Crime Modeling and Mapping Using Geospatial Technologies Crime Modeling and Mapping Using Geospatial Technologies, edited by Michael Leitner Springer
Bibliographic record
Abstract
Crime mapping and spatial analysis of crime remains one of the most active fields regarding the applications of geospatial technologies. Included in the book series of Geotechnologies and Environment by Springer, Crime modeling and Mapping Using Geospatial Technologies, edited by Michael Leitner, presents a rich collection of the latest research applying cutting edge geo-techniques to crime mapping and spatial analysis of crime. This book is made up of eighteen chapters which cover a broad range of topics such as spatial analysis of drug market areas, spatiotemporal clustering of crime hot spots, journey to crime, and campus crime. Whereas most chapters of this book focused on the analysis of crime occurred in U.S. cities, several chapters investigated the geography of criminal activity in Belgium, Canada, England, and Mexico. Each chapter addresses different problems and uses unique GIS-based approaches; yet, they are grouped into four sections according their emphases: Fundamental spatial problems, crime analysis, crime modeling, crime mapping, and applications and implementations. In fact, it may be more reasonable to classify those works by units of analysis (e.g., street crime, journey to crime, neighborhood crime, campus crime, and spatiotemporal clustering of crime) because mapping, analysis, and modeling of crime are techniques intertwined with each other and some chapters still fit well if being placed into other sections
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".