地理学报 ›› 2019, Vol. 74 ›› Issue (6): 1236-1251.doi: 10.11821/dlxb201906012

• 交通地理 • 上一篇    下一篇

秦巴山区乡村交通环境脆弱性及影响因素——以陕西省洛南县为例

杨晴青1,刘倩1,尹莎1,张戬1,杨新军1,高岩辉2()   

  1. 1. 西北大学城市与环境学院,西安 710127
    2. 西安外国语大学旅游学院·人文地理研究所,西安 710128;
  • 收稿日期:2018-03-30 修回日期:2019-03-02 出版日期:2019-06-25 发布日期:2019-06-20
  • 通讯作者: 高岩辉 E-mail:rwdl_gyh@163.com
  • 作者简介:杨晴青(1992-), 女, 湖南益阳人, 博士生, 研究方向为人居环境与区域可持续发展。E-mail: yqq@mails.ccnu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41571163);西北大学研究生自主创新资助项目(YZZ17149)

Vulnerability and influencing factors of rural transportation environment in Qinling-Daba mountainous areas: A case study of Luonan county in Shaanxi province

YANG Qingqing1,LIU Qian1,YIN Sha1,ZHANG Jian1,YANG Xinjun1,GAO Yanhui2()   

  1. 1. College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
    2. School of Tourism & Research Institute of Human Geography, Xi'an International Studies University, Xi'an 710128, China;
  • Received:2018-03-30 Revised:2019-03-02 Online:2019-06-25 Published:2019-06-20
  • Contact: GAO Yanhui E-mail:rwdl_gyh@163.com
  • Supported by:
    National Natural Science Foundation of China(41571163);Northwest University Graduate Innovation and Creativity Funds(YZZ17149)

摘要:

山区乡村长期处于地形地貌制约、自然灾害频发的风险胁迫之下,乡村交通系统网络化水平低、抗灾能力弱,交通环境脆弱性问题突出。以秦巴山区洛南县为例,基于人地关系脆弱性的暴露、敏感、应对能力3个维度构建了涵盖风险事件、地理特征、关键出行路径、路网结构、交通工具、家庭资本等要素的乡村交通环境脆弱性基本构成框架,并针对性建立了评估指标体系。依托ArcGIS和GeoDa软件解析了洛南县交通环境脆弱性的空间结构和空间自相关特征,运用地理加权回归模型探寻了自然、人口、社会、经济因素对交通风险应对能力的影响及空间差异。结果显示:交通环境脆弱性以县城及城郊为中心向外递增形成圈层结构,且垂直差异显著;暴露度、敏感性均与应对能力呈现显著的空间负相关性,脆弱性局部“热点”区域广泛分布于北部中山地带,且陷入了高暴露、高敏感、低应对能力的窘境。“冷点”区域多为城郊或邻近镇区的村庄,敏感性低,应对能力高;地形条件、产业分布、人口结构与受教育程度、家庭规模对交通风险应对能力有显著影响,影响性质及强度存在空间差异。

关键词: 交通环境脆弱性, 风险应对能力, 空间自相关, 地理加权回归, 秦巴山区

Abstract:

Villages in mountainous areas are under the risk of topography, geomorphology and frequent natural disasters in a long term. Rural transportation system is characterized by low network degree and weak capacity to resist disasters, and the problem of vulnerability of traffic environment is prominent. Taking Luonan county in the Qinling-Daba mountainous areas as an example and based on exposure, sensitivity and response capacity of human-environment system vulnerability, this paper constructed a basic framework of rural transportation environment vulnerability, which contained the key elements of risk events, geographical features, key travel path, road network structures, public and private vehicles, family capital etc., and established a targeted evaluation index system. With the aid of ArcGIS and GeoDa, this research examined the spatial structure and spatial autocorrelation of the transportation environment vulnerability in Luonan county at the village level. It also utilized a geographical weighted regression model to explore the factors of natural conditions, population, socio-economic development, which had influence on response capacity of traffic risk and its spatial difference. The results showed that the vulnerability of transportation environment took the county seat and the suburbs as the center increasing outward, which presents a circle structure featured by great difference in vertical direction. Simultaneously, the vulnerability of the transportation environment in village-level residential areas showed a significant positive spatial autocorrelation, but both exposure degree and sensitivity showed a significant spatial negative correlation with the response capacity. There were three patterns of local spatial correlation in transportation environment vulnerability: the vulnerability of the local "hot spots" areas was widely observed in the north- central mountainous area and fell into the dilemma of high exposure, high sensitivity and low response capacity, while the "cold spots" villages were founded in suburbs or areas adjacent to the towns with higher income, which had low sensitivity and high response capacity. There were a few "heterogeneity points", and these villages were adjacent to low-vulnerable villages, but they belonged to high-vulnerable areas. Moreover, topographical condition, industrial distribution, population structure, education level and family size had a significant impact on response capacity of transportation risk. In addition, the effect direction and intensity of the influencing factors had significant spatial differences.

Key words: transportation environment vulnerability, response capacity of risk, spatial autocorrelation, geographical weighted regression, Qinling-Daba mountainous areas