地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (8): 1575-1588.doi: 10.12082/dqxxkx.2022.210226

• 地理空间分析综合应用 • 上一篇    下一篇

融合多源地理大数据的城市街区综合活力评价

唐璐(), 许捍卫(), 丁彦文   

  1. 河海大学水文水资源学院,南京 210024
  • 收稿日期:2021-04-25 修回日期:2021-06-14 出版日期:2022-08-25 发布日期:2022-10-25
  • 通讯作者: *许捍卫(1969— ),男,博士,副教授,主要从事地理国情监测技术研究、地理大数据应用、3S集成研究、城市空间 信息共享平台研究。E-mail: xuhanwei@hhu.edu.cn
  • 作者简介:唐 璐(1996— ),女,安徽马鞍山人,硕士生,主要从事GIS应用研究。E-mail: t392258110@outlook.com
  • 基金资助:
    国家自然科学基金项目(41101374)

Comprehensive Vitality Evaluation of Urban Blocks based on Multi-source Geographic Big Data

TANG Lu(), XU Hanwei(), DING Yanwen   

  1. College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China
  • Received:2021-04-25 Revised:2021-06-14 Online:2022-08-25 Published:2022-10-25
  • Contact: XU Hanwei
  • Supported by:
    National Natural Science Foundation of China(41101374)

摘要:

随着城市人口、物资、信息流动的日益频繁,城市居民活动特征和生产生活方式更加复杂多变,同时,城市空间无序扩张,发展规划不足,引发了交通堵塞、人口流失、公共空间缺乏等一系列问题,最终引发了城市活力消解难题。因此,如何科学高效地进行城市活力定量分析成为了重点研究问题。本文基于OpenStreetMap、百度地图兴趣点(Point of Interest,POI)、微信宜出行、美团、高德建筑物轮廓等多源地理大数据,从人与空间双重角度,分别对人群活力、活力多样性、活动满意度和空间交互潜能进行量化研究;引入空间权重矩阵,构建了改进的空间优劣解距离法(Technique for Order Preference by Similarity to Ideal Solution,TOPSIS)综合活力评价模型,实现对南京市中心城区综合活力的评价,最后分析了工作日、周末的街区活力空间分布特征及活力极的异同,并比较了传统的熵值TOPSIS综合活力评价结果,以此探究空间关系对城市街区活力的影响,以求帮助城市规划者系统的认识当前城市活力现状,为城市规划研究提供一种可行性方案。

关键词: 地理大数据, OSM路网, POI, 南京市, 城市街区活力, 引力模型, 优劣解距离法, 空间相互作用

Abstract:

How to conduct quantitative analysis of urban vitality in a scientific and efficient manner has become a key research issue nowadays. Based on the multi-source geographic big data such as OpenStreetMap, Baidu Map POI, WeChat Travel, Meituan, Gaode building outlines, etc., and from the dual perspectives of people and space, this study selects indicators from four aspects, including crowd vitality, diversity of vitality, activity satisfaction, and spatial interaction potential, to construct a comprehensive vitality evaluation model of Spatial TOPSIS. Using the model, this study evaluates the comprehensive vitality of the downtown area of Nanjing, analyzes the spatial distribution characteristics of the vitality of the neighborhood, and explores the similarities and differences of vitality poles between weekdays and weekends. The evaluation results are compared with that of Entropy TOPSIS, in an effort to explore the impact of spatial interaction on the vitality of blocks. This study aims to help urban planners to understand the current status of urban vitality systematically, and provide a feasible plan for urban planning research. The research shows that, firstly, the spatial distribution characteristics of the comprehensive vitality of the downtown blocks in Nanjing urban center are similar between weekdays and weekends. However, the comprehensive vitality of the blocks on weekdays is higher than that on weekends. From the perspective of block functions, the high-value areas of comprehensive vitality are mainly concentrated in commercial centers, tourist attractions, and transportation hubs, which are closely related to the distribution of transportation stations (e.g. subway stations). Secondly, based on the vitality analysis of weekdays and weekends, it is found that Hunanlu - Xinjiekou - Confucius temple scenic area is the largest and most stable vitality pole. Among the small vitality poles, only Longjiang Metro Station has begun to take shape. Other small vitality poles, including Jiqingmen Street, Olympic Sports Center, Baijiahu Commercial District, and Wanda Commercial District, are unstable. Their vitality is still growing. Thus, they may become bigger vitality poles in the future. Thirdly, both the high vitality blocks in the center of the study area and the low vitality blocks receive less spatial effects. The areas with the greatest vitality change are generally distributed in a ring shape around the periphery of the central city. Combining the block functions, it is found that the comprehensive vitality of block units of other land and industrial land is less affected by the spatial interaction. In comparison, residential, commercial, scientific, educational, and cultural land are greatly affected by spatial interaction.

Key words: geographic big data, OSM road network, POI, Nanjing, urban block vitality, gravity model, TOPSIS, spatial interaction