地理信息系统技术在某市道路交通伤害中的应用研究

Application of Geographic Information System to Road Traffic Injuries in a City

  • 摘要:
    目的 探讨柳州城市道路交通伤害的空间规律, 对交通伤害的预防和控制提供科学依据。

    方法 应用地理信息系统(geographic information system, GIS)技术中反距离加权插值法(inverse distance weighted, IDW)探测柳州城市道路交通伤害的空间分布规律, 并对结果进行可视化分析。

    结果 2000-2009年柳州城市道路交通的伤害事故发生起数和受伤人数波动较大, 但总体上呈下降趋势, 死亡人数和直接经济损失在高值过后呈小范围内波动; 柳州城市道路交通伤害发生的事故起数排序为鱼峰区 > 柳北区 > 城中区 > 柳南区, 城市道路交通伤害事故发生起数的空间自相关分析表明, 平均 Moran's I的 Z值为 6.113>1.96, P < 0.05, 存在空间聚集性。道路伤害死亡数的插值分析结果显示, 聚集区主要集中在东环大道鱼峰区路段、柳江一桥至鱼峰路路段、屏山大道箭盘山路路段, 柳北区跃进路路段, 潭中中路、西路路段; 2004年道路伤害专题地图显示城市中心道路伤害严重, 2005-2009年伤害渐渐远离城市中心, 主要发生在城乡结合部道路上。

    结论 柳州市 2000-2009年城市道路伤害发生情况依然比较严重, 应用 GIS可以探测出伤害的聚集区, 且准确定位聚集区并对区域进行可视化, 为预防和控制道路伤害提供可视化依据。

     

    Abstract:
    Objective To investigate the spatial patterns of road traffic injuries in Liuzhou city, and to provide scientific basis for traffic injury prevention and control.

    Methods The method of inverse distance weighted (IDW) interpolation of geographic information system (GIS) technology was employed to detect the spatial distribution pattern of road traffic injuries in Liuzhou city and to make visual analysis.

    Results From 2000-2009, a major fluctuation was observed in the number of traffic accidents and casualties of road traffic injuries in Liuzhou city, and a minor one in death toll and direct economic losses after peak value. The order of accident frequency was Yufeng District > Liubei District > Chengzhong District > Liunan District. Spatial autocorrelation analysis showed that the average Moran's I Z was 6.113>1.96, P<0.05, indicating there existed spatial clustering. IDW analysis showed that the road traffic injuries concentrated mainly in the sections of Donghuan Road of Yufeng District, Liujiangyiqiao to Yufeng Road, Pingshan Avenue of Jianpanshan Road, Yuejin Road of Liubei District, Middle Tanzhong Road and West Tanzhong Road. The road traffic injury thematic maps showed that traffic accidents gathered in the downtown area in 2004, and gradually removed away from the downtown area and concentrated in the urban-rural fringe in 2005-2009.

    Conclusion The road traffic injuries of Liuzhou city are serious during 2000-2009. GIS can be used to detect and locate the high-risk areas, and to provide a visual basis for road traffic injury control and prevention.

     

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