Identifying Potential Zones of Crime Commitment against Tourists in a Park: Conceptual and Logical Spatial Data Modeling
Bibliographic record
Abstract
Location is considered as an important element in studying tourism security. Therefore, mapping crime hotspots has recently been an interesting research topic in tourism development. In order to identify crime patterns and hotspots, it is essential to create a database containing the required spatial data. It should also be integrated with additional qualitative/quantitative attributes affecting criminal actions. Designing a geographic information system (GIS) can be considered as the most efficient way to deal with this problem considering the complex nature of tourism security. This paper presents the theoretical scheme of spatial data modeling with the purpose of indentifying potential crime zones within a developed park. From the spatial point of view, the factors and the constraints, which make a location vulnerable, are defined. The entities are identified by their attributes and characterized by their relationships. Finally, the conceptual and the logical models to create the crime suitability maps are generated. The models provided in this paper are designed in an explicit way; therefore, they can be easily modified or generalized for any specific case study. The presented data modeling procedure can be applied to generate essential databases for crime mapping via any GIS software.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".