A GIS Class Exercise to Study Environmental Risk
Bibliographic record
Abstract
Geographic Information System (GIS) software can be used to determine the spatial distribution of environmental hazards. The ability to look at multiple layers of information on one map enables investigators to visually compare areas that contain high numbers of hazardous industries with variables such as socio-economic status and race. We used GIS in a classroom exercise to examine the distribution of toxic release sites in Queens, New York. Using 1990 U.S. Census tract data along with Toxic Release Inventory (TRI) sites registered by the Environmental Protection Agency (EPA) for Queens in 2000, we created a series of maps to examine the relationships between the locations of known toxic releases and demographic factors such as race, education, income levels, and linguistic isolation. By using readily available digital data like TRI sites and census tract data this classroom project shows students the utility of GIS for analysis of environmental hazards. Our in-class exercise revealed 1) distinct divides between neighborhoods by race; 2) an association between the locations of TRI sites and Asian and Hispanic linguistic isolation; 3) correspondence between the locations of TRI sites and limited level of education; and 4) overlap between the locations of TRI sites and neighborhoods of low income. Although not a definitive environmental risk study, these findings suggest that neighborhoods with limited resources to prevent the siting of undesirable technologies in their communities or to move out of harm's way may be disproportionately subjected to environmental risks. Exercises of this sort are easily carried out by students with access to GIS. Such studies demonstrate to students the societal importance of integrating natural and social sciences.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.012 |
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".