Spatial Analysis for Environmental Health Research: Concepts, Methods, and Examples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spatial analysis can illuminate environmental health research in two ways. First, spatial analysis may suggest possible causal factors in disease pathogenesis. Association between disease and place may imply that the population living there either possesses inherent traits that make it more susceptible to disease or experiences elevated exposure to a risk factor such as air pollution. Second, spatial analysis can help identify how populations adapt and relate to their environment. This knowledge may lead to improved understanding of how people perceive and avoid health risks of environmental origin. The potential for spatial analysis to uncover these aspects of the association between health and the environment is limited by data and methodological problems that are discussed in the article. To familiarize researchers and policymakers with this increasingly important approach, we review spatial-analytic methods under three headings: visualization, exploration, and modeling. We use illustrative examples to assist readers in understanding the strengths and weaknesses of specific methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it