The Impacts of Green Spaces on Crime in New York City
Bibliographic record
Abstract
From the early 1960s through the mid-1990s, crime in New York City ran rampant. With a gradually dwindling police during this time, a high unemployment rate, and an rapidly increasing metropolitan population, crime peaked in the early 1990s, with the murder rate hitting a record-high of 2,245 in 1990. When Mayor Rudy Giuliani took office in 1994 and appoint Bill Bratton as the NYPD police commissioner, these rates immediately plunged. Numerous factors may have contributed to this sudden decline in crime: the police force grew significantly through the 1990s, more criminals were placed and held in prison, and the economic boom of the 1990s brought with it a tremendous drop in the national and city unemployment rate. While economic factors have traditionally been regarded as the leading factor in impacting the occurrence of crime, recent research into the effects of green spaces on crime rates have opened the door to alternate explanations. Some studies indicate that greening areas helps to deter crime by “signaling to potential criminals that a house is better cared for and, therefore, subject to more effective authority.” Other studies have gone as far as to draw a link between mental fatigue and an increase in crime, claiming that green spaces serve as a calming and crime-deterring agent. While the field of environmental criminology is relatively young in its depth of research, this study aims to further only a small component of the discipline: the effects of green spaces on social disorder and social cohesion. Based off of the findings from previous research conducted by Matthew Iannone regarding the presence of green spaces in Manhattan, this study looked at the occurrence of 8,149 violent crimes (assault, murder, rape, and robbery) in the Bronx from January 1, 2016 to December 31, 2017.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".