Discovering spatial contrast and common sets with statistically significant co-location patterns
Bibliographic record
Abstract
Co-location pattern mining is a spatial data mining technique which can be used to find associations among spatial features. Our work is motivated by an application in environmental health where the goal is to investigate whether the maternal exposure during pregnancy to air pollutants could be potentially associated with adverse birth outcomes. Discovering such relationships can be defined as finding spatial associations (i.e. co-location patterns) between adverse birth outcomes and air pollutant emissions. In particular, our application problem requires to find specific co-location patterns which are common to many spatial groups and co-location patterns which can discriminate one spatial group from the others. Traditional co-location pattern mining methods are not capable of finding such specific patterns. Hence, to achieve the spatial group comparison task, we introduce two new spatial patterns: spatial contrast sets and spatial common sets, and techniques to efficiently mine them based on co-location pattern mining. Traditional co-location pattern mining methods rely on frequency based thresholds which discard rare patterns and find exaggerated noisy patterns which may not be equally prevalent in unseen data. Addressing these limitations, we propose to use statistical significance tests instead of frequency to quantify the strength of a pattern. Towards this end, we propose to apply Fisher's exact test to efficiently find statistically significant co-location rules and use them to discover spatial contrast and common sets. Our experiments reveal that the Fisher's test based method could indeed help in finding co-location patterns with a better statistical significance leading to find valid spatial contrast and common sets. With the proposed methods we discovered that air pollutants such as heavy metals, NO2 and PM are significantly associated with adverse birth outcomes conforming to the existing domain knowledge thus validating our approach. We also evaluated our methods with synthetic datasets which confirmed that our methods indeed extract the patterns we seek to find.
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.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".