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

doi:10.11821/dlxb201906012

摘要/Abstract

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

杨晴青,刘倩,尹莎,张戬,杨新军,高岩辉. 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[J]. 地理学报, 2019, 74(6): 1236-1251.

YANG Qingqing,LIU Qian,YIN Sha,ZHANG Jian,YANG Xinjun,GAO Yanhui. Vulnerability and influencing factors of rural transportation environment in Qinling-Daba mountainous areas: A case study of Luonan county in Shaanxi province[J]. Acta Geographica Sinica, 2019, 74(6): 1236-1251.

图/表 9

图1

表1

山区乡村交通环境脆弱性评估指标体系

准则层	指标层	指标计算说明	指标方向
暴露度 (E)	E₁地形起伏度	基于精度为30 m的DEM数据,通过均值变点法寻找最佳统计单元为0.29 km²,地形起伏度为该单元内最高点与最低点之高程差	+
	E₂地质灾害风险等级	基于《洛南县地质灾害分布及易发程度分区图》,将地质灾害低、中、高易发等级依次赋分1~3分	+
	E₃暴雨风险等级	基于《洛南县暴雨风险区划图》,根据暴雨等值线梯度依次赋分1~4分	+
	E₄未铺装公路里程比	镇域范围内,未铺装公路里程与全部公路里程之比	+
	E₅关键路径隐患成本	根据《洛南县农村公路安全隐患表》,依据不同类型的危害程度,将地质类、水毁类、积水或缺口类道路隐患点依次赋值1~3分,并将得分叠加至车行路网	+
敏感性 (S)	S₁关键路径最短通行时长	基于已有成果、研究区交通状况,将道路行车速度设为国道70 km/h,省道50 km/h,县道30 km/h,乡道25 km/h,其他或无道路通过区域设定为15 km/h^[15]	+
	S₂关键路径临河长度	将河道风险缓冲区内路段视为临河路段。按照河道等级建立河道风险缓冲区,即一级河道200 m,二级河道150 m,三级及以下河道95 m^[26]	+
	S₃关键路径单位道路质量	以公路技术水平表征道路质量,依次将等外公路、四级、三级、二级赋权1~4分	-
应对能力(RC)	R₁村庄客运班线拥有数	依据洛南县境内农村客运班线明细图表,统计各行政村客运班线拥有数量	+
	R₂农村家庭交通工具拥有数	农村家庭户均拥有主要交通工具(电动自行车、摩托车、民用汽车)数量,依次将电动自行车、摩托车、民用汽车赋权重1~3分	+
	R₃农村人均纯收入	数值型变量	+
	R₄加权路网密度	根据公路技术等级差异,依次将等外公路、四级、三级、二级赋权1~4分	+

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图4

表2

表3

图5

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解释变量	系数	标准差	T值	P值	VIF	OLS诊断
常数项	0.406	0.090	4.485	0.000^***	-	R²	0.176
地形高程	-0.247	0.064	-3.868	0.000^***	1.260	校正R²	0.147
耕地拥有量	0.139	0.083	1.664	0.097^*	1.129	联合F统计量/P值	0.000^***
老龄化程度	-0.205	0.085	-2.419	0.016^**	1.185	多重共线性条件数	23.728
受教育程度	0.235	0.091	2.582	0.011^**	1.108	Koenker(BP)统计量/P值	0.731
家庭规模	0.141	0.075	1.882	0.061^*	1.065	残差平方和	4.850
村庄规模	-0.019	0.066	-0.294	0.768	1.050	赤池信息准则(AICc)	-166.850
村庄资产	-0.003	0.058	-0.055	0.955	1.035	Sigma	0.157

模型参数	数值
带宽	13363.050
残差平方和	3.165
赤池信息准则AICc	-191.100
Sigma	0.140
R²	0.462
校正R²	0.318

解释变量	最小值	上四分位值	中位值	下四分位值	最大值	平均值
地形高程	-0.607	-0.490	-0.324	-0.066	0.159	-0.277
耕地拥有量	-0.317	-0.046	0.104	0.232	0.563	0.092
老龄化程度	-0.448	-0.230	-0.162	-0.114	0.179	-0.179
受教育程度	-0.106	0.049	0.182	0.242	0.413	0.151
家庭规模	-0.146	0.082	0.161	0.236	0.384	0.143