The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- Science and technology studies
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: ObservationalConsensus signal: Observational
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.086
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.312 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Crime rate is a statistic used to summarize the risk of criminal events. However, research has shown that choosing the appropriate denominator is non-trivial. Different crime types exhibit different spatial opportunities and so does the population at risk. The residential population is the most commonly used population at risk, but is unlikely to be suitable for crimes that involve mobile populations. In this article, we use “crowd-sourced” data in Leeds, England, to measure the population at risk, considering violent crime. These new data sources have the potential to represent mobile populations at higher spatial and temporal resolutions than other available data. Through the use of two local spatial statistics (Getis-Ord GI* and the Geographical Analysis Machine) and visualization, we show that when the volume of social media messages, as opposed to the residential population, is used as a proxy for the population at risk, criminal event hot spots shift spatially. Specifically, the results indicate a significant shift in the city center, eliminating its hot spot. Consequently, if crime reduction/prevention efforts are based on resident population based crime rates, such efforts may not only be ineffective in reducing criminal event risk, but be a waste of public resources.
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.
The record
- Venue
- Cartography and Geographic Information Science
- Topic
- Crime Patterns and Interventions
- Field
- Social Sciences
- Canadian institutions
- Simon Fraser University
- Funders
- Economic and Social Research Council
- Keywords
- PopulationGeographyProxy (statistics)Crime rateStatisticCriminologySocial mediaCartographyDemographyComputer scienceStatisticsPsychologySociology
- Has abstract in OpenAlex
- yes