Systematic Literature Review of Crime Prediction and Data Mining
Bibliographic record
Abstract
Crime is a social menace that impacts negatively on social economic development of a nation.Crime has been in existence from time immemorial and violent crime is the main enemy of the society.One of the primary responsibilities of any government is security of life and properties which translates to reduction of crime rate and provisioning of adequate security to its citizenry.To this end, government must wake up to its responsibilities by reducing crime rate and provide adequate security to its citizenry through effective, efficient and proactive policing.Any research in this direction that can help in analyzing and predicting the future occurrence of violent crime by using crime dataset is laudable.Predicting future occurrence of crime from crime dataset is well reported in literature, therefore it has become imperative to come up with an overview of the present state of the art on crime prediction and control.The systematic review present in this study focuses on crime prediction and data mining as well as the techniques employed in the past studies.The existing work is classified and grouped into different categories and are presented by using visualization approach.It is found that more studies adopted supervised learning approaches to crime prediction and control compared to other methods.The challenges encountered were also reported.Crime prediction has become hot research area in recent time because of its intending benefits to socio-economic development of a nation.
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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 | 0.009 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".