however not significant. CONCLUSIONS: Given the evidence from the literature and information from our data indicating relatively strong relationships between PAO distance and physiological outcomes, our current hypothesis that there are direct predictors available using the GIS continuous measures is unconfirmed. The challenge of including this novel measure with the traditional categorical descriptions of our target relationships requires additional investigation before we can include it in methodologies of determining and promoting activity older adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".