Spatial Delimitation of Urban-Rural Fringe Based on POI and Nighttime Light Data： A Case Study of Wuhan City
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摘要:
城乡结合部边界识别是城乡精细规划与治理的基础工作,对于土地可持续利用、城乡一体化等进程具有推动作用。以往城乡结合部划分存在数据源单一、获取困难、时空分辨率低的不足,基于电子地图的兴趣点（point of interest,POI）和国家极地轨道伙伴关系（national polar-orbiting partnership,NPP）卫星的夜间灯光融合数据,构建NPP &POI综合指数；结合城乡空间结构关系,提出了一种新的城乡结合部空间识别方法。以武汉市为例,采用断裂点分析法识别空间突变点,求得城乡结合部边界,利用土地利用结构信息熵、归一化植被指数(normalized difference vegetation index,NDVI)以及人口密度数据对划定结果进行验证和比较,并对典型区域进行了野外实地校核。结果表明,相较于单独采用POI、夜间灯光和人口密度数据,NPP &POI综合指数考虑了夜间灯光与POI中设施类型、光照强度和分辨率差异,其划分识别出的城乡结合部边界准确度更高、时效性更强；相较于土地利用、景观等数据,NPP &POI综合指数更能表征城乡发展活力,定量识别出城乡潜在中心区与多层结构对于城乡基础设施的配置、产业分工、生态职能划分等研究具有参考意义。NPP &POI综合指数在城乡空间上的二次突变规律证实了城乡结合部作为城市扩张过程中产生的地域实体客观存在,为城乡三元结构理论提供了实证支撑。
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关键词:
- NPP &POI综合指数 /
- 城乡结合部 /
- 断裂点分析法 /
- 城乡空间结构 /
- 夜间灯光数据
Abstract:ObjectivesThe identification of urban-rural fringe boundaries is a fundamental task for fine urban and rural planning and governance, and it plays a vital role in promoting sustainable land use and urban-rural integration. The traditional methods for delineating urban-rural boundaries have many limitations, such as reliance on single data sources, difficulty in data acquisition, and low temporal and spatial resolution.
MethodsThis paper proposes a novel method for identifying urban-rural fringe boundaries, based on the fusion of points of interest (POI) from electronic map and nighttime light data of national polar-orbiting partnership (NPP) satellite, in conjunction with the spatial structure of urban and rural space. Taking Wuhan City as a case study, this paper employs break point analysis to identify spatial mutation points and determine urban-rural fringe boundaries. The results are validated and compared using land use structure entropy, normalized difference vegetation index, and population density data, with field verification conducted in selected typical areas.
ResultsThe results show that by considering differences in facility types, light intensity, and resolution in POI and nighttime light data, the NPP &POI composite index for boundary delineation offers higher accuracy and timeliness compared to the boundaries identified using POI, nighttime light, and population density data individually. NPP &POI data can more effectively reflect the vitality of urban-rural development compared to land use and landscape data. The quantitative identification of potential central areas and multi-layered structures in urban-rural settings is significant for research on urban-rural infrastructure allocation, industrial division, and ecological function distribution.
ConclusionsThe secondary mutation pattern of NPP &POI data in urban-rural spaces confirms the objective existence of urban-rural fringe areas as territorial entities emerging from urban expansion, providing empirical support for the urban-rural triad structure theory.
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http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20220597
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图 1 不同带宽下武汉市NPP &POI处理效果对比
Figure 1. Comparison of NPP &POI Processing Effects Under Different Search Distances in Wuhan City
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图 2 NPP &POI剖面线与等值线示意图
Figure 2. Schematic Diagram of NPP &POI Section Lines and Isolines
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图 3 NPP &POI及其等值面面积随距离变化图
Figure 3. Variation of NPP &POI and the Isosurface Area with Distance
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图 6 武汉市城乡结合部土地利用类型分布图
Figure 6. Distribution Map of Land Use Types in Urban Rural Fringe of Wuhan City
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图 11 武汉市城乡结合部边界与人口密度分布图
Figure 11. Distribution Map of Boundary and Population Density in the Urban Rural Area of Wuhan City
表 1 武汉市POI分类统计
Table 1 Statistics of POI Classification in Wuhan City
功能类别 一级行业分类 数量/条 文体类 旅游景点、休闲娱乐、运动健身、教育培训、文化传媒、自然地物 53 966 商业类 美食、酒店、购物、生活服务、丽人、汽车服务、金融 68 508 产业类 公司企业 70 204 公服类 医疗、交通设施、政府机构 33 640 居住类 房地产 119 543 
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表 2 不同剖面线上NPP &POI断裂点数值统计表
Table 2 Statistics of NPP &POI Breakpoint Values on Different Profile Lines
剖面线序号 NPP &POI综合指数断裂点平均值 1~30 0.109 31~60 0.095 61~90 0.105 91~120 0.103 121~150 0.097 151~180 0.114
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表 3 2010-2020年武汉市不同区域的NDVI分布情况
Table 3 Distribution of NDVI in Different Areas of Wuhan City from 2010 to 2020
年份 城市核心区 城乡结合部 乡村 平均值 标准差 平均值 标准差 平均值 标准差 2010 0.412 0.122 0.557 0.175 0.708 0.120 2011 0.418 0.102 0.576 0.176 0.721 0.124 2012 0.407 0.113 0.541 0.190 0.710 0.145 2013 0.410 0.110 0.535 0.196 0.691 0.151 2014 0.393 0.110 0.536 0.187 0.713 0.147 2015 0.394 0.103 0.547 0.193 0.710 0.155 2016 0.383 0.103 0.515 0.189 0.692 0.153 2017 0.386 0.100 0.541 0.188 0.719 0.148 2018 0.404 0.093 0.538 0.183 0.725 0.136 2019 0.410 0.094 0.501 0.183 0.694 0.144 2020 0.431 0.101 0.523 0.184 0.718 0.158 总变化量 0.019 -0.021 -0.034 0.009 0.010 0.038
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