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
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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。
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, socioeconomic 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.
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, socioeconomic 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.
引文
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[11] Yang Luping, Qian Dalin. Vulnerability analysis of road networks. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(1):105-110.[杨露萍,钱大琳.道路交通网络脆弱性研究.交通运输系统工程与信息, 2012, 12(1):105-110.]
[12] He X, Liu H X. Modeling the day-to-day traffic evolution process after an unexpected network disruption.Transportation Research Part B:Methodological, 2012, 46(1):50-71.
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[14] Jin Cheng, Lu Yuqi, Zhang Li, et al. An analysis of accessibility of scenic spots based on land traffic network:A case study of Nanjing. Geographical Research, 2009, 28(1):246-258.[靳诚,陆玉麒,张莉,等.基于路网结构的旅游景点可达性分析:以南京市区为例.地理研究, 2009, 28(1):246-258.]
[15] Li Yiman, Xiu Chunliang, Sun Pingjun. Analyzing spatial pattern and accessibility of comprehensive transport in Zhejiang province. Human Geography, 2014, 29(4):155-160.[李一曼,修春亮,孙平军.基于加权平均旅行时间的浙江省交通可达性时空格局研究.人文地理, 2014, 29(4):155-160.]
[16] Chen Bowen, Lu Yuqi, Ke Wenqian, et al. Analysis on the measuring of the relationship between transportation accessibility and level of regional economic growth in Jiangsu:Based on spatial econometric perspective. Geographical Research, 2015, 34(12):2283-2294.[陈博文,陆玉麒,柯文前,等.江苏交通可达性与区域经济发展水平关系测度:基于空间计量视角.地理研究, 2015, 34(12):2283-2294.]
[17] Yin Jiangbin, Huang Xiaoyan, Hong Guozhi, et al. The effect of transport accessibility on urban growth convergence in China:A spatial econometric analysis. Acta Geographica Sinica, 2016, 71(10):1767-1783.[殷江滨,黄晓燕,洪国志,等.交通通达性对中国城市增长趋同影响的空间计量分析.地理学报, 2016, 71(10):1767-1783.]
[18] Yang Xinjun, Shi Yuzhong, Wang Ziqiao. Exploring the impacts of road construction on a local social-ecological system in Qinling mountainous area. Acta Geographica Sinica, 2015, 70(8):1313-1326.[杨新军,石育中,王子侨.道路建设对秦岭山区社会—生态系统的影响:一个社区恢复力的视角.地理学报, 2015, 70(8):1313-1326.]
[19] Tan Zhangzhi, Li Shaoyin, Li Xia, et al. Spatio-temporal effects of urban rail transit on complex land-use change. Acta Geographica Sinica, 2017, 72(5):850-862.[谭章智,李少英,黎夏,等.城市轨道交通对土地利用变化的时空效应.地理学报, 2017, 72(5):850-862.]
[20] Jenelius E. Network structure and travel patterns:Explaining the geographical disparities of road network vulnerability.Journal of Transport Geography, 2009, 17(3):234-244.
[21] Jenelius E, Mattsson L G. Road network vulnerability analysis:Conceptualization, implementation and application.Computers Environment&Urban Systems, 2015, 49:136-147.
[22] Taylor M A P, Susilawati. Remoteness and accessibility in the vulnerability analysis of regional road networks.Transportation Research Part A, 2012, 46(5):761-771.
[23] Nyberg R, Johansson M. Indicators of road network vulnerability to storm-felled trees. Natural Hazards, 2013, 69(1):185-199.
[24] Yang J, Sun H, Wang L, et al. Vulnerability evaluation of the highway transportation system against meteorological disasters. Procedia:Social and Behavioral Sciences, 2013, 96:280-293.
[25] Angeon V, Bates S. Reviewing composite vulnerability and resilience indexes:A sustainable approach and application.World Development, 2015, 72:140-162.
[26] Luo Rihong, Huang Jinlin, Tang Zaozao. Study on freshet disaster risk zonation at small mountainous watershed base on AHP and GIS. Journal of Catastrophology, 2018, 33(2):64-69.[罗日洪,黄锦林,唐造造.基于AHP和GIS的山区小流域山洪灾害风险区划研究.灾害学, 2018, 33(2):64-69.]
[27] Anselin L, Kelejian H H. Testing for spatial error autocorrelation in the presence of endogenous regressors. International Regional Science Review, 1997, 20(1/2):153-182.
[28] Anselin L. The local indicators of spatial association:LISA. Geographical Analysis, 1995, 27(2):93-115.
[29] Brunsdon C, Fotheringham S, Charlton M. Geographically weighted regression-modelling spatial non-stationarity.Journal of the Royal Statistical Society, 1998, 47(3):431-443.
[30] Liu Weidong, Liu Hongguang, Fan Xiaomei, et al. Sector-specific spatial statistic model for estimating inter-regional trade flows:A case study of agricultural, chemical and electronic sectors in China. Acta Geographica Sinica, 2012, 67(2):147-156.[刘卫东,刘红光,范晓梅,等.地区间贸易流量的产业—空间模型构建与应用.地理学报, 2012, 67(2):147-156.]
[31] Zeng Juxin, Yang Qingqing, Liu Yajing, et al. Research on evolution and influential mechanism for rural human settlement in national key ecological function areas:A case of Lichuan. Human Geography, 2016, 31(1):81-88.[曾菊新,杨晴青,刘亚晶,等.国家重点生态功能区乡村人居环境演变及影响机制:以湖北省利川市为例.人文地理,2016, 31(1):81-88.]
[32] Yang Qingqing, Chen Jia, Li Bohua, et al. Evolution and driving force detection of urban human settlement environment at urban agglomeration in the middle reaches of the Yangtze River. Scientia Geographica Sinica, 2018, 38(2):195-205.[杨晴青,陈佳,李伯华,等.长江中游城市群城市人居环境演变及驱动力研究.地理科学, 2018, 38(2):195-205.]
[33] Maru Y T, Smith M S, Sparrow A, et al. A linked vulnerability and resilience framework for adaptation pathways in remote disadvantaged communities. Global Environmental Change, 2014, 28(1):337-350.
[2] Nicholson A, Du Z P. Degradable transportation systems:An integrated equilibrium model. Transportation Research Part B:Methodological, 1997, 31(3):209-223.
[3] Berdica K. An introduction to road vulnerability:What has been done, is done and should be done. Transport Policy,2002, 9(2):117-127.
[4] Duan Y, Feng L. Robustness of city road networks at different granularities. Physica A Statistical Mechanics&Its Applications, 2014, 411:21-34.
[5] Mattsson L G, Jenelius E. Vulnerability and resilience of transport systems:A discussion of recent research.Transportation Research Part A:Policy&Practice, 2015, 81:16-34.
[6] Yin Hongying, Xu Liqun. Vulnerability assessment of transportation road networks. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(3):7-13.[尹洪英,徐丽群.道路交通网络脆弱性评估研究现状与展望.交通运输系统工程与信息, 2010, 10(3):7-13.]
[7] Murray A T, Matisziw T C, Grubesic T H. A methodological overview of network vulnerability analysis. Growth&Change, 2008, 39(4):573-592.
[8] Scott D M, Novak D C, Aultman-Hall L, et al. Network robustness index:A new method for identifying critical links and evaluating the performance of transportation networks. Journal of Transport Geography, 2006, 14(3):215-227.
[9] Jenelius E, Petersen T, Mattsson L G. Importance and exposure in road network vulnerability analysis. Transportation Research Part A:Policy&Practice, 2006, 40(7):537-560.
[10] Rupi F, Bernardi S, Rossi G, et al. The evaluation of road network vulnerability in mountainous areas:A case study.Networks&Spatial Economics, 2015, 15(2):397-411.
[11] Yang Luping, Qian Dalin. Vulnerability analysis of road networks. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(1):105-110.[杨露萍,钱大琳.道路交通网络脆弱性研究.交通运输系统工程与信息, 2012, 12(1):105-110.]
[12] He X, Liu H X. Modeling the day-to-day traffic evolution process after an unexpected network disruption.Transportation Research Part B:Methodological, 2012, 46(1):50-71.
[13] Alexakis D D, Agapiou A, Tzouvaras M, et al. Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements:The case study of Paphos area in Cyprus. Natural Hazards, 2014, 72(1):119-141.
[14] Jin Cheng, Lu Yuqi, Zhang Li, et al. An analysis of accessibility of scenic spots based on land traffic network:A case study of Nanjing. Geographical Research, 2009, 28(1):246-258.[靳诚,陆玉麒,张莉,等.基于路网结构的旅游景点可达性分析:以南京市区为例.地理研究, 2009, 28(1):246-258.]
[15] Li Yiman, Xiu Chunliang, Sun Pingjun. Analyzing spatial pattern and accessibility of comprehensive transport in Zhejiang province. Human Geography, 2014, 29(4):155-160.[李一曼,修春亮,孙平军.基于加权平均旅行时间的浙江省交通可达性时空格局研究.人文地理, 2014, 29(4):155-160.]
[16] Chen Bowen, Lu Yuqi, Ke Wenqian, et al. Analysis on the measuring of the relationship between transportation accessibility and level of regional economic growth in Jiangsu:Based on spatial econometric perspective. Geographical Research, 2015, 34(12):2283-2294.[陈博文,陆玉麒,柯文前,等.江苏交通可达性与区域经济发展水平关系测度:基于空间计量视角.地理研究, 2015, 34(12):2283-2294.]
[17] Yin Jiangbin, Huang Xiaoyan, Hong Guozhi, et al. The effect of transport accessibility on urban growth convergence in China:A spatial econometric analysis. Acta Geographica Sinica, 2016, 71(10):1767-1783.[殷江滨,黄晓燕,洪国志,等.交通通达性对中国城市增长趋同影响的空间计量分析.地理学报, 2016, 71(10):1767-1783.]
[18] Yang Xinjun, Shi Yuzhong, Wang Ziqiao. Exploring the impacts of road construction on a local social-ecological system in Qinling mountainous area. Acta Geographica Sinica, 2015, 70(8):1313-1326.[杨新军,石育中,王子侨.道路建设对秦岭山区社会—生态系统的影响:一个社区恢复力的视角.地理学报, 2015, 70(8):1313-1326.]
[19] Tan Zhangzhi, Li Shaoyin, Li Xia, et al. Spatio-temporal effects of urban rail transit on complex land-use change. Acta Geographica Sinica, 2017, 72(5):850-862.[谭章智,李少英,黎夏,等.城市轨道交通对土地利用变化的时空效应.地理学报, 2017, 72(5):850-862.]
[20] Jenelius E. Network structure and travel patterns:Explaining the geographical disparities of road network vulnerability.Journal of Transport Geography, 2009, 17(3):234-244.
[21] Jenelius E, Mattsson L G. Road network vulnerability analysis:Conceptualization, implementation and application.Computers Environment&Urban Systems, 2015, 49:136-147.
[22] Taylor M A P, Susilawati. Remoteness and accessibility in the vulnerability analysis of regional road networks.Transportation Research Part A, 2012, 46(5):761-771.
[23] Nyberg R, Johansson M. Indicators of road network vulnerability to storm-felled trees. Natural Hazards, 2013, 69(1):185-199.
[24] Yang J, Sun H, Wang L, et al. Vulnerability evaluation of the highway transportation system against meteorological disasters. Procedia:Social and Behavioral Sciences, 2013, 96:280-293.
[25] Angeon V, Bates S. Reviewing composite vulnerability and resilience indexes:A sustainable approach and application.World Development, 2015, 72:140-162.
[26] Luo Rihong, Huang Jinlin, Tang Zaozao. Study on freshet disaster risk zonation at small mountainous watershed base on AHP and GIS. Journal of Catastrophology, 2018, 33(2):64-69.[罗日洪,黄锦林,唐造造.基于AHP和GIS的山区小流域山洪灾害风险区划研究.灾害学, 2018, 33(2):64-69.]
[27] Anselin L, Kelejian H H. Testing for spatial error autocorrelation in the presence of endogenous regressors. International Regional Science Review, 1997, 20(1/2):153-182.
[28] Anselin L. The local indicators of spatial association:LISA. Geographical Analysis, 1995, 27(2):93-115.
[29] Brunsdon C, Fotheringham S, Charlton M. Geographically weighted regression-modelling spatial non-stationarity.Journal of the Royal Statistical Society, 1998, 47(3):431-443.
[30] Liu Weidong, Liu Hongguang, Fan Xiaomei, et al. Sector-specific spatial statistic model for estimating inter-regional trade flows:A case study of agricultural, chemical and electronic sectors in China. Acta Geographica Sinica, 2012, 67(2):147-156.[刘卫东,刘红光,范晓梅,等.地区间贸易流量的产业—空间模型构建与应用.地理学报, 2012, 67(2):147-156.]
[31] Zeng Juxin, Yang Qingqing, Liu Yajing, et al. Research on evolution and influential mechanism for rural human settlement in national key ecological function areas:A case of Lichuan. Human Geography, 2016, 31(1):81-88.[曾菊新,杨晴青,刘亚晶,等.国家重点生态功能区乡村人居环境演变及影响机制:以湖北省利川市为例.人文地理,2016, 31(1):81-88.]
[32] Yang Qingqing, Chen Jia, Li Bohua, et al. Evolution and driving force detection of urban human settlement environment at urban agglomeration in the middle reaches of the Yangtze River. Scientia Geographica Sinica, 2018, 38(2):195-205.[杨晴青,陈佳,李伯华,等.长江中游城市群城市人居环境演变及驱动力研究.地理科学, 2018, 38(2):195-205.]
[33] Maru Y T, Smith M S, Sparrow A, et al. A linked vulnerability and resilience framework for adaptation pathways in remote disadvantaged communities. Global Environmental Change, 2014, 28(1):337-350.