标题： 英格兰、威尔士和苏格兰道路事故的月度城乡规模变化（2019-2023） 显示英文标题

标题： Monthly Rural-Urban Scaling of Road Accidents in England, Wales and Scotland (2019-2023)

摘要： 道路交通事故仍然是全球重大的公共卫生挑战，城市化和人口密度被确定为影响风险的关键因素。 本研究分析了2009年至2023年间英格兰、威尔士和苏格兰632个议会选区的月度事故数据，采用基于人口密度的区域标准化方法。 分段幂律模型一致识别出将亚线性农村与超线性城市缩放行为分开的断点。 在农村地区，缩放指数的季节性变化明显，而在城市地区则不那么明显。 基于傅里叶的交叉谱分析显示年度周期存在系统相位偏移：农村指数比预指数因素滞后4.5个月，而城市指数则滞后2.7个月，导致农村和城市指数之间出现5.3个月的偏移。 这些发现强调了预指数的定义——即单位人口密度下的预期事故密度——作为可比较的去量纲指标的重要性，揭示了长期国家下降趋势和反复出现的季节性高峰。 值得注意的是，相位偏移表明农村和城市事故风险有结构上不同的原因，城市地区表现出事故缩放的加速增加，可能与车辆数量、大小和重量的增长有关。 用第一类广义逻辑分布（GLD）建模的残差比正态假设更能有效捕捉偏度和异质性。 地理空间制图显示了持续的城市热点，以及农村和沿海选区的事故密度系统性低于预测值。 综上所述，这些发现推进了对密度和城市化如何塑造事故风险的理解，并提供了支持更针对性的道路安全干预和政策规划的证据。 显示英文摘要

摘要： Road traffic accidents remain a major public health challenge worldwide, with urbanisation and population density identified as key factors influencing risk. This study analyses monthly accident data from 2009 to 2023 across 632 parliamentary constituencies in England, Wales, and Scotland, using an area-normalised approach based on population density. Segmented power law models consistently identified breakpoints separating sublinear rural from superlinear urban scaling behaviours. Seasonal variation in scaling exponents was pronounced in rural regions but less evident in urban ones. Fourier-based cross-spectral analysis of yearly cycles revealed systematic phase shifts: rural exponents lagged pre-exponential factors by 4.5 months, while urban exponents were 2.7 months out of phase, producing a 5.3 month shift between rural and urban exponents. These findings highlight the importance of pre-exponentials-defined as the expected density of accidents at unit population density-as comparable descaled metrics, revealing both long-term national declines and recurring seasonal peaks. Notably, the phase offsets suggest structurally distinct causes of rural and urban accident risk, with urban regions exhibiting increasing acceleration in accident scaling, potentially linked to growth in vehicle numbers, size, and weight. Residuals, modelled with the Type I Generalised Logistic Distribution (GLD), captured skewness and heterogeneity more effectively than normal assumptions. Geospatial mapping highlighted persistent urban hotspots alongside rural and coastal constituencies with systematically lower accident densities than predicted. Together, these findings advance understanding of how density and urbanisation shape accident risk and provide evidence to support more targeted road safety interventions and policy planning.