Investigating the Influences of Tree Coverage and Road Network Density on Property Crime: A Case Study in the City of Vancouver, British Columbia, Canada
Bibliographic record
Abstract
With the development of Geographic Information Systems (GIS), crime mapping becomes an effective approach to investigate the spatial pattern of crime in a defined area. Understanding the relationship between crime and its surrounding environment can reveal possible strategies that can reduce crime in a neighbourhood. The relationship between vegetation density and crime has been under debate for a long time. On the one hand, dense vegetation is usually used as shield by criminals when committing crime. On the other hand, green spaces can attract people to spend time outdoors and thus create nature surveillance around the area. The convenience of road network is another important factor that can influence criminal’s selection of locations. This research investigates the impacts of tree coverage and road network density on crime in the City of Vancouver. Temporal analysis was conducted based on detected vegetation changes and crime data from 2008 to 2013. High spatial resolution airborne LiDAR data collected in 2013 provided by the City of Vancouver and road network file provided by Statistics Canada were used for the extraction of tree-covered area and the calculation of road density for cross-sectional analysis. The two independent variables were put into Ordinary Least-Squares (OLS) regression, Spatial Lag regression, and Geographically Weighted Regression (GWR) models to examine their influences on property crime rates. Other independent variables taken into consideration included population density, unemployment rate, lone-parent families, low-income families, streetlights and graffiti. According to the results, the temporal analysis provided qualitative evidence of vegetation coverage having inverse impact on property crime, and the cross-sectional analysis demonstrated statistical evidences that property crime rates had negative correlations with both tree coverage and road density, with greater influences occurred around Downtown Vancouver.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".