Fig 1.
Cycle superhighways in the study area of Inner London.
Fig 2.
Flowchart of the experimental design to identify risk indicators for regression modeling of bicycle theft.
Fig 3.
Seasonal bicycle theft statistics for Inner London from May 2013 to April 2016 (n = 36,987 events).
Table 1.
The distribution of risk indicators over Inner London’s street segments (n = 51,216 segments).
Fig 4.
Incidence rate ratios (IRRs) and 95% confidence intervals of negative binomial models.
Estimates correspond to effects of risky and risk-mitigating amenities and socioeconomic factors on bicycle theft. Effects are measured using bicycle theft counts (May 2013 to April 2016) for 51,216 street segments. Models account for the seasonal effects shown in Table 2 and assess risk exposure over four threshold distances: (a) 160 m—Model 1; (b) 320m -Model 2; (c) 480m—Model 3; and (d) 640m—Model 4. The commuter-adjusted population is modeled as an offset variable.
Table 2.
Effect estimates of negative binomial bicycle theft models for the seasonal variables a at four threshold distances of risk exposure measurement (models are adjusted for risk factors).