Measuring Interaction Potential: Mobility Triangles
Bibliographic record
Abstract
Crime analysis is of upcoming importance for both theory and practice in criminology. The study of offenders’ spatial behavior is an integral part of a more comprehensive understanding of the convergence in physical space between the offenders and their victims (Bernasco, 2014). The journey to crime has been defined as ‘the distances traveled by offenders from their home to crime locations’ (Beauregard & Busina, 2013, p. 2053), and Bernasco (2014) noted that ‘A crime journey thus contain[s] the complete whereabouts of the individual between leaving home and returning, provided a crime was committed during the journey’ (p. 2). Based on these definitions, criminologists mostly studied the offender’s mobility patterns (i.e., the analysis of the spatial patterns from offenders’ residences to crime locations) neglecting to consider the victim’s spatial behavior. The mobility triangles analysis is an approach that allows for a more comprehensive analysis of the journey to crime. This approach integrates a third address to combine with the offender’s residence and the crime location. In the first studies using this approach, this third address corresponded to offenders’ partner residences (Burgess, 1925) or co-offenders’ living spaces (Lind, 1930). However, since Normandeau’s seminal work (1968) this third apex corresponds to the victim’s residence in order to investigate the spatial proximity of the offender’s residence, the victim’s residence, and the crime location. The combination of these geographical points forms a triangle pattern which varies according to the three distances that compose it: the distance between the victim’s and offender’s residences (V-O), the distance between the victim’s residence and the crime location (V-P), and the distance between the offender’s residence and the crime location (O-P). In this chapter we present the fundamentals of the crime mobility triangles approach, focusing on geometric and geographic patterns of crime. Second, we provide an overview of the main published studies in the field. Third, we provide an example using a step-by-step procedure with elderly sexual abuse cases perpetrated by stranger rapists. Fourth, we discuss the main limitations of this approach. The final section deals with the next step to the mobility triangles analysis: the association of covariates to improve our understanding of offenders’ spatial behaviors.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".