A framework for statistical inferential decisions in spatial pattern analysis
Bibliographic record
Abstract
The desire of many geographical information science (GIS) practitioners to undertake sophisticated spatial pattern analysis has been facilitated by the increasing availability of specialised software and the appearance of pedagogic papers illustrating the application of various techniques. However, the appropriate use of these techniques also requires an understanding of the nature of hypothesis testing and statistical inference for spatial data. Since there is little information currently available to aid the GIS practitioner in this regard, we offer such guidance here. We do so by revisiting the steps involved in spatial pattern analysis. Our perspective is based on the notion of spatial stochastic models and is presented as a decision tree. The four levels of the tree (i.e., sequential decisions) are associated with the assumptions, the type of data representation and the types of questions asked by the analyst. We emphasise the scientific and educational challenges involved.
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 | 0.073 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".