Remote Sensing and Spatial Statistics as Tools in Crime Analysis
Bibliographic record
Abstract
This chapter explores the feasibility and utility of using aerial photography or remotely sensed satellite imagery to identify geographic or “place” features that may be associated with criminal activity. It assesses whether or not variables derived from satellite images can provide surrogate relationships between land use and crime. A review of the remote sensing literature suggests two basic approaches to the use of remotely sensed images in law enforcement: (1) tactical; and (2) analytical. The tactical approach uses the imagery as a background to the maps and other spatial information that an officer on the beat might have as he or she is investigating a crime or emergency situation. The analytical approach uses the remotely sensed images to create new variables that may serve as proxies for the risk of crime in particular locations. In this study we employ the analytical approach to the use of remotely sensed images, classifying images according to the presence or absence of vegetation within a pixel, as well as the classification of specific urban attributes, such as parking lots. We also employ spatial statistics to quantify the relationship between features of the images and crime events on the ground, and these analyses may be particularly useful as input to policy decisions about policing within the community.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".