基于多源开放数据的中国农村可达指数评估

Evaluating Rural Access Index across China with Multi-source Open Data

农村可达指数(RAI)是联合国可持续发展目标(SDGs)的重要评估指标(SDG 9.1.1),用于衡量享受道路交通服务的农村人口比例,但是目前存在指标不全、范围有限、数据有偏和解释不足等问题。因此,本文基于1:25万道路数据、1:100万行政区划数据、100 m分辨率人口数据、城市建成区数据、高程数据和GDP数据六种全球或区域开放的地理空间数据,评估了全国2852个区县单元的RAI和NSRP(难以享受道路交通服务的农村人口)指标,并引入社会经济变量和地形变量理解这2个指标的空间格局。研究发现:①虽然我国仍有485.3万难以享受道路交通服务的农村人口;但是享受道路交通服务的农村人口比例为99.5%,且该值远高于世界银行给出的评估结果(71.8%);②RAI和NSRP的空间格局均沿“胡焕庸线”呈两极化分布:“胡焕庸线”以东地区的RAI值较高、NSRP值较低;而“胡焕庸线”以西地区的RAI值较低、NSRP值较高;③RAI和NSRP与社会经济和地形变量显著相关,且与地形变量的相关性更高,表明地形对2个指标空间格局影响显著。本研究首次在区县级尺度上揭示了我国农村交通服务的空间格局,可以为改善农村道路交通设施提供决策支持。

Rural Access Index(RAI)is an indicator(SDG 9.1.1)of the UN's Sustainable Development Goals(SDGs),and it measures the proportion of the rural population who live within 2 km of all-season roads.Currently,there is a lack of evaluation on RAI's spatial pattern in China and its influencing factors in existing studies.To fill this gap,our study proposes another indicator,Non-Served Rural Population(NSRP),and employs six open datasets including 1:250000 road data,1:1000000 county-level division data,100 m population data,urban extent data,DEM data,and GDP data to evaluate both the RAI and NSRP indicators for 2852 counties in China.We also select two categories of variables(i.e.,socio-economic and terrain variables)to analyze the spatial patterns of RAI and NSRP.Results show that:(1)The RAI and NSRP of China are 99.5%and 4.8 million,respectively.It means that 99.5%of rural population in China live within 2 km of all-season roads,and this value is much higher than that published by The World Bank.However,there still are 4.8 million rural population that live outside the 2 km of all-season roads;(2)The spatial patterns of both RAI and NSRP can be divided into two parts.That is,a relatively high RAI and low NSRP in the southeast of the“Hu Huanyong Line”,but on the contrary,a relatively low RAI and high NSRP in the northwest of the“Hu Huanyong Line”;(3)Both the RAI and NSRP are significantly correlated with socio-economic and terrain variables,and the correlation between RAI(or NSRP)and the terrain variable is the strongest,which indicates that the terrain may be a main factor that affecting the spatial patterns of these two indicators.This study first maps the spatial pattern of SDG 9.1.1 in China,which provides basic data,helpful information,and knowledge for improving road infrastructure in rural areas of China.