Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze the spatial patterns of different phenomena in the same geographical space. Andresen’s spatial point pattern test computes a global index (the S-index) that informs on the similarity or dissimilarity of spatial patterns. This paper suggests a generalized S-index that allows perfect similarity and dissimilarity in all situations. Design/methodology/approach The relevance of the generalized S-index is illustrated with police data from the San Francisco Police Department. In all cases, the original S-index, its robust version – which excludes zero-crime areas – and the generalized alternative were computed. Findings In the first example, the number of crimes greatly exceeds the number of areas and there are no zero-value areas. A key feature of the second example is that most street segments were free of any criminal activity in both patterns. Finally, in the third case, one type of event is considerably rarer than the other. The original S-index is equal to the generalized index (Case 1) or theoretically irrelevant (Cases 2 and 3). Furthermore, the robust index is unnecessary and potentially biased when the number of at least one phenomenon being compared is lower than the number of areas under study. Thus, this study suggests to replace the S-index with its generalized version. Originality/value The generalized S-index is relevant for situations when events are relatively rare –as is the case with crime – and the unit of analysis is small but plentiful – such as addresses or street segments.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".